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Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach*

Vranas, Kelly C. MD1,2; Jopling, Jeffrey K. MD, MSHS1,3; Sweeney, Timothy E. MD, PhD4; Ramsey, Meghan C. MD1,5; Milstein, Arnold S. MD, MPH1; Slatore, Christopher G. MD, MS6,2; Escobar, Gabriel J. MD7; Liu, Vincent X. MD, MS7

doi: 10.1097/CCM.0000000000002548
Clinical Investigations

Objectives: Identifying subgroups of ICU patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care through the design of care platforms tailored around patients’ shared needs. However, objective methods for identifying these ICU patient subgroups are lacking. We used a machine learning approach to empirically identify ICU patient subgroups through clustering analysis and evaluate whether these groups might represent appropriate targets for care redesign efforts.

Design: We performed clustering analysis using data from patients’ hospital stays to retrospectively identify patient subgroups from a large, heterogeneous ICU population.

Setting: Kaiser Permanente Northern California, a healthcare delivery system serving 3.9 million members.

Patients: ICU patients 18 years old or older with an ICU admission between January 1, 2012, and December 31, 2012, at one of 21 Kaiser Permanente Northern California hospitals.

Interventions: None.

Measurements and Main Results: We used clustering analysis to identify putative clusters among 5,000 patients randomly selected from 24,884 ICU patients. To assess cluster validity, we evaluated the distribution and frequency of patient characteristics and the need for invasive therapies. We then applied a classifier built from the sample cohort to the remaining 19,884 patients to compare the derivation and validation clusters. Clustering analysis successfully identified six clinically recognizable subgroups that differed significantly in all baseline characteristics and clinical trajectories, despite sharing common diagnoses. In the validation cohort, the proportion of patients assigned to each cluster was similar and demonstrated significant differences across clusters for all variables.

Conclusions: A machine learning approach revealed important differences between empirically derived subgroups of ICU patients that are not typically revealed by admitting diagnosis or severity of illness alone. Similar data-driven approaches may provide a framework for future organizational innovations in ICU care tailored around patients’ shared needs.

Supplemental Digital Content is available in the text.

1Department of Medicine, Clinical Excellence Research Center, Stanford University, Stanford, CA.

2Division of Pulmonary and Critical Care, Department of Medicine, Oregon Health and Science University, Portland, OR.

3Department of Surgery, Stanford University, Stanford, CA.

4Biomedical Informatics Research, Stanford University, Stanford, CA.

5Division of Pulmonary and Critical Care, Department of Medicine, Stanford University, Stanford, CA.

6Health Services Research and Development, Portland VA Medical Center, Portland, OR.

7Division of Research, Kaiser Permanente, Oakland, CA.

*See also p. 1775.

The Department of Veterans Affairs did not have a role in the conduct of the study, in the collection, management, analysis, interpretation of data, or in the preparation of the article. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs or the U.S. Government.

Drs. Vranas, Jopling, Ramsey, and Liu contributed to the conception and design of this study. Dr. Liu contributed to data acquisition. Drs. Sweeney and Liu contributed to the analysis and interpretation of data. All authors contributed to the preparation and/or revision of this article.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (

Dr. Vranas received support for article research from the National Institutes of Health (NIH), and she was supported by T32 HL083808 07. Dr. Jopling received funding and support for article research from NIH UL1 TR001085 grant. Dr. Sweeney received funding from Inflammatix. Dr. Ramsey received funding from the American Thoracic Society. Dr. Slatore disclosed government work, and he was supported by resources from the VA Portland Health Care System, Portland, Oregon. Dr. Escobar’s institution received funding from Merck and the Gordon and Betty Moore Foundation. Dr. Liu received support for article research from the NIH; his institution received funding from NIH K23GM112018; and he received funding from the Permanente Medical Group (employee). Dr. Milstein has disclosed that he does not have any potential conflicts of interest.

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