ABSTRACT

Background High-dimensional electronic health records (EHR) data can be used to phenotype complex diseases. The aim of this study is to apply unsupervised clustering to EHR-based traits derived in a cohort of patients with heart failure (HF) from a large integrated health system.

Methods Using the institutional EHR, we identified 8569 patients with HF and extracted 1263 EHR-based input features, including clinical, echocardiographic, and comorbidity data, prior to the time of HF diagnosis. Principal component analysis, Uniform Manifold Approximation and Projection, and spectral clustering were applied to the input features after sex stratification of the cohort. The optimal number of clusters for each sex-stratified group was selected by highest Silhouette score and by within-cluster and between-cluster sums of squares. Determinants of cluster assignment were evaluated.

Results We identified four clusters in each of the female-only (44%) and male-only (56%) cohorts. Sex-specific cohorts differed significantly by age of HF diagnosis, left ventricular chamber size, markers of renal and hepatic function, and comorbidity burden (all p<0.001). Left ventricular ejection fraction was not a strong driver of cluster assignment.

Conclusion Readily available EHR data collected in the course of routine care can be leveraged to accurately classify patients into major phenotypic HF subtypes using data driven approaches.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

NIH KL2TR001879 (N.R.) and R01HL141232 (M.R., T.C.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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:

Institutional Review Board of the University of Pennsylvania gave ethical approval for this work.

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Yes

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Yes

Data Availability

All data produced in the present study are available upon reasonable request to the authors

Non-standard Abbreviations and AcronymsHERelectronic health recordICD-10-CMInternational Classification of Diseases, Tenth Revision, Clinical ModificationHFheart failureHfpEFheart failure with preserved ejection fractionHfrEFheart failure with reduced ejection fractionLVEFleft ventricular ejection fractionNYHANew York Heart AssociationPCAPrincipal Component AnalysisUMAPUniform Manifold Approximation and Projection