Abstract 15783: A Simplified Machine Learning Algorithm for Identification of Patients At-risk for Wild-type Transthyretin Amyloid Cardiomyopathy

Abstract

Introduction: Wild-type transthyretin amyloid cardiomyopathy (wtATTR-CM) is a progressive, life-threatening, increasingly recognized but underdiagnosed cause of heart failure (HF). A previously validated machine learning (ML) model trained on medical claims data from 300 million US patients predicted wtATTR-CM among individuals diagnosed with HF with high sensitivity and specificity. Here, we simplified the ML model by reducing the number of predictive variables and tested it in a cohort of patients with confirmed wtATTR-CM at a large academic amyloid referral center. Methods: A retrospective, case-control study was conducted using electronic health records (EHR) from a random 1:1 sample of patients diagnosed with wtATTR-CM (cases) and non-amyloid HF (controls) at OHSU (Jul 2005-Nov 2019). Inclusion criteria were age ≥50 years; HF diagnosis (based on ICD-10 codes/SNOMED CT); and ≥1 of the following: ≥12 months of medical history, ≥5 clinical visits, or ≥10 documented diagnosis codes. The original 1,871 variables were systematically reduced to 15 based on recursive feature elimination and clinical relevance to ATTR-CM. After confirmation of the full ML model algorithm performance, the simplified model, validated against Optum EHR, was applied to the OHSU cohort of patients with wtATTR-CM. Results: Of 25,233 patients who met study criteria, 38 (0.2%) had wtATTR-CM and were evaluated along with 38 patients with non-amyloid HF. Performance of the simplified ML model was consistent with the previously validated model, with an ROC AUC of 0.812 and 0.804, respectively, and improved at lower thresholds (Table). Conclusions: A simplified ML algorithm to estimate the empirical probability of wtATTR-CM in patients with HF performed well at an academic amyloid referral center. This may serve as a practical approach to aid physicians in identifying HF patients who may be at-risk for wtATTR-CM. Additional studies are needed to confirm these findings in larger cohorts.