Clinical Epidemiology (Oct 2020)
Co-Morbidity Patterns Identified Using Latent Class Analysis of Medications Predict All-Cause Mortality Independent of Other Known Risk Factors: The COPDGene® Study
Abstract
Yisha Li,1 Margaret Ragland,1 Erin Austin,2 Kendra Young,1 Katherine Pratte,3 John E Hokanson,1 Terri H Beaty,4 Elizabeth A Regan,3 Stephen I Rennard,5 Christina Wern,6 Michael R Jacobs,7 Ruth Tal-Singer,8 Barry J Make,3 Gregory L Kinney1 On Behalf of theCOPDGene investigators1Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; 2Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA; 3National Jewish Health, Denver, CO, USA; 4Bloomberg School of Public Health, University of John Hopkins, Baltimore, MD, USA; 5Department of Internal Medicine, University of Nebraska Medical Center, Omaha, NB, USA; 6Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA; 7School of Pharmacy, Temple University, PA, Pennsylvania, USA; 8COPD Foundation, Washington, D.C., USACorrespondence: Gregory L KinneyDepartment of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USATel +1 303-724-4437Email [email protected]: Medication patterns include all medications in an individual’s clinical profile. We aimed to identify chronic co-morbidity treatment patterns through medication use among COPDGene participants and determine whether these patterns were associated with mortality, acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and quality of life.Materials and Methods: Participants analyzed here completed Phase 1 (P1) and/or Phase 2 (P2) of COPDGene. Latent class analysis (LCA) was used to identify medication patterns and assign individuals into unobserved LCA classes. Mortality, AECOPD, and the St. George’s Respiratory Questionnaire (SGRQ) health status were compared in different LCA classes through survival analysis, logistic regression, and Kruskal–Wallis test, respectively.Results: LCA identified 8 medication patterns from 32 classes of chronic comorbid medications. A total of 8110 out of 10,127 participants with complete covariate information were included. Survival analysis adjusted for covariates showed, compared to a low medication use class, mortality was highest in participants with hypertension+diabetes+statin+antiplatelet medication group. Participants in hypertension+SSRI+statin medication group had the highest odds of AECOPD and the highest SGRQ score at both P1 and P2.Conclusion: Medication pattern can serve as a good indicator of an individual’s comorbidities profile and improves models predicting clinical outcomes.Keywords: COPDGene, co-morbidities, latent class analysis, medication patterns, mortality
