Implementing a Machine-Learning-Adapted Algorithm to Identify Possible Transthyretin Amyloid Cardiomyopathy at an Academic Medical Center. Issue 16 (November 2022)

• Record Type: Journal Article
• Authors: Mitchell, Joshua D; Lenihan, Daniel J; Reed, Casey; Huda, Ahsan; Nolen, Kim; Bruno, Marianna; Kannampallil, Thomas
• Is Part Of: Clinical Medicine Insights. Issue 16(2022)
• Publication Date: 2022-11
• Subjects: Cardiac amyloidosis -- electronic health record -- machine learning -- identification -- transthyretin amyloidosis; Cardiovascular system -- Diseases -- Periodicals; Cardiology -- Periodicals; Cardiovascular Diseases; Cardiology; Cardiovascular system -- Diseases; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodicals; 616.12
• DOI: 10.1177/11795468221133608
• Languages: English
• ISSNs: 1179-5468

Abstract

Background: Wild-type transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequently under-recognized cause of heart failure (HF) in older patients. To improve identification of patients at risk for the disease, we initiated a pilot program in which 9 cardiac/non-cardiac phenotypes and 20 high-performing phenotype combinations predictive of wild-type ATTR-CM were operationalized in electronic health record (EHR) configurations at a large academic medical center. Methods: Inclusion criteria were age >50 years and HF; exclusion criteria were end-stage renal disease and prior amyloidosis diagnoses. The different Epic EHR configurations investigated were a clinical decision support tool (Best Practice Advisory) and operational/analytical reports (Clarity™, Reporting Workbench™, and SlicerDicer); the different data sources employed were problem list, visit diagnosis, medical history, and billing transactions. Results: With Clarity, among 45 051 patients with HF, 4006 patients (8.9%) had ⩾1 phenotype combination associated with increased risk of wild-type ATTR-CM. Across all data sources, 2 phenotypes (cardiomegaly; osteoarthrosis) and 2 combinations (carpal tunnel syndrome + HF; atrial fibrillation + heart block + cardiomegaly + osteoarthrosis) generated the highest proportions of patients for wild-type ATTR-CM screening. Conclusion: All EHR configurations tested were capable of operationalizing phenotypes or phenotype combinations to identify at-risk patients; the Clarity report was the most comprehensive.