Diagnostic signature for heart failure with preserved ejection fraction (HFpEF): a machine learning approach using multi-modality electronic health record data

TY - JOUR

T1 - Diagnostic signature for heart failure with preserved ejection fraction (HFpEF)

T2 - a machine learning approach using multi-modality electronic health record data

AU - Farajidavar, Nazli

AU - O’Gallagher, Kevin

AU - Bean, Daniel

AU - Nabeebaccus, Adam

AU - Zakeri, Rosita

AU - Bromage, Daniel

AU - Kraljevic, Zeljko

AU - Teo, James T.H.

AU - Dobson, Richard J.

AU - Shah, Ajay M.

N1 - Funding Information: This work was supported by the British Heart Foundation [RE/18/2/34213; CH/1999001/11735]; the NIHR Biomedical Research Centres at Guy’s & St Thomas’ NHS Foundation Trust [IS-BRC-1215-20006] and South London and Maudsley NHS Foundation Trust [IS-BRC-1215-20018], both with King’s College London. KOG is supported by a Medical Research Council Clinical Training Fellowship [MR/R017751/1]. DMB is funded by a UKRI Innovation Fellowship as part of Health Data Research UK MR/S00310X/1 ( https://www.hdruk.ac.uk ). The views expressed are those of the authors and not necessarily those of NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2022, The Author(s).

PY - 2022/12

Y1 - 2022/12

N2 - Background: Heart failure with preserved ejection fraction (HFpEF) is thought to be highly prevalent yet remains underdiagnosed. Evidence-based treatments are available that increase quality of life and decrease hospitalization. We sought to develop a data-driven diagnostic model to predict from electronic health records (EHR) the likelihood of HFpEF among patients with unexplained dyspnea and preserved left ventricular EF. Methods and results: The derivation cohort comprised patients with dyspnea and echocardiography results. Structured and unstructured data were extracted using an automated informatics pipeline. Patients were retrospectively diagnosed as HFpEF (cases), non-HF (control cohort I), or HF with reduced EF (HFrEF; control cohort II). The ability of clinical parameters and investigations to discriminate cases from controls was evaluated by extreme gradient boosting. A likelihood scoring system was developed and validated in a separate test cohort. The derivation cohort included 1585 consecutive patients: 133 cases of HFpEF (9%), 194 non-HF cases (Control cohort I) and 1258 HFrEF cases (Control cohort II). Two HFpEF diagnostic signatures were derived, comprising symptoms, diagnoses and investigation results. A final prediction model was generated based on the averaged likelihood scores from these two models. In a validation cohort consisting of 269 consecutive patients [with 66 HFpEF cases (24.5%)], the diagnostic power of detecting HFpEF had an AUROC of 90% (P < 0.001) and average precision of 74%. Conclusion: This diagnostic signature enables discrimination of HFpEF from non-cardiac dyspnea or HFrEF from EHR and can assist in the diagnostic evaluation in patients with unexplained dyspnea. This approach will enable identification of HFpEF patients who may then benefit from new evidence-based therapies.

AB - Background: Heart failure with preserved ejection fraction (HFpEF) is thought to be highly prevalent yet remains underdiagnosed. Evidence-based treatments are available that increase quality of life and decrease hospitalization. We sought to develop a data-driven diagnostic model to predict from electronic health records (EHR) the likelihood of HFpEF among patients with unexplained dyspnea and preserved left ventricular EF. Methods and results: The derivation cohort comprised patients with dyspnea and echocardiography results. Structured and unstructured data were extracted using an automated informatics pipeline. Patients were retrospectively diagnosed as HFpEF (cases), non-HF (control cohort I), or HF with reduced EF (HFrEF; control cohort II). The ability of clinical parameters and investigations to discriminate cases from controls was evaluated by extreme gradient boosting. A likelihood scoring system was developed and validated in a separate test cohort. The derivation cohort included 1585 consecutive patients: 133 cases of HFpEF (9%), 194 non-HF cases (Control cohort I) and 1258 HFrEF cases (Control cohort II). Two HFpEF diagnostic signatures were derived, comprising symptoms, diagnoses and investigation results. A final prediction model was generated based on the averaged likelihood scores from these two models. In a validation cohort consisting of 269 consecutive patients [with 66 HFpEF cases (24.5%)], the diagnostic power of detecting HFpEF had an AUROC of 90% (P < 0.001) and average precision of 74%. Conclusion: This diagnostic signature enables discrimination of HFpEF from non-cardiac dyspnea or HFrEF from EHR and can assist in the diagnostic evaluation in patients with unexplained dyspnea. This approach will enable identification of HFpEF patients who may then benefit from new evidence-based therapies.

KW - Dyspnea

KW - HFpEF

KW - Machine learning

UR - http://www.scopus.com/inward/record.url?scp=85144637425&partnerID=8YFLogxK

U2 - 10.1186/s12872-022-03005-w

DO - 10.1186/s12872-022-03005-w

M3 - Article

C2 - 36567336

AN - SCOPUS:85144637425

SN - 1471-2261

VL - 22

JO - BMC Cardiovascular Disorders

JF - BMC Cardiovascular Disorders

IS - 1

M1 - 567

ER -