Phenotyping of heart failure with preserved ejection faction using health electronic records and echocardiography

Abstract

Abstract Patients suffering from heart failure (HF) symptoms and a preserved left ventricular ejection fraction (HFpEF) present very different clinical phenotypes that could influence their survival. This study aims to identify phenotypes of this type of HF by using the medical information database from our hospital center. Methods and results We develop a POC of machine learning model to predict the phenotype of patients suffering from HFpEF from both clinical and echocardiography data from data warehouse of our hospital center. We performed a retrospective observational study. We used the inpatient clinical data warehouse at our hospital center to identify patients suffering from HFpEF and having echocardiography between January 2018 and December 2019. A list of relevant clinical and echocardiographic variables was established by cardiologists. For clinical variables, we extracted information from the data warehouse using either structured data (ICD-10 codes, lab results, ...) or regular expressions for narratives. The echocardiographic parameters consisted only in structured data. We performed a two-step cluster analysis to identify common characteristics among patients. The final sample eligible included 2500 patients with echocardiography and diagnosed for HFpEF. The variables included in the two-step cluster analysis were both clinical (13 variables) and echographic (17 variables). Four clusters were identified from the clustering algorithm. The groups were relatively well balanced with respectively: 753, 744, 519 and 545 patients. After performing survival analysis, we observed that clusters have significative different survival curves in particular for death. The performances of these two models are satisfying with an AUC upper than 0.97 and an accuracy upper than 0.92) (Figure 1). Conclusion In the future, this work could be deployed as a tool for the physician to assess risks and contribute to support better care for patients. The phenotyping of HFpEF could improve the characterization of patients, the definition of the most appropriate treatments, and the care pathways. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Fédération française de cardiologie