Use of Latent Class Analysis and k-Means Clustering to Identify Complex Patient Profiles

• Published in: JAMA network open Vol. 3; no. 12; p. e2029068
• Main Authors: Grant, Richard W, McCloskey, Jodi, Hatfield, Meghan, Uratsu, Connie, Ralston, James D, Bayliss, Elizabeth, Kennedy, Chris J
• Format: Journal Article
• Language: English
• Published: United States American Medical Association 01.12.2020
• Subjects: Aged; California - epidemiology; Cluster Analysis; Complex patients; Ethnic Groups - statistics & numerical data; Female; Frailty; Health Care Rationing - methods; Health Informatics; Hospitalization - trends; Humans; Latent Class Analysis; Male; Mental Disorders - epidemiology; Mortality; Multiple Chronic Conditions - classification; Multiple Chronic Conditions - economics; Multiple Chronic Conditions - epidemiology; Multiple Chronic Conditions - therapy; Online Only; Original Investigation; Patient Acceptance of Health Care - statistics & numerical data; Patient Care Management - economics; Patient Care Management - standards; Patients; Quality Improvement - organization & administration; Resource Allocation - methods; California
• ISSN: 2574-3805; 2574-3805
• DOI: 10.1001/jamanetworkopen.2020.29068

Medically complex patients are a heterogeneous group that contribute to a substantial proportion of health care costs. Coordinated efforts to improve care and reduce costs for this patient population have had limited success to date. To define distinct patient clinical profiles among the most medically complex patients through clinical interpretation of analytically derived patient clusters. This cohort study analyzed the most medically complex patients within Kaiser Permanente Northern California, a large integrated health care delivery system, based on comorbidity score, prior emergency department admissions, and predicted likelihood of hospitalization, from July 18, 2018, to July 15, 2019. From a starting point of over 5000 clinical variables, we used both clinical judgment and analytic methods to reduce to the 97 most informative covariates. Patients were then grouped using 2 methods (latent class analysis, generalized low-rank models, with k-means clustering). Results were interpreted by a panel of clinical stakeholders to define clinically meaningful patient profiles. Complex patient profiles, 1-year health care utilization, and mortality outcomes by profile. The analysis included 104 869 individuals representing 3.3% of the adult population (mean [SD] age, 70.7 [14.5] years; 52.4% women; 39% non-White race/ethnicity). Latent class analysis resulted in a 7-class solution. Stakeholders defined the following complex patient profiles (prevalence): high acuity (9.4%), older patients with cardiovascular complications (15.9%), frail elderly (12.5%), pain management (12.3%), psychiatric illness (12.0%), cancer treatment (7.6%), and less engaged (27%). Patients in these groups had significantly different 1-year mortality rates (ranging from 3.0% for psychiatric illness profile to 23.4% for frail elderly profile; risk ratio, 7.9 [95% CI, 7.1-8.8], P < .001). Repeating the analysis using k-means clustering resulted in qualitatively similar groupings. Each clinical profile suggested a distinct collaborative care strategy to optimize management. The findings suggest that highly medically complex patient populations may be categorized into distinct patient profiles that are amenable to varying strategies for resource allocation and coordinated care interventions.