ArticleJACC. Advances2023
Identifying Patients With Peripheral Artery Disease Using the Electronic Health Record: A Pragmatic Approach.
Article in JACC. Advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 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
- 2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association.Circulation · 2025Review
- Relationships between ankle blood pressure indices and major adverse cardiovascular events in people with and without type 2 diabetes.Cardiovascular diabetology · 2024Article
- Trends in peripheral artery disease and critical limb ischemia hospitalizations among cocaine and methamphetamine users in the United States: a nationwide study.Frontiers in cardiovascular medicine · 2024Article
- Machine Learning in Vascular Medicine: Optimizing Clinical Strategies for Peripheral Artery Disease.Current cardiovascular risk reports · 2024Review
- A Glimpse Into the Black Box: Using Machine Learning to Prioritize Predictors of Vascular Disease.JACC. Advances · 2023Article
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10 authors.
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Abstract
backgroundPeripheral artery disease (PAD) is underdiagnosed due to poor patient and clinician awareness. Despite this, no widely accepted PAD screening is recommended.
objectivesThe authors used machine learning to develop an automated risk stratification tool for identifying patients with a high likelihood of PAD.
methodsUsing data from the electronic health record (EHR), ankle-brachial indices (ABIs) were extracted for 3,298 patients. In addition to ABI, we extracted 60 other patient characteristics and used a random forest model to rank the features by association with ABI. The model identified several features independently correlated with PAD. We then built a logistic regression model to predict PAD status on a validation set of patients (n = 1,089), an external cohort of patients (n = 2,922), and a national database (n = 2,488). The model was compared to an age-based and random forest model.
resultsThe model had an area under the curve (AUC) of 0.68 in the validation set. When evaluated on an external population using EHR data, it performed similarly with an AUC of 0.68. When evaluated on a national database, it had an AUC of 0.72. The model outperformed an age-based model (AUC: 0.62;
conclusionsStatistical techniques can be used to build models which identify individuals at high risk for PAD using information accessible from the EHR. Models such as this may allow large health care systems to efficiently identify patients that would benefit from aggressive preventive strategies or targeted-ABI screening.
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