Artificial intelligence methods to detect heart failure with preserved ejection fraction within electronic health...

Heart failure with preserved ejection fraction (HFpEF) accounts for approximately half of all heart failure cases, with high levels of morbidity and mortality. However, many patients who meet diagnostic criteria for HFpEF do not have a documented diagnosis, particularly in non-White populations where conventional risk scores may underestimate risk. Our aim was to develop and validate a diagnostic prediction model to detect HFpEF based on ESC criteria, AIM-HFpEF.

We applied natural language processing (NLP) and machine learning methods to routinely collected electronic health record (EHR) data from a tertiary centre hospital trust in London, UK, to derive the AIM-HFpEF model. We then externally validated the model and performed benchmarking against existing HFpEF prediction models (H2FPEF and HFpEF-ABA) for diagnostic power on the entire external cohort and in patients of non-White ethnicity and patients from areas of increased socioeconomic deprivation. An XGBoost model combining demographic, clinical, and echocardiogram data showed strong diagnostic performance in the derivation dataset [n = 3173, AUC = 0.88, (95% CI, 0.85-0.91)] and validation cohort [n = 5383, AUC: 0.88 (95% CI, 0.86-0.90)]. Diagnostic performance was maintained in patients of non-White ethnicity [AUC = 0.89 (95% CI, 0.85-0.93)] and patients from areas of high socioeconomic deprivation [AUC = 0.90 (95% CI, 0.85-0.95)]. In contrast, AIM-HFpEF demonstrated favourable performance relative to the H2FPEF and HFpEF-ABA models. AIM-HFpEF model probabilities were associated with an increased risk of death, hospitalization, and stroke in the external validation cohort (P < 0.001, P = 0.01, P < 0.001, respectively, for highest vs. middle tertile).

AIM-HFpEF represents a validated equitable diagnostic model for HFpEF, which can be embedded within an EHR to allow for fully automated HFpEF detection.