Evaluation of patient state index, bispectral index, and entropy during drug induced sleep endoscopy with dexmedetomidine.
Journal of clinical monitoring and computing
Multiple electroencephalographic (EEG) monitors and their associated EEG markers have been developed to aid in assessing the level of sedation in the operating room. While many studies have assessed the response of these markers to propofol sedation and anesthetic gases, few studies have compared these markers when using dexmedetomidine, an alpha-2 agonist. Fifty-one patients underwent drug induced sleep endoscopy with dexmedetomidine sedation. Continuous EEG was captured using SedLine (Masimo, Inc), and a playback system was used to extract the bispectral index (BIS) (Medtronic Inc), the patient state index (PSI) (Masimo, Inc), the state and response Entropy (GE Healthcare), and calculate the spectral edge frequency 95% (SEF95). Richmond Agitation-Sedation Scale (RASS) scores were assessed continually throughout the procedure and in recovery. We assessed the correlation between EEG markers and constructed ordinal logistic regression models to predict the RASS score and compare EEG markers. All three commercial EEG metrics were significantly associated with the RASS score (p<0.001 for all metrics) whereas SEF95 alone was insufficient at characterizing dexmedetomidine sedation. PSI and Entropy achieved higher accuracy at predicing deeper levels of sedation as compared to BIS (PSI: 58.3%, Entropy: 58.3%, BIS: 44.4%). Lightening secondary to RASS score assessment is significantly captured by all three commercial EEG metrics (p<0.001). Commercial EEG monitors can capture changes in the brain state associated with the RASS score during dexmedetomidine sedation. PSI and Entropy were highly correlated and may be better suited for assessing deeper levels of sedation.
View details for DOI 10.1007/s10877-022-00952-9
View details for PubMedID 36550344
A probabilistic pathway score (PROPS) for classification with applications to inflammatory bowel disease
2018; 34 (6): 985–93
Gene-based supervised machine learning classification models have been widely used to differentiate disease states, predict disease progression and determine effective treatment options. However, many of these classifiers are sensitive to noise and frequently do not replicate in external validation sets. For complex, heterogeneous diseases, these classifiers are further limited by being unable to capture varying combinations of genes that lead to the same phenotype. Pathway-based classification can overcome these challenges by using robust, aggregate features to represent biological mechanisms. In this work, we developed a novel pathway-based approach, PRObabilistic Pathway Score, which uses genes to calculate individualized pathway scores for classification. Unlike previous individualized pathway-based classification methods that use gene sets, we incorporate gene interactions using probabilistic graphical models to more accurately represent the underlying biology and achieve better performance. We apply our method to differentiate two similar complex diseases, ulcerative colitis (UC) and Crohn's disease (CD), which are the two main types of inflammatory bowel disease (IBD). Using five IBD datasets, we compare our method against four gene-based and four alternative pathway-based classifiers in distinguishing CD from UC. We demonstrate superior classification performance and provide biological insight into the top pathways separating CD from UC.PROPS is available as a R package, which can be downloaded at http://simtk.org/home/props or on Bioconductor.email@example.com.Supplementary data are available at Bioinformatics online.
View details for PubMedID 29048458
View details for PubMedCentralID PMC5860179
MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
2018; 23: 331–42
Glioblastoma Multiforme (GBM), a malignant brain tumor, is among the most lethal of all cancers. Temozolomide is the primary chemotherapy treatment for patients diagnosed with GBM. The methylation status of the promoter or the enhancer regions of the O6-methylguanine methyltransferase (MGMT) gene may impact the efficacy and sensitivity of temozolomide, and hence may affect overall patient survival. Microscopic genetic changes may manifest as macroscopic morphological changes in the brain tumors that can be detected using magnetic resonance imaging (MRI), which can serve as noninvasive biomarkers for determining methylation of MGMT regulatory regions. In this research, we use a compendium of brain MRI scans of GBM patients collected from The Cancer Imaging Archive (TCIA) combined with methylation data from The Cancer Genome Atlas (TCGA) to predict the methylation state of the MGMT regulatory regions in these patients. Our approach relies on a bi-directional convolutional recurrent neural network architecture (CRNN) that leverages the spatial aspects of these 3-dimensional MRI scans. Our CRNN obtains an accuracy of 67% on the validation data and 62% on the test data, with precision and recall both at 67%, suggesting the existence of MRI features that may complement existing markers for GBM patient stratification and prognosis. We have additionally presented our model via a novel neural network visualization platform, which we have developed to improve interpretability of deep learning MRI-based classification models.
View details for PubMedID 29218894
Imputing gene expression to maximize platform compatibility
2017; 33 (4): 522-528
Microarray measurements of gene expression constitute a large fraction of publicly shared biological data, and are available in the Gene Expression Omnibus (GEO). Many studies use GEO data to shape hypotheses and improve statistical power. Within GEO, the Affymetrix HG-U133A and HG-U133 Plus 2.0 are the two most commonly used microarray platforms for human samples; the HG-U133 Plus 2.0 platform contains 54 220 probes and the HG-U133A array contains a proper subset (21 722 probes). When different platforms are involved, the subset of common genes is most easily compared. This approach results in the exclusion of substantial measured data and can limit downstream analysis. To predict the expression values for the genes unique to the HG-U133 Plus 2.0 platform, we constructed a series of gene expression inference models based on genes common to both platforms. Our model predicts gene expression values that are within the variability observed in controlled replicate studies and are highly correlated with measured data. Using six previously published studies, we also demonstrate the improved performance of the enlarged feature space generated by our model in downstream analysis.The gene inference model described in this paper is available as a R package (affyImpute), which can be downloaded at http://firstname.lastname@example.org.Supplementary data are available at Bioinformatics online.
View details for DOI 10.1093/bioinformatics/btw664
View details for Web of Science ID 000397264100008
View details for PubMedCentralID PMC5408923
Distinct clinical phenotypes for Crohn's disease derived from patient surveys.
2021; 21 (1): 160
BACKGROUND: Defining clinical phenotypes provides opportunities for new diagnostics and may provide insights into early intervention and disease prevention. There is increasing evidence that patient-derived health data may contain information that complements traditional methods of clinical phenotyping. The utility of these data for defining meaningful phenotypic groups is of great interest because social media and online resources make it possible to query large cohorts of patients with health conditions.METHODS: We evaluated the degree to which patient-reported categorical data is useful for discovering subclinical phenotypes and evaluated its utility for discovering new measures of disease severity, treatment response and genetic architecture. Specifically, we examined the responses of 1961 patients with inflammatory bowel disease to questionnaires in search of sub-phenotypes. We applied machine learning methods to identify novel subtypes of Crohn's disease and studied their associations with drug responses.RESULTS: Using the patients' self-reported information, we identified two subpopulations of Crohn's disease; these subpopulations differ in disease severity, associations with smoking, and genetic transmission patterns. We also identified distinct features of drug response for the two Crohn's disease subtypes. These subtypes show a trend towards differential genotype signatures.CONCLUSION: Our findings suggest that patient-defined data can have unplanned utility for defining disease subtypes and may be useful for guiding treatment approaches.
View details for DOI 10.1186/s12876-021-01740-6
View details for PubMedID 33836648
Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease.
PLoS computational biology
2021; 17 (2): e1008631
For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices. Drug repurposing strategies for IBD have had limited clinical success and have not typically offered individualized patient-level treatment recommendations. In this work, we present NetPTP, a Network-based Personalized Treatment Prediction framework which models measured drug effects from gene expression data and applies them to patient samples to generate personalized ranked treatment lists. To accomplish this, we combine publicly available network, drug target, and drug effect data to generate treatment rankings using patient data. These ranked lists can then be used to prioritize existing treatments and discover new therapies for individual patients. We demonstrate how NetPTP captures and models drug effects, and we apply our framework to individual IBD samples to provide novel insights into IBD treatment.
View details for DOI 10.1371/journal.pcbi.1008631
View details for PubMedID 33544718
Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.
Circulation. Cardiovascular quality and outcomes
2019; 12 (10): e005595
BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitoring data from cardiac implantable electronic devices, we sought to evaluate if machine learned signatures of AF burden could provide prognostic information on near-term risk of stroke when compared to conventional risk scores.METHODS AND RESULTS: We retrospectively identified Veterans Health Administration serviced patients with cardiac implantable electronic device remote monitoring data and at least one day of device-registered AF. The first 30 days of remote monitoring in nonstroke controls were compared against the past 30 days of remote monitoring before stroke in cases. We trained 3 types of models on our data: (1) convolutional neural networks, (2) random forest, and (3) L1 regularized logistic regression (LASSO). We calculated the CHA2DS2-VASc score for each patient and compared its performance against machine learned indices based on AF burden in separate test cohorts. Finally, we investigated the effect of combining our AF burden models with CHA2DS2-VASc. We identified 3114 nonstroke controls and 71 stroke cases, with no significant differences in baseline characteristics. Random forest performed the best in the test data set (area under the curve [AUC]=0.662) and convolutional neural network in the validation dataset (AUC=0.702), whereas CHA2DS2-VASc had an AUC of 0.5 or less in both data sets. Combining CHA2DS2-VASc with random forest and convolutional neural network yielded a validation AUC of 0.696 and test AUC of 0.634, yielding the highest average AUC on nontraining data.CONCLUSIONS: This proof-of-concept study found that machine learning and ensemble methods that incorporate daily AF burden signature provided incremental prognostic value for risk stratification beyond CHA2DS2-VASc for near-term risk of stroke.
View details for DOI 10.1161/CIRCOUTCOMES.118.005595
View details for PubMedID 31610712
Mendelian Disease Associations Reveal Novel Insights into Inflammatory Bowel Disease
INFLAMMATORY BOWEL DISEASES
2018; 24 (3): 471–81
Monogenic diseases have been shown to contribute to complex disease risk and may hold new insights into the underlying biological mechanism of Inflammatory Bowel Disease (IBD).We analyzed Mendelian disease associations with IBD using over 55 million patients from the Optum's deidentified electronic health records dataset database. Using the significant Mendelian diseases, we performed pathway enrichment analysis and constructed a model using gene expression datasets to differentiate Crohn's disease (CD), ulcerative colitis (UC), and healthy patient samples.We found 50 Mendelian diseases were significantly associated with IBD, with 40 being significantly associated with both CD and UC. Our results for CD replicated those from previous studies. Pathways that were enriched consisted of mainly immune and metabolic processes with a focus on tolerance and oxidative stress. Our 3-way classifier for UC, CD, and healthy samples yielded an accuracy of 72%.Mendelian diseases that are significantly associated with IBD may reveal novel insights into the genetic architecture of IBD.
View details for PubMedID 29462399
Identification of UT-A1-and AQP2-interacting proteins in rat inner medullary collecting duct
AMERICAN JOURNAL OF PHYSIOLOGY-CELL PHYSIOLOGY
2018; 314 (1): C99–C117
The urea channel UT-A1 and the water channel aquaporin-2 (AQP2) mediate vasopressin-regulated transport in the renal inner medullary collecting duct (IMCD). To identify the proteins that interact with UT-A1 and AQP2 in native rat IMCD cells, we carried out chemical cross-linking followed by detergent solubilization, immunoprecipitation, and LC-MS/MS analysis of the immunoprecipitated material. The analyses revealed 133 UT-A1-interacting proteins and 139 AQP2-interacting proteins, each identified in multiple replicates. Fifty-three proteins that were present in both the UT-A1 and the AQP2 interactomes can be considered as mediators of housekeeping interactions, likely common to all plasma membrane proteins. Among proteins unique to the UT-A1 list were those involved in posttranslational modifications: phosphorylation (protein kinases Cdc42bpb, Phkb, Camk2d, and Mtor), ubiquitylation/deubiquitylation (Uba1, Usp9x), and neddylation (Nae1 and Uba3). Among the proteins unique to the AQP2 list were several Rab proteins (Rab1a, Rab2a, Rab5b, Rab5c, Rab7a, Rab11a, Rab11b, Rab14, Rab17) involved in membrane trafficking. UT-A1 was found to interact with UT-A3, although quantitative proteomics revealed that most UT-A1 molecules in the cell are not bound to UT-A3. In vitro incubation of UT-A1 peptides with the protein kinases identified in the UT-A1 interactome revealed that all except Mtor were capable of phosphorylating known sites in UT-A1. Overall, the UT-A1 and AQP2 interactomes provide a snapshot of a dynamic process in which UT-A1 and AQP2 are produced in the rough endoplasmic reticulum, processed through the Golgi apparatus, delivered to endosomes that move into and out of the plasma membrane, and are regulated in the plasma membrane.
View details for DOI 10.1152/ajpcell.00082.2017
View details for Web of Science ID 000423562400009
View details for PubMedID 29046292
View details for PubMedCentralID PMC5866378
Development of an automated assessment tool for MedWatch reports in the FDA adverse event reporting system.
Journal of the American Medical Informatics Association
As the US Food and Drug Administration (FDA) receives over a million adverse event reports associated with medication use every year, a system is needed to aid FDA safety evaluators in identifying reports most likely to demonstrate causal relationships to the suspect medications. We combined text mining with machine learning to construct and evaluate such a system to identify medication-related adverse event reports.FDA safety evaluators assessed 326 reports for medication-related causality. We engineered features from these reports and constructed random forest, L1 regularized logistic regression, and support vector machine models. We evaluated model accuracy and further assessed utility by generating report rankings that represented a prioritized report review process.Our random forest model showed the best performance in report ranking and accuracy, with an area under the receiver operating characteristic curve of 0.66. The generated report ordering assigns reports with a higher probability of medication-related causality a higher rank and is significantly correlated to a perfect report ordering, with a Kendall's tau of 0.24 ( P = .002).Our models produced prioritized report orderings that enable FDA safety evaluators to focus on reports that are more likely to contain valuable medication-related adverse event information. Applying our models to all FDA adverse event reports has the potential to streamline the manual review process and greatly reduce reviewer workload.
View details for DOI 10.1093/jamia/ocx022
View details for PubMedID 28371826
Predicting hospital visits from geo-tagged Internet search logs.
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
2016; 2016: 15-24
The steady rise in healthcare costs has deprived over 45 million Americans of healthcare services (1, 2) and has encouraged healthcare providers to look for opportunities to improve their operational efficiency. Prior studies have shown that evidence of healthcare seeking intent in Internet searches correlates well with healthcare resource utilization. Given the ubiquitous nature of mobile Internet search, we hypothesized that analyzing geo-tagged mobile search logs could enable us to machine-learn predictors of future patient visits. Using a de-identified dataset of geo-tagged mobile Internet search logs, we mined text and location patterns that are predictors of healthcare resource utilization and built statistical models that predict the probability of a user's future visit to a medical facility. Our efforts will enable the development of innovative methods for modeling and optimizing the use of healthcare resources-a crucial prerequisite for securing healthcare access for everyone in the days to come.
View details for PubMedID 27570641
Repolarization lability measured on 10-second ECG by spatial TT' angle: reproducibility and agreement with QT variability.
Journal of electrocardiology
2014; 47 (5): 708-15
Reproducibility of spatial TT' angle on the 10-second ECG and its agreement with QT variability has not been previously studied.We analyzed 2 randomly selected 10-second segments within 3-minute resting orthogonal ECG in 172 healthy IDEAL study participants (age 38.1±15.2years, 50% male, 94% white). Repolarization lability was measured by the QT variance (QTV), short-term QT variability (STV(QT)), and spatial TT' angle. Bland-Altman analysis was used to assess the agreement between different log-transformed metrics of repolarization lability, and to assess the reproducibility.The heart rate showed a very high reproducibility (bias 0.14%, Lin's rho_c=0.99). As expected, noise suppression by averaging improves reproducibility. Agreement between two 10-second LogQTV was poor (bias -0.04; 95% limits of agreement [-1.89; 1.81]), while LogSTV(QT) (0.04 [-1.01; 1.10]), and especially LogTT' angle (-0.009 [-0.84; 0.82]) was better.TT' angle is a satisfactory reproducible metric of repolarization lability on the 10-second ECG.
View details for DOI 10.1016/j.jelectrocard.2014.06.003
View details for PubMedID 25012076
View details for PubMedCentralID PMC4143436
Mechanical alternans is associated with mortality in acute hospitalized heart failure: prospective mechanical alternans study (MAS).
Circulation. Arrhythmia and electrophysiology
2014; 7 (2): 259-266
Acute hospitalized heart failure (AHHF) is associated with 40% to 50% risk of death or rehospitalization within 6 months after discharge. Timely (before hospital discharge) risk stratification of patients with AHHF is crucial. We hypothesized that mechanical alternans (MA) and T-wave alternans (TWA) are associated with postdischarge outcomes in patients with AHHF.A prospective cohort study was conducted in the intensive cardiac care unit and enrolled 133 patients (59.6±15.7 years; 65% men) admitted with AHHF. Surface ECG and peripheral arterial blood pressure waveform via arterial line were recorded continuously during the intensive cardiac care unit stay. MA and TWA were measured by enhanced modified moving average method. All-cause death or heart transplant served as a combined primary end point. MA was observed in 28 patients (25%), whereas TWA was detected in 33 patients (33%). If present, MA was tightly coupled with TWA. Mean TWA amplitude was larger in patients with both TWA and MA when compared with patients with lone TWA (median, 37 [interquartile range, 26-61] versus 22 [21-23] μV; P=0.045). After a median of 10-month postdischarge, 42 (38%) patients died and 2 had heart transplants. MA was associated with the primary end point in univariable Cox model (hazard ratio, 1.84; 95% confidence interval, 1.00-3.40; P=0.05) and after adjustment for left ventricular ejection fraction, New York Heart Association HF class, and implanted implantable cardioverter defibrillator/cardiac resynchronization therapy defibrillator (hazard ratio, 2.12 95% confidence interval, 1.13-3.98; P=0.020). TWA without consideration of simultaneous MA was not significantly associated with primary end point (hazard ratio, 1.42; 95% confidence interval, 0.77-2.64; P=0.260).MA is independently associated with outcomes in AHHF.URL: http://www.clinicaltrials.gov. Unique identifier: NCT01557465.
View details for DOI 10.1161/CIRCEP.113.000958
View details for PubMedID 24585716
- Complex assessment of the temporal lability of repolarization. International journal of cardiology 2013; 166 (2): 543-545
ELECTRICAL REMODELING PREDICTS RISK OF SUDDEN CARDIAC DEATH IN ASYMPTOMATIC ADULTS
62nd Annual Scientific Session of the American-College-of-Cardiology
ELSEVIER SCIENCE INC. 2013: E1340–E1340
View details for Web of Science ID 000316555201445
Comparison of Sum Absolute QRST Integral, and Temporal Variability in Depolarization and Repolarization, Measured by Dynamic Vectorcardiography Approach, in Healthy Men and Women
2013; 8 (2)
Recently we showed the predictive value of sum absolute QRST integral (SAI QRST) and repolarization lability for risk stratification of sudden cardiac death (SCD) in heart failure patients. The goal of this study was to compare SAI QRST and metrics of depolarization and repolarization variability in healthy men and women.Orthogonal ECGs were recorded at rest for 10 minutes in 160 healthy men and women (mean age 39.6±14.6, 80 men). Mean spatial TT' angle, and normalized variances of T loop area, of spatial T vector amplitude, of QT interval and Tpeak-Tend area were measured for assessment of repolarization lability. Normalized variances of spatial QRS vector and QRS loop area characterized variability of depolarization. In addition, variability indices (VI) were calculated to adjust for normalized heart rate variance. SAI QRST was measured as the averaged arithmetic sum of areas under the QRST curve.Men were characterized by shorter QTc (430.3±21.7 vs. 444.7±22.2 ms; P<0.0001) and larger SAI QRST (282.1±66.7 vs. 204.9±58.5 mV*ms; P<0.0001). Repolarization lability negatively correlated with spatial T vector amplitude. Adjusted by normalized heart rate variance, QT variability index was significantly higher in women than in men (-1.54±0.38 vs. -1.70±0.33; P = 0.017). However, in multivariate logistic regression after adjustment for body surface area, QTc, and spatial T vector amplitude, healthy men had 1.5-3 fold higher probability of having larger repolarization lability, as compared to healthy women (T vector amplitude variability index odds ratio 3.88 (95%CI 1.4-11.1; P = 0.012).Healthy men more likely than women have larger repolarization lability.
View details for DOI 10.1371/journal.pone.0057175
View details for Web of Science ID 000316658800087
View details for PubMedID 23451181
View details for PubMedCentralID PMC3579786
ECG Marker of Adverse Electrical Remodeling Post-Myocardial Infarction Predicts Outcomes in MADIT II Study
2012; 7 (12)
Post-myocardial infarction (MI) structural remodeling is characterized by left ventricular dilatation, fibrosis, and hypertrophy of the non-infarcted myocardium.The goal of our study was to quantify post-MI electrical remodeling by measuring the sum absolute QRST integral (SAI QRST). We hypothesized that adverse electrical remodeling predicts outcomes in MADIT II study participants.Baseline orthogonal ECGs of 750 MADIT II study participants (448 [59.7%] ICD arm) were analyzed. SAI QRST was measured as the arithmetic sum of absolute QRST integrals over all three orthogonal ECG leads. The primary endpoint was defined as sudden cardiac death (SCD) or sustained ventricular tachycardia (VT)/ventricular fibrillation (VF) with appropriate ICD therapies. All-cause mortality served as a secondary endpoint.Adverse electrical remodeling in post-MI patients was characterized by wide QRS, increased magnitudes of spatial QRS and T vectors, J-point deviation, and QTc prolongation. In multivariable Cox regression analysis after adjustment for age, QRS duration, atrial fibrillation, New York Heart Association heart failure class and blood urea nitrogen, SAI QRST predicted SCD/VT/VF (HR 1.33 per 100 mV*ms (95%CI 1.11-1.59); P = 0.002), and all-cause death (HR 1.27 per 100 mV*ms (95%CI 1.03-1.55), P = 0.022) in both arms. No interaction with therapy arm and bundle branch block (BBB) status was found.In MADIT II patients, increased SAI QRST is associated with increased risk of sustained VT/VF with appropriate ICD therapies and all-cause death in both ICD and in conventional medical therapy arms, and in patients with and without BBB. Further studies of SAI QRST are warranted.
View details for DOI 10.1371/journal.pone.0051812
View details for Web of Science ID 000312386800089
View details for PubMedID 23251630
View details for PubMedCentralID PMC3522579
QT variability paradox after premature ventricular contraction in patients with structural heart disease and ventricular arrhythmias
JOURNAL OF ELECTROCARDIOLOGY
2012; 45 (6): 652-657
Increased repolarization lability is known to be associated with the risk of ventricular tachycardia (VT)/ventricular fibrillation (VF). Premature ventricular contractions (PVCs) are excluded from the analysis of QT variability. However, QT dynamics after PVCs is poorly understood.We analyzed data of 33 patients with structural heart disease (mean age 60.5 ± 12.1; 24 (73%) men; 26 (79%) whites; 22 (67%) ischemic cardiomyopathy) and single-chamber ICD implanted for primary (28 patients, 85%) or secondary prevention of SCD. Arrhythmia group comprised 16 patients with VT/VF/death outcomes. Alive patients (n = 17) without VT/VF served as controls. The baseline far-field (FF) ICD electrogram (EGM) was recorded at rest. RR and QT intervals of 15 sinus beats before and after PVC in 33 patients were analyzed. The prematurity index, C(i)Mean(RR), where C(i) is coupling interval, was used to select the most premature PVC. QT variability index (QTVI) was calculated. Difference in QTVI was calculated as QTVI(diff) = QTVI(after)-QTVI(before.)In paired analysis QTVI significantly increased after PVC in controls (0.64 ± 1.02 vs. 0.26 ± 1.15; P = 0.046), but decreased in patients in the arrhythmia group (0.16 ± 0.85 vs. 0.43 ± 0.84; P = 0.190). QTVI(diff) was significantly lower in patients with VT/VF, as compared to controls (-0.197 ± 0.650 vs. 0.207 ± 0.723; P=0.030). In multivariate logistic regression after adjustment for the type of cardiomyopathy and NYHA class the decrease in QTVI after PVC was associated with increased risk of VT/VF (OR 9.24; 95% CI 1.11-76.82; P=0.040).Elevated at baseline QTVI is decreased during first 15 beats after PVC in patients at risk for VT/VF.
View details for DOI 10.1016/j.jelectrocard.2012.07.016
View details for Web of Science ID 000310763300021
View details for PubMedID 22995383
Intracardiac J-point elevation before the onset of polymorphic ventricular tachycardia and ventricular fibrillation in patients with an implantable cardioverter-defibrillator
2012; 9 (10): 1594-1602
The clinical importance of the J-point elevation on electrocardiogram is controversial.To study intracardiac J-point amplitude before ventricular arrhythmia.Baseline 12-lead electrocardiogram and far-field right ventricular intracardiac implantable cardioverter-defibrillator electrograms were recorded at rest in 494 patients (mean age 60.4 ± 13.1 years; 360 [72.9%] men) with structural heart disease (278 [56.3%] ischemic cardiomyopathy) who received primary (463 [93.9%] patients) or secondary prevention implantable cardioverter-defibrillator. Ten-second intracardiac far-field electrograms before the onset of arrhythmia were compared with the baseline. The J-point amplitude was measured on the baseline 12-lead surface electrocardiogram and the intracardiac far-field electrogram. The relative J-point amplitude was calculated as the ratio of J-point amplitude to peak-to-peak R-wave.The paired t test showed that the relative intracardiac J-point amplitude was significantly higher before polymorphic ventricular tachycardia/ventricular fibrillation (VF) onset (0.28 ± 0.08 vs -0.19 ± 0.39; P = .012) than at baseline. In a mixed-effects logistic regression model, adjusted for multiple episodes per patient, each 10% increase in relative J-point amplitude increased the odds of having ventricular tachycardia/VF by 13% (odds ratio 1.13 [95% confidence interval 1.07-1.19]; P < .0001) and increased the odds of having polymorphic ventricular tachycardia/VF by 27% (odds ratio 1.27 [95% confidence interval 1.11-1.46]; P = .001).The relative intracardiac J-point amplitude is augmented immediately before the onset of polymorphic ventricular tachycardia/VF in patients with structural heart disease.
View details for DOI 10.1016/j.hrthm.2012.06.036
View details for Web of Science ID 000309292600005
View details for PubMedID 22750217
View details for PubMedCentralID PMC3459168
Predictive Value of Beat-to-Beat QT Variability Index Across the Continuum of Left Ventricular Dysfunction Competing Risks of Noncardiac or Cardiovascular Death and Sudden or Nonsudden Cardiac Death
CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY
2012; 5 (4): 719-727
The goal of the present study was to determine the predictive value of beat-to-beat QT variability in heart failure patients across the continuum of left ventricular dysfunction.Beat-to-beat QT variability index (QTVI), log-transformed heart rate variance, normalized QT variance, and coherence between heart rate variability and QT variability have been measured at rest during sinus rhythm in 533 participants of the Muerte Subita en Insuficiencia Cardiaca heart failure study (mean age, 63.1±11.7; men, 70.6%; left ventricular ejection fraction >35% in 254 [48%]) and in 181 healthy participants from the Intercity Digital Electrocardiogram Alliance database. During a median of 3.7 years of follow-up, 116 patients died, 52 from sudden cardiac death (SCD). In multivariate competing risk analyses, the highest QTVI quartile was associated with cardiovascular death (subhazard ratio, 1.67 [95% CI, 1.14-2.47]; P=0.009) and, in particular, with non-SCD (subhazard ratio, 2.91 [1.69-5.01]; P<0.001). Elevated QTVI separated 97.5% of healthy individuals from subjects at risk for cardiovascular (subhazard ratio, 1.57 [1.04-2.35]; P=0.031) and non-SCD in multivariate competing risk model (subhazard ratio, 2.58 [1.13-3.78]; P=0.001). No interaction between QTVI and left ventricular ejection fraction was found. QTVI predicted neither noncardiac death (P=0.546) nor SCD (P=0.945). Decreased heart rate variability rather than increased QT variability was the reason for increased QTVI in the present study.Increased QTVI because of depressed heart rate variability predicts cardiovascular mortality and non-SCD but neither SCD nor extracardiac mortality in heart failure across the continuum of left ventricular dysfunction. Abnormally augmented QTVI separates 97.5% of healthy individuals from heart failure patients at risk.
View details for DOI 10.1161/CIRCEP.112.970541
View details for Web of Science ID 000313584500020
View details for PubMedID 22730411
View details for PubMedCentralID PMC3432262
INTRACARDIAC J-WAVE IS AUGMENTED IMMEDIATELY BEFORE THE ONSET OF POLYMORPHIC VENTRICULAR TACHYCARDIA OR VENTRICULAR FIBRILLATION, BUT DOES NOT CORRELATE WITH BASELINE J-POINT ELEVATION ON SURFACE ECG
61st Annual Scientific Session and Expo of the American-College-of-Cardiology (ACC)/Conference on ACC-i2 with TCT
ELSEVIER SCIENCE INC. 2012: E625–E625
View details for Web of Science ID 000302326700627
Antiarrhythmic Effect of Reverse Electrical Remodeling Associated with Cardiac Resynchronization Therapy
PACE-PACING AND CLINICAL ELECTROPHYSIOLOGY
2011; 34 (3): 357-364
Antiarrhythmic and proarrhythmic effects of cardiac resynchronization therapy (CRT) remain controversial. We hypothesized that reverse electrical remodeling (RER) with CRT is associated with reduced frequency of ventricular tachyarrhythmias (VTs).The width of native and paced QRS was measured in lead II electrocardiogram before and 13 ± 7 months after implantation of a CRT defibrillator device in 69 patients (mean age 66.3 ± 13.9; 39 males [83%]) with bundle branch block (BBB) (41 patients with left BBB and three patients with bifascicular block) or nonspecific intraventricular conduction delay (25 patients, 36%), and New York Heart Association class III-IV heart failure. Biventricular pacing was inhibited for 10 seconds to record native QRS. RER was defined as a decrease in the native QRS duration ≥10 ms compared to preimplant. Patients were followed prospectively 24 ± 13 months after assessment for electrical remodeling.RER was observed in 22 patients (32%), among whom QRS duration decreased by 30.9 ± 14.1 ms (P < 0.00001) with similar heart rate and QRS morphology. Native QRS duration increased by 10.3 ± 16.6 ms in the other 47 patients (68%) (P = 0.0001). Baseline mean ejection fraction did not differ between patients with and those without RER (24.9 ± 10.0 vs 24.2 ± 8.6%, NS). During 2 ± 1 years of further follow-up, 19 patients had VTs and 12 patients died. RER was associated with a fourfold decrease in the risk of death or sustained VTs requiring appropriate implantable cardioverter-defibrillator therapies, whichever came first (hazard ratio 0.25; 95% confidence interval 0.08-0.85; P = 0.026).RER of the native conduction with CRT is associated with decreased mortality and antiarrhythmic effect of CRT.
View details for DOI 10.1111/j.1540-8159.2010.02974.x
View details for Web of Science ID 000288918300017
View details for PubMedID 21091740
Lability of R- and T-wave peaks in three-dimensional electrocardiograms in implantable cardioverter defibrillator patients with ventricular tachyarrhythmia during follow-up
35th Annual Conference of the International-Society-for-Computerized-Electrocardiology
CHURCHILL LIVINGSTONE INC MEDICAL PUBLISHERS. 2010: 577–82
From experiments, we know that the heterogeneity of action potential duration and morphology is an important mechanism of ventricular tachyarrhythmia. Electrocardiogram (ECG) markers of repolarization lability are known; however, lability of depolarization has not been systematically studied. We propose a novel method for the assessment of variability of both depolarization and repolarization phases of the cardiac cycle.Baseline orthogonal ECGs of 81 patients (mean ± SD age, 56 ± 13 years; 61 male [75%]) with structural heart disease and implanted single-chamber implantable cardioverter defibrillator (ICD) were analyzed. Clean 30-beat intervals with absence of premature beats were then selected. Baseline wandering was corrected before analysis. Peaks of R wave and peaks of T wave were detected for each beat, and the axis magnitude was calculated. The peaks were plotted to show clouds of peaks and then used to construct a convex hull, and the volumes of the R peaks cloud and T peaks cloud and ratio of volumes were calculated.During a mean (SD) follow-up period of 13 (10) months, 9 of the 81 patients had sustained ventricular tachycardia or ventricular fibrillation (VT/VF) and received appropriate ICD therapies. All ICD events were adjudicated by three independent electrophysiologists. There was no statistically significant difference in the volume of T-wave peaks or R-wave peaks between patients with and without VT or VF during follow-up; however, R/T peaks cloud volume ratio was significantly lower in patients with subsequent VT/VF (22.4 ± 25.4 versus 13.1 ± 7.9, P = .024).Larger volume of T peaks cloud, measured during 30 beats of three-dimensional ECG, is associated with higher risk of sustained ventricular tachyarrhythmias and appropriate ICD therapies. New method to assess temporal variability of repolarization in three-dimensional ECGs by measuring volume of peak clouds shows potential for further exploration for VT/VF risk stratification.
View details for DOI 10.1016/j.jelectrocard.2010.05.011
View details for Web of Science ID 000284514700016
View details for PubMedID 20655057
Beat-to-beat three-dimensional ECG variability predicts ventricular arrhythmia in ICD recipients
2010; 7 (11): 1606-1613
Methodological difficulties associated with QT measurements prompt the search for new electrocardiographic markers of repolarization heterogeneity.We hypothesized that beat-to-beat 3-dimensional vectorcardiogram variability predicts ventricular arrhythmia (VA) in patients with structural heart disease, left ventricular systolic dysfunction, and implanted implantable cardioverter-defibrillators (ICDs).Baseline orthogonal electrocardiograms were recorded in 414 patients with structural heart disease (mean age 59.4 ± 12.0; 280 white [68%] and 134 black [32%]) at rest before implantation of ICD for primary prevention of sudden cardiac death. R and T peaks of 30 consecutive sinus beats were plotted in 3 dimensions to form an R peaks cloud and a T peaks cloud. The volume of the peaks cloud was calculated as the volume within the convex hull. Patients were followed up for at least 6 months; sustained VA with appropriate ICD therapies served as an end point.During a mean follow-up time of 18.4 ± 12.5 months, 61 of the 414 patients (14.73% or 9.6% per person-year of follow-up) experienced sustained VA with appropriate ICD therapies: 41 of them were white and 20 were black. In the multivariate Cox model that included inducibility of VA and use of beta-blockers, the highest tertile of T/R peaks cloud volume ratio significantly predicted VA (hazard ratio 1.68, 95% confidence interval 1.01 to 2.80; P = .046) in all patients. T peaks cloud volume and T/R peaks cloud volume ratio were significantly smaller in black subjects (median 0.09 [interquartile range 0.04 to 0.15] vs. median 0.11 [interquartile range 0.06 to 0.22], P = .002).A relatively large T peaks cloud volume is associated with increased risk of VA in patients with structural heart disease and systolic dysfunction.
View details for DOI 10.1016/j.hrthm.2010.08.022
View details for Web of Science ID 000283648200019
View details for PubMedID 20816873
View details for PubMedCentralID PMC2990972