Race- and Ethnicity-Related Differences in Heart Failure With Preserved Ejection Fraction Using Natural Language Processing

TY - JOUR

T1 - Race- and Ethnicity-Related Differences in Heart Failure With Preserved Ejection Fraction Using Natural Language Processing

AU - Brown, Sam

AU - Biswas, Dhruva

AU - Wu, Jack

AU - Ryan, Matthew

AU - Bernstein, Brett S

AU - Fairhurst, Natalie

AU - Kaye, George

AU - Baral, Ranu

AU - Cannata, Antonio

AU - Searle, Tom

AU - Melikian, Narbeh

AU - Sado, Daniel

AU - Lüscher, Thomas F

AU - Teo, James

AU - Dobson, Richard

AU - Bromage, Daniel I

AU - McDonagh, Theresa A

AU - Vazir, Ali

AU - Shah, Ajay M

AU - O'Gallagher, Kevin

N1 - Publisher Copyright: © 2024 The Authors

PY - 2024/8

Y1 - 2024/8

N2 - 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: A validated Natural Language Processing (NLP) algorithm was applied to the electronic health records of a large London hospital to identify patients meeting the European Society of Cardiology criteria for a diagnosis of HFpEF. NLP extracted patient demographics (including self-reported ethnicity and socioeconomic status), comorbidities, investigation results (N-terminal pro-B-type natriuretic peptide, H2FPEF scores, and echocardiogram reports), and mortality. Analyses were stratified by ethnicity and adjusted for socioeconomic status. Results: Our cohort consisted of 1,261 (64%) White, 578 (29%) Black, and 134 (7%) Asian patients meeting the European Society of Cardiology HFpEF diagnostic criteria. Compared to White patients, Black patients were younger at diagnosis and more likely to have metabolic comorbidities (obesity, diabetes, and hypertension) but less likely to have atrial fibrillation (30% vs 13%; P < 0.001). Black patients had lower N-terminal pro-B-type natriuretic peptide levels and a lower frequency of H2FPEF scores ≥6, indicative of likely HFpEF (26% vs 44%; P < 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.

AB - 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: A validated Natural Language Processing (NLP) algorithm was applied to the electronic health records of a large London hospital to identify patients meeting the European Society of Cardiology criteria for a diagnosis of HFpEF. NLP extracted patient demographics (including self-reported ethnicity and socioeconomic status), comorbidities, investigation results (N-terminal pro-B-type natriuretic peptide, H2FPEF scores, and echocardiogram reports), and mortality. Analyses were stratified by ethnicity and adjusted for socioeconomic status. Results: Our cohort consisted of 1,261 (64%) White, 578 (29%) Black, and 134 (7%) Asian patients meeting the European Society of Cardiology HFpEF diagnostic criteria. Compared to White patients, Black patients were younger at diagnosis and more likely to have metabolic comorbidities (obesity, diabetes, and hypertension) but less likely to have atrial fibrillation (30% vs 13%; P < 0.001). Black patients had lower N-terminal pro-B-type natriuretic peptide levels and a lower frequency of H2FPEF scores ≥6, indicative of likely HFpEF (26% vs 44%; P < 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.

KW - AI (artificial intelligence)

KW - health equity

KW - heart failure

KW - Natural Language Processing

KW - preserved ejection fraction

UR - https://www.scopus.com/pages/publications/85197092538

U2 - 10.1016/j.jacadv.2024.101064

DO - 10.1016/j.jacadv.2024.101064

M3 - Article

C2 - 39050815

AN - SCOPUS:85197092538

SN - 2772-963X

VL - 3

JO - JACC: Advances

JF - JACC: Advances

IS - 8

M1 - 101064

ER -