A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history

A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history

Maurits, Marc P. (Leiden University Medical Center)
Korsunsky, Ilya (Harvard Medical School)
Raychaudhuri, Soumya (Harvard Medical School)
Murphy, Shawn N. (Mass General Brigham, Boston)
Smoller, Jordan W. (Massachusetts General Hospital)
Weiss, Scott T. (Harvard Medical School)
Huizinga, Thomas W.J. (Leiden University Medical Center)
Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center; Delft Bioinformatics Lab)
van den Akker, E.B. (Leiden University Medical Center)

2022

Objective: To facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects. Material and Methods: We used 1872 billing codes in EHRs of 102 880 patients from 12 healthcare systems. Using tools borrowed from single-cell omics, we mitigated center-specific batch effects and performed clustering to identify patients with highly similar medical history patterns across the various centers. Our visualization method (PheSpec) depicts the phenotypic profile of clusters, applies a novel filtering of noninformative codes (Ranked Scope Pervasion), and indicates the most distinguishing features. Results: We observed 114 clinically meaningful profiles, for example, linking prostate hyperplasia with cancer and diabetes with cardiovascular problems and grouping pediatric developmental disorders. Our framework identified disease subsets, exemplified by 6 "other headache"clusters, where phenotypic profiles suggested different underlying mechanisms: migraine, convulsion, injury, eye problems, joint pain, and pituitary gland disorders. Phenotypic patterns replicated well, with high correlations of ≥0.75 to an average of 6 (2-8) of the 12 different cohorts, demonstrating the consistency with which our method discovers disease history profiles. Discussion: Costly clinical research ventures should be based on solid hypotheses. We repurpose methods from single-cell omics to build these hypotheses from observational EHR data, distilling useful information from complex data. Conclusion: We establish a generalizable pipeline for the identification and replication of clinically meaningful (sub)phenotypes from widely available high-dimensional billing codes. This approach overcomes datatype problems and produces comprehensive visualizations of validation-ready phenotypes.

clustering
electronic health records
electronic medical records
eMERGE
ICD
PhenoGraph

http://resolver.tudelft.nl/uuid:3ab2510b-bbe1-4bee-895b-fdd49b2f5a00

https://doi.org/10.1093/jamia/ocac008

1067-5027

Journal of the American Medical Informatics Association, 29 (5), 761-769

Institutional Repository

journal article

© 2022 Marc P. Maurits, Ilya Korsunsky, Soumya Raychaudhuri, Shawn N. Murphy, Jordan W. Smoller, Scott T. Weiss, Thomas W.J. Huizinga, M.J.T. Reinders, E.B. van den Akker, More Authors