ArticleCirculation. Cardiovascular quality and outcomes2019
Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data.
Article in Circulation. Cardiovascular quality and outcomes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Performance and Fairness of a Peripheral Artery Disease Detection Model Across Patient Phenotypes and Health Systems.medRxiv : the preprint server for health sciences · 2026Article
- Trefoil Factor 3 as a Biomarker for Peripheral Artery Disease.Biomolecules · 2026Article
- Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.Circulation. Population health and outcomes · 2026Review
- Deep-learning-based diagnostic models for transcutaneous oxygen pressure-defined peripheral arterial disease and lower extremity wound healing in patients with diabetic foot.BMC medical informatics and decision making · 2026Article
- Reassessing Cardiovascular Risk in Patients With Peripheral Artery Disease Undergoing Myocardial Perfusion Imaging.The Canadian journal of cardiology · 2025Observational
- Prediction of Major Adverse Limb Events in Females with Peripheral Artery Disease using Blood-Based Biomarkers and Clinical Features.Journal of cardiovascular translational research · 2025Article
- Data source and utilization of artificial intelligence technologies in vascular surgery-a scoping review.Frontiers in cardiovascular medicine · 2025Review
- Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting.Journal of the American Heart Association · 2024Article
- Investigating the Prognostic Potential of Plasma ST2 in Patients with Peripheral Artery Disease: Identification and Evaluation.Proteomes · 2024Article
- Inflammatory Protein Panel: Exploring Diagnostic Insights for Peripheral Artery Disease Diagnosis in a Cross-Sectional Study.Diagnostics (Basel, Switzerland) · 2024Article
- Machine Learning Identifies Metabolic Dysfunction-Associated Steatotic Liver Disease in Patients With Diabetes Mellitus.The Journal of clinical endocrinology and metabolism · 2024Article
- Random Forest and Feature Importance Measures for Discriminating the Most Influential Environmental Factors in Predicting Cardiovascular and Respiratory Diseases.International journal of environmental research and public health · 2024Article
- The Identification and Evaluation of Interleukin-7 as a Myokine Biomarker for Peripheral Artery Disease Prognosis.Journal of clinical medicine · 2024Article
- A machine learning algorithm for peripheral artery disease prognosis using biomarker data.iScience · 2024Article
- A machine learning-based approach to identify peripheral artery disease using texture features from contrast-enhanced magnetic resonance imaging.Magnetic resonance imaging · 2024Article
- Machine Learning in Vascular Medicine: Optimizing Clinical Strategies for Peripheral Artery Disease.Current cardiovascular risk reports · 2024Review
- Prediction of Peripheral Artery Disease Prognosis Using Clinical and Inflammatory Biomarker Data.Journal of inflammation research · 2024Article
- Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review.PLOS digital health · 2023Article
- A Glimpse Into the Black Box: Using Machine Learning to Prioritize Predictors of Vascular Disease.JACC. Advances · 2023Article
- Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence.Seminars in vascular surgery · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundPatients 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
resultsData 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).
conclusionsMachine 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.
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