ArticleJMIR medical informatics2020
Model-Based Algorithms for Detecting Peripheral Artery Disease Using Administrative Data From an Electronic Health Record Data System: Algorithm Development Study.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysis.Scientific reports · 2025Pooled it
- Performance and Fairness of a Peripheral Artery Disease Detection Model Across Patient Phenotypes and Health Systems.medRxiv : the preprint server for health sciences · 2026Article
- Extending the Observational Medical Outcomes Partnership Common Data Model to Support Observational Peripheral Vascular Disease Research.The Journal of surgical research · 2026Observational
- Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.Circulation. Population health and outcomes · 2026Review
- Risk of lower extremity complications with GLP-1 receptor agonists, SGLT2 inhibitors, and DPP-4 inhibitors in peripheral artery disease.Diabetes research and clinical practice · 2025Article
- Intravitreal anti-VEGF therapy and risk of limb complications in individuals with diabetic eye disease.Diabetes research and clinical practice · 2025Article
- Validation ofCirculation. Cardiovascular quality and outcomes · 2025Article
- Real-World Vein Mapping Practice Patterns Before Endovascular Treatment of Limb Ischemia.The Journal of surgical research · 2024Article
- Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study.JMIR formative research · 2023Article
- A Reassessment of the Causal Effects of Dysglycemia on Atherosclerotic and Thrombotic Events.Diabetes · 2022Article
- Impact of risk factor control on peripheral artery disease outcomes and health disparities.Vascular medicine (London, England) · 2022Article
- Clinician Specialty, Access to Care, and Outcomes Among Patients with Peripheral Artery Disease.The American journal of medicine · 2022Article
- Use of Natural Language Processing to Improve Identification of Patients With Peripheral Artery Disease.Circulation. Cardiovascular interventions · 2020Article
Corrections and comments
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
Abstract
backgroundPeripheral artery disease (PAD) affects 8 to 10 million Americans, who face significantly elevated risks of both mortality and major limb events such as amputation. Unfortunately, PAD is relatively underdiagnosed, undertreated, and underresearched, leading to wide variations in treatment patterns and outcomes. Efforts to improve PAD care and outcomes have been hampered by persistent difficulties identifying patients with PAD for clinical and investigatory purposes.
objectiveThe aim of this study is to develop and validate a model-based algorithm to detect patients with peripheral artery disease (PAD) using data from an electronic health record (EHR) system.
methodsAn initial query of the EHR in a large health system identified all patients with PAD-related diagnosis codes for any encounter during the study period. Clinical adjudication of PAD diagnosis was performed by chart review on a random subgroup. A binary logistic regression to predict PAD was built and validated using a least absolute shrinkage and selection operator (LASSO) approach in the adjudicated patients. The algorithm was then applied to the nonsampled records to further evaluate its performance.
resultsThe initial EHR data query using 406 diagnostic codes yielded 15,406 patients. Overall, 2500 patients were randomly selected for ground truth PAD status adjudication. In the end, 108 code flags remained after removing rarely- and never-used codes. We entered these code flags plus administrative encounter, imaging, procedure, and specialist flags into a LASSO model. The area under the curve for this model was 0.862.
conclusionsThe algorithm we constructed has two main advantages over other approaches to the identification of patients with PAD. First, it was derived from a broad population of patients with many different PAD manifestations and treatment pathways across a large health system. Second, our model does not rely on clinical notes and can be applied in situations in which only administrative billing data (eg, large administrative data sets) are available. A combination of diagnosis codes and administrative flags can accurately identify patients with PAD in large cohorts.
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