Detection of transthyretin amyloid cardiomyopathy by automated data extraction from electronic health records

Abstract

AbstractAimsTransthyretin amyloid cardiomyopathy (ATTR‐CM), a progressive and fatal cardiomyopathy, is frequently misdiagnosed or entails diagnostic delays, hindering patients from timely treatment. This study aimed to generate a systematic framework based on data from electronic health records (EHRs) to assess patients with ATTR‐CM in a real‐world population of heart failure (HF) patients. Predictive factors or combinations of predictive factors related to ATTR‐CM in a European population were also assessed.Methods and resultsRetrospective unstructured and semi‐structured data from EHRs of patients from OLV Hospital Aalst, Belgium (2012–20), were processed using natural language processing (NLP) to generate an Observational Medical Outcomes Partnership Common Data Model database. NLP model performance was assessed on a random subset of EHRs by comparing algorithm outputs to a physician‐generated standard (using precision, recall, and their harmonic mean, or F1‐score). Of the 3127 HF patients, 103 potentially had ATTR‐CM (age 78 ± 9 years; male 55%; ejection fraction of 48% ± 16). The mean diagnostic delay between HF and ATTR‐CM diagnosis was 1.8 years. Besides HF and cardiomyopathy‐related phenotypes, the strongest cardiac predictor was atrial fibrillation (AF; 72% in ATTR‐CM vs. 60% in non‐ATTR‐CM, P = 0.02), whereas the strongest non‐cardiac predictor was carpal tunnel syndrome (21% in ATTR‐CM vs. 3% in non‐ATTR‐CM, P < 0.001). The strongest combination predictor was AF, joint disorders, and HF with preserved ejection fraction (29% in ATTR‐CM vs. 18% in non‐ATTR‐CM: odds ratio = 2.03, 95% confidence interval = 1.28–3.22).ConclusionsNot only well‐known variables associated with ATTR‐CM but also unique combinations of cardiac and non‐cardiac phenotypes are able to predict ATTR‐CM in a real‐world HF population, aiding in early identification of ATTR‐CM patients.