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

Author:

Mitchell Joshua D1,Lenihan Daniel J1,Reed Casey2ORCID,Huda Ahsan2,Nolen Kim2ORCID,Bruno Marianna2,Kannampallil Thomas34

Affiliation:

1. Amyloid Center of Excellence, Division of Cardiology, Washington University School of Medicine, St. Louis, MO, USA

2. Pfizer Inc, New York, NY, USA

3. Institute for Informatics, Washington University School of Medicine, St. Louis, MO, USA

4. Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, USA

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.

Funder

Pfizer

Publisher

SAGE Publications

Subject

Cardiology and Cardiovascular Medicine

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