Identifying Peripheral Artery Disease in Persons with and without CKD from Electronic Health Records

Background: Peripheral artery disease (PAD) may be more difficult to identify in CKD due to altered pathophysiology. We aimed to develop a model to identify PAD in persons with and without CKD from electronic health records (EHR) to facilitate future studies. Methods: We used the Stanford Medicine Research Repository (STARR) EHR mapped to the OMOP Common Data Model to identify adults with ≥1 PAD encounter 1/2020 to 9/2022. We randomly selected 993 patients for chart review for ground truth PAD status. Potential variables included PAD billing codes, demographics, comorbidities, diagnostic testing, and specialist visits. We built logistic regression models using age, diabetes and the top 10 percentile of variables selected by random forest. We assessed sensitivity, specificity, positive predicted value (PPV), negative predicted value (NPV), and accuracy overall and by CKD status (defined using ≥2 codes/labs). We chose the threshold probability to maximize Youden’s index with specificity>sensitivity. We calculated 95% confidence intervals [CI] by bootstrapping 1,000 samples. Results: In our cohort (Table), 222 (22%) had PAD (130 with CKD, 92 without). Our model had high sensitivity, specificity, NPV and accuracy but low PPV (Figure). Results were similar by CKD status. Conclusion: Our study is among the first to focus on PAD ascertainment in CKD. Using discrete EHR data elements, our model generally performed well regardless of CKD status, but due to the lower prevalence of PAD, the PPV was relatively low. Future approaches will use natural language processing to incorporate clinical notes and diagnostic reports to improve model performance. Funding: Other NIH Support - NHLBIBaseline CharacteristicsModel Performance