Co-Morbidity Patterns Identified Using Latent Class Analysis of Medications Predict All-Cause Mortality Independent of Other Known Risk Factors: The COPDGene^® Study

Abstract

Purpose

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

Acknowledgment

We thank COPDGene investigators who helped acquiring the data for this study. We thank COPD foundation for their support. We thank the Colorado School of Public Health Laboratory for Analytical and Computational Epidemiology (LACE) for their support. Special thanks to Annika Czizik, Nicole E. Reed, and Chandler Zachary from LACE to help support this project.

Abbreviations

ACEi, angiotensin-converting enzyme inhibitors; ARB, angiotensin II receptor blockers; AECOPD, acute exacerbation of COPD; AIC, Akaike Information Criterion; APT, antiplatelet; BIC, Bayesian information criterion; COPD, chronic obstructive pulmonary disease; COPDGene, Genetic Epidemiology of Chronic Obstructive Pulmonary Disease; CI, confidence interval; GOLD, Global initiative for chronic Obstructive Lung Disease; HR, hazard ratio; HTN, hypertension; LCA, latent class analysis; OR, odds ratio; P1, Phase 1; P2, Phase 2; PRISm, preserved ratio impaired spirometry; SD, standard deviation; SGRQ, St. George’s Respiratory Questionnaire; SSRI, selective serotonin reuptake inhibitors; SABIC, sample size adjusted BIC; T2D, type II diabetes; VIF, variance inflation factor.

Data Sharing Statement

Data supporting this manuscript is available to all COPDGene investigators. Data are available upon request through https://dccweb.njhealth.org/sec/COPDGene/MainPage.cfm.

Ethics Approval and Consent Form

All study participants were informed about the purpose of the study. Confirmation of consent forms were received from study participants. Protocols for all phases (HS-2778) and data evaluation in the COPDGene cohort followed the principles of the Declaration of Helsinki and was approved by the COPDGene committee.

Consent for Publication

We confirmed that details of words, tables, figures included in the manuscript can be published.

Author Contributions

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.

Disclosure

COPDGene is also supported by the COPD Foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, Siemens, and Sunovion.

Kendra Young reports grants from NIH, during the conduct of the study.

Terri H Beaty reports grants from NIHBI, during the conduct of the study.

Elizabeth A Regan reports grants from National Heart Lung and Blood Institute, during the conduct of the study.

Stephen I Rennard reports salary and shareholder from AstraZeneca, personal fees from GlaxoSmithKline, nothing from BerGenBio, nothing from Verona Pharma, outside the submitted work.,

Ruth Tal-Singer is a former employee and current shareholder of GSK, reports personal fees form Immunomet, Vocalis Health, and ENA Pharmaceuticals, and consultancy for Ena respiratory and Vocalis, outside the submitted work.

Barry J Make reports funding from the NHLBI, grants and medical advisory board work from Boehringer Ingelheim, GlaxoSmithKline, AstraZeneca and Sunovion, personal fees from Spiration, Shire, Circassia, CME personal fees from WebMD, National Jewish Health, American College of Chest Physicians, Projects in Knowledge, Hybrid Communications, Ultimate Medical Academy, Catamount Medical, Eastern Pulmonary Society, Medscape, Eastern VA Medical Center, Academy Continued Healthcare Learning, Mt. Sinai Medical Center, Theravance, Third Pole, Novartis, Phillips, Science 24/7, Wolter Kluwer Health and Verona, grants from Pearl, during the conduct of the study.

The authors report no other potential conflicts of interest for this work.

Additional information

Funding

The study was supported by Award Number U01 HL089897 and Award Number U01 HL089856 from the National Heart, Lung, and Blood Institute.