ESC heart failure · 2023
Detection of transthyretin amyloid cardiomyopathy by automated data extraction from electronic health records.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Moya Ana, Oeste Clara L, Beles Monika, Verstreken Sofie, Dierckx Riet, Heggermont Ward, Bartunek Jozef, Bogaerts Eline, Masuy Imke, Hens Dries, Bertolone Dario, Vanderheyden Marc
Аннотация
AIMS: Transthyretin 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 RESULTS: Retrospective 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).
CONCLUSIONS: Not 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.
METHODS AND RESULTS: Retrospective 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).
CONCLUSIONS: Not 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.