Dr. Ross is a vascular surgeon and research scientist. She graduated from Stanford University School of Medicine in 2011 and completed her vascular surgery 0+5 residency at Stanford University School of Medicine in 2018. During her residency, she completed a two-year post-doctoral fellowship in biomedical informatics. Her current research focuses on using machine learning and electronic health records for early disease identification, precision medicine, and evaluating opportunities to engage in patient education beyond the clinic.
- Vascular Surgery
- Preventative health
- Peripheral vascular disease
- Carotid disease
- Venous disease
- Vascular and endovascular treatment of abdominal and thoracic aortic aneurysms
- Vascular trauma
Assistant Professor - University Medical Line, Surgery - Vascular Surgery
Assistant Professor - University Medical Line, Medicine - Biomedical Informatics Research
Member, Cardiovascular Institute
Honors & Awards
US-UK Fulbright Scholar, US-UK Fulbright Commission (2008-09)
Soros Fellow, Paul & Daisy Soros Fellowship for New Americans (2008-2010)
Association for Academic Surgery Young Investigators Award, Association for Academic Surgery (AAS) (2018-19)
Society of University Surgeons Junior Faculty Award, Society of University Surgeons (SUS) (2018-2019)
K01 Mentored Scientist Development Award, National Institutes of Health (2019-2024)
2021 Clinical Scientist Development Award, Doris Duke Charitable Foundation (2021-2024)
Board Certification: American Board of Surgery, Vascular Surgery (2021)
Medical Education: Stanford University School of Medicine (2011) CA
Residency: Stanford University Vascular Surgery Fellowship (2018) CA
Post Doc, Stanford University School of Medicine, Biomedical informatics (2015)
MSc, London School of Hygiene and Tropical Medicine, London School of Economics, Health Policy, Planning and Financing (2009)
BA, Stanford University, Human Biology (2004)
Evaluation of an Electronic Health Record-based Screening Tool for Peripheral Artery Disease
This protocol represents a pilot randomized-controlled trial evaluating the effect of an electronic health record (EHR)-based peripheral artery disease (PAD) screening tool on rates of new non-invasive testing, diagnosis and treatment of PAD over a 6-month period. An EHR-based PAD screening tool will be applied to the Stanford EHR, which will generate a group of patients of varying risks of having undiagnosed PAD. Patients with the highest risk of having undiagnosed PAD will then be evaluated for inclusion in this study. 1:1 randomization will be performed on a consecutive basis until study enrollment is completed (25 patients per arm). Physicians of patients randomized to the intervention arm will be sent notification via an EHR message detailing the patient's risk of undiagnosed PAD and suggestions for referral to vascular medicine for risk assessment and/or non-invasive ankle brachial index (ABI) testing. The primary outcome is number of patients receiving ABI testing for PAD at 6 months, with secondary outcomes including number of new PAD diagnoses, number of new referrals to cardiovascular specialists (vascular medicine, vascular surgery, and/or cardiology) and number of patients receiving initiation of new cardiovascular medications (anti-platelet agents, statins, and/or antihypertensive agents).
Stanford is currently not accepting patients for this trial. For more information, please contact Elsie Ross, MD, MSc, 650-723-5477.
Independent Studies (2)
- Directed Reading and Research
BIOMEDIN 299 (Win, Spr)
- Undergraduate Research
SURG 199 (Aut)
- Directed Reading and Research
Med Scholar Project Advisor
Postdoctoral Faculty Sponsor
Validity of the Global Vascular Guidelines in Predicting Outcomes Based on First-Time Revascularization Strategy.
Annals of vascular surgery
The Global Vascular Guidelines (GVG) recommend selecting an endovascular vs open-surgical approach to revascularization for chronic limb-threatening ischemia (CLTI), based on the Global Limb Anatomic Staging System (GLASS) and Wound, Ischemia, and Foot Infection (WIfI) classification systems. We assessed the utility of GVG-recommended strategies in predicting clinical outcomes.We conducted a single-center, retrospective review of first-time lower-extremity revascularizations within a comprehensive limb-preservation program from 2010-2018. Procedures were stratified by 1) treatment concordance with GVG-recommended strategy (concordant vs non-concordant groups), 2) GLASS stages I-III, and 3) endovascular vs open strategies. The primary outcome was 5-year freedom from major adverse limb events (FF-MALE), defined as freedom from reintervention or major amputation, and secondary outcomes included 5-year overall survival, freedom from major amputation, freedom from reintervention, and immediate technical failure during initial revascularization. Kaplan-Meier (KM) survival analysis and multivariate analysis with Cox proportional hazard models were performed on the primary and secondary outcomes, RESULTS: Of 281 first-time revascularizations for CLTI, 251 (89.3%) were endovascular and 186 (66.2%) were in the concordant group, with a mean clinical follow-up of 3.02±2.40 years. Within the concordant group alone, 167 (89.8%) of revascularizations were endovascular. The concordant group had a higher rate of chronic kidney disease (60.8% vs 45.3%, P=.02), WIfI foot infection grade (0.81±1.1 vs 0.56±0.80, P=.03), and WIfI stage (3.1±0.79 vs 2.8±1.2, P<.01) compared to the non-concordant group. After both KM and multivariate analyses, there were no significant differences in 5-year FF-MALE or overall survival between concordant and non-concordant groups. There was higher freedom from major amputation in the non-concordant group on KM analysis (83.9% vs 74.2%, P=.025), though this difference was non-significant on multivariate analysis (HR 0.49, 95% CI 0.21-1.15, P=.10). The open group had lower MALE compared to the endovascular group (HR 0.39, 95% CI 0.17-0.91, P=.029) attributed to a lower reintervention rate in the open group (HR 0.31, 95% CI 0.11-0.87, P=.026). GLASS stage was not associated with significant differences in outcomes, but the severity of GLASS stage was associated with immediate technical failure (2.1% in stage 1, 6.4% in stage 2, and 11.7% in stage 3, P=.01).In this study, CLTI treatment outcomes did not differ significantly based on whether treatment was received in concordance with GVG-recommended strategy. There was no difference in overall survival between the endovascular and open groups, though there was a higher reintervention rate in the endovascular group. The GVG guidelines are an important resource to help guide the management of CLTI patients. However, in this study, both concordance with GVG guidelines and GLASS staging were found to be indeterminate in differentiating outcomes between complex CLTI patients treated primarily with an endovascular-first approach. The revascularization approach for a CLTI patient is a nuanced decision that must take into account patient anatomy and clinical status, as well as physician skill and experience and institutional resources.
View details for DOI 10.1016/j.avsg.2023.02.001
View details for PubMedID 36828135
APLUS: A Python Library for Usefulness Simulations of Machine Learning Models in Healthcare.
Journal of biomedical informatics
Despite the creation of thousands of machine learning (ML) models, the promise of improving patient care with ML remains largely unrealized. Adoption into clinical practice is lagging, in large part due to disconnects between how ML practitioners evaluate models and what is required for their successful integration into care delivery. Models are just one component of care delivery workflows whose constraints determine clinicians' abilities to act on models' outputs. However, methods to evaluate the usefulness of models in the context of their corresponding workflows are currently limited. To bridge this gap we developed APLUS, a reusable framework for quantitatively assessing via simulation the utility gained from integrating a model into a clinical workflow. We describe the APLUS simulation engine and workflow specification language, and apply it to evaluate a novel ML-based screening pathway for detecting peripheral artery disease at Stanford Health Care.
View details for DOI 10.1016/j.jbi.2023.104319
View details for PubMedID 36791900
Guideline medication adherence patterns and physical activity in people with peripheral artery disease: a multiethnic study of atherosclerosis study
SAGE PUBLICATIONS LTD. 2022: 647-648
View details for Web of Science ID 000923957000085
Real-world Experience With Drug-coated Balloon Angioplasty in Dysfunctional Arteriovenous Fistulae
MOSBY-ELSEVIER. 2022: E302
View details for Web of Science ID 000798307600339
Disparities in peripheral artery disease care: A review and call for action.
Seminars in vascular surgery
2022; 35 (2): 141-154
Peripheral artery disease (PAD), the pathophysiologic narrowing of arterial blood vessels of the lower leg due to atherosclerosis, is a highly prevalent disease that affects more than 6 million individuals 40 years and older in the United States, with sharp increases in prevalence with age. Morbidity and mortality rates in patients with PAD range from 30% to 70% during the 5- to 15-year period after diagnosis and PAD is associated with poor health outcomes and reduced functionality and quality of life. Despite advances in medical, endovascular, and open surgical techniques, there is striking variation in care among population subgroups defined by sex, race and ethnicity, and socioeconomic status, with concomitant differences in preoperative medication optimization, amputation risk, and overall health outcomes. We reviewed studies from 1995 to 2021 to provide a comprehensive analysis of the current impact of disparities on the treatment and management of PAD and offer action items that require strategic partnership with primary care providers, researchers, patients, and their communities.With new technologies and collaborative approaches, optimal management across all population subgroups is possible.
View details for DOI 10.1053/j.semvascsurg.2022.05.003
View details for PubMedID 35672104
Clustering cancers by shared transcriptional risk reveals novel targets for cancer therapy.
2022; 21 (1): 116
View details for DOI 10.1186/s12943-022-01592-y
View details for PubMedID 35585548
Machine Learning Can Predict Recurrence Patterns After Liver Transplantation in Hepatocellular Carcinoma Patients: Analysis from the US Multicenter HCC Transplant Consortium
SPRINGER. 2022: 348-349
View details for Web of Science ID 000789811800036
Development of a polygenic risk score to improve detection of peripheral artery disease.
Vascular medicine (London, England)
INTRODUCTION: Peripheral artery disease (PAD) is a major cause of cardiovascular morbidity and mortality, yet timely diagnosis is elusive. Larger genome-wide association studies (GWAS) have now provided the ability to evaluate whether genetic data, in the form of genome-wide polygenic risk scores (PRS), can help improve our ability to identify patients at high risk of having PAD.METHODS: Using summary statistic data from the largest PAD GWAS from the Million Veteran Program, we developed PRSs with genome data from UK Biobank. We then evaluated the clinical utility of adding the best-performing PRS to a PAD clinical risk score.RESULTS: A total of 487,320 participants (5759 PAD cases) were included in our final genetic analysis. Compared to participants in the lowest 10% of PRS, those in the highest decile had 3.1 higher odds of having PAD (95% CI, 3.06-3.21). Additionally, a PAD PRS was associated with increased risk of having coronary artery disease, congestive heart failure, and cerebrovascular disease. The PRS significantly improved a clinical risk model (Net Reclassification Index = 0.07, p < 0.001), with most of the performance seen in downgrading risk of controls. Combining clinical and genetic data to detect risk of PAD resulted in a model with an area under the curve of 0.76 (95% CI, 0.75-0.77).CONCLUSION: We demonstrate that a genome-wide PRS can discriminate risk of PAD and other cardiovascular diseases. Adding a PAD PRS to clinical risk models may help improve detection of prevalent, but undiagnosed disease.
View details for DOI 10.1177/1358863X211067564
View details for PubMedID 35287516
Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care.
Frontiers in cardiovascular medicine
2022; 9: 840262
Today's digital health revolution aims to improve the efficiency of healthcare delivery and make care more personalized and timely. Sources of data for digital health tools include multiple modalities such as electronic medical records (EMR), radiology images, and genetic repositories, to name a few. While historically, these data were utilized in silos, new machine learning (ML) and deep learning (DL) technologies enable the integration of these data sources to produce multi-modal insights. Data fusion, which integrates data from multiple modalities using ML and DL techniques, has been of growing interest in its application to medicine. In this paper, we review the state-of-the-art research that focuses on how the latest techniques in data fusion are providing scientific and clinical insights specific to the field of cardiovascular medicine. With these new data fusion capabilities, clinicians and researchers alike will advance the diagnosis and treatment of cardiovascular diseases (CVD) to deliver more timely, accurate, and precise patient care.
View details for DOI 10.3389/fcvm.2022.840262
View details for PubMedID 35571171
Unsupervised Learning for Automated Detection of Coronary Artery Disease Subgroups.
Journal of the American Heart Association
Background The promise of precision population health includes the ability to use robust patient data to tailor prevention and care to specific groups. Advanced analytics may allow for automated detection of clinically informative subgroups that account for clinical, genetic, and environmental variability. This study sought to evaluate whether unsupervised machine learning approaches could interpret heterogeneous and missing clinical data to discover clinically important coronary artery disease subgroups. Methods and Results The Genetic Determinants of Peripheral Arterial Disease study is a prospective cohort that includes individuals with newly diagnosed and/or symptomatic coronary artery disease. We applied generalized low rank modeling and K-means cluster analysis using 155 phenotypic and genetic variables from 1329 participants. Cox proportional hazard models were used to examine associations between clusters and major adverse cardiovascular and cerebrovascular events and all-cause mortality. We then compared performance of risk stratification based on clusters and the American College of Cardiology/American Heart Association pooled cohort equations. Unsupervised analysis identified 4 phenotypically and prognostically distinct clusters. All-cause mortality was highest in cluster 1 (oldest/most comorbid; 26%), whereas major adverse cardiovascular and cerebrovascular event rates were highest in cluster 2 (youngest/multiethnic; 41%). Cluster 4 (middle-aged/healthiest behaviors) experienced more incident major adverse cardiovascular and cerebrovascular events (30%) than cluster 3 (middle-aged/lowest medication adherence; 23%), despite apparently similar risk factor and lifestyle profiles. In comparison with the pooled cohort equations, cluster membership was more informative for risk assessment of myocardial infarction, stroke, and mortality. Conclusions Unsupervised clustering identified 4 unique coronary artery disease subgroups with distinct clinical trajectories. Flexible unsupervised machine learning algorithms offer the ability to meaningfully process heterogeneous patient data and provide sharper insights into disease characterization and risk assessment. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT00380185.
View details for DOI 10.1161/JAHA.121.021976
View details for PubMedID 34845917
Dynamic changes in chromatin accessibility are associated with the atherogenic transitioning of vascular smooth muscle cells.
AIMS: De-differentiation and activation of pro-inflammatory pathways are key transitions vascular smooth muscle cells (SMCs) make during atherogenesis. Here, we explored the upstream regulators of this 'atherogenic transition'.METHODS AND RESULTS: Genome-wide sequencing studies, including ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) and RNA-seq, were performed on cells isolated from both murine SMC-lineage tracing models of atherosclerosis and human atherosclerotic lesions. At the bulk level, alterations in chromatin accessibility were associated with the atherogenic transitioning of lesional SMCs, especially in relation to genes that govern differentiation status and complement-dependent inflammation. Using computational biology, we observed that a transcription factor previously related to coronary artery disease, ATF3 (Activating transcription factor 3), was predicted to be an upstream regulator of genes altered during the transition. At the single-cell level, our results indicated that ATF3 is a key repressor of SMC transitioning towards the subset of cells that promote vascular inflammation by activating the complement cascade. The expression of ATF3 and complement component C3 were negatively correlated in SMCs from human atherosclerotic lesions, suggesting translational relevance. Phenome-wide association studies indicated that genetic variation that results in reduced expression of ATF3 is correlated with an increased risk for atherosclerosis, and the expression of ATF3 was significantly downregulated in humans with advanced vascular disease.CONCLUSION: Our study indicates that the plasticity of atherosclerotic SMCs may in part be explained by dynamic changes in their chromatin architecture, which in turn may contribute to their maladaptive response to inflammation-induced stress.TRANSLATIONAL PERSPECTIVE: The recent CANTOS and COLCOT trials have shown that targeting inflammatory pathways lowers the risk of major adverse cardiovascular events. However, more specific targets are needed to avoid immunosuppressive side effects. Our data identify an upstream regulator of pro-inflammatory SMCs, ATF3, which is involved in the initial atherogenic transitioning of lesional SMCs. Restoring ATF3 activity may prevent the de-differentiation of SMCs and offer a novel translational approach for the suppression of complement-dependent inflammation in atherosclerotic lesions.
View details for DOI 10.1093/cvr/cvab347
View details for PubMedID 34849613
Validity of the Global Vascular Guidelines in Predicting Outcomes in a Comprehensive Wound Care Program
MOSBY-ELSEVIER. 2021: E405-E406
View details for Web of Science ID 000707158200193
Collagen Fibril Orientation in Tissue Specimens from Atherosclerotic Plaque Explored Using Small Angle X-Ray Scattering.
Journal of biomechanical engineering
Atherosclerotic plaques can gradually develop in certain arteries. Disruption of fibrous tissue in plaques can result in plaque rupture and thromboembolism, leading to heart attacks and strokes. Collagen fibrils are important tissue building blocks and tissue strength depends on how fibrils are oriented. Fibril orientation in plaque tissue may potentially influence vulnerability to disruption. While X-ray scattering has previously been used to characterize fibril orientations in soft tissues and bones, it has never been used for characterization of human atherosclerotic plaque tissue. This study served to explore fibril orientation in specimens from human plaques using small angle X-ray scattering. Plaque tissue was extracted from human femoral and carotid arteries, and each tissue specimen contained a region of calcified material. 3D collagen fibril orientation was determined along scan lines that started away from and then extended towards a given calcification. At locations several millimeters or more from a calcification, fibrils were found to be oriented predominantly in the circumferential direction of the plaque tissue. However, in a number of cases, the dominant fibril direction changed markedly near a calcification, from circumferential to longitudinal. Further study is needed to elucidate how these fibril patterns may change plaque tissue behavior.
View details for DOI 10.1115/1.4052432
View details for PubMedID 34529040
Validity of the Global Vascular Guidelines in Predicting Outcomes in a Comprehensive Wound Care Program
MOSBY-ELSEVIER. 2021: E250-E251
View details for Web of Science ID 000691401100388
Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes.
2021; 128 (12): 1833-1850
Peripheral artery disease is an atherosclerotic disorder which, when present, portends poor patient outcomes. Low diagnosis rates perpetuate poor management, leading to limb loss and excess rates of cardiovascular morbidity and death. Machine learning algorithms and artificially intelligent systems have shown great promise in application to many areas in health care, such as accurately detecting disease, predicting patient outcomes, and automating image interpretation. Although the application of these technologies to peripheral artery disease are in their infancy, their promises are tremendous. In this review, we provide an introduction to important concepts in the fields of machine learning and artificial intelligence, detail the current state of how these technologies have been applied to peripheral artery disease, and discuss potential areas for future care enhancement with advanced analytics.
View details for DOI 10.1161/CIRCRESAHA.121.318224
View details for PubMedID 34110911
Comparison of Pre-Amputation Evaluation in Patients with and without Chronic Kidney Disease.
American journal of nephrology
INTRODUCTION: Patients with chronic kidney disease (CKD) and peripheral artery disease (PAD) are more likely to undergo lower extremity amputation than patients with preserved kidney function. We sought to determine whether patients with CKD were less likely to receive pre-amputation care in the 1-year prior to lower extremity amputation compared to patients without CKD.METHODS: We conducted a retrospective observational study of patients with PAD-related lower extremity amputation between January 2014 and December 2017 using a large commercial insurance database. The primary exposure was CKD identified using billing codes and laboratory values. The primary outcomes were receipt of pre-amputation care, defined as diagnostic evaluation (ankle-brachial index, duplex ultrasound, and computed tomographic angiography), specialty care (vascular surgery, cardiology, orthopedic surgery, and podiatry), and lower extremity revascularization in the 1-year prior to amputation. We conducted separate logistic regression models to estimate the adjusted odds ratio (aOR) and 95% confidence intervals (CIs) among patients with and without CKD. We assessed for effect modification by age, sex, Black race, and diabetes status.RESULTS: We identified 8,554 patients with PAD-related amputation. In fully adjusted models, patients with CKD were more likely to receive diagnostic evaluation (aOR 1.30; 95% CI 1.17-1.44) and specialty care (aOR 1.45, 95% CI 1.27-1.64) in the 1-year prior to amputation. There was no difference in odds of revascularization by CKD status (aOR 1.03, 0.90-1.19). Age, sex, Black race, and diabetes status did not modify these associations.DISCUSSION/CONCLUSION: Patients with CKD had higher odds of receiving diagnostic testing and specialty care and similar odds of lower extremity revascularization in the 1-year prior to amputation than patients without CKD. Disparities in access to pre-amputation care do not appear to explain the higher amputation rates seen among patients with CKD.
View details for DOI 10.1159/000516017
View details for PubMedID 33957619
US National Trends in Vascular Surgical Practice During the COVID-19 Pandemic.
View details for DOI 10.1001/jamasurg.2021.1708
View details for PubMedID 33856428
Update on workforce diversity in vascular surgery.
Journal of vascular surgery
OBJECTIVES: Creating a diverse workforce is paramount to the success of the surgical field. A diverse workforce allows us to meet the health needs of an increasingly diverse population and to bring new ideas to spur technical innovation. The purpose of this study was to assess trends in workforce diversity within vascular surgery (VS) and general surgery (GS) as compared to orthopedic surgery (OS)-a specialty that instituted a formal diversity initiative over a decade ago.METHODS: Data on the trainee pool for VS (fellowships and integrated residencies), GS, and OS were obtained from the U.S. Graduate Medical Education reports for 1999 through 2017. Medical student demographic data were obtained from the Association of American Medical Colleges U.S. medical school enrollment reports. Representation of surgical trainee populations (female, Hispanic and Black) were normalized by their representation in medical school. We also performed Chi-squared test to compare proportions of residents over dichotomized time periods (1999-2005 and 2013-2017) as well as a more sensitive trend of proportions test.RESULTS: The proportion of female trainees increased significantly between the time periods for the three surgical disciplines examined (P<0.001). Hispanic trainees also represented an increasing proportion of all three disciplines (P<0.001). The proportion of Black trainees did not significantly change in any discipline between the two periods. Relative to their proportion in medical school, Hispanic trainees were well represented in all surgical specialties studied (Normalized Ratio 0.95-1.52: 0.95 OS, 1.00 GS, 1.53 VS fellowship, and 1.23 VS residency). Compared to their representation in medical school, women were under-represented as surgical trainees (NR 0.32 OS, 0.82 GS, 0.56 VS fellowship, and 0.78 VS residency) as were Black trainees (NR 0.63 OS, 0.90 GS, 0.99 VS fellowship, and 0.81 VS residency).CONCLUSION: While there were significant increases in the number of women and Hispanic trainees in these three surgical disciplines, only Hispanic trainees enter the surgical field at a rate higher than their proportion in medical school. The lack of increase in Black trainees across all specialties was particularly discouraging. Women and Black trainees were underrepresented in all specialties as compared to their representation in medical school. The data presented suggest potential problems with recruitment at multiple levels of the pipeline. Particular attention should be paid to increasing the pool of minority medical school graduates who are both interested in and competitive for surgical specialties.
View details for DOI 10.1016/j.jvs.2020.12.063
View details for PubMedID 33348000
Toward Automated Detection of Peripheral Artery Disease Using Electronic Health Records
MOSBY-ELSEVIER. 2020: E41
View details for Web of Science ID 000544100700060
Update on Workforce Diversity in Vascular Surgery: What Has Changed in 20 Years?
MOSBY-ELSEVIER. 2020: E25
View details for Web of Science ID 000544100700033
Identifying dietary and nutritional risk factors for symptomatic peripheral arterial disease using the UK biobank cohort study
SAGE PUBLICATIONS LTD. 2020: NP5
View details for Web of Science ID 000542416200027
Evaluation of regional variations in length of stay after elective, uncomplicated carotid endarterectomy in North America.
Journal of vascular surgery
OBJECTIVE: The objective of this study was to evaluate factors affecting regional variation in length of stay (LOS) after elective, uncomplicated carotid endarterectomy (CEA).METHODS: Data were obtained from the Vascular Quality Initiative database and included patients with complete data who received elective CEA without complications between 2012 and 2017 across 18 regions in North America and 294 centers. The main outcome measure was LOS >1day after surgery (LOS >1 postoperative day [POD]). Using least absolute shrinkage and selection operator regression, multivariable modeling, and mixed-effects general linear modeling, we evaluated whether regional variations in LOS were independent of demographic, clinical, or center-related factors and to what extent these factors accounted for postoperative variation in LOS.RESULTS: A total of 36,004 patients were included. Mean postprocedure LOS was 1.6± 6.6days. Overall, 24% of patients had an LOS >1 POD. After adjustment for important demographic, clinical, and center-related factors, the region in which a patient was treated independently and significantly affected LOS after elective, uncomplicated CEA. Region and center of treatment accounted for 18% of LOS variation. Demographic, clinical, and surgical factors accounted for another 32% of variation in LOS. Of these factors, postoperative discharge to a facility other than home (odds ratio [OR], 6.3; confidence interval [CI], 5.2-7.6), use of intravenous (IV) vasoactive agents (OR, 3.2; CI, 3-3.4), intraoperative drain placement (OR, 1.4; CI, 1.3-1.55), and female sex (OR, 1.4; CI, 1.3-1.5) were associated with longer LOS. Factors associated with LOS ≤1 POD included preoperative aspirin (OR, 0.88; CI, 0.8-0.96) and statin use (OR, 0.9; CI, 0.83-0.98), high surgeon volume (highest quartile: OR, 0.68; CI, 0.5-0.87), and completion evaluation after CEA (eg, Doppler, ultrasound; OR, 0.87; CI, 0.8-0.95). We also found that use of IV vasoactive medications varied significantly across regions, independent of demographic and clinical factors.CONCLUSIONS: Significant regional variation in LOS exists after elective, uncomplicated CEA even after controlling for a wide range of important factors, indicating that there remain unmeasured causes of longer LOS in some regions. Even so, modification of certain clinical practices may reduce overall LOS. Regional differences in use of IV vasoactive medications not driven by clinical factors warrant further analysis, given the strong association with longer LOS.
View details for DOI 10.1016/j.jvs.2019.02.071
View details for PubMedID 31280981
A Comprehensive Evaluation of Lifestyle and Social Factors Related to Peripheral Artery Disease Events in a Large Longitudinal Study
MOSBY-ELSEVIER. 2019: E54–E55
View details for DOI 10.1016/j.jvs.2019.04.021
View details for Web of Science ID 000469220300023
Diagnosis and management of external iliac endofibrosis: A case report
JOURNAL OF VASCULAR NURSING
2019; 37 (2): 86–90
External iliac artery endofibrosis is an uncommon, nonatherosclerotic disease seen in endurance cyclists. It is poorly identified by providers. These otherwise healthy patients usually present with symptoms of arterial insufficiency, such as thigh or buttock pain, loss of power, or weakness occurring during strenuous exercises. These symptoms subside rapidly with rest. As these patients lack traditional risk factors of peripheral artery disease, their symptoms are often overlooked or are attributed to other etiologies, resulting in mismanagement and delayed treatment. In this case study, we report our experience with the successful management of a 48-year-old male who is a longstanding, avid cyclist. He self-referred to our institution after extensive research of providers familiar with his problem and at the recommendation of other cyclists with similar experiences. The patient underwent a successful left external iliac to common femoral artery endarterectomy and patch angioplasty. Three months after operation, he returned to cycling and, for the most part, has remained without symptoms.
View details for DOI 10.1016/j.jvn.2018.11.008
View details for Web of Science ID 000469492800003
View details for PubMedID 31155167
Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data.
Circulation. Cardiovascular quality and outcomes
2019; 12 (3): e004741
BACKGROUND: Patients with peripheral artery disease (PAD) are at risk of major adverse cardiac and cerebrovascular events. There are no readily available risk scores that can accurately identify which patients are most likely to sustain an event, making it difficult to identify those who might benefit from more aggressive intervention. Thus, we aimed to develop a novel predictive model-using machine learning methods on electronic health record data-to identify which PAD patients are most likely to develop major adverse cardiac and cerebrovascular events.METHODS AND RESULTS: Data were derived from patients diagnosed with PAD at 2 tertiary care institutions. Predictive models were built using a common data model that allowed for utilization of both structured (coded) and unstructured (text) data. Only data from time of entry into the health system up to PAD diagnosis were used for modeling. Models were developed and tested using nested cross-validation. A total of 7686 patients were included in learning our predictive models. Utilizing almost 1000 variables, our best predictive model accurately determined which PAD patients would go on to develop major adverse cardiac and cerebrovascular events with an area under the curve of 0.81 (95% CI, 0.80-0.83).CONCLUSIONS: Machine learning algorithms applied to data in the electronic health record can learn models that accurately identify PAD patients at risk of future major adverse cardiac and cerebrovascular events, highlighting the great potential of electronic health records to provide automated risk stratification for cardiovascular diseases. Common data models that can enable cross-institution research and technology development could potentially be an important aspect of widespread adoption of newer risk-stratification models.
View details for PubMedID 30857412
Evaluation of Cell Therapy on Exercise Performance and Limb Perfusion in Peripheral Artery Disease: The CCTRN Patients with Intermittent Claudication Injected with ALDH Bright Cells (PACE) Trial.
Atherosclerotic peripheral artery disease affects 8% to 12% of Americans >65 years of age and is associated with a major decline in functional status, increased myocardial infarction and stroke rates, and increased risk of ischemic amputation. Current treatment strategies for claudication have limitations. PACE (Patients With Intermittent Claudication Injected With ALDH Bright Cells) is a National Heart, Lung, and Blood Institute-sponsored, randomized, double-blind, placebo-controlled, phase 2 exploratory clinical trial designed to assess the safety and efficacy of autologous bone marrow-derived aldehyde dehydrogenase bright (ALDHbr) cells in patients with peripheral artery disease and to explore associated claudication physiological mechanisms.All participants, randomized 1:1 to receive ALDHbr cells or placebo, underwent bone marrow aspiration and isolation of ALDHbr cells, followed by 10 injections into the thigh and calf of the index leg. The coprimary end points were change from baseline to 6 months in peak walking time (PWT), collateral count, peak hyperemic popliteal flow, and capillary perfusion measured by magnetic resonance imaging, as well as safety.A total of 82 patients with claudication and infrainguinal peripheral artery disease were randomized at 9 sites, of whom 78 had analyzable data (57 male, 21 female patients; mean age, 66±9 years). The mean±SEM differences in the change over 6 months between study groups for PWT (0.9±0.8 minutes; 95% confidence interval [CI] -0.6 to 2.5; P=0.238), collateral count (0.9±0.6 arteries; 95% CI, -0.2 to 2.1; P=0.116), peak hyperemic popliteal flow (0.0±0.4 mL/s; 95% CI, -0.8 to 0.8; P=0.978), and capillary perfusion (-0.2±0.6%; 95% CI, -1.3 to 0.9; P=0.752) were not significant. In addition, there were no significant differences for the secondary end points, including quality-of-life measures. There were no adverse safety outcomes. Correlative relationships between magnetic resonance imaging measures and PWT were not significant. A post hoc exploratory analysis suggested that ALDHbr cell administration might be associated with an increase in the number of collateral arteries (1.5±0.7; 95% CI, 0.1-2.9; P=0.047) in participants with completely occluded femoral arteries.ALDHbr cell administration did not improve PWT or magnetic resonance outcomes, and the changes in PWT were not associated with the anatomic or physiological magnetic resonance imaging end points. Future peripheral artery disease cell therapy investigational trial design may be informed by new anatomic and perfusion insights.URL: http://www.clinicaltrials.gov. Unique identifier: NCT01774097.
View details for DOI 10.1161/CIRCULATIONAHA.116.025707
View details for PubMedID 28209728
Enhanced Quality Measurement Event Detection: An Application to Physician Reporting.
EGEMS (Washington, DC)
2017; 5 (1): 5
The wide-scale adoption of electronic health records (EHR)s has increased the availability of routinely collected clinical data in electronic form that can be used to improve the reporting of quality of care. However, the bulk of information in the EHR is in unstructured form (e.g., free-text clinical notes) and not amenable to automated reporting. Traditional methods are based on structured diagnostic and billing data that provide efficient, but inaccurate or incomplete summaries of actual or relevant care processes and patient outcomes. To assess the feasibility and benefit of implementing enhanced EHR- based physician quality measurement and reporting, which includes the analysis of unstructured free- text clinical notes, we conducted a retrospective study to compare traditional and enhanced approaches for reporting ten physician quality measures from multiple National Quality Strategy domains. We found that our enhanced approach enabled the calculation of five Physician Quality and Performance System measures not measureable in billing or diagnostic codes and resulted in over a five-fold increase in event at an average precision of 88 percent (95 percent CI: 83-93 percent). Our work suggests that enhanced EHR-based quality measurement can increase event detection for establishing value-based payment arrangements and can expedite quality reporting for physician practices, which are increasingly burdened by the process of manual chart review for quality reporting.
View details for PubMedID 29881731
The use of machine learning for the identification of peripheral artery disease and future mortality risk.
Journal of vascular surgery
2016; 64 (5): 1515-1522 e3
A key aspect of the precision medicine effort is the development of informatics tools that can analyze and interpret "big data" sets in an automated and adaptive fashion while providing accurate and actionable clinical information. The aims of this study were to develop machine learning algorithms for the identification of disease and the prognostication of mortality risk and to determine whether such models perform better than classical statistical analyses.Focusing on peripheral artery disease (PAD), patient data were derived from a prospective, observational study of 1755 patients who presented for elective coronary angiography. We employed multiple supervised machine learning algorithms and used diverse clinical, demographic, imaging, and genomic information in a hypothesis-free manner to build models that could identify patients with PAD and predict future mortality. Comparison was made to standard stepwise linear regression models.Our machine-learned models outperformed stepwise logistic regression models both for the identification of patients with PAD (area under the curve, 0.87 vs 0.76, respectively; P = .03) and for the prediction of future mortality (area under the curve, 0.76 vs 0.65, respectively; P = .10). Both machine-learned models were markedly better calibrated than the stepwise logistic regression models, thus providing more accurate disease and mortality risk estimates.Machine learning approaches can produce more accurate disease classification and prediction models. These tools may prove clinically useful for the automated identification of patients with highly morbid diseases for which aggressive risk factor management can improve outcomes.
View details for DOI 10.1016/j.jvs.2016.04.026
View details for PubMedID 27266594
View details for PubMedCentralID PMC5079774
The Promise and Challenge of Induced Pluripotent Stem Cells for Cardiovascular Applications.
JACC. Basic to translational science
2016; 1 (6): 510-523
The recent discovery of human-induced pluripotent stem cells (iPSCs) has revolutionized the field of stem cells. iPSCs have demonstrated that biological development is not an irreversible process and that mature adult somatic cells can be induced to become pluripotent. This breakthrough is projected to advance our current understanding of many disease processes and revolutionize the approach to effective therapeutics. Despite the great promise of iPSCs, many translational challenges still remain. In this article, we review the basic concept of induction of pluripotency as a novel approach to understand cardiac regeneration, cardiovascular disease modeling and drug discovery. We critically reflect on the current results of preclinical and clinical studies using iPSCs for these applications with appropriate emphasis on the challenges facing clinical translation.
View details for DOI 10.1016/j.jacbts.2016.06.010
View details for PubMedID 28580434
National Comparison of Hybrid and Open Repair for Aortoiliac-Femoral Occlusive Disease
MOSBY-ELSEVIER. 2016: 551
View details for DOI 10.1016/j.jvs.2016.05.036
View details for Web of Science ID 000380753300071
Use of Predictive Analytics for the Identification of Latent Vascular Disease and Future Adverse Cardiac Events
MOSBY-ELSEVIER. 2016: 28S–29S
View details for DOI 10.1016/j.jvs.2016.03.209
View details for Web of Science ID 000376230600042
Use of Machine Learning to Accurately Predict Adverse Events in Patients with Peripheral Artery Disease Using Electronic Health Record Data
SAGE PUBLICATIONS LTD. 2016: 290
View details for Web of Science ID 000377101000015
Statin Intensity or Achieved LDL? Practice-based Evidence for the Evaluation of New Cholesterol Treatment Guidelines
2016; 11 (5)
The recently updated American College of Cardiology/American Heart Association cholesterol treatment guidelines outline a paradigm shift in the approach to cardiovascular risk reduction. One major change included a recommendation that practitioners prescribe fixed dose statin regimens rather than focus on specific LDL targets. The goal of this study was to determine whether achieved LDL or statin intensity was more strongly associated with major adverse cardiac events (MACE) using practice-based data from electronic health records (EHR).We analyzed the EHR data of more than 40,000 adult patients on statin therapy between 1995 and 2013. Demographic and clinical variables were extracted from coded data and unstructured clinical text. To account for treatment selection bias we performed propensity score stratification as well as 1:1 propensity score matched analyses. Conditional Cox proportional hazards modeling was used to identify variables associated with MACE.We identified 7,373 adults with complete data whose cholesterol appeared to be actively managed. In a stratified propensity score analysis of the entire cohort over 3.3 years of follow-up, achieved LDL was a significant predictor of MACE outcome (Hazard Ratio 1.1; 95% confidence interval, 1.05-1.2; P < 0.0004), while statin intensity was not. In a 1:1 propensity score matched analysis performed to more aggressively control for covariate balance between treatment groups, achieved LDL remained significantly associated with MACE (HR 1.3; 95% CI, 1.03-1.7; P = 0.03) while treatment intensity again was not a significant predictor.Using EHR data we found that on-treatment achieved LDL level was a significant predictor of MACE. Statin intensity alone was not associated with outcomes. These findings imply that despite recent guidelines, achieved LDL levels are clinically important and LDL titration strategies warrant further investigation in clinical trials.
View details for DOI 10.1371/journal.pone.0154952
View details for Web of Science ID 000376882500009
View details for PubMedID 27227451
View details for PubMedCentralID PMC4881915
Learning statistical models of phenotypes using noisy labeled training data.
Journal of the American Medical Informatics Association
Traditionally, patient groups with a phenotype are selected through rule-based definitions whose creation and validation are time-consuming. Machine learning approaches to electronic phenotyping are limited by the paucity of labeled training datasets. We demonstrate the feasibility of utilizing semi-automatically labeled training sets to create phenotype models via machine learning, using a comprehensive representation of the patient medical record.We use a list of keywords specific to the phenotype of interest to generate noisy labeled training data. We train L1 penalized logistic regression models for a chronic and an acute disease and evaluate the performance of the models against a gold standard.Our models for Type 2 diabetes mellitus and myocardial infarction achieve precision and accuracy of 0.90, 0.89, and 0.86, 0.89, respectively. Local implementations of the previously validated rule-based definitions for Type 2 diabetes mellitus and myocardial infarction achieve precision and accuracy of 0.96, 0.92 and 0.84, 0.87, respectively.We have demonstrated feasibility of learning phenotype models using imperfectly labeled data for a chronic and acute phenotype. Further research in feature engineering and in specification of the keyword list can improve the performance of the models and the scalability of the approach.Our method provides an alternative to manual labeling for creating training sets for statistical models of phenotypes. Such an approach can accelerate research with large observational healthcare datasets and may also be used to create local phenotype models.
View details for DOI 10.1093/jamia/ocw028
View details for PubMedID 27174893
View details for PubMedCentralID PMC5070523
Factors impacting follow-up care after placement of temporary inferior vena cava filters
27th Annual Meeting of the Western-Vascular-Society
MOSBY-ELSEVIER. 2013: 440–45
Rates of inferior vena cava (IVC) filter retrieval have remained suboptimal, in part because of poor follow-up. The goal of our study was to determine demographic and clinical factors predictive of IVC filter follow-up care in a university hospital setting.We reviewed 250 consecutive patients who received an IVC filter placement with the intention of subsequent retrieval between March 2009 and October 2010. Patient demographics, clinical factors, and physician specialty were evaluated. Multivariate logistic regression analysis was performed to identify variables predicting follow-up care.In our cohort, 60.7% of patients received follow-up care; of those, 93% had IVC filter retrieval. Major indications for IVC filter placement were prophylaxis for high risk surgery (53%) and venous thromboembolic event with contraindication and/or failure of anticoagulation (39%). Follow-up care was less likely for patients discharged to acute rehabilitation or skilled nursing facilities (P < .0001), those with central nervous system pathology (eg, cerebral hemorrhage or spinal fracture; P < .0001), and for those who did not receive an IVC filter placement by a vascular surgeon (P < .0001). In a multivariate analysis, discharge home (odds ratio [OR], 4.0; 95% confidence interval [CI], 1.99-8.2; P < .0001), central nervous system pathology (OR, 0.46; 95% CI, 0.22-0.95; P = .04), and IVC filter placement by the vascular surgery service (OR, 4.7; 95% CI, 2.3-9.6; P < .0001) remained independent predictors of follow-up care. Trauma status and distance of residence did not significantly impact likelihood of patient follow-up.Service-dependent practice paradigms play a critical role in patient follow-up and IVC filter retrieval rates. Nevertheless, specific patient populations are more prone to having poorer rates of follow-up. Such trends should be factored into institutional quality control goals and patient-centered care.
View details for DOI 10.1016/j.jvs.2012.12.085
View details for PubMedID 23588109
Effect of chronic red cell transfusion therapy on vasculopathies and silent infarcts in patients with sickle cell disease
AMERICAN JOURNAL OF HEMATOLOGY
2011; 86 (1): 104-106
Regular, chronic red cell transfusions (CTX) have been shown to be effective prophylaxis against stroke in sickle cell disease (SCD) in those at risk. Because serial brain imaging is not routinely performed, little is known about the impact of CTX on silent infarcts (SI) and cerebral vascular pathology. Thus, we retrospectively evaluated the magnetic resonance imaging reports of a cohort of SCD patients who were prescribed CTX for either primary or secondary stroke prophylaxis. Seventeen patients with Hb SS were included (mean age 15 years, mean follow-up 4.3 years). Eight patients were on CTX for primary prophylaxis. New SI occurred in 17.6% of patients corresponding to an SI rate of 5.42 per 100 patient-years. Vasculopathy of the cerebral arteries was present in 65% of patients and progressed in 63% of these patients. Those who developed progressive vasculopathy were on CTX for an average of 8 years before lesions progressed. Patients on CTX for secondary prophylaxis had more SIs and evidence of progressive vascular disease than patients on CTX for primary prophylaxis. We conclude that adherence to CTX does not necessarily prevent SI or halt cerebral vasculopathy progression, especially in those with a history of overt stroke.
View details for DOI 10.1002/ajh.21901
View details for PubMedID 21117059