Race- and Ethnicity-Related Differences in Heart Failure With Preserved Ejection Fraction Using Natural Language Processing
Sam Brown, Dhruva Biswas, Jack Wu, M. J. Ryan, Brett Sydney Bernstein, Natalie Fairhurst, George Kaye, Ranu Baral, Antonio Cannatà, Thomas Searle, Narbeh Melikian, Daniel Sado, Thomas F. Lüscher, James Teo, Richard Dobson, Daniel I. Bromage, Theresa A. McDonagh, Ali Vazir, Ajay M. Shah, Kevin O’Gallagher
Abstract
Background: Heart failure with preserved ejection fraction (HFpEF) is the predominant form of HF in older adults. It represents a heterogenous clinical syndrome that is less well understood across different ethnicities. Objectives: This study aimed to compare the clinical presentation and assess the diagnostic performance of existing HFpEF diagnostic tools between ethnic groups. Methods: FPEF scores, and echocardiogram reports), and mortality. Analyses were stratified by ethnicity and adjusted for socioeconomic status. Results: < 0.0001). Conclusions: Leveraging an NLP-based artificial intelligence approach to quantify health inequities in HFpEF diagnosis, we discovered that established markers systematically underdiagnose HFpEF in Black patients, possibly due to differences in the underlying comorbidity patterns. Clinicians should be aware of these limitations and its implications for treatment and trial recruitment.