Albert "A.J." Rogers, MD, MBA, FAHA

All Publications

• Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms. JACC. Clinical electrophysiology Chiu, I. M., Wu, P. J., Zhang, H., Hughes, J. W., Rogers, A. J., Jalilian, L., Perez, M., Lin, C. R., Lee, C. T., Zou, J., Ouyang, D. 2024

  Abstract

  Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk for individuals with chronic kidney disease and end-stage renal disease (ESRD). Traditional laboratory test monitoring is resource-heavy, invasive, and unable to provide continuous tracking. Wearable technologies like smartwatches with electrocardiogram (ECG) capabilities are emerging as valuable tools for remote monitoring, potentially allowing for personalized monitoring with artificial intelligence (AI)-ECG interpretation.The purpose of this study was to develop an AI-ECG algorithm to predict serum potassium level in ESRD patients with smartwatch-generated ECG waveforms.A cohort of 152,508 patients with 293,557 ECGs paired serum potassium levels obtained within 1 hour at Cedars Sinai Medical Center was used to train an AI-ECG model ("Kardio-Net") to predict serum potassium level. The model was further fine-tuned on 4,337 ECGs from 1,463 patients with ESRD using inputs from 12- and single-lead ECGs. Kardio-Net was evaluated in held-out test cohorts from Cedars Sinai Medical Center and Stanford Healthcare (SHC) as well as a prospective international cohort of 40 ESRD patients with smartwatch ECGs at Chang Gung Memorial Hospital.The Kardio-Net, when applied to 12-lead ECGs, identified severe hyperkalemia (>6.5 mEq/L) with an AUC of 0.852 (95% CI: 0.745-0.956) and a mean absolute error (MAE) of 0.527 mEq/L. In external validation at SHC, the model achieved an AUC of 0.849 (95% CI: 0.823-0.875) and an MAE of 0.599 mEq/L. For single-lead ECGs, Kardio-Net detected severe hyperkalemia with an AUC of 0.876 (95% CI: 0.765-0.987) in the primary cohort and had an MAE of 0.575 mEq/L. In the external SHC validation, the AUC was 0.807 (95% CI: 0.778-0.835) with an MAE of 0.740 mEq/L. Using prospectively obtained smartwatch data, the AUC was 0.831 (95% CI: 0.693-0.975), with an MAE of 0.580 mEq/L.We validate a deep learning model to predict serum potassium levels from both 12-lead ECGs and single-lead smartwatch data, demonstrating its utility for remote monitoring of hyperkalemia.

  View details for DOI 10.1016/j.jacep.2024.07.023

  View details for PubMedID 39387744

• Periprocedural Management and Multidisciplinary Care Pathways for Patients With Cardiac Implantable Electronic Devices: A Scientific Statement From the American Heart Association. Circulation Wan, E. Y., Rogers, A. J., Lavelle, M., Marcus, M., Stone, S. A., Ottoboni, L., Srivatsa, U., Leal, M. A., Russo, A. M., Jackson, L. R., Crossley, G. H. 2024; 150 (8): e183-e196

  Abstract

  The rapid technological advancements in cardiac implantable electronic devices such as pacemakers, implantable cardioverter defibrillators, and loop recorders, coupled with a rise in the number of patients with these devices, necessitate an updated clinical framework for periprocedural management. The introduction of leadless pacemakers, subcutaneous and extravascular defibrillators, and novel device communication protocols underscores the imperative for clinical updates. This scientific statement provides an inclusive framework for the periprocedural management of patients with these devices, encompassing the planning phase, procedure, and subsequent care coordinated with the primary device managing clinic. Expert contributions from anesthesiologists, cardiac electrophysiologists, and cardiac nurses are consolidated to appraise current evidence, offer patient and health system management strategies, and highlight key areas for future research. The statement, pertinent to a wide range of health care professionals, underscores the importance of quality care pathways for patient safety, optimal device function, and minimization of hemodynamic disturbances or arrhythmias during procedures. Our primary objective is to deliver quality care to the expanding patient cohort with cardiac implanted electronic devices, offering direction in the era of evolving technologies and laying a foundation for sustained education and practice enhancement.

  View details for DOI 10.1161/CIR.0000000000001264

  View details for PubMedID 38984417

• Novel Domain Knowledge-Encoding Algorithm Enables Label-Efficient Deep Learning for Cardiac CT Segmentation to Guide Atrial Fibrillation Treatment in a Pilot Dataset. Diagnostics (Basel, Switzerland) Ganesan, P., Feng, R., Deb, B., Tjong, F. V., Rogers, A. J., Ruipérez-Campillo, S., Somani, S., Clopton, P., Baykaner, T., Rodrigo, M., Zou, J., Haddad, F., Zaharia, M., Narayan, S. M. 2024; 14 (14)

  Abstract

  Background: Segmenting computed tomography (CT) is crucial in various clinical applications, such as tailoring personalized cardiac ablation for managing cardiac arrhythmias. Automating segmentation through machine learning (ML) is hindered by the necessity for large, labeled training data, which can be challenging to obtain. This article proposes a novel approach for automated, robust labeling using domain knowledge to achieve high-performance segmentation by ML from a small training set. The approach, the domain knowledge-encoding (DOKEN) algorithm, reduces the reliance on large training datasets by encoding cardiac geometry while automatically labeling the training set. The method was validated in a hold-out dataset of CT results from an atrial fibrillation (AF) ablation study. Methods: The DOKEN algorithm parses left atrial (LA) structures, extracts "anatomical knowledge" by leveraging digital LA models (available publicly), and then applies this knowledge to achieve high ML segmentation performance with a small number of training samples. The DOKEN-labeled training set was used to train a nnU-Net deep neural network (DNN) model for segmenting cardiac CT in N = 20 patients. Subsequently, the method was tested in a hold-out set with N = 100 patients (five times larger than training set) who underwent AF ablation. Results: The DOKEN algorithm integrated with the nn-Unet model achieved high segmentation performance with few training samples, with a training to test ratio of 1:5. The Dice score of the DOKEN-enhanced model was 96.7% (IQR: 95.3% to 97.7%), with a median error in surface distance of boundaries of 1.51 mm (IQR: 0.72 to 3.12) and a mean centroid-boundary distance of 1.16 mm (95% CI: -4.57 to 6.89), similar to expert results (r = 0.99; p < 0.001). In digital hearts, the novel DOKEN approach segmented the LA structures with a mean difference for the centroid-boundary distances of -0.27 mm (95% CI: -3.87 to 3.33; r = 0.99; p < 0.0001). Conclusions: The proposed novel domain knowledge-encoding algorithm was able to perform the segmentation of six substructures of the LA, reducing the need for large training data sets. The combination of domain knowledge encoding and a machine learning approach could reduce the dependence of ML on large training datasets and could potentially be applied to AF ablation procedures and extended in the future to other imaging, 3D printing, and data science applications.

  View details for DOI 10.3390/diagnostics14141538

  View details for PubMedID 39061675

  View details for PubMedCentralID PMC11276420

• Simple models vs. deep learning in detecting low ejection fraction from the electrocardiogram. European heart journal. Digital health Hughes, J. W., Somani, S., Elias, P., Tooley, J., Rogers, A. J., Poterucha, T., Haggerty, C. M., Salerno, M., Ouyang, D., Ashley, E., Zou, J., Perez, M. V. 2024; 5 (4): 427-434

  Abstract

  Deep learning methods have recently gained success in detecting left ventricular systolic dysfunction (LVSD) from electrocardiogram (ECG) waveforms. Despite their high level of accuracy, they are difficult to interpret and deploy broadly in the clinical setting. In this study, we set out to determine whether simpler models based on standard ECG measurements could detect LVSD with similar accuracy to that of deep learning models.Using an observational data set of 40 994 matched 12-lead ECGs and transthoracic echocardiograms, we trained a range of models with increasing complexity to detect LVSD based on ECG waveforms and derived measurements. The training data were acquired from the Stanford University Medical Center. External validation data were acquired from the Columbia Medical Center and the UK Biobank. The Stanford data set consisted of 40 994 matched ECGs and echocardiograms, of which 9.72% had LVSD. A random forest model using 555 discrete, automated measurements achieved an area under the receiver operator characteristic curve (AUC) of 0.92 (0.91-0.93), similar to a deep learning waveform model with an AUC of 0.94 (0.93-0.94). A logistic regression model based on five measurements achieved high performance [AUC of 0.86 (0.85-0.87)], close to a deep learning model and better than N-terminal prohormone brain natriuretic peptide (NT-proBNP). Finally, we found that simpler models were more portable across sites, with experiments at two independent, external sites.Our study demonstrates the value of simple electrocardiographic models that perform nearly as well as deep learning models, while being much easier to implement and interpret.

  View details for DOI 10.1093/ehjdh/ztae034

  View details for PubMedID 39081946

  View details for PubMedCentralID PMC11284011

• Competing Risks for Monomorphic versus Non-Monomorphic Ventricular Arrhythmias in Primary Prevention Implantable Cardioverter Defibrillator Recipients: Global Electrical Heterogeneity and Clinical Outcomes (GEHCO) Study. Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology Tereshchenko, L. G., Waks, J. W., Tompkins, C., Rogers, A. J., Ehdaie, A., Henrikson, C. A., Dalouk, K., Raitt, M., Kewalramani, S., Kattan, M. W., Santangeli, P., Wilkoff, B. W., Kapadia, S. R., Narayan, S. M., Chugh, S. S. 2024

  Abstract

  Ablation of monomorphic ventricular tachycardia (MMVT) has been shown to reduce shock frequency and improve survival. We aimed to compare cause-specific risk factors of MMVT and polymorphic ventricular tachycardia (PVT)/ventricular fibrillation (VF) and to develop predictive models.The multicenter retrospective cohort study included 2,668 patients (age 63.1±13.0 y; 23% female; 78% white; 43% nonischemic cardiomyopathy, left ventricular ejection fraction 28.2±11.1%). Cox models were adjusted for demographic characteristics, heart failure severity and treatment, device programming, and ECG metrics. Global electrical heterogeneity was measured by spatial QRS-T angle (QRSTa), spatial ventricular gradient elevation (SVGel), azimuth, magnitude (SVGmag), and sum absolute QRST integral (SAIQRST). We compared the out-of-sample performance of the lasso and elastic net for Cox proportional hazards and the Fine-Gray competing risk model.During a median follow-up of 4 years, 359 patients experienced their first sustained MMVT with appropriate ICD therapy, and 129 patients had their first PVT/VF with appropriate ICD shock. The risk of MMVT was associated with wider QRSTa (HR 1.16; 95%CI 1.01-1.34), larger SVGel (HR 1.17; 95%CI 1.05-1.30), and smaller SVGmag (HR 0.74; 95%CI 0.63-0.86) and SAIQRST (HR 0.84; 95%CI 0.71-0.99). The best-performing 3-year competing risk Fine-Gray model for MMVT (ROC(t)AUC 0.728; 95%CI 0.668-0.788) identified high-risk (> 50%) patients with 75% sensitivity, 65% specificity, and PVT/VF prediction model had ROC(t)AUC 0.915 (95%CI 0.868-0.962), both satisfactory calibration.We developed and validated models to predict the competing risks of MMVT or PVT/VF that could inform procedural planning and future RCTs of prophylactic VT ablation.

  View details for DOI 10.1093/europace/euae127

  View details for PubMedID 38703375

• Simple models vs. deep learning in detecting low ejection fraction from the electrocardiogram EUROPEAN HEART JOURNAL - DIGITAL HEALTH Hughes, J., Somani, S., Elias, P., Tooley, J., Rogers, A. J., Poterucha, T., Haggerty, C. M., Salerno, M., Ouyang, D., Ashley, E., Zou, J., Perez, M. 2024

  View details for DOI 10.1093/ehjdh/ztae034

  View details for Web of Science ID 001219734900001

• AUTOMATED, ACCURATE IDENTIFICATION OF VENTRICULAR TACHYCARDIA FROM ELECTRONIC HEALTH RECORDS USING NATURAL LANGUAGE PROCESSING Brennan, K., Azizi, Z., Feng, R., Goyal, J., Liu, X., Ganesan, P., Ruiperez-Campillo, S., Baykaner, T., Badhwar, N., John, R. M., Viswanathan, M., Perino, A., Wang, P. J., Rogers, A. J., Narayan, S. M. ELSEVIER SCIENCE INC. 2024: 2644

  View details for Web of Science ID 001324901502678

• Just in time: detecting cardiac arrest with smartwatch technology. The Lancet. Digital health Somani, S., Rogers, A. J. 2024; 6 (3): e148-e149

  View details for DOI 10.1016/S2589-7500(24)00020-7

  View details for PubMedID 38395532

• Latent drivers for atrial fibrillation and specific patterns of localized fibrosis. Cardiovascular research Rogers, A. J., Narayan, S. M. 2024

  View details for DOI 10.1093/cvr/cvae032

  View details for PubMedID 38376986

• Spatially Conserved Spiral Wave Activity During Human Atrial Fibrillation. Circulation. Arrhythmia and electrophysiology Rappel, W. J., Baykaner, T., Zaman, J., Ganesan, P., Rogers, A. J., Narayan, S. M. 2024: e012041

  Abstract

  Atrial fibrillation is the most common cardiac arrhythmia in the world and increases the risk for stroke and morbidity. During atrial fibrillation, the electric activation fronts are no longer coherently propagating through the tissue and, instead, show rotational activity, consistent with spiral wave activation, focal activity, collision, or partial versions of these spatial patterns. An unexplained phenomenon is that although simulations of cardiac models abundantly demonstrate spiral waves, clinical recordings often show only intermittent spiral wave activity.In silico data were generated using simulations in which spiral waves were continuously created and annihilated and in simulations in which a spiral wave was intermittently trapped at a heterogeneity. Clinically, spatio-temporal activation maps were constructed using 60 s recordings from a 64 electrode catheter within the atrium of n=34 patients (n=24 persistent atrial fibrillation). The location of clockwise and counterclockwise rotating spiral waves was quantified and all intervals during which these spiral waves were present were determined. For each interval, the angle of rotation as a function of time was computed and used to determine whether the spiral wave returned in step or changed phase at the start of each interval.In both simulations, spiral waves did not come back in phase and were out of step." In contrast, spiral waves returned in step in the majority (68%; P=0.05) of patients. Thus, the intermittently observed rotational activity in these patients is due to a temporally and spatially conserved spiral wave and not due to ones that are newly created at the onset of each interval.Intermittency of spiral wave activity represents conserved spiral wave activity of long, but interrupted duration or transient spiral activity, in the majority of patients. This finding could have important ramifications for identifying clinically important forms of atrial fibrillation and in guiding treatment.

  View details for DOI 10.1161/CIRCEP.123.012041

  View details for PubMedID 38348685

• Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure. Circulation. Heart failure Kaur, D., Hughes, J. W., Rogers, A. J., Kang, G., Narayan, S. M., Ashley, E. A., Perez, M. V. 2023: e010879

  Abstract

  Deep learning models may combat widening racial disparities in heart failure outcomes through early identification of individuals at high risk. However, demographic biases in the performance of these models have not been well-studied.This retrospective analysis used 12-lead ECGs taken between 2008 and 2018 from 326 518 patient encounters referred for standard clinical indications to Stanford Hospital. The primary model was a convolutional neural network model trained to predict incident heart failure within 5 years. Biases were evaluated on the testing set (160 312 ECGs) using the area under the receiver operating characteristic curve, stratified across the protected attributes of race, ethnicity, age, and sex.There were 59 817 cases of incident heart failure observed within 5 years of ECG collection. The performance of the primary model declined with age. There were no significant differences observed between racial groups overall. However, the primary model performed significantly worse in Black patients aged 0 to 40 years compared with all other racial groups in this age group, with differences most pronounced among young Black women. Disparities in model performance did not improve with the integration of race, ethnicity, sex, and age into model architecture, by training separate models for each racial group, or by providing the model with a data set of equal racial representation. Using probability thresholds individualized for race, age, and sex offered substantial improvements in F1 scores.The biases found in this study warrant caution against perpetuating disparities through the development of machine learning tools for the prognosis and management of heart failure. Customizing the application of these models by using probability thresholds individualized by race, ethnicity, age, and sex may offer an avenue to mitigate existing algorithmic disparities.

  View details for DOI 10.1161/CIRCHEARTFAILURE.123.010879

  View details for PubMedID 38126168

• Sleep apnea independently predicts incident atrial fibrillation in the young - Implications for targeted screening Deb, B., Vasireddi, S. K., Bhatia, N. K., Rogers, A. J., Clopton, P., Narayan, S. M. OXFORD UNIV PRESS. 2023

  View details for Web of Science ID 001115619400451

• Predicting success of atrial fibrillation ablation: comparing machine learning approaches of intracardiac electrograms Feng, R., Ganesan, P., Deb, B., Ruiperez-Campillo, S., Chang, H. J., Clopton, P., Rogers, A. J., Rodrigo, M., Tjong, F. Y., Zaharia, M., Narayan, S. M. OXFORD UNIV PRESS. 2023

  View details for Web of Science ID 001115619400518

• Novel scoring system for predicting mortality in patients with ventricular arrhythmias: Analysis from a 24,000 patient cohort Deb, B., Rogers, A. J., Tjong, F. Y., Somani, S., Desai, Y., Azizi, Z., Chang, H. J., Bhatia, N. K., Clopton, P., Narayan, S. M. OXFORD UNIV PRESS. 2023

  View details for Web of Science ID 001115619401063

• Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding. Frontiers in cardiovascular medicine Feng, R., Deb, B., Ganesan, P., Tjong, F. V., Rogers, A. J., Ruipérez-Campillo, S., Somani, S., Clopton, P., Baykaner, T., Rodrigo, M., Zou, J., Haddad, F., Zahari, M., Narayan, S. M. 2023; 10: 1189293

  Abstract

  Segmentation of computed tomography (CT) is important for many clinical procedures including personalized cardiac ablation for the management of cardiac arrhythmias. While segmentation can be automated by machine learning (ML), it is limited by the need for large, labeled training data that may be difficult to obtain. We set out to combine ML of cardiac CT with domain knowledge, which reduces the need for large training datasets by encoding cardiac geometry, which we then tested in independent datasets and in a prospective study of atrial fibrillation (AF) ablation.We mathematically represented atrial anatomy with simple geometric shapes and derived a model to parse cardiac structures in a small set of N = 6 digital hearts. The model, termed "virtual dissection," was used to train ML to segment cardiac CT in N = 20 patients, then tested in independent datasets and in a prospective study.In independent test cohorts (N = 160) from 2 Institutions with different CT scanners, atrial structures were accurately segmented with Dice scores of 96.7% in internal (IQR: 95.3%-97.7%) and 93.5% in external (IQR: 91.9%-94.7%) test data, with good agreement with experts (r = 0.99; p < 0.0001). In a prospective study of 42 patients at ablation, this approach reduced segmentation time by 85% (2.3 ± 0.8 vs. 15.0 ± 6.9 min, p < 0.0001), yet provided similar Dice scores to experts (93.9% (IQR: 93.0%-94.6%) vs. 94.4% (IQR: 92.8%-95.7%), p = NS).Encoding cardiac geometry using mathematical models greatly accelerated training of ML to segment CT, reducing the need for large training sets while retaining accuracy in independent test data. Combining ML with domain knowledge may have broad applications.

  View details for DOI 10.3389/fcvm.2023.1189293

  View details for PubMedID 37849936

  View details for PubMedCentralID PMC10577270

• Advances in cardiac pacing with leadless pacemakers and conduction system pacing. Current opinion in cardiology Somani, S., Rogers, A. J. 2023

  Abstract

  The field of cardiac pacing has undergone significant evolution with the introduction and adoption of conduction system pacing (CSP) and leadless pacemakers (LLPMs). These innovations provide benefits over conventional pacing methods including avoiding lead related complications and achieving more physiological cardiac activation. This review critically assesses the latest advancements in CSP and LLPMs, including their benefits, challenges, and potential for future growth.CSP, especially of the left bundle branch area, enhances ventricular depolarization and cardiac mechanics. Recent studies show CSP to be favorable over traditional pacing in various patient populations, with an increase in its global adoption. Nevertheless, challenges related to lead placement and long-term maintenance persist. Meanwhile, LLPMs have emerged in response to complications from conventional pacemaker leads. Two main types, Aveir and Micra, have demonstrated improved outcomes and adoption over time. The incorporation of new technologies allows LLPMs to cater to broader patient groups, and their integration with CSP techniques offers exciting potential.The advancements in CSP and LLPMs present a transformative shift in cardiac pacing, with evidence pointing towards enhanced clinical outcomes and reduced complications. Future innovations and research are likely to further elevate the clinical impact of these technologies, ensuring improved patient care for those with conduction system disorders.

  View details for DOI 10.1097/HCO.0000000000001092

  View details for PubMedID 37751365

• Optimising Patient Selection for Primary Prevention ICD Implantation: Utilising Multimodal Machine Learning to Assess Risk of ICD Non-Benefit. Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology Kolk, M. Z., Ruiperez-Campillo, S., Deb, B., Bekkers, E., Allaart, C. P., Rogers, A. J., Van Der Lingen, A. C., Alvarez Florez, L., Isgum, I., De Vos, B., Clopton, P., Wilde, A. A., Knops, R. E., Narayan, S. M., Tjong, F. V. 2023

  Abstract

  BACKGROUND: Left ventricular ejection fraction (LVEF) is suboptimal as a sole marker for predicting sudden cardiac death (SCD). Machine learning (ML) provides new opportunities for personalised predictions using complex, multimodal data. This study aimed to determine if risk stratification for implantable cardioverter defibrillator (ICD) implantation can be improved by ML models that combine clinical variables with 12-lead electrocardiograms (ECG) time-series features.METHODS: A multicentre study of 1010 patients (64.9 ±10.8 years, 26.8% female) with ischaemic, dilated, or non-ischaemic cardiomyopathy, and LVEF≤35% implanted with an ICD between 2007 and 2021 for primary prevention of SCD in two academic hospitals was performed. For each patient, a raw 12-lead, 10-second ECG obtained within 90 days before ICD implantation and clinical details were collected. Supervised ML models were trained and validated on a development cohort (n=550) from Hospital A to predict ICD non-arrhythmic mortality at 3-year follow-up (i.e. mortality without prior appropriate ICD-therapy). Model performance was evaluated on an external patient cohort from Hospital B (n=460).RESULTS: At 3-year follow-up, 16.0% of patients had died, with 72.8% meeting criteria for non-arrhythmic mortality. Extreme gradient boosting models identified patients with non-arrhythmic mortality with an area under the receiver operating characteristic curve (AUROC) of 0.90 (95% CI 0.80-1.00) during internal validation. In the external cohort, the AUROC was 0.79 (95% CI 0.75-0.84).CONCLUSIONS: ML models combining ECG time-series features and clinical variables were able to predict non-arrhythmic mortality within 3 years after device implantation in a primary prevention population, with robust performance in an independent cohort.

  View details for DOI 10.1093/europace/euad271

  View details for PubMedID 37712675

• A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease. NPJ digital medicine Hughes, J. W., Tooley, J., Torres Soto, J., Ostropolets, A., Poterucha, T., Christensen, M. K., Yuan, N., Ehlert, B., Kaur, D., Kang, G., Rogers, A., Narayan, S., Elias, P., Ouyang, D., Ashley, E., Zou, J., Perez, M. V. 2023; 6 (1): 169

  Abstract

  The electrocardiogram (ECG) is the most frequently performed cardiovascular diagnostic test, but it is unclear how much information resting ECGs contain about long term cardiovascular risk. Here we report that a deep convolutional neural network can accurately predict the long-term risk of cardiovascular mortality and disease based on a resting ECG alone. Using a large dataset of resting 12-lead ECGs collected at Stanford University Medical Center, we developed SEER, the Stanford Estimator of Electrocardiogram Risk. SEER predicts 5-year cardiovascular mortality with an area under the receiver operator characteristic curve (AUC) of 0.83 in a held-out test set at Stanford, and with AUCs of 0.78 and 0.83 respectively when independently evaluated at Cedars-Sinai Medical Center and Columbia University Irving Medical Center. SEER predicts 5-year atherosclerotic disease (ASCVD) with an AUC of 0.67, similar to the Pooled Cohort Equations for ASCVD Risk, while being only modestly correlated. When used in conjunction with the Pooled Cohort Equations, SEER accurately reclassified 16% of patients from low to moderate risk, uncovering a group with an actual average 9.9% 10-year ASCVD risk who would not have otherwise been indicated for statin therapy. SEER can also predict several other cardiovascular conditions such as heart failure and atrial fibrillation. Using only lead I of the ECG it predicts 5-year cardiovascular mortality with an AUC of 0.80. SEER, used alongside the Pooled Cohort Equations and other risk tools, can substantially improve cardiovascular risk stratification and aid in medical decision making.

  View details for DOI 10.1038/s41746-023-00916-6

  View details for PubMedID 37700032

  View details for PubMedCentralID 8145781

• Safety of transvenous cardiac defibrillator and magnetic titanium beads system for gastroesophageal reflux disease: a case report. Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing Vasireddi, S. K., Greif, S., Fazal, M., Wei, C., Gomez, S., Shah, S., Rogers, A. J., Narayan, S. M., Wang, P. J., Kapoor, R., Baykaner, T. 2023

  View details for DOI 10.1007/s10840-023-01604-x

  View details for PubMedID 37421563

  View details for PubMedCentralID 3667475

• Evaluating Recommendations About Atrial Fibrillation for Patients and Clinicians Obtained From Chat-Based Artificial Intelligence Algorithms. Circulation. Arrhythmia and electrophysiology Azizi, Z., Alipour, P., Gomez, S., Broadwin, C., Islam, S., Sarraju, A., Rogers, A. J., Sandhu, A. T., Rodriguez, F. 2023: e012015

  View details for DOI 10.1161/CIRCEP.123.012015

  View details for PubMedID 37334705

• Comparative arrhythmia patterns among patients on tyrosine kinase inhibitors. Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing Wei, C., Fazal, M., Loh, A., Kapoor, R., Gomez, S. E., Shah, S., Rogers, A. J., Narayan, S. M., Wang, P. J., Witteles, R. M., Perino, A. C., Cheng, P., Rhee, J. W., Baykaner, T. 2023

  Abstract

  Tyrosine kinase inhibitors (TKIs) are widely used in the treatment of hematologic malignancies. Limited studies have shown an association between treatment-limiting arrhythmias and TKI, particularly ibrutinib, a Bruton's tyrosine kinase (BTK) inhibitor. We sought to comprehensively assess the arrhythmia burden in patients receiving ibrutinib vs non-BTK TKI vs non-TKI therapies.We performed a retrospective analysis of consecutive patients who received long-term cardiac event monitors while on ibrutinib, non-BTK TKIs, or non-TKI therapy for a hematologic malignancy between 2014 and 2022.One hundred ninety-three patients with hematologic malignancies were included (ibrutinib = 72, non-BTK TKI = 46, non-TKI therapy = 75). The average duration of TKI therapy was 32 months in the ibrutinib group vs 64 months in the non-BTK TKI group (p = 0.003). The ibrutinib group had a higher prevalence of atrial fibrillation (n = 32 [44%]) compared to the non-BTK TKI (n = 7 [15%], p = 0.001) and non-TKI (n = 15 [20%], p = 0.002) groups. Similarly, the prevalence of non-sustained ventricular tachycardia was higher in the ibrutinib group (n = 31, 43%) than the non-BTK TKI (n = 8 [17%], p = 0.004) and non-TKI groups (n = 20 [27%], p = 0.04). TKI therapy was held in 25% (n = 18) of patients on ibrutinib vs 4% (n = 2) on non-BTK TKIs (p = 0.005) secondary to arrhythmias.In this large retrospective analysis of patients with hematologic malignancies, patients receiving ibrutinib had a higher prevalence of atrial and ventricular arrhythmias compared to those receiving other TKI, with a higher rate of treatment interruption due to arrhythmias.

  View details for DOI 10.1007/s10840-023-01575-z

  View details for PubMedID 37256462

• Atrial Fibrillation Ablation Outcome Prediction with a Machine Learning Fusion Framework Incorporating Cardiac Computed Tomography. Journal of cardiovascular electrophysiology Razeghi, O., Kapoor, R., Alhusseini, M. I., Fazal, M., Tang, S., Roney, C. H., Rogers, A. J., Lee, A., Wang, P. J., Clopton, P., Rubin, D. L., Narayan, S. M., Niederer, S., Baykaner, T. 2023

  Abstract

  BACKGROUND: Structural changes in the left atrium (LA) modestly predict outcomes in patients undergoing catheter ablation for atrial fibrillation (AF). Machine learning (ML) is a promising approach to personalize AF management strategies and improve predictive risk models after catheter ablation by integrating atrial geometry from cardiac computed tomography (CT) scans and patient-specific clinical data. We hypothesized that ML approaches based on a patient's specific data can identify responders to AF ablation.METHODS: Consecutive patients undergoing AF ablation, who had preprocedural CT scans, demographics, and 1-year follow-up data, were included in the study for a retrospective analysis. The inputs of models were CT-derived morphological features from left atrial segmentation (including the shape, volume of the LA, LA appendage, and pulmonary vein ostia) along with deep features learned directly from raw CT images, and clinical data. These were merged intelligently in a framework to learn their individual importance and produce the optimal classification.RESULTS: 321 patients (64.2 + 10.6 years, 69% male, 40% paroxysmal AF) were analyzed. Post 10-fold nested cross-validation, the model trained to intelligently merge and learn appropriate weights for clinical, morphological, and imaging data (AUC 0.821) outperformed those trained solely on clinical data (AUC 0.626), morphological (AUC 0.659) or imaging data (AUC 0.764).CONCLUSION: Our machine learning approach provides an end-to-end automated technique to predict AF ablation outcomes using deep learning from CT images, derived structural properties of LA, augmented by incorporation of clinical data in a merged ML framework. This can help develop personalized strategies for patient selection in invasive management of AF. This article is protected by copyright. All rights reserved.

  View details for DOI 10.1111/jce.15890

  View details for PubMedID 36934383

• Quantifying a spectrum of clinical response in atrial tachyarrhythmias using spatiotemporal synchronization of electrograms. Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology Ganesan, P., Deb, B., Feng, R., Rodrigo, M., Ruiperez-Campillo, S., Rogers, A. J., Clopton, P., Wang, P. J., Zeemering, S., Schotten, U., Rappel, W., Narayan, S. M. 2023

  Abstract

  AIMS: There is a clinical spectrum for atrial tachyarrhythmias wherein most patients with atrial tachycardia (AT) and some with atrial fibrillation (AF) respond to ablation, while others do not. It is undefined if this clinical spectrum has pathophysiological signatures. This study aims to test the hypothesis that the size of spatial regions showing repetitive synchronized electrogram (EGM) shapes over time reveals a spectrum from AT, to AF patients who respond acutely to ablation, to AF patients without acute response.METHODS AND RESULTS: We studied n = 160 patients (35% women, 65.0 ± 10.4 years) of whom (i) n = 75 had AF terminated by ablation propensity matched to (ii) n = 75 without AF termination and (iii) n = 10 with AT. All patients had mapping by 64-pole baskets to identify areas of repetitive activity (REACT) to correlate unipolar EGMs in shape over time. Synchronized regions (REACT) were largest in AT, smaller in AF termination, and smallest in non-termination cohorts (0.63 ± 0.15, 0.37 ± 0.22, and 0.22 ± 0.18, P < 0.001). Area under the curve for predicting AF termination in hold-out cohorts was 0.72 ± 0.03. Simulations showed that lower REACT represented greater variability in clinical EGM timing and shape. Unsupervised machine learning of REACT and extensive (50) clinical variables yielded four clusters of increasing risk for AF termination (P < 0.01, chi2), which were more predictive than clinical profiles alone (P < 0.001).CONCLUSION: The area of synchronized EGMs within the atrium reveals a spectrum of clinical response in atrial tachyarrhythmias. These fundamental EGM properties, which do not reflect any predetermined mechanism or mapping technology, predict outcome and offer a platform to compare mapping tools and mechanisms between AF patient groups.

  View details for DOI 10.1093/europace/euad055

  View details for PubMedID 36932716

• Can Machine Learning Disrupt the Prediction of Sudden Death? Journal of the American College of Cardiology Narayan, S. M., Rogers, A. J. 2023; 81 (10): 962-963

  View details for DOI 10.1016/j.jacc.2022.12.027

  View details for PubMedID 36889874

• VENTRICULAR TACHYCARDIA PREDICTS ATRIAL FIBRILLATION RECURRENCE POST ABLATION: A PROPENSITY SCORE-MATCHED ANALYSIS OF A LARGE PROSPECTIVE STUDY Azizi, Z., Deb, B., Feng, R., Ganesan, P., Rogers, A. J., Chang, H., Clopton, P., Narayan, S. M. ELSEVIER SCIENCE INC. 2023: 186

  View details for Web of Science ID 000990866100187

• OBSTRUCTIVE SLEEP APNEA PORTENDS STROKE IN YOUNG INDIVIDUALS WITHOUT ATRIAL FIBRILLATION: A LARGE REGISTRY STUDY Deb, B., Vasireddi, S., Bhatia, N. K., Rogers, A. J., Clopton, P., Baykaner, T., Ganesan, P., Feng, R., Azizi, Z., Narayan, S. M. ELSEVIER SCIENCE INC. 2023: 130

  View details for Web of Science ID 000990866100131

• Artificial Intelligence in cardiac electrophysiology Artificial Intelligence in Clinical Practice Somani, S., Narayan, S. M., Rogers, A. J. Elsevier. 2023
• Improving Cardiac Segmentation for Atrial Fibrillation Ablation: A Prospective Trial of Machine Learned Geometric Dissection vs Experts Feng, R., Deb, B., Ganesan, P., Tjong, F. V., Ruiperez-Campillo, S., Clopton, P. L., Rogers, A. J., Somani, S., Rodrigo, M., Zou, J., Zaharia, M., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2022

  View details for Web of Science ID 000890856908114

• Intelligent Machine Learning Fusion Framework Predicts Atrial Fibrillation Ablation Outcomes With Demographic, Morphological, and Imaging Features Fazal, M., Rogers, A. J., Kapoor, R., Wang, P. J., Narayan, S. M., Razeghi, O., Baykaner, T. LIPPINCOTT WILLIAMS & WILKINS. 2022

  View details for Web of Science ID 000890856909132

• Arrhythmia Patterns in Patients Managed With and Without Tyrosine Kinase Inhibitors Wei, C., Fazal, M., Kapoor, R., Cheng, P., Rogers, A. J., Perino, A. C., Narayan, S. M., Rhee, J. W., Baykaner, T. LIPPINCOTT WILLIAMS & WILKINS. 2022

  View details for Web of Science ID 000890856906165

• Machine Learning to Probe Variability in Patient Outcomes After Atrial Fibrillation Ablation - The Stanford AF Registry (start) Deb, B., Bhatia, N., Rogers, A. J., Baykaner, T., Chang, H., Clopton, P. L., Ganesan, P., Feng, R., Ruiperez-Campillo, S., Turakhia, M., Perino, A. C., Perez, M. V., Zei, P., Badhwar, N., Brodt, C., Narayan, S. M., Wang, P. J., Viswanathan, M. N. LIPPINCOTT WILLIAMS & WILKINS. 2022

  View details for Web of Science ID 000890856903397

• Multivariate Predictors of Long-Term Outcome From Ventricular Tachycardia Ablation in a Large Registry Goyal, J., Deb, B., Le Menestrel, T., Chang, H., Tjong, F. V., Rogers, A. J., Azizi, Z., Ruiperez-Campillo, S., Feng, R., Ganesan, P., Baykaner, T., John, R., Perez, M. V., Perino, A. C., Wang, P. J., Viswanathan, M. N., Badhwar, N., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2022

  View details for Web of Science ID 000890856906317

• Machine Learning of Action Potential Shape to Define Refractory Periods in Ischemiccardiomyopathy Ruiperez-Campillo, S., Deb, B., Selvalingam, A., Feng, R., Ganesan, P., Tjong, F. V., Chang, H., Kowalewski, C., Kolk, M. H., Clopton, P. L., Vasireddi, S. K., Rogers, A. J., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2022

  View details for Web of Science ID 000890856903098

• Spectrum of Clinical Response in Atrial Tachyarrhythmias Identified by Spatiotemporal Synchronization of Electrograms Ganesan, P., Deb, B., Feng, R., Rodrigo, M., Ruiperez-Campillo, S., Rogers, A. J., Clopton, P. L., Wang, P. J., Rappel, W., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2022

  View details for Web of Science ID 000890856902300

• Automatic Left Atrial Volume and Sphericity Index Calculation From Cardiac CT Using Computer Graphics Imaging and Deep Learning Feng, R., Deb, B., Ganesan, P., Tjong, F. V., Rogers, A. J., Ruiperez-Campillo, S., Somani, S., Rodrigo, M., Clopton, P. L., Zou, J., Zaharia, M., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2022

  View details for Web of Science ID 000890856908203

• ECG-Derived Features Enable Prediction of Sudden Death in Ischemic Cardiomyopathy Ruiperez-Campillo, S., Deb, B., Selvalingam, A., Feng, R., Ganesan, P., Tjong, F. V., Chang, H., Kolk, M. H., Kowalewski, C., Vasireddi, S. K., Goyal, J., Rogers, A. J., Clopton, P. L., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2022

  View details for Web of Science ID 000890856903096

• Machine learned clusters explain heterogeneity in outcomes from map-guided ablation of Atrial Fibrillation results from the large PROspective STanford AF Registry (ProSTAR) Deb, B., Rogers, A. J., Bhatia, N. K., Baykaner, T., Turakhia, M., Clopton, P. L., Chang, H. J., Brodt, C., Narayan, S. M., Wang, P. J., Viswanathan, M. N. OXFORD UNIV PRESS. 2022: 474

  View details for Web of Science ID 000894947902573

• Spatiotemporal signatures of response to atrial fibrillation ablation Ganesan, P., Rogers, A. J., Deb, B., Feng, R., Rodrigo, M., Ruiperez-Campillo, S., Tjong, F. V., Bhatia, N., Clopton, P., Rappel, W. J., Narayan, S. M. OXFORD UNIV PRESS. 2022: 601

  View details for Web of Science ID 000894947902700

• Novel electrogram featurization reveals a spectrum of response to ablation from atrial tachycardia to types of atrial fibrillation Ganesan, P., Rogers, A. J., Deb, B., Feng, R., Ruiperez-Campillo, S., Tjong, F. V., Bhatia, N., Clopton, P., Rappel, W. J., Narayan, S. M. OXFORD UNIV PRESS. 2022: 471

  View details for Web of Science ID 000894947902570

• Artificial intelligence to reduce artifact in cardiac electrophysiological signals Ruiperez-Campillo, S., Deb, B., Feng, R., Ganesan, P., Tjong, F. Y., Clopton, P., Rogers, A. J., Narayan, S. M. OXFORD UNIV PRESS. 2022: 422

  View details for Web of Science ID 000894947902521

• Reduction of artifacts and noise in small electrogram datasets without manual annotation using transfer machine learning Ruiperez-Campillo, S., Deb, B., Feng, R., Ganesan, P., Tjong, F. Y., Clopton, P., Rogers, A. J., Narayan, S. M. OXFORD UNIV PRESS. 2022: 2976

  View details for Web of Science ID 000894947902282

• Automatic left atrial segmentation from cardiac CT using computer graphics imaging and deep learning Feng, R., Deb, B., Ganesan, P., Rogers, A. J., Ruiperez-Campillo, S., Clopton, P., Tjong, F. V., Chang, H. J., Rodrigo, M., Zaharia, M., Narayan, S. M. OXFORD UNIV PRESS. 2022: 472

  View details for Web of Science ID 000894947902571

• Differential Cardiac Remodeling Profile Of Immunosuppression Drugs Sallam, K., Thomas, D., Gaddam, S., Lopez, N., Beck, A., Dexheimer, R., Beach, L. Y., Rogers, A. J., Zhang, H., Chen, I. Y., Ameen, M., Hiesinger, W., Teuteberg, J., Rhee, J. W., Wang, K., Sayed, N., Wu, J. C. LIPPINCOTT WILLIAMS & WILKINS. 2022

  View details for DOI 10.1161/res.131.suppl_1.P2115

  View details for Web of Science ID 000899250500118

• Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes. Circulation. Arrhythmia and electrophysiology Tang, S., Razeghi, O., Kapoor, R., Alhusseini, M. I., Fazal, M., Rogers, A. J., Rodrigo Bort, M., Clopton, P., Wang, P., Rubin, D., Narayan, S. M., Baykaner, T. 2022: 101161CIRCEP122010850

  Abstract

  BACKGROUND: Machine learning is a promising approach to personalize atrial fibrillation management strategies for patients after catheter ablation. Prior atrial fibrillation ablation outcome prediction studies applied classical machine learning methods to hand-crafted clinical scores, and none have leveraged intracardiac electrograms or 12-lead surface electrocardiograms for outcome prediction. We hypothesized that (1) machine learning models trained on electrograms or ECG signals can perform better at predicting patient outcomes after atrial fibrillation ablation than existing clinical scores and (2) multimodal fusion of electrogram, ECG, and clinical features can further improve the prediction of patient outcomes.METHODS: Consecutive patients who underwent catheter ablation between 2015 and 2017 with panoramic left atrial electrogram before ablation and clinical follow-up for at least 1 year following ablation were included. Convolutional neural network and a novel multimodal fusion framework were developed for predicting 1-year atrial fibrillation recurrence after catheter ablation from electrogram, ECG signals, and clinical features. The models were trained and validated using 10-fold cross-validation on patient-level splits.RESULTS: One hundred fifty-six patients (64.5±10.5 years, 74% male, 42% paroxysmal) were analyzed. Using electrogram signals alone, the convolutional neural network achieved an area under the receiver operating characteristics curve of 0.731, outperforming the existing APPLE scores (area under the receiver operating characteristics curve=0.644) and CHA2DS2-VASc scores (area under the receiver operating characteristics curve=0.650). Similarly using 12-lead ECG alone, the convolutional neural network achieved an AUROC of 0.767. Combining electrogram, ECG, and clinical features, the fusion model achieved an AUROC of 0.859, outperforming single and dual modality models.CONCLUSIONS: Deep neural networks trained on electrogram or ECG signals improved the prediction of catheter ablation outcome compared with existing clinical scores, and fusion of electrogram, ECG, and clinical features further improved the prediction. This suggests the promise of using machine learning to help treatment planning for patients after catheter ablation.

  View details for DOI 10.1161/CIRCEP.122.010850

  View details for PubMedID 35867397

• Mapping Atrial Fibrillation After Surgical Therapy to Guide Endocardial Ablation. Circulation. Arrhythmia and electrophysiology Bhatia, N. K., Shah, R. L., Deb, B., Pong, T., Kapoor, R., Rogers, A., Badhwar, N., Brodt, C., Wang, P. J., Narayan, S. M., Lee, A. M. 2022: 101161CIRCEP121010502

  Abstract

  Surgical ablation for atrial fibrillation (AF) can be effective, yet has mixed results. It is important to improve the success of AF surgery, yet unclear which endocardial lesions will best augment surgical lesion sets in individual patients. We addressed this question by systematically mapping AF endocardially after surgical ablation and relating findings to early recurrence.We studied 81 consecutive patients undergoing epicardial surgical ablation (stage 1 hybrid), of whom 64 proceeded to endocardial catheter mapping and ablation (stage 2). Stage 2 comprised high-density mapping of pulmonary vein (PV) or posterior wall (PW) reconnections, low-voltage zones (LVZs), and potential localized AF drivers. We related findings to postsurgical recurrence of AF.Mapping at stage 2 revealed PW isolation reconnection in 59.4%, PV isolation reconnection in 28.1%, and LVZ in 42.2% of patients. Postsurgical recurrence of AF occurred in 36 patients (56.3%), particularly those with long-standing persistent AF (P=0.017), but had no relationship to reconnection of PVs (P=0.53) or PW isolation (P=0.75) when compared with those without postsurgical recurrence of AF. LVZs were more common in patients with postsurgical recurrence of AF (P=0.002), long-standing persistent AF (P=0.002), advanced age (P=0.03), and elevated CHA2DS2-VASc (P=0.046). AF mapping revealed 4.4±2.7 localized focal/rotational sites near and also remote from PV or PW reconnection. After ablation at patient-specific targets, arrhythmia freedom at 1 year was 81.0% including and 73.0% excluding previously ineffective antiarrhythmic medications.After surgical ablation, AF may recur by several modes including recovery of PW or PV isolation, mechanisms related to localized LVZ, or other sustaining mechanisms. LVZs are more common in patients at high clinical risk for recurrence. Patient-specific targeting of these mechanisms yields excellent long-term outcomes from hybrid ablation.

  View details for DOI 10.1161/CIRCEP.121.010502

  View details for PubMedID 35622437

• Modeling Effects of Immunosuppressive Drugs on Human Hearts Using Induced Pluripotent Stem Cell-Derived Cardiac Organoids and Single-Cell RNA Sequencing. Circulation Sallam, K., Thomas, D., Gaddam, S., Lopez, N., Beck, A., Beach, L., Rogers, A. J., Zhang, H., Chen, I. Y., Ameen, M., Hiesinger, W., Teuteberg, J. J., Rhee, J. W., Wang, K. C., Sayed, N., Wu, J. C. 2022; 145 (17): 1367-1369

  View details for DOI 10.1161/CIRCULATIONAHA.121.054317

  View details for PubMedID 35467958

• Atrial fibrillation signatures on intracardiac electrograms identified by deep learning. Computers in biology and medicine Rodrigo, M., Alhusseini, M. I., Rogers, A. J., Krittanawong, C., Thakur, S., Feng, R., Ganesan, P., Narayan, S. M. 2022; 145: 105451

  Abstract

  BACKGROUND: Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy.OBJECTIVE: We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures.METHODS: We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65±11 years). Custom DL architectures were trained to identify AF using N=29,340 unipolar and N=23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate and timing on AF/AT classification in 246,067 EGMs reconstructed from clinical data.RESULTS: DL identified AF with AUC of 0.97±0.04 (unipolar) and 0.92±0.09 (bipolar). Rule-based classifiers misclassified 10-12% of cases. DL classification was explained by regularity in EGM shape (13%) or timing (26%), and rate (60%; p<0.001), and also by a set of unipolar EGM shapes that classified as AF independent of rate or regularity. Overall, the optimal AF 'fingerprint' comprised these specific EGM shapes, >15% timing variation, <0.48 correlation in beat-to-beat EGM shapes and CL<190ms (p<0.001).CONCLUSIONS: Deep learning of intracardiac EGMs can identify AF or AT via signatures of rate, regularity in timing or shape, and specific EGM shapes. Future work should examine if these signatures differ between different clinical subpopulations with AF.

  View details for DOI 10.1016/j.compbiomed.2022.105451

  View details for PubMedID 35429831

• TARGETING SYNCHRONIZED ELECTROGRAM ISLANDS WITHIN ATRIAL FIBRILLATION FOR ABLATION Ganesan, P., Deb, B., Feng, R., Rodrigo, M., Ruiperez-Campillo, S., Bhatia, N. K., Rogers, A. J., Clopton, P., Rappel, W., Narayan, S. M. ELSEVIER SCIENCE INC. 2022: 3

  View details for Web of Science ID 000781026600004

• A MORPHOLOGICAL OPERATION-BASED APPROACH TO AUTOMATICALLY SEPARATE AND LABEL LEFT ATRIUM BODY AND PULMONARY VEINS Feng, R., Ganesan, P., Deb, B., Rogers, A. J., Ruiperez-Campillo, S., Rodrigo, M., Zaharia, M., Clopton, P., Rappel, W., Narayan, S. M. ELSEVIER SCIENCE INC. 2022: 1244

  View details for Web of Science ID 000781026601343

• UNSUPERVISED MACHINE LEARNING IDENTIFIES PHENOTYPES FOR ATRIAL FIBRILLATION THAT PREDICT ACUTE ABLATION SUCCESS Deb, B., Ganesan, P., Feng, R., Bhatia, N. K., Rogers, A. J., Ruiperez-Campillo, S., Clopton, P., Narayan, S. M. ELSEVIER SCIENCE INC. 2022: 51

  View details for Web of Science ID 000781026600052

• Wide Complex QRS During Sotalol Administration. JAMA cardiology Rogers, A. J., Wang, P. J., Badhwar, N. 1800

  View details for DOI 10.1001/jamacardio.2021.5788

  View details for PubMedID 35080582

• Spatially Synchronized Electrogram Islands Within Atrial Fibrillation Predict Termination by Ablation Ganesan, P., Deb, B., Bhatia, N., Rodrigo, M., Feng, R., Alhusseini, M., Rogers, A. J., Krummen, D., Clopton, P. L., Rappel, W., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2021

  View details for Web of Science ID 000752020008183

• Spatial Electrical Synchronization in Patients With Atrial Fibrillation and Atrial Tachycardia Ganesan, P., Deb, B., Rodrigo, M., Feng, R., Bhatia, N., Rogers, A. J., Krummen, D., Wang, P. J., Clopton, P. L., Rappel, W., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2021

  View details for Web of Science ID 000752020008308

• Immediate and Delayed Response of Simulated Human Atrial Myocytes to Clinically-Relevant Hypokalemia FRONTIERS IN PHYSIOLOGY Clerx, M., Mirams, G. R., Rogers, A. J., Narayan, S. M., Giles, W. R. 2021; 12: 651162

  Abstract

  Although plasma electrolyte levels are quickly and precisely regulated in the mammalian cardiovascular system, even small transient changes in K+, Na+, Ca2+, and/or Mg2+ can significantly alter physiological responses in the heart, blood vessels, and intrinsic (intracardiac) autonomic nervous system. We have used mathematical models of the human atrial action potential (AP) to explore the electrophysiological mechanisms that underlie changes in resting potential (Vr) and the AP following decreases in plasma K+, [K+]o, that were selected to mimic clinical hypokalemia. Such changes may be associated with arrhythmias and are commonly encountered in patients (i) in therapy for hypertension and heart failure; (ii) undergoing renal dialysis; (iii) with any disease with acid-base imbalance; or (iv) post-operatively. Our study emphasizes clinically-relevant hypokalemic conditions, corresponding to [K+]o reductions of approximately 1.5 mM from the normal value of 4 to 4.5 mM. We show how the resulting electrophysiological responses in human atrial myocytes progress within two distinct time frames: (i) Immediately after [K+]o is reduced, the K+-sensing mechanism of the background inward rectifier current (IK1) responds. Specifically, its highly non-linear current-voltage relationship changes significantly as judged by the voltage dependence of its region of outward current. This rapidly alters, and sometimes even depolarizes, Vr and can also markedly prolong the final repolarization phase of the AP, thus modulating excitability and refractoriness. (ii) A second much slower electrophysiological response (developing 5-10 minutes after [K+]o is reduced) results from alterations in the intracellular electrolyte balance. A progressive shift in intracellular [Na+]i causes a change in the outward electrogenic current generated by the Na+/K+ pump, thereby modifying Vr and AP repolarization and changing the human atrial electrophysiological substrate. In this study, these two effects were investigated quantitatively, using seven published models of the human atrial AP. This highlighted the important role of IK1 rectification when analyzing both the mechanisms by which [K+]o regulates Vr and how the AP waveform may contribute to "trigger" mechanisms within the proarrhythmic substrate. Our simulations complement and extend previous studies aimed at understanding key factors by which decreases in [K+]o can produce effects that are known to promote atrial arrhythmias in human hearts.

  View details for DOI 10.3389/fphys.2021.651162

  View details for Web of Science ID 000659214100001

  View details for PubMedID 34122128

  View details for PubMedCentralID PMC8188899

• CONSISTENT SPATIOTEMPORAL VECTORS IN ATRIAL FIBRILLATION PREDICT RESPONSE TO ABLATION Ganesan, P., Bhatia, N., Beck, T. C., Ravi, N., Rogers, A., Krummen, D., Wang, P., Rappel, W., Narayan, S. ELSEVIER SCIENCE INC. 2021: 334

  View details for Web of Science ID 000647487500334

• CLASSIFICATION OF INDIVIDUAL ATRIAL INTRACARDIAC ELECTROGRAMS BY DEEP LEARNING Rodrigo, M., Rogers, A., Ganesan, P., Krittanawong, C., Alhusseini, M., Narayan, S. ELSEVIER SCIENCE INC. 2021: 3217

  View details for Web of Science ID 000647487503241

• PROBING MACHINE LEARNING TO SEPARATE ATRIAL FIBRILLATION FROM OTHER ARRHYTHMIAS Rodrigo, M., Rogers, A., Ganesan, P., Alhusseini, M., Krittanawong, C., Narayan, S. ELSEVIER SCIENCE INC. 2021: 3410

  View details for Web of Science ID 000647487503431

• MACHINE LEARNING CLASSIFIES INTRACARDIAC ELECTROGRAMS OF ATRIAL FIBRILLATION FROM OTHER ARRHYTHMIAS Rodrigo, M., Rogers, A., Ganesan, P., Krittanawong, C., Alhusseini, M., Narayan, S. ELSEVIER SCIENCE INC. 2021: 279

  View details for Web of Science ID 000647487500279

• VALIDATING NON-INVASIVE INDICES OF AF COMPLEXITY AGAINST INTRACARDIAC MEASUREMENTS Rodrigo, M., Alhusseini, M., Rogers, A., Narayan, S. ELSEVIER SCIENCE INC. 2021: 1354

  View details for Web of Science ID 000647487501362

• IDENTIFICATION OF AREAS OF ORGANIZED 1:1 ACTIVITY IN ATRIAL FIBRILLATION IN PATIENTS POST MAZE SURGERY Bhatia, N. K., Shah, R., Ganesan, P., Rogers, A., Pong, T., Purewal, S., Baykaner, T., Wang, P., Lee, A., Rappel, W., Narayan, S. ELSEVIER SCIENCE INC. 2021: 333

  View details for Web of Science ID 000647487500333

• Non-invasive Spatial Mapping of Frequencies in Atrial Fibrillation: Correlation With Contact Mapping FRONTIERS IN PHYSIOLOGY Rodrigo, M., Waddell, K., Magee, S., Rogers, A. J., Alhusseini, M., Hernandez-Romero, I., Costoya-Sanchez, A., Liberos, A., Narayan, S. M. 2021; 11

  View details for DOI 10.3389/fphys.2020.611266

  View details for Web of Science ID 000608806200001

• Competing Risks in Patients with Primary Prevention Implantable Cardioverter-Defibrillators: Global Electrical Heterogeneity and Clinical Outcomes (GEHCO) Study. Heart rhythm Waks, J. W., Haq, K. T., Tompkins, C. n., Rogers, A. J., Ehdaie, A. n., Bender, A. n., Minnier, J. n., Dalouk, K. n., Howell, S. n., Peiris, A. n., Raitt, M. n., Narayan, S. M., Chugh, S. S., Tereshchenko, L. G. 2021

  Abstract

  Global electrical heterogeneity (GEH) is associated with sudden cardiac death in the general population. Its utility in patients with systolic heart failure (HF) who are candidates for primary prevention (PP) implantable cardioverter-defibrillators (ICDs) is unclear.To investigate whether GEH is associated with sustained ventricular tachycardia (VT)/ventricular fibrillation (VF) leading to appropriate ICD therapies in HF patients with PP ICDs.We conducted a multicenter retrospective cohort study. GEH was measured by spatial ventricular gradient (SVG) direction (azimuth and elevation) and magnitude, QRS-T angle, and sum absolute QRST integral (SAIQRST) on pre-implant 12-lead ECGs. Survival analysis using cause-specific hazard functions compared the strength of associations with two competing outcomes: sustained VT/VF leading to appropriate ICD therapies and all-cause death without appropriate ICD therapies.We analyzed 2,668 patients (age 63±12y; 23% female; 78% white; 43% nonischemic cardiomyopathy (NICM); left ventricular ejection fraction 28±11% from 6 academic medical centers). After adjustment for demographic, clinical, device, and traditional ECG characteristics, SVG elevation (Hazard Ratio (HR) per 1 standard deviation (SD) 1.14 (95% CI 1.04-1.25); P=0.004), SVG azimuth (HR per 1 SD 1.12(1.01-1.24); P=0.039); SVG magnitude (HR per 1 SD 0.75(0.66-0.85); P<0.0001), and QRS-T angle (HR per 1 SD 1.21 (95% CI 1.08-1.36); P=0.001) were associated with appropriate ICD therapies. SAIQRST had different associations in infarct-related [HR 1.29(1.04-1.60)] and NICM [HR 0.78(0.62-0.96); Pinteraction=0.022].In patients with PP ICDs, GEH is independently associated with appropriate ICD therapies. The SVG vector points in distinctly different directions in patients with two competing outcomes.

  View details for DOI 10.1016/j.hrthm.2021.03.006

  View details for PubMedID 33684549

• Deep Neural Network Trained on Surface ECG Improves Diagnostic Accuracy of Prior Myocardial Infarction Over Q Wave Analysis Yildirim, O., Baloglu, U. B., Talo, M., Ganesan, P., Tung, J. S., Kang, G., Tooley, J., Alhusseini, M., Baykaner, T., Wang, P. J., Perez, M., Tereshchenko, L., Narayan, S. M., Rogers, A. J., IEEE IEEE. 2021

  View details for DOI 10.22489/CinC.2021.010

  View details for Web of Science ID 000821955000130

• Arrhythmia Patterns in Patients on Ibrutinib. Frontiers in cardiovascular medicine Fazal, M., Kapoor, R., Cheng, P., Rogers, A. J., Narayan, S. M., Wang, P., Witteles, R. M., Perino, A. C., Baykaner, T., Rhee, J. 1800; 8: 792310

  Abstract

  Introduction: Ibrutinib, a Bruton's tyrosine kinase inhibitor (TKI) used primarily in the treatment of hematologic malignancies, has been associated with increased incidence of atrial fibrillation (AF), with limited data on its association with other tachyarrhythmias. There are limited reports that comprehensively analyze atrial and ventricular arrhythmia (VA) burden in patients on ibrutinib. We hypothesized that long-term event monitors could reveal a high burden of atrial and VAs in patients on ibrutinib. Methods: A retrospective data analysis at a single center using electronic medical records database search tools and individual chart review was conducted to identify consecutive patients who had event monitors while on ibrutinib therapy. Results: Seventy-two patients were included in the analysis with a mean age of 76.9 ± 9.9 years and 13 patients (18%) had a diagnosis of AF prior to the ibrutinib therapy. During ibrutinib therapy, most common arrhythmias documented were non-AF supraventricular tachycardia (n = 32, 44.4%), AF (n = 32, 44%), and non-sustained ventricular tachycardia (n = 31, 43%). Thirteen (18%) patients had >1% premature atrial contraction burden; 16 (22.2%) patients had >1% premature ventricular contraction burden. In 25% of the patients, ibrutinib was held because of arrhythmias. Overall 8.3% of patients were started on antiarrhythmic drugs during ibrutinib therapy to manage these arrhythmias. Conclusions: In this large dataset of ambulatory cardiac monitors on patients treated with ibrutinib, we report a high prevalence of atrial and VAs, with a high incidence of treatment interruption secondary to arrhythmias and related symptoms. Further research is warranted to optimize strategies to diagnose, monitor, and manage ibrutinib-related arrhythmias.

  View details for DOI 10.3389/fcvm.2021.792310

  View details for PubMedID 35047578

• Three dimensional reconstruction to visualize atrial fibrillation activation patterns on curved atrial geometry. PloS one Abad, R., Collart, O., Ganesan, P., Rogers, A. J., Alhusseini, M. I., Rodrigo, M., Narayan, S. M., Rappel, W. 2021; 16 (4): e0249873

  Abstract

  BACKGROUND: The rotational activation created by spiral waves may be a mechanism for atrial fibrillation (AF), yet it is unclear how activation patterns obtained from endocardial baskets are influenced by the 3D geometric curvature of the atrium or 'unfolding' into 2D maps. We develop algorithms that can visualize spiral waves and their tip locations on curved atrial geometries. We use these algorithms to quantify differences in AF maps and spiral tip locations between 3D basket reconstructions, projection onto 3D anatomical shells and unfolded 2D surfaces.METHODS: We tested our algorithms in N = 20 patients in whom AF was recorded from 64-pole baskets (Abbott, CA). Phase maps were generated by non-proprietary software to identify the tips of spiral waves, indicated by phase singularities. The number and density of spiral tips were compared in patient-specific 3D shells constructed from the basket, as well as 3D maps from clinical electroanatomic mapping systems and 2D maps.RESULTS: Patients (59.4±12.7 yrs, 60% M) showed 1.7±0.8 phase singularities/patient, in whom ablation terminated AF in 11/20 patients (55%). There was no difference in the location of phase singularities, between 3D curved surfaces and 2D unfolded surfaces, with a median correlation coefficient between phase singularity density maps of 0.985 (0.978-0.990). No significant impact was noted by phase singularities location in more curved regions or relative to the basket location (p>0.1).CONCLUSIONS: AF maps and phase singularities mapped by endocardial baskets are qualitatively and quantitatively similar whether calculated by 3D phase maps on patient-specific curved atrial geometries or in 2D. Phase maps on patient-specific geometries may be easier to interpret relative to critical structures for ablation planning.

  View details for DOI 10.1371/journal.pone.0249873

  View details for PubMedID 33836026

• Electrical Substrate Ablation for Refractory Ventricular Fibrillation: Results of the AVATAR Study. Circulation. Arrhythmia and electrophysiology Krummen, D. E., Ho, G. n., Hoffmayer, K. S., Schweis, F. n., Baykaner, T. n., Rogers, A. J., Han, F. T., Hsu, J. C., Viswanathan, M. N., Wang, P. J., Rappel, W. J., Narayan, S. M. 2021

  Abstract

  Background - Refractory ventricular fibrillation (VF) is a challenging clinical entity, for which ablation of triggering premature ventricular complexes (PVCs) is described. When PVCs are infrequent and multifocal, the optimal treatment strategy is uncertain. Methods - We prospectively enrolled consecutive patients presenting with multiple ICD shocks for VF refractory to antiarrhythmic drug therapy, exhibiting infrequent (≤3%), multifocal PVCs (≥3 morphologies). Procedurally, VF was induced with rapid pacing and mapped, identifying sites of conduction slowing and rotation or rapid focal activation. VF electrical substrate ablation (VESA) was then performed. Outcomes were compared against reference patients with VF who were unable or unwilling to undergo catheter ablation. The primary outcome was a composite of ICD shock, electrical storm, or all-cause mortality. Results - VF was induced and mapped in 6 patients (60±10 y, LVEF 46±19%) with ischemic (n=3) and nonischemic cardiomyopathy. An average of 3.3±0.5 sites of localized reentry during VF were targeted for radiofrequency ablation (38.3±10.9 minutes) during sinus rhythm, rendering VF non-inducible with pacing. Freedom from the primary outcome was 83% in the VF ablation group versus 17% in 6 non-ablation reference patients at a median of 1.0 years (IQR 0.5-1.5 years, p=0.046) follow-up. Conclusions - VESA is associated with a reduction in the combined endpoint compared with the non-ablation reference group. Additional work is required to understand the precise pathophysiologic changes which promote VF in order to improve preventative and therapeutic strategies.

  View details for DOI 10.1161/CIRCEP.120.008868

  View details for PubMedID 33550811

• Arrhythmias Other Than Atrial Fibrillation in Those With an Irregular Pulse Detected With a Smartwatch: Findings From the Apple Heart Study. Circulation. Arrhythmia and electrophysiology Perino, A. C., Gummidipundi, S. E., Lee, J., Hedlin, H., Garcia, A., Ferris, T., Balasubramanian, V., Gardner, R. M., Cheung, L., Hung, G., Granger, C. B., Kowey, P., Rumsfeld, J. S., Russo, A. M., True Hills, M., Talati, N., Nag, D., Tsay, D., Desai, S., Desai, M., Mahaffey, K. W., Turakhia, M. P., Perez, M. V. 2021: CIRCEP121010063

  Abstract

  The Apple watch irregular pulse detection algorithm was found to have a positive predictive value of 0.84 for identification of atrial fibrillation (AF). We sought to describe the prevalence of arrhythmias other than AF in those with an irregular pulse detected on a smartwatch.The Apple Heart Study investigated a smartwatch-based irregular pulse notification algorithm to identify AF. For this secondary analysis, we analyzed participants who received an ambulatory ECG patch after index irregular pulse notification. We excluded participants with AF identified on ECG patch and described the prevalence of other arrhythmias on the remaining participant ECG patches. We also reported the proportion of participants self-reporting subsequent AF diagnosis.Among 419 297 participants enrolled in the Apple Heart Study, 450 participant ECG patches were analyzed, with no AF on 297 ECG patches (66%). Non-AF arrhythmias (excluding supraventricular tachycardias <30 beats and pauses <3 seconds) were detected in 119 participants (40.1%) with ECG patches without AF. The most common arrhythmias were frequent PACs (burden ≥1% to <5%, 15.8%; ≥5% to <15%, 8.8%), atrial tachycardia (≥30 beats, 5.4%), frequent PVCs (burden ≥1% to <5%, 6.1%; ≥5% to <15%, 2.7%), and nonsustained ventricular tachycardia (4-7 beats, 6.4%; ≥8 beats, 3.7%). Of 249 participants with no AF detected on ECG patch and patient-reported data available, 76 participants (30.5%) reported subsequent AF diagnosis.In participants with an irregular pulse notification on the Apple Watch and no AF observed on ECG patch, atrial and ventricular arrhythmias, mostly PACs and PVCs, were detected in 40% of participants. Defining optimal care for patients with detection of incidental arrhythmias other than AF is important as AF detection is further investigated, implemented, and refined.

  View details for DOI 10.1161/CIRCEP.121.010063

  View details for PubMedID 34565178

• Role of 3.3fr Mapping Catheters in Defining and Ablating Mechanisms of Ventricular Arrhythmias: A Multicenter Experience Rogers, A. J., Greif, S., Perino, A. C., Shah, R. L., Viswanathan, M. N., Tholakanahalli, V. N., Singh, D., Badhwar, N. LIPPINCOTT WILLIAMS & WILKINS. 2020

  View details for DOI 10.1161/circ.142.suppl_3.14910

  View details for Web of Science ID 000607190402319

• Machine Learning of the Electrocardiogram to Detect Regional Structural Abnormalities of the Heart Rogers, A. J., Tooley, J., Thakkar, V., Torres, J., Xu, J., Bhatia, N. K., Tung, J., Alhusseini, M., Baykaner, T., Clifford, G., Zaharia, M., Ashley, E. A., Perez, M. V., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2020

  View details for Web of Science ID 000607190400008

• Deep Learning of Intracardiac Electrograms in Atrial Arrhythmia Rodrigo, M., Rogers, A. J., Ganesan, P., Alhusseini, M., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2020

  View details for Web of Science ID 000607190402201

• Sotalol-induced Wide Complex Rhythm: What is the Mechanism? Rogers, A. J., Wang, P. J., Badhwar, N. LIPPINCOTT WILLIAMS & WILKINS. 2020

  View details for DOI 10.1161/circ.142.suppl_3.14912

  View details for Web of Science ID 000607190402320

• Classification of Atrial Fibrillation by Deep Learning of Electrogram Shapes versus Rate and Regularity Rodrigo, M., Rogers, A. J., Ganesan, P., Alhusseini, M., Xu, J., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2020

  View details for Web of Science ID 000607190402197

• Islands of Organized 1:1 Conduction Within Atrial Fibrillation as Potential Targets for Ablation Ganesan, P., Bhatia, N. K., Rogers, A. J., Krummen, D. E., Alhusseini, M., Clopton, P., Rappel, W., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2020

  View details for DOI 10.1161/circ.142.suppl_3.16313

  View details for Web of Science ID 000607190404293

• Machine Learned Cellular Phenotypes Predict Outcome in Ischemic Cardiomyopathy. Circulation research Rogers, A. J., Selvalingam, A., Alhusseini, M. I., Krummen, D. E., Corrado, C., Abuzaid, F., Baykaner, T., Meyer, C., Clopton, P., Giles, W. R., Bailis, P., Niederer, S. A., Wang, P. J., Rappel, W., Zaharia, M., Narayan, S. M. 2020

  Abstract

  RATIONALE: Susceptibility to ventricular arrhythmias (VT/VF) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside.OBJECTIVE: To develop computational phenotypes of patients with ischemic cardiomyopathy, by training then interpreting machine learning (ML) of ventricular monophasic action potentials (MAPs) to reveal phenotypes that predict long-term outcomes.METHODS AND RESULTS: We recorded 5706 ventricular MAPs in 42 patients with coronary disease (CAD) and left ventricular ejection fraction (LVEF) {less than or equal to}40% during steady-state pacing. Patients were randomly allocated to independent training and testing cohorts in a 70:30 ratio, repeated K=10 fold. Support vector machines (SVM) and convolutional neural networks (CNN) were trained to 2 endpoints: (i) sustained VT/VF or (ii) mortality at 3 years. SVM provided superior classification. For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each endpoint. Patient-level predictions in independent test cohorts yielded c-statistics of 0.90 for sustained VT/VF (95% CI: 0.76-1.00) and 0.91 for mortality (95% CI: 0.83-1.00) and were the most significant multivariate predictors. Interpreting trained SVM revealed MAP morphologies that, using in silico modeling, revealed higher L-type calcium current or sodium calcium exchanger as predominant phenotypes for VT/VF.CONCLUSIONS: Machine learning of action potential recordings in patients revealed novel phenotypes for long-term outcomes in ischemic cardiomyopathy. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions.

  View details for DOI 10.1161/CIRCRESAHA.120.317345

  View details for PubMedID 33167779

• Comparing machine learning approaches to identify myocardial scar from the ECG Tung, J., Rogers, A. J., Ravi, N., Bhatia, N. K., Shah, R. L., Purewal, S. K., Baykaner, T., Rappel, W. J., Viswanathan, M. N., Brodt, C. R., Wang, P. J., Clifford, G., Tereshchenko, L., Narayan, S. M. OXFORD UNIV PRESS. 2020: 2048

  View details for Web of Science ID 000606106302053

• Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management. Nature reviews. Cardiology Krittanawong, C., Rogers, A. J., Johnson, K. W., Wang, Z., Turakhia, M. P., Halperin, J. L., Narayan, S. M. 2020

  Abstract

  Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in patients. Technological advances in computing have led to the introduction of novel physiological biosignals that can increase the frequency at which abnormalities in cardiovascular parameters can be detected, making expert-level, automated diagnosis a reality. However, use of these biosignals for diagnosis also raises numerous concerns related to accuracy and actionability within clinical guidelines, in addition to medico-legal and ethical issues. Analytical methods such as machine learning can potentially increase the accuracy and improve the actionability of device-based diagnoses. Coupled with interoperability of data to widen access to all stakeholders, seamless connectivity (an internet of things) and maintenance of anonymity, this approach could ultimately facilitate near-real-time diagnosis and therapy. These tools are increasingly recognized by regulatory agencies and professional medical societies, but several technical and ethical issues remain. In this Review, we describe the current state of cardiovascular monitoring along the continuum from biosignal acquisition to the identification of novel biosensors and the development of analytical techniques and ultimately to regulatory and ethical issues. Furthermore, we outline new paradigms for cardiovascular monitoring.

  View details for DOI 10.1038/s41569-020-00445-9

  View details for PubMedID 33037325

• Machine Learning to Classify Intracardiac Electrical Patterns during Atrial Fibrillation. Circulation. Arrhythmia and electrophysiology Alhusseini, M. I., Abuzaid, F., Rogers, A. J., Zaman, J. A., Baykaner, T., Clopton, P., Bailis, P., Zaharia, M., Wang, P. J., Rappel, W., Narayan, S. M. 2020

  Abstract

  Background - Advances in ablation for atrial fibrillation (AF) continue to be hindered by ambiguities in mapping, even between experts. We hypothesized that convolutional neural networks (CNN) may enable objective analysis of intracardiac activation in AF, which could be applied clinically if CNN classifications could also be explained. Methods - We performed panoramic recording of bi-atrial electrical signals in AF. We used the Hilbert-transform to produce 175,000 image grids in 35 patients, labeled for rotational activation by experts who showed consistency but with variability (kappa=0.79). In each patient, ablation terminated AF. A CNN was developed and trained on 100,000 AF image grids, validated on 25,000 grids, then tested on a separate 50,000 grids. Results - In the separate test cohort (50,000 grids), CNN reproducibly classified AF image grids into those with/without rotational sites with 95.0% accuracy (CI 94.8-95.2%). This accuracy exceeded that of support vector machines, traditional linear discriminant and k-nearest neighbor statistical analyses. To probe the CNN, we applied Gradient-weighted Class Activation Mapping which revealed that the decision logic closely mimicked rules used by experts (C-statistic 0.96). Conclusions - Convolutional neural networks improved the classification of intracardiac AF maps compared to other analyses, and agreed with expert evaluation. Novel explainability analyses revealed that the CNN operated using a decision logic similar to rules used by experts, even though these rules were not provided in training. We thus describe a scaleable platform for robust comparisons of complex AF data from multiple systems, which may provide immediate clinical utility to guide ablation.

  View details for DOI 10.1161/CIRCEP.119.008160

  View details for PubMedID 32631100

• PREDICTING SUDDEN CARDIAC DEATH BY MACHINE LEARNING OF VENTRICULAR ACTION POTENTIALS Selvalingam, A., Alhusseini, M., Rogers, A. J., Krummen, D., Abuzaid, F. M., Baykaner, T., Clopton, P., Bailis, P., Zaharia, M., Wang, P., Narayan, S. ELSEVIER SCIENCE INC. 2020: 427

  View details for Web of Science ID 000522979100416

• LARGER ORGANIZED AREAS IN PERSISTENT ATRIAL FIBRILLATION PREDICTS TERMINATION DURING ABLATION Ravi, N., Rogers, A. J., Bhatia, N., Tung, J. S., Krummen, D., Sauer, W., Alhusseini, M., Baykaner, T., Wang, P., Rappel, W., Narayan, S. ELSEVIER SCIENCE INC. 2020: 279

  View details for Web of Science ID 000522979100273

• Non-invasive Spatial Mapping of Frequencies in Atrial Fibrillation: Correlation With Contact Mapping. Frontiers in physiology Rodrigo, M., Waddell, K., Magee, S., Rogers, A. J., Alhusseini, M., Hernandez-Romero, I., Costoya-Sánchez, A., Liberos, A., Narayan, S. M. 2020; 11: 611266

  Abstract

  Introduction: Regional differences in activation rates may contribute to the electrical substrates that maintain atrial fibrillation (AF), and estimating them non-invasively may help guide ablation or select anti-arrhythmic medications. We tested whether non-invasive assessment of regional AF rate accurately represents intracardiac recordings. Methods: In 47 patients with AF (27 persistent, age 63 ± 13 years) we performed 57-lead non-invasive Electrocardiographic Imaging (ECGI) in AF, simultaneously with 64-pole intracardiac signals of both atria. ECGI was reconstructed by Tikhonov regularization. We constructed personalized 3D AF rate distribution maps by Dominant Frequency (DF) analysis from intracardiac and non-invasive recordings. Results: Raw intracardiac and non-invasive DF differed substantially, by 0.54 Hz [0.13 - 1.37] across bi-atrial regions (R2 = 0.11). Filtering by high spectral organization reduced this difference to 0.10 Hz (cycle length difference of 1 - 11 ms) [0.03 - 0.42] for patient-level comparisons (R2 = 0.62), and 0.19 Hz [0.03 - 0.59] and 0.20 Hz [0.04 - 0.61] for median and highest DF, respectively. Non-invasive and highest DF predicted acute ablation success (p = 0.04). Conclusion: Non-invasive estimation of atrial activation rates is feasible and, when filtered by high spectral organization, provide a moderate estimate of intracardiac recording rates in AF. Non-invasive technology could be an effective tool to identify patients who may respond to AF ablation for personalized therapy.

  View details for DOI 10.3389/fphys.2020.611266

  View details for PubMedID 33584334

  View details for PubMedCentralID PMC7873897

• The interconnected atrium: Acute impact of pulmonary vein isolation on remote atrial tissue. Journal of cardiovascular electrophysiology Rogers, A. J., Baykaner, T. n., Narayan, S. M. 2020

  View details for DOI 10.1111/jce.14389

  View details for PubMedID 32090385

• Non-invasive Assessment of Complexity of Atrial Fibrillation: Correlation with Contact Mapping and Impact of Ablation Circulation: Arrhythmia and Electrophysiology Rodrigo, M., Climent, A. M., Hernández-Romero, I., et al 2020
• Continuous Ablation Improves Lesion Maturation Compared with Intermittent Ablation Strategies. Journal of cardiovascular electrophysiology Rogers, A. J., Borne, R. T., Ho, G. n., Sauer, W. H., Wang, P. J., Narayan, S. M., Zheng, L. n., Nguyen, D. T. 2020

  Abstract

  Interrupted ablation is increasingly proposed as part of high-power short duration radiofrequency ablation (RFA) strategies and may also result from loss of contact from respiratory patterns or cardiac motion.To study the extent that ablation interruption affects lesions.In ex vivo and in vivo experiments, lesion characteristics and tissue temperatures were compared between continuous (Group 1) and interrupted (Groups 2,3) RFA with equal total ablation duration and contact force. Extended duration ablation lesions were also characterized from 1 to 5 minutes.In the ex vivo study, continuous RFA (Group 1) produced larger total lesion volumes compared with each interrupted ablation lesion group (273.8±36.5 mm3 vs. 205.1±34.2 and vs. 174.3±32.3, all p<0.001). Peak temperatures for Group 1 were higher at 3mm and 5mm than Groups 2 and 3. In vivo, continuous ablation resulted in larger lesions, greater lesion depths, and higher tissue temperatures. Longer ablation durations created larger lesion volumes and increased lesion depths. However, after 3 min of ablation, the rate of lesion volume and depth formation decreased.Continuous RFA delivery resulted in larger and deeper lesions with higher tissue temperatures compared to interrupted ablation. This work may have implications for high-power short duration ablation strategies, motivates strategies to reduce variations in ablation delivery, and provides an upper limit for ablation duration beyond which power delivery has diminishing returns. This article is protected by copyright. All rights reserved.

  View details for DOI 10.1111/jce.14510

  View details for PubMedID 32323395

• Non-Invasive Assessment of Complexity of Atrial Fibrillation: Correlation with Contact Mapping and Impact of Ablation. Circulation. Arrhythmia and electrophysiology Rodrigo, M. n., Climent, A. M., Hernández-Romero, I. n., Liberos, A. n., Baykaner, T. n., Rogers, A. J., Alhusseini, M. n., Wang, P. J., Fernández-Avilés, F. n., Guillem, M. S., Narayan, S. M., Atienza, F. n. 2020

  Abstract

  Background - It is difficult to non-invasively phenotype atrial fibrillation (AF) in a way that reflects clinical endpoints such as response to therapy. We set out to map electrical patterns of disorganization and regions of reentrant activity in AF from the body surface using electrocardiographic imaging (ECGI), calibrated to panoramic intracardiac recordings and referenced to AF termination by ablation. Methods - Bi-atrial intracardiac electrograms of 47 AF patients at ablation (30 persistent, 29 male, 63±9 years) were recorded with 64-pole basket catheters and simultaneous 57-lead body surface ECGs. Atrial epicardial electrical activity was reconstructed and organized sites were invasively and non-invasively tracked in 3D using phase singularity (PS). In a subset of 17 patients, sites of AF organization were targeted for ablation. Results - Body surface mapping showed greater AF organization near intracardially-detected drivers than elsewhere, both in PS density (2.3±2.1 vs 1.9±1.6, p=0.02) and number of drivers (3.2±2.3 vs 2.7±1.7, p=0.02). Complexity, defined as the number of stable AF reentrant sites, was concordant between non-invasive and invasive methods (r2 =0.5, CC=0.71). In the subset receiving targeted ablation, AF complexity showed lower values in those in whom AF terminated than those in whom AF did not terminate (p<0.01). Conclusions - AF complexity tracked non-invasively correlates well with organized and disorganized regions detected by panoramic intracardiac mapping, and correlates with the acute outcome by ablation. This approach may assist in bedside monitoring of therapy or in improving the efficacy of ongoing ablation procedures.

  View details for DOI 10.1161/CIRCEP.119.007700

  View details for PubMedID 32078374

• Termination of persistent atrial fibrillation by ablating sites that control large atrial areas. Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology Bhatia, N. K., Rogers, A. J., Krummen, D. E., Hossainy, S. n., Sauer, W. n., Miller, J. M., Alhusseini, M. I., Peszek, A. n., Armenia, E. n., Baykaner, T. n., Brachmann, J. n., Turakhia, M. P., Clopton, P. n., Wang, P. J., Rappel, W. J., Narayan, S. M. 2020

  Abstract

  Persistent atrial fibrillation (AF) has been explained by multiple mechanisms which, while they conflict, all agree that more disorganized AF is more difficult to treat than organized AF. We hypothesized that persistent AF consists of interacting organized areas which may enlarge, shrink or coalesce, and that patients whose AF areas enlarge by ablation are more likely to respond to therapy.We mapped vectorial propagation in persistent AF using wavefront fields (WFF), constructed from raw unipolar electrograms at 64-pole basket catheters, during ablation until termination (Group 1, N = 20 patients) or cardioversion (Group 2, N = 20 patients). Wavefront field mapping of patients (age 61.1 ± 13.2 years, left atrium 47.1 ± 6.9 mm) at baseline showed 4.6 ± 1.0 organized areas, each separated by disorganization. Ablation of sites that led to termination controlled larger organized area than competing sites (44.1 ± 11.1% vs. 22.4 ± 7.0%, P < 0.001). In Group 1, ablation progressively enlarged unablated areas (rising from 32.2 ± 15.7% to 44.1 ± 11.1% of mapped atrium, P < 0.0001). In Group 2, organized areas did not enlarge but contracted during ablation (23.6 ± 6.3% to 15.2 ± 5.6%, P < 0.0001).Mapping wavefront vectors in persistent AF revealed competing organized areas. Ablation that progressively enlarged remaining areas was acutely successful, and sites where ablation terminated AF were surrounded by large organized areas. Patients in whom large organized areas did not emerge during ablation did not exhibit AF termination. Further studies should define how fibrillatory activity is organized within such areas and whether this approach can guide ablation.

  View details for DOI 10.1093/europace/euaa018

  View details for PubMedID 32243508

• Letter in reply: Continuous radiofrequency ablation in scar-based arrhythmia substrate. Journal of cardiovascular electrophysiology Rogers, A. J., Nguyen, D. T. 2020

  View details for DOI 10.1111/jce.14534

  View details for PubMedID 32430949

• Presence of Ablation of Atrial Organized Zones May Predict Response to Ablation Rogers, A. J., Bhatia, N. K., Alhusseini, M., Moosvi, N. F., Baykaner, T., Brodt, C., Clopton, P. L., Wang, P. J., Rappel, W., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2019

  View details for Web of Science ID 000529998006166

• Persistent Atrial Fibrillation as a Dynamic Network of Competing Zones of Control Bhatia, N. K., Rogers, A. J., Krummen, D. E., Alhusseini, M., Moosvi, N., Brodt, C., Baykaner, T., Wang, P. J., Rappel, W., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2019

  View details for Web of Science ID 000529998001027

• Developing Convolutional Neural Networks for Deep Learning of Ventricular Action Potentials to Predict Risk for Ventricular Arrhythmias Selvalingam, A., Alhusseini, M., Rogers, A. J., Krummen, D. E., Abuzaid, F. M., Zaman, J. A., Baykaner, T., Clopton, P. L., Bailis, P., Zaharia, M., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2019

  View details for Web of Science ID 000529998005215

• Machine Learning of Remodeled Ventricular Action Potentials and Long-Term Follow-Up of Ventricular Arrhythmias Rogers, A. J., Alhusseini, M., Selvalingam, A., Krummen, D. E., Abuzaid, F., Zaman, J. A., Baykaner, T., Clopton, P., Bailis, P., Zaharia, M., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2019

  View details for Web of Science ID 000529998007250

• Ablation of Sites That Control Large Areas During Atrial Fibrillation Cause Acute Termination Rogers, A. J., Bhatia, N. K., Alhusseini, M., Moosvi, N. F., Baykaner, T., Brodt, C., Clopton, P., Wang, P. J., Rappel, W., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2019

  View details for Web of Science ID 000529998006173

• Electrographic flow mapping in persistent atrial fibrillation Baykaner, T., Alhusseini, M., Rogers, A., Sauer, W., Ruppersberg, P., Narayan, S. WILEY. 2019: 1745–46

  View details for Web of Science ID 000485280500073

• Wavefront Field Mapping Reveals a Physiologic Network Between Drivers Where Ablation Terminates Atrial Fibrillation. Circulation. Arrhythmia and electrophysiology Leef, G., Shenasa, F., Bhatia, N. K., Rogers, A. J., Sauer, W., Miller, J. M., Swerdlow, M., Tamboli, M., Alhusseini, M. I., Armenia, E., Baykaner, T., Brachmann, J., Turakhia, M. P., Atienza, F., Rappel, W., Wang, P. J., Narayan, S. M. 2019; 12 (8): e006835

  Abstract

  BACKGROUND: Localized drivers are proposed mechanisms for persistent atrial fibrillation (AF) from optical mapping of human atria and clinical studies of AF, yet are controversial because drivers fluctuate and ablating them may not terminate AF. We used wavefront field mapping to test the hypothesis that AF drivers, if concurrent, may interact to produce fluctuating areas of control to explain their appearance/disappearance and acute impact of ablation.METHODS: We recruited 54 patients from an international registry in whom persistent AF terminated by targeted ablation. Unipolar AF electrograms were analyzed from 64-pole baskets to reconstruct activation times, map propagation vectors each 20 ms, and create nonproprietary phase maps.RESULTS: Each patient (63.6±8.5 years, 29.6% women) showed 4.0±2.1 spatially anchored rotational or focal sites in AF in 3 patterns. First, a single (type I; n=7) or, second, paired chiral-antichiral (type II; n=5) rotational drivers controlled most of the atrial area. Ablation of 1 to 2 large drivers terminated all cases of types I or II AF. Third, interaction of 3 to 5 drivers (type III; n=42) with changing areas of control. Targeted ablation at driver centers terminated AF and required more ablation in types III versus I (P=0.02 in left atrium).CONCLUSIONS: Wavefront field mapping of persistent AF reveals a pathophysiologic network of a small number of spatially anchored rotational and focal sites, which interact, fluctuate, and control varying areas. Future work should define whether AF drivers that control larger atrial areas are attractive targets for ablation.

  View details for DOI 10.1161/CIRCEP.118.006835

  View details for PubMedID 31352796

• Propagation velocity at atrial fibrillation sources: Go with the flow INTERNATIONAL JOURNAL OF CARDIOLOGY Rogers, A. J., Bhatia, N. K., Brodt, C. R., Narayan, S. M. 2019; 286: 76–77

  View details for DOI 10.1016/j.ijcard.2019.04.007

  View details for Web of Science ID 000465271500018

• Editorial: High density mapping of atrial fibrillation sources JOURNAL OF CARDIOVASCULAR ELECTROPHYSIOLOGY Rogers, A. J., Bhatia, N. K., Brodt, C., Narayan, S. M. 2019; 30 (6): 964–65

  View details for DOI 10.1111/jce.13949

  View details for Web of Science ID 000472680300020

• Comparing phase and electrographic flow mapping for persistent atrial fibrillation PACE-PACING AND CLINICAL ELECTROPHYSIOLOGY Swerdlow, M., Tamboli, M., Alhusseini, M. I., Moosvi, N., Rogers, A. J., Leef, G., Wang, P. J., Rillig, A., Brachmann, J., Sauer, W. H., Ruppersberg, P., Narayan, S. M., Baykaner, T. 2019; 42 (5): 499–507

  View details for DOI 10.1111/pace.13649

  View details for Web of Science ID 000465228600002

• Predictability in complex atrial arrhythmias: The N/N-1 algorithm to guide ablation of atrial tachycardias HEART RHYTHM Kaiser, D. W., Rogers, A. J., Narayan, S. M. 2019; 16 (4): 562–63

  View details for DOI 10.1016/j.hrthm.2018.11.018

  View details for Web of Science ID 000462618400013

• SITES THAT CONTROL LARGER AREAS DURING ATRIAL FIBRILLATION MAY DETERMINE TERMINATION DURING ABLATION Bhatia, N. K., Hossainy, S., Rogers, A., Alhusseini, M., Brodt, C., Moosvi, N., Baykaner, T., Wang, P., Rappel, W., Narayan, S. ELSEVIER SCIENCE INC. 2019: 400

  View details for Web of Science ID 000460565900400

• INTRACLASS CORRELATIONS OF VOLTAGE, FRACTIONATED ELECTROGRAMS, AND DOMINANT FREQUENCY IN PATIENTS WHERE LOCALIZED ABLATION TERMINATED PERSISTENT ATRIAL FIBRILLATION Rogers, A. J., Moosvi, N., Singh, A., Alhusseini, M., Baykaner, T., Clopton, P., Rappel, W., Wang, P., Narayan, S. ELSEVIER SCIENCE INC. 2019: 521

  View details for Web of Science ID 000460565900521

• MACHINE LEARNING IDENTIFIES SITES WHERE ABLATION TERMINATES PERSISTENT ATRIAL FIBRILLATION Alhusseini, M., Abuzaid, F., Clopton, P., Rogers, A., Rodrigo, M., Baykaner, T., Wang, P., Rappel, W., Narayan, S. ELSEVIER SCIENCE INC. 2019: 301

  View details for Web of Science ID 000460565900301

• Structurally-based electrical predictors of atrial arrhythmias INTERNATIONAL JOURNAL OF CARDIOLOGY Rogers, A. J., Moosvi, N. F., Brodt, C. R., Narayan, S. M. 2019; 278: 151–52

  View details for DOI 10.1016/j.ijcard.2018.12.002

  View details for Web of Science ID 000456396200036

• Integrating blockchain technology with artificial intelligence for cardiovascular medicine. Nature reviews. Cardiology Krittanawong, C. n., Rogers, A. J., Aydar, M. n., Choi, E. n., Johnson, K. W., Wang, Z. n., Narayan, S. M. 2019

  View details for DOI 10.1038/s41569-019-0294-y

  View details for PubMedID 31605093

• Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. The New England journal of medicine Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., Hung, G., Lee, J., Kowey, P., Talati, N., Nag, D., Gummidipundi, S. E., Beatty, A., Hills, M. T., Desai, S., Granger, C. B., Desai, M., Turakhia, M. P., Apple Heart Study Investigators, Perez, M. V., Turakhia, M. P., Lhamo, K., Smith, S., Berdichesky, M., Sharma, B., Mahaffey, K., Parizo, J., Olivier, C., Nguyen, M., Tallapalli, S., Kaur, R., Gardner, R., Hung, G., Mitchell, D., Olson, G., Datta, S., Gerenrot, D., Wang, X., McCoy, P., Satpathy, B., Jacobsen, H., Makovey, D., Martin, A., Perino, A., O'Brien, C., Gupta, A., Toruno, C., Waydo, S., Brouse, C., Dorfman, D., Stein, J., Huang, J., Patel, M., Fleischer, S., Doll, E., O'Reilly, M., Dedoshka, K., Chou, M., Daniel, H., Crowley, M., Martin, C., Kirby, T., Brumand, M., McCrystale, K., Haggerty, M., Newberger, J., Keen, D., Antall, P., Holbrook, K., Braly, A., Noone, G., Leathers, B., Montrose, A., Kosowsky, J., Lewis, D., Finkelmeier, J. R., Bemis, K., Mahaffey, K. W., Desai, M., Talati, N., Nag, D., Rajmane, A., Desai, S., Caldbeck, D., Cheung, L., Granger, C., Rumsfeld, J., Kowey, P. R., Hills, M. T., Russo, A., Rockhold, F., Albert, C., Alonso, A., Wruck, L., Friday, K., Wheeler, M., Brodt, C., Park, S., Rogers, A., Jones, R., Ouyang, D., Chang, L., Yen, A., Dong, J., Mamic, P., Cheng, P., Shah, R., Lorvidhaya, P. 2019; 381 (20): 1909–17

  Abstract

  BACKGROUND: Optical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown.METHODS: Participants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10.RESULTS: We recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed - which had been applied, on average, 13 days after notification - atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events.CONCLUSIONS: The probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.).

  View details for DOI 10.1056/NEJMoa1901183

  View details for PubMedID 31722151

• Editorial: High density mapping of atrial fibrillation sources. Journal of cardiovascular electrophysiology Rogers, A. J., Bhatia, N. K., Brodt, C. n., Narayan, S. M. 2019

  View details for PubMedID 31056801

• Online webinar training to analyse complex atrial fibrillation maps: A randomized trial. PloS one Mesquita, J. n., Maniar, N. n., Baykaner, T. n., Rogers, A. J., Swerdlow, M. n., Alhusseini, M. I., Shenasa, F. n., Brizido, C. n., Matos, D. n., Freitas, P. n., Santos, A. R., Rodrigues, G. n., Silva, C. n., Rodrigo, M. n., Dong, Y. n., Clopton, P. n., Ferreira, A. M., Narayan, S. M. 2019; 14 (7): e0217988

  Abstract

  Specific tools have been recently developed to map atrial fibrillation (AF) and help guide ablation. However, when used in clinical practice, panoramic AF maps generated from multipolar intracardiac electrograms have yielded conflicting results between centers, likely due to their complexity and steep learning curve, thus limiting the proper assessment of its clinical impact.The main purpose of this trial was to assess the impact of online training on the identification of AF driver sites where ablation terminated persistent AF, through a standardized training program. Extending this concept to mobile health was defined as a secondary objective.An online database of panoramic AF movies was generated from a multicenter registry of patients in whom targeted ablation terminated non-paroxysmal AF, using a freely available method (Kuklik et al-method A) and a commercial one (RhythmView-method B). Cardiology Fellows naive to AF mapping were enrolled and randomized to training vs no training (control). All participants evaluated an initial set of movies to identify sites of AF termination. Participants randomized to training evaluated a second set of movies in which they received feedback on their answers. Both groups re-evaluated the initial set to assess the impact of training. This concept was then migrated to a smartphone application (App).12 individuals (median age of 30 years (IQR 28-32), 6 females) read 480 AF maps. Baseline identification of AF termination sites by ablation was poor (40%±12% vs 42%±11%, P = 0.78), but similar for both mapping methods (P = 0.68). Training improved accuracy for both methods A (P = 0.001) and B (p = 0.012); whereas controls showed no change in accuracy (P = NS). The Smartphone App accessed AF maps from multiple systems on the cloud to recreate this training environment.Digital online training improved interpretation of panoramic AF maps in previously inexperienced clinicians. Combining online clinical data, smartphone apps and other digital resources provides a powerful, scalable approach for training in novel techniques in electrophysiology.

  View details for DOI 10.1371/journal.pone.0217988

  View details for PubMedID 31269029

• Dielectric-Based Imaging And Navigation Of The Heart. Heart rhythm Rogers, A. J., Narayan, S. M. 2019

  View details for DOI 10.1016/j.hrthm.2019.07.016

  View details for PubMedID 31323349

• Structurally-based electrical predictors of atrial arrhythmias. International journal of cardiology Rogers, A. J., Moosvi, N. F., Brodt, C. R., Narayan, S. M. 2018

  View details for PubMedID 30528625

• Predictability in Complex Atrial Arrhythmias: the N/N-1 Algorithm to Guide Ablation of Atrial Tachycardias. Heart rhythm Kaiser, D. W., Rogers, A. J., Narayan, S. M. 2018

  View details for PubMedID 30465903

• AF Drivers Where Ablation Terminates Persistent AF Fluctuate Due to Competing Drivers but Remain Anchored in Specific Locations Meckler, G. L., Kowalewski, C. A., Rogers, A. J., Rodrigo, M., Clopton, P., Shenasa, F., Alhusseini, M., Swerdlow, M., Joshi, V., Hossainy, S., Zaman, J., Baykaner, T., Brachmann, J., Miller, J., Krummen, D. E., Sauer, W., Viswanathan, M., Wang, P., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2018

  View details for Web of Science ID 000528619407352

• Electrode Density is Greater at Sites Where Ablation Acutely Terminates Atrial Fibrillation Rogers, A. J., Juan, R. C., Collart, O., Swerdlow, M., Alhusseini, M., Rodrigo, M., Kowalewski, C., Baykaner, T., Zaman, J., Wang, P. J., Rappel, W., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2018

  View details for Web of Science ID 000528619405137

• Comparing Multiple Mapping Methods at Sites of AF Termination: The COMPARE-AF Registry. Zaman, J. A., Baykaner, T., Meckler, G., Clopton, P., Alhusseini, M., Shenasa, F., Kowalewski, C., Rogers, A., Vidmar, D., Krummen, D., Viswanathan, M., Rappel, W., Brachmann, J., Peters, N., Miller, J., Wang, P., Sauer, W., Atienza, F., Narayan, S. LIPPINCOTT WILLIAMS & WILKINS. 2018

  View details for Web of Science ID 000528619405012

• Sites Where Ablation Terminated Atrial Fibrillation Identified by Machine Learning Models Alhusseini, M., Abuzaid, F., Swerdlow, M., Meckler, G., Clopton, P., Rogers, A., Rodrigo, M., Baykaner, T., Zaman, J., Kowalewski, C., Shenasa, F., Atienza, F., Mohan, N., Wang, P. J., Rappel, W. J., Bailis, P., Zaharia, M., Narayan, S. LIPPINCOTT WILLIAMS & WILKINS. 2018

  View details for Web of Science ID 000528619403044

• Machine Learning Reveals That Drivers for Persistent Atrial Fibrillation at Termination Sites Show Irregular Rotational Cycles and Domain Size Alhusseini, M., Abuzaid, F., Swerdlow, M., Clopton, P., Meckler, G. L., Maniar, N. M., Rogers, A., Rodrigo, M., Baykaner, T., Zaman, J., Kowalewski, C., Shenasa, F., Tamboli, M., Viswanathan, M., Wang, P., Atienza, F., Rappel, W. J., Bailis, P., Zaharia, M., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2018

  View details for Web of Science ID 000528619404020

• Vector Propagation Automatically Identifies Sites of Termination of Persistent Atrial Fibrillation by Ablation Leef, G., Shenasa, F., Rogers, A. J., Baykaner, T., Atienza, F., Wang, P. J., Rappel, W., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2018

  View details for Web of Science ID 000528619402355

• Procedural and Clinical Determinants of Acute Success of Driver Ablation for Persistent Atrial Fibrillation Baykaner, T., Rogers, A. J., Rodrigo, M., Alhusseini, M., Zaman, J. A., Wang, P. J., Narayan, S. M., Spitzer, S., Szili-Torok, T. LIPPINCOTT WILLIAMS & WILKINS. 2018

  View details for Web of Science ID 000528619404248

• Localized Driver Regions That Control Larger Regions of the Atria May Be Critical to Sustaining Atrial Fibrillation: Analyses From Novel Vector Mapping Leef, G., Shenasa, F., Sauer, W., Miller, J. M., Vidmar, D., Swerdlow, M. A., Tomboli, M., Rogers, A. J., Alhusseini, M., Armenia, E., Baykaner, T., Brachmann, J., Atienza, F., Wang, P. J., Rappel, W., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2018

  View details for Web of Science ID 000528619402146

• Integrating mapping methods for atrial fibrillation. Pacing and clinical electrophysiology : PACE Rogers, A. J., Tamboli, M., Narayan, S. M. 2018

  View details for DOI 10.1111/pace.13476

  View details for PubMedID 30144115

• Interaction of Localized Drivers and Disorganized Activation in Persistent Atrial Fibrillation: Reconciling Putative Mechanisms Using Multiple Mapping Techniques CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY Kowalewski, C. B., Shenasa, F., Rodrigo, M., Clopton, P., Meckler, G., Alhusseini, M. I., Swerdlow, M. A., Joshi, V., Hossainy, S., Zaman, J. B., Baykaner, T., Rogers, A. J., Brachmann, J., Miller, J. M., Krummen, D. E., Sauer, W. H., Peters, N. S., Wang, P. J., Narayan, S. M. 2018; 11 (6): e005846

  Abstract

  Mechanisms for persistent atrial fibrillation (AF) are unclear. We hypothesized that putative AF drivers and disorganized zones may interact dynamically over short time scales. We studied this interaction over prolonged durations, focusing on regions where ablation terminates persistent AF using 2 mapping methods.We recruited 55 patients with persistent AF in whom ablation terminated AF prior to pulmonary vein isolation from a multicenter registry. AF was mapped globally using electrograms for 360±45 cycles using (1) a published phase method and (2) a commercial activation/phase method.Patients were 62.2±9.7 years, 76% male. Sites of AF termination showed rotational/focal patterns by methods 1 and 2 (51/55 vs 55/55; P=0.13) in spatially conserved regions, yet fluctuated over time. Time points with no AF driver showed competing drivers elsewhere or disordered waves. Organized regions were detected for 61.6±23.9% and 70.6±20.6% of 1 minute per method (P=nonsignificant), confirmed by automatic phase tracking (P<0.05). To detect AF drivers with >90% sensitivity, 8 to 32 s of AF recordings were required depending on driver definition.Sites at which persistent AF terminated by ablation show organized activation that fluctuate over time, because of collision from concurrent organized zones or fibrillatory waves, yet recur in conserved spatial regions. Results were similar by 2 mapping methods. This network of competing mechanisms should be reconciled with existing disorganized or driver mechanisms for AF, to improve clinical mapping and ablation of persistent AF.URL: http://www.clinicaltrials.gov. Unique identifier: NCT02997254.

  View details for PubMedID 29884620

• Clinical Implications of Ablation of Drivers for Atrial Fibrillation A Systematic Review and Meta-Analysis CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY Baykaner, T., Rogers, A. J., Meckler, G. L., Zaman, J., Navara, R., Rodrigo, M., Alhusseini, M., Kowalewski, C. B., Viswanathan, M. N., Narayan, S. M., Clopton, P., Wang, P. J., Heidenreich, P. A. 2018; 11 (5)

  View details for DOI 10.1161/CIRCEP.117.006119

  View details for Web of Science ID 000439150300011

• Independent mapping methods reveal rotational activation near pulmonary veins where atrial fibrillation terminates before pulmonary vein isolation JOURNAL OF CARDIOVASCULAR ELECTROPHYSIOLOGY Navara, R., Leef, G., Shenasa, F., Kowalewski, C., Rogers, A. J., Meckler, G., Zaman, J. B., Baykaner, T., Park, S., Turakhia, M. P., Zei, P., Viswanathan, M., Wang, P. J., Narayan, S. M. 2018; 29 (5): 687–95

  View details for DOI 10.1111/jce.13446

  View details for Web of Science ID 000433580000005

• CAVOTRICUSPID ISTHMUS ABLATION FOR TREATMENT OF RECURRENT ATRIAL TACHYARRHYTHMIA IN PATIENT WITH DRUG-INDUCED TORSADE DE POINTES AND SEVERE SYSTOLIC HEART FAILURE Rogers, A., Viswanathan, M. ELSEVIER SCIENCE INC. 2018: 2565

  View details for DOI 10.1016/S0735-1097(18)33106-1

  View details for Web of Science ID 000429659705015

• Independent mapping methods reveal rotational activation near pulmonary veins where atrial fibrillation terminates before pulmonary vein isolation. Journal of cardiovascular electrophysiology Navara, R., Leef, G., Shenasa, F., Kowalewski, C., Rogers, A. J., Meckler, G., Zaman, J. A., Baykaner, T., Park, S., Turakhia, M. P., Zei, P., Viswanathan, M., Wang, P. J., Narayan, S. M. 2018

  Abstract

  OBJECTIVE: To investigate mechanisms by which atrial fibrillation (AF) may terminate during ablation near the pulmonary veins before the veins are isolated (PVI).INTRODUCTION: It remains unstudied how AF may terminate during ablation before PVs are isolated, or how patients with PV reconnection can be arrhythmia-free. We studied patients in whom PV antral ablation terminated AF before PVI, using two independent mapping methods.METHODS: We studied patients with AF referred for ablation, in whom biatrial contact basket electrograms were studied by both an activation/phase mapping method and by a second validated mapping method reported not to create false rotational activity.RESULTS: In 22 patients (age 60.1 ± 10.4, 36% persistent AF), ablation at sites near the PVs terminated AF (77% to sinus rhythm) prior to PVI. AF propagation revealed rotational (n=20) and focal (n=2) patterns at sites of termination by mapping method 1 and method 2. Both methods showed organized sites that were spatially concordant (P<0.001) with similar stability (P<0.001). Vagal slowing was not observed at sites of AF termination.DISCUSSION: PV antral regions where ablation terminated AF before PVI exhibited rotational and focal activation by two independent mapping methods. These data provide an alternative mechanism for the success of PVI, and may explain AF termination before PVI or lack of arrhythmias despite PV reconnection. Mapping such sites may enable targeted PV lesion sets and improved freedom from AF.

  View details for PubMedID 29377478

• Clinical Implications of Ablation of Drivers for Atrial Fibrillation: A Systematic Review and Meta-Analysis. Circulation. Arrhythmia and electrophysiology Baykaner, T. n., Rogers, A. J., Meckler, G. L., Zaman, J. n., Navara, R. n., Rodrigo, M. n., Alhusseini, M. n., Kowalewski, C. A., Viswanathan, M. N., Narayan, S. M., Clopton, P. n., Wang, P. J., Heidenreich, P. A. 2018; 11 (5): e006119

  Abstract

  The outcomes from pulmonary vein isolation (PVI) for atrial fibrillation (AF) are suboptimal, but the benefits of additional lesion sets remain unproven. Recent studies propose ablation of AF drivers improves outcomes over PVI, yet with conflicting reports in the literature. We undertook a systematic literature review and meta-analysis to determine outcomes from ablation of AF drivers in addition to PVI or as a stand-alone procedure.Database search was done using the terms atrial fibrillation and ablation or catheter ablation and driver or rotor or focal impulse or FIRM (Focal Impulse and Rotor Modulation). We pooled data using random effects model and assessed heterogeneity with I2 statistic.Seventeen studies met inclusion criteria, in a cohort size of 3294 patients. Adding AF driver ablation to PVI reported freedom from AF of 72.5% (confidence interval [CI], 62.1%-81.8%; P<0.01) and from all arrhythmias of 57.8% (CI, 47.5%-67.7%; P<0.01). AF driver ablation when added to PVI or as stand-alone procedure compared with controls produced an odds ratio of 3.1 (CI, 1.3-7.7; P=0.02) for freedom from AF and an odds ratio of 1.8 (CI, 1.2-2.7; P<0.01) for freedom from all arrhythmias in 4 controlled studies. AF termination rate was 40.5% (CI, 30.6%-50.9%) and predicted favorable outcome from ablation(P<0.05).In controlled studies, the addition of AF driver ablation to PVI supports the possible benefit of a combined approach of AF driver ablation and PVI in improving single-procedure freedom from all arrhythmias. However, most studies are uncontrolled and are limited by substantial heterogeneity in outcomes. Large multicenter randomized trials are needed to precisely define the benefits of adding driver ablation to PVI.

  View details for PubMedID 29743170

• Minimizing Radiation in the Modern Electrophysiology Laboratory. The Journal of innovations in cardiac rhythm management Rogers, A. J., Brodt, C. R. 2018; 9 (8): 3265–70

  Abstract

  Historically, the electrophysiology laboratory has relied heavily on the use of ionizing radiation in the form of fluoroscopy for a broad range of interventions and diagnostics. As the harmful effects of radiation have become increasingly recognized and procedural technologies have advanced, electrophysiologists have adopted new workflows. The purpose of this article is to review the available literature and experience in minimizing radiation in the modern electrophysiology laboratory. This review first covers general approaches to reducing fluoroscopy radiation in the electrophysiology suite, with concepts that apply across all procedure types. These include the reduction of infrared emission through fastidious fluoroscopy settings, new and proven solutions for radiation shielding, and methods of creating distance between the radiation source and the operator to reduce exposure. Following this discussion, we review specific task-based techniques for reducing radiation during special electrophysiologic procedures and workflows such as vascular access, coronary sinus lead placement, catheter manipulation, and periprocedural planning studies.

  View details for DOI 10.19102/icrm.2018.090805

  View details for PubMedID 32494501

  View details for PubMedCentralID PMC7252826

• Minimizing Radiation in the Modern Electrophysiology Laboratory The Journal of Innovations in Cardiac Rhythm Management Rogers, A. J., Brodt, C. R. 2018; 2018 (9): 3265-3270

  View details for DOI 10.19102/icrm.2018.090805

• Rotational Drivers in Atrial Fibrillation: Are Multiple Techniques Circling Similar Mechanisms? Circulation. Arrhythmia and electrophysiology Zaman, J. A., Rogers, A. J., Narayan, S. M. 2017; 10 (12)

  View details for PubMedID 29254949

• Rotational Drivers in Atrial Fibrillation Are Multiple Techniques Circling Similar Mechanisms? CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY Zaman, J. B., Rogers, A. J., Narayan, S. M. 2017; 10 (12)

  View details for DOI 10.1161/CIRCEP.117.006022

  View details for Web of Science ID 000418461100018

• Mechanisms for Persistent Atrial Fibrillation - Comparing Multiple Mapping Methods at Sites of Termination: The International COMPARE-AF Registry Baykaner, T., Zaman, J., AlHusseini, M., Vidmar, D., Meckler, G., Shenasa, F., Kowalewski, C., Rogers, A. J., Rodrigo, M., Krummen, D. E., Peters, N. S., Wang, P. J., Brachmann, J., Miller, J. M., Sauer, W. H., Rappel, W. J., Narayan, S. M. LIPPINCOTT WILLIAMS & WILKINS. 2017

  View details for Web of Science ID 000437035901294

• Drivers of persistent atrial fibrillation: do focal or rotational regions differ in their stability over time? Navara, R., Leef, G., Shenasa, F., Meckler, G., Kowalewski, C., Baykaner, T., Alhusseini, M., Hossainy, S., Joshi, V., Rogers, A. J., Zaman, J., Park, S., Zei, P., Wang, P., Narayan, S. OXFORD UNIV PRESS. 2017: 638

  View details for Web of Science ID 000440509302454

• Drivers of Persistent Atrial Fibrillation: Are Focal and Rotational Sites Transient or Stable Over Time? Navara, R., Leef, G., Shenasa, F., Kowalewski, C., Baykaner, T., Rogers, A., Zaman, J., Park, H., Zei, P., Wang, P. J., Narayan, S. M. WILEY. 2017: 606–7

  View details for Web of Science ID 000403296100062

• Spatial relationship of sites for atrial fibrillation drivers and atrial tachycardia in patients with both arrhythmias. International journal of cardiology Baykaner, T. n., Zaman, J. A., Rogers, A. J., Navara, R. n., AlHusseini, M. n., Borne, R. T., Park, S. n., Wang, P. J., Krummen, D. E., Sauer, W. H., Narayan, S. M. 2017; 248: 188–95

  Abstract

  Atrial fibrillation (AF) often converts to and from atrial tachycardia (AT), but it is undefined if these rhythms are mechanistically related in such patients. We tested the hypothesis that critical sites for AT may be related to regional AF sources in patients with both rhythms, by mapping their locations and response to ablation on transitions to and from AF.From 219 patients undergoing spatial mapping of AF prior to ablation at 3 centers, we enrolled 26 patients in whom AF converted to AT by ablation (n=19) or spontaneously (n=7; left atrial size 42±6cm, 38% persistent AF). Both atria were mapped in both rhythms by 64-electrode baskets, traditional activation maps and entrainment.Each patient had a single mapped AT (17 reentrant, 9 focal) and 3.7±1.7 AF sources. The mapped AT spatially overlapped one AF source in 88% (23/26) of patients, in left (15/23) or right (8/23) atria. AF transitioned to AT by 3 mechanisms: (a) ablation anchoring AF rotor to AT (n=13); (b) residual, unablated AF source producing AT (n=6); (c) spontaneous slowing of AF rotor leaving reentrant AT at this site without any ablation (n=7). Electrogram analysis revealed a lower peak-to-peak voltage at overlapping sites (0.36±0.2mV vs 0.49±0.2mV p=0.03).Mechanisms responsible for AT and AF may arise in overlapping atrial regions. This mechanistic inter-relationship may reflect structural and/or functional properties in either atrium. Future work should delineate how acceleration of an organized AT may produce AF, and whether such regions can be targeted a priori to prevent AT recurrence post AF ablation.

  View details for PubMedID 28733070

• Editorial commentary: What can lung transplantation teach us about the mechanisms of atrial arrhythmias? Trends in cardiovascular medicine Baykaner, T. n., Rogers, A. J., Zaman, J. A., Narayan, S. M. 2017

  View details for PubMedID 28893519

• The impact of endoscopic ultrasound findings on clinical decision making in Barrett's esophagus with high-grade dysplasia or early esophageal adenocarcinoma. Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus Bulsiewicz, W. J., Dellon, E. S., Rogers, A. J., Pasricha, S., Madanick, R. D., Grimm, I. S., Shaheen, N. J. 2014; 27 (5): 409-17

  Abstract

  The clinical utility of endoscopic ultrasound (EUS) for staging patients with Barrett's esophagus and high-grade dysplasia (HGD) or intramucosal carcinoma (IMC) prior to endoscopic therapy is unclear. We performed a retrospective analysis of patients with HGD or IMC referred to an American medical center for endoscopic treatment between 2004 and 2010. All patients had pretreatment staging by EUS. We examined the frequency that EUS findings consistent with advanced disease (tumor invasion into the submucosa, lymph node involvement, or regional metastasis) led to a change in management. The analysis was stratified by nodularity and pre-EUS histology. We identified one hundred thirty-five patients with HGD (n = 106, 79%) or IMC (n = 29, 21%) had staging by EUS (79 non-nodular, 56 nodular). Pathologic lymph nodes or metastases were not found by EUS. There were no endosonographic abnormalities noted in any patient with non-nodular mucosa (0/79). Abnormal EUS findings were present in 8/56 patients (14%) with nodular neoplasia (five IMC, three HGD). Endoscopic mucosal resection was performed in 44 patients with a nodule, with 13% (6/44) having invasive cancer. In nodular neoplasia, the EUS and endoscopic mucosal resection were abnormal in 24% (5/21) and 40% (6/15) of those with IMC and 9% (3/35) and 0% (0/29) of those with HGD, respectively. In this study we found that EUS did not alter management in patients with non-nodular HGD or IMC. Because the diagnostic utility of EUS in subjects with non-nodular Barrett's esophagus is low, the value of performing endoscopic mucosal resection in this setting is questionable. For patients with nodular neoplasia, resection of the nodule with histological examination had greater utility than staging by EUS.

  View details for DOI 10.1111/j.1442-2050.2012.01408.x

  View details for PubMedID 23016606

  View details for PubMedCentralID PMC4369130

• FMN fluorescence in inducible NOS constructs reveals a series of conformational states involved in the reductase catalytic cycle FEBS JOURNAL Ghosh, D. K., Ray, K., Rogers, A. J., Nahm, N. J., Salerno, J. C. 2012; 279 (7): 1306-1317

  Abstract

  Nitric oxide synthases (NOSs) produce NO as a molecular signal in the nervous and cardiovascular systems and as a cytotoxin in the immune response. NO production in the constitutive isoforms is controlled by calmodulin regulation of electron transfer. In the tethered shuttle model for NOS reductase function, the FMN domain moves between NADPH dehydrogenase and oxygenase catalytic centers. Crystal structures of neuronal NOS reductase domain and homologs correspond to an 'input state', with FMN in close contact with FAD. We recently produced two domain 'output state' (oxyFMN) constructs showing calmodulin dependent FMN domain association with the oxygenase domain. FMN fluorescence is sensitive to enzyme conformation and calmodulin binding. The inducible NOS (iNOS) oxyFMN construct is more fluorescent than iNOS holoenzyme. The difference in steady state fluorescence is rationalized by the observation of a series of characteristic states in the two constructs, which we assign to FMN in different environments. OxyFMN and holoenzyme share open conformations with an average lifetime of ~4.3 ns. The majority state in holoenzyme has a short lifetime of ~90 ps, probably because of FAD-FMN interactions. In oxyFMN about 25-30% of the FMN is in a state with a lifetime of 0.9 ns, which we attribute to quenching by heme in the output state. Occupancy of the output state together with our previous kinetic results yields a heme edge to FMN distance estimate of 12-15 Å. These results indicate that FMN fluorescence is a valuable tool to study conformational states involved in the NOS reductase catalytic cycle.

  View details for DOI 10.1111/j.1742-4658.2012.08525.x

  View details for Web of Science ID 000301571800015

  View details for PubMedID 22325715

• A High-Fiber Diet Does Not Protect Against Asymptomatic Diverticulosis GASTROENTEROLOGY Peery, A. F., Barrett, P. R., Park, D., Rogers, A. J., Galanko, J. A., Martin, C. F., Sandler, R. S. 2012; 142 (2): 266-U158

  Abstract

  The complications of diverticulosis cause considerable morbidity in the United States; health care expenditures for this disorder are estimated to be $2.5 billion per year. Many physicians and patients believe that a high-fiber diet and frequent bowel movements prevent the development of diverticulosis. Evidence for these associations is poor. We sought to determine whether low-fiber or high-fat diets, diets that include large quantities of red meat, constipation, or physical inactivity increase risk for asymptomatic diverticulosis.We performed a cross-sectional study of 2104 participants, 30-80 years old, who underwent outpatient colonoscopy from 1998 to 2010. Diet and physical activity were assessed in interviews using validated instruments.The prevalence of diverticulosis increased with age, as expected. High intake of fiber did not reduce the prevalence of diverticulosis. Instead, the quartile with the highest fiber intake had a greater prevalence of diverticulosis than the lowest (prevalence ratio = 1.30; 95% confidence interval, 1.13-1.50). Risk increased when calculated based on intake of total fiber, fiber from grains, soluble fiber, and insoluble fiber. Constipation was not a risk factor. Compared to individuals with <7 bowel movements per week, individuals with >15 bowel movements per week had a 70% greater risk for diverticulosis (prevalence ratio = 1.70; 95% confidence interval, 1.24-2.34). Neither physical inactivity nor intake of fat or red meat was associated with diverticulosis.A high-fiber diet and increased frequency of bowel movements are associated with greater, rather than lower, prevalence of diverticulosis. Hypotheses regarding risk factors for asymptomatic diverticulosis should be reconsidered.

  View details for DOI 10.1053/j.gastro.2011.10.035

  View details for Web of Science ID 000299540000033

  View details for PubMedID 22062360

  View details for PubMedCentralID PMC3724216

• Dietary Fiber is Not Associated With Diverticulosis Peery, A. F., Barrett, P. R., Park, D., Rogers, A. J., Locklear, T., Galanko, J. A., Martin, C. F., Sandler, R. S. W B SAUNDERS CO-ELSEVIER INC. 2011: S61

  View details for Web of Science ID 000290167300250

• Simulation of Autonomous Robotic Multiple-Core Biopsy by 3D Ultrasound Guidance ULTRASONIC IMAGING Liang, K., Rogers, A. J., Light, E. D., von Allmen, D., Smith, S. W. 2010; 32 (2): 118-127

  Abstract

  An autonomous multiple-core biopsy system guided by real-time 3D ultrasound and operated by a robotic arm with 6+1 degrees of freedom has been developed. Using a specimen of turkey breast as a tissue phantom, our system was able to first autonomously locate the phantom in the image volume and then perform needle sticks in each of eight sectors in the phantom in a single session, with no human intervention required. Based on the fraction of eight sectors successfully sampled in an experiment of five trials, a success rate of 93% was recorded. This system could have relevance in clinical procedures that involve multiple needle-core sampling such as prostate or breast biopsy.

  View details for Web of Science ID 000280155300005

  View details for PubMedID 20687279

  View details for PubMedCentralID PMC3018680

• THREE-DIMENSIONAL ULTRASOUND GUIDANCE OF AUTONOMOUS ROBOTIC BREAST BIOPSY: FEASIBILITY STUDY ULTRASOUND IN MEDICINE AND BIOLOGY Liang, K., Rogers, A. J., Light, E. D., von Allmen, D., Smith, S. W. 2010; 36 (1): 173-177

  Abstract

  Feasibility studies of autonomous robot biopsies in tissue have been conducted using real-time three-dimensional (3-D) ultrasound combined with simple thresholding algorithms. The robot first autonomously processed 3-D image volumes received from the ultrasound scanner to locate a metal rod target embedded in turkey breast tissue simulating a calcification, and in a separate experiment, the center of a water-filled void in the breast tissue simulating a cyst. In both experiments the robot then directed a needle to the desired target, with no user input required. Separate needle-touch experiments performed by the image-guided robot in a water tank yielded an rms error of 1.15 mm. (E-mail: kaicheng.liang@duke.edu).

  View details for DOI 10.1016/j.ultrasmedbio.2009.08.014

  View details for Web of Science ID 000278012200019

  View details for PubMedID 19900753

  View details for PubMedCentralID PMC2800959

• 3-D Ultrasound Guidance of Autonomous Robot for Location of Ferrous Shrapnel IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL Rogers, A. J., Light, E. D., Smith, S. W. 2009; 56 (7): 1301-1303

  Abstract

  Vibrations can be induced in ferromagnetic shrapnel by a variable electromagnet. Real time 3-D color Doppler ultrasound located the induced motion in a needle fragment and determined its 3-D position in the scanner coordinates. This information was used to guide a robot which moved a probe to touch the shrapnel fragment.

  View details for DOI 10.1109/TUFFC.2009.1185

  View details for Web of Science ID 000267222400006

  View details for PubMedID 19574140

  View details for PubMedCentralID PMC2810201

• Real-time 3D ultrasound guidance of autonomous surgical robot for shrapnel detection and breast biopsy Rogers, A. J., Light, E. D., von Allmen, D., Smith, S. W. Medical Imaging 2009: Ultrasonic Imaging and signal Processing. 2009