Artificial Intelligence methods for Improved Detection of undiagnosed Heart Failure with Preserved Ejection Fraction (HFpEF)

Background and aim Heart Failure with preserved Ejection Fraction (HFpEF) remains under-diagnosed in clinical practice despite accounting for nearly half of all Heart Hailure (HF) cases. Accurate and timely diagnosis of HFpEF is crucial for proper patient management and treatment. In this study, we explored the potential of natural language processing (NLP) to improve the detection and diagnosis of HFpEF according to the European Society of Cardiology (ESC) diagnostic criteria. Methods In a retrospective cohort study, we used an NLP pipeline applied to the Electronic Health Record (EHR) to identify patients with a clinical diagnosis of HF between 2010-2022. We collected demographic, clinical, echocardiographic and outcome data from the EHR. Patients were categorised according to the left ventricular ejection fraction (LVEF). Those with LVEF ≥ 50% were further categorised based on whether they had a clinician-assigned diagnosis of HFpEF and if not, whether they met the ESC diagnostic criteria. Results were validated in a second, independent centre. Results We identified 8606 patients with HF. Of 3727 consecutive patients with HF and LVEF ≥ 50% on echocardiogram, only 8.3% had a clinician-assigned diagnosis of HFpEF, while 75.4% met ESC criteria but did not have a formal diagnosis of HFpEF. Patients with confirmed HFpEF were hospitalised more frequently; however the ESC criteria group had a higher 5-year mortality, despite being less co-morbid and experiencing fewer acute cardiovascular events. Conclusions This study demonstrates that patients with undiagnosed HFpEF are an at-risk group with high mortality. It is possible to use NLP methods to identify likely HFpEF patients from EHR data who would likely then benefit from expert clinical review and complement the use of diagnostic algorithms. Graphical Abstract Of 3727 consecutive patients with a clinical diagnosis of HF and left ventricular ejection fraction (LVEF) >50% on echocardiogram, only 8.3% had a clinician-assigned diagnosis of HFpEF, while 75.4% met ESC criteria but did not have a formal diagnosis of HFpEF. The two groups had similar rates of hospitalisation however the ESC criteria group had a higher 5-year mortality. ![Figure][1] ### Competing Interest Statement AMS serves as an advisor to Forcefield Therapeutics and CYTE - Global Network for Clinical Research. TAM has received speaker's fees or advisory board fees from Abbott, Edwards, Boehringer Ingelheim, and Astra Zeneca. ### Funding Statement This work was supported by grants from the British Heart Foundation (CH/1999001/11735, RG/20/3/34823 and RE/18/2/34213 to AMS; CC/22/250022 to RJDB, AMS, JT and KOG) and King's College Hospital Charity (D3003/122022/Shah/1188 to AMS). KOG and DIB are each supported by MRC Clinician Scientist Fellowships (MR/Y001311/1 to KOG, MR/X001881/1 to DiB). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This project operated under London South-East Research Ethics Committee approval (18/LO/2048) granted to the King's Electronic Records Research Interface (KERRI) and London Dulwich Research Ethics Committee approval (19/LO/1957), which did not require written informed patient consent. This study complies with the Declaration of Helsinki. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The datasets analysed during the current study are not publicly available due to hospital information governance regulations but are available from the corresponding author on reasonable request. [1]: pending:yes