Implementing a Machine-Learning-Adapted Algorithm to Identify Possible Transthyretin Amyloid Cardiomyopathy at an Academic Medical Center

• Published in: Clinical Medicine Insights. Cardiology Vol. 16; p. 11795468221133608
• Main Authors: Mitchell, Joshua D, Lenihan, Daniel J, Reed, Casey, Huda, Ahsan, Nolen, Kim, Bruno, Marianna, Kannampallil, Thomas
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-01-2022; Sage Publications Ltd; SAGE Publishing
• Subjects: Cardiomyopathy; Electronic health records; Genotype & phenotype; Original Research; identification; Cardiac amyloidosis; machine learning; electronic health record; transthyretin amyloidosis
• ISSN: 1179-5468; 1179-5468
• DOI: 10.1177/11795468221133608

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.