Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: an individual participant data meta-analysis (original) (raw)

Butterly, E. W. et al. (2023) Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: an individual participant data meta-analysis.PLoS Medicine, 20(1), e1004154. (doi: 10.1371/journal.pmed.1004154) (PMID:36649256) (PMCID:PMC9844862)

Abstract

Background: Health-related quality of life metrics evaluate treatments in ways that matter to patients, so are often included in randomised clinical trials (hereafter trials). Multimorbidity, where individuals have 2 or more conditions, is negatively associated with quality of life. However, whether multimorbidity predicts change over time or modifies treatment effects for quality of life is unknown. Therefore, clinicians and guideline developers are uncertain about the applicability of trial findings to people with multimorbidity. We examined whether comorbidity count (higher counts indicating greater multimorbidity) (i) is associated with quality of life at baseline; (ii) predicts change in quality of life over time; and/or (iii) modifies treatment effects on quality of life. Methods and findings: Included trials were registered on the United States trials registry for selected index medical conditions and drug classes, phase 2/3, 3 or 4, had ≥300 participants, a nonrestrictive upper age limit, and were available on 1 of 2 trial repositories on 21 November 2016 and 18 May 2018, respectively. Of 124 meeting these criteria, 56 trials (33,421 participants, 16 index conditions, and 23 drug classes) collected a generic quality of life outcome measure (35 EuroQol-5 dimension (EQ-5D), 31 36-item short form survey (SF-36) with 10 collecting both). Blinding and completeness of follow up were examined for each trial. Using trials where individual participant data (IPD) was available from 2 repositories, a comorbidity count was calculated from medical history and/or prescriptions data. Linear regressions were fitted for the association between comorbidity count and (i) quality of life at baseline; (ii) change in quality of life during trial follow up; and (iii) treatment effects on quality of life. These results were then combined in Bayesian linear models. Posterior samples were summarised via the mean, 2.5th and 97.5th percentiles as credible intervals (95% CI) and via the proportion with values less than 0 as the probability (PBayes) of a negative association. All results are in standardised units (obtained by dividing the EQ-5D/SF-36 estimates by published population standard deviations). Per additional comorbidity, adjusting for age and sex, across all index conditions and treatment comparisons, comorbidity count was associated with lower quality of life at baseline and with a decline in quality of life over time (EQ-5D −0.02 [95% CI −0.03 to −0.01], PBayes > 0.999). Associations were similar, but with wider 95% CIs crossing the null for SF-36-PCS and SF-36-MCS (−0.05 [−0.10 to 0.01], PBayes = 0.956 and −0.05 [−0.10 to 0.01], PBayes = 0.966, respectively). Importantly, there was no evidence of any interaction between comorbidity count and treatment efficacy for either EQ-5D or SF-36 (EQ-5D −0.0035 [95% CI −0.0153 to −0.0065], PBayes = 0.746; SF-36-MCS (−0.0111 [95% CI −0.0647 to 0.0416], PBayes = 0.70 and SF-36-PCS −0.0092 [95% CI −0.0758 to 0.0476], PBayes = 0.631. Conclusions: Treatment effects on quality of life did not differ by multimorbidity (measured via a comorbidity count) at baseline—for the medical conditions studied, types and severity of comorbidities and level of quality of life at baseline, suggesting that evidence from clinical trials is likely to be applicable to settings with (at least modestly) higher levels of comorbidity. Trial registration: A prespecified protocol was registered on PROSPERO (CRD42018048202).

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Funder and Project Information

Combining efficacy estimates from clinical trials with the natural history obtained from large routine healthcare databases to determine net overall treatment benefits

David McAllister

201492/Z/16/Z

Institute of Health & Wellbeing

Understanding prevalence and impact of frailty in chronic illness and implications for clinical management

Frances Mair

MR/S021949/1

HW - General Practice and Primary Care

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