Is gain in health-related quality of life after a total hip arthroplasty depended on the comorbidity burden?

Glassou, Eva Natalia; Pedersen, Alma B; Aalund, Peter Kloster; Mosegaard, Sebastian Breddam; Hansen, Torben Bæk

doi:10.1080/17453674.2018.1457885

Cited by 10 publications

(8 citation statements)

References 35 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a cohort study of 1,211 adults in Japan, multimorbidity at baseline predicted more rapid decline in SF-36 over 12 months [ 27 ]. Similarly, among 1,582 people undergoing total hip arthroplasty in Denmark comorbidity at baseline was associated with a smaller increase in quality of life (high comorbidity versus no comorbidity EQ-5D change 0.09; 95% CI 0.02 to 0.16) [ 28 ]. In a study comprising 351 individuals attending an Australian clinic for people with complex chronic diseases, comorbidity count predicted more rapid decline in SF-36 (−0.11 per comorbidity; −0.96 to 0.76) [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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).

show abstract

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Although this indicator has been widely used in orthopedic research, its suitability might still affect its effectiveness. Fifth, another limitation of using CCI is that the only mental illness it considers is dementia ( 69 ). Sixth, cementless and cemented implants were used in the patient cohort, although there are fewer cemented prostheses, different fixation methods can have an impact on postoperative outcomes.…”

Section: Discussionmentioning

confidence: 99%

Effects of Comorbidities on Pain and Function After Total Hip Arthroplasty

et al. 2022

View full text Add to dashboard Cite

BackgroundThe growing number of patients undergoing total hip arthroplasty (THA) and postoperative outcomes receive increasing attention from doctors and patients. This study aimed to elucidate the effects of comorbidities on postoperative function, pain, complications, readmission rate, and mortality.MethodsWe included consecutive patients who underwent primary unilateral THA between 2017 and 2019. The Charlson comorbidity index (CCI) and the WOMAC and SF-36 (physical function, body pain) scales were assessed preoperatively and at 3, 6, 12, and 24 months postoperatively. The complications, 30-day readmission, and mortality rates assessed the impact of comorbidities and their changes over time on the WOMAC and SF-36 scores during follow-up. We used mixed model linear regression to examine the association of worsening comorbidity post-THA with change in WOMAC and SF-36 scores in the subsequent follow-up periods, controlling for age, length of follow-up, and repeated observations.ResultsThis study included 468 patients, divided into four groups based on comorbidity burden (CCI-0, 1, 2, and ≥3). The physiological function recovery and pain scores in the CCI ≥ 3 group were inferior to the other groups and took longer than the other groups (6 vs. 3 months) to reach their best level. The four groups preoperative waiting times were 2.41 ± 0.74, 2.97 ± 0.65, 3.80 ± 0.53, and 5.01 ± 0.71 days, respectively. The complications, 30-day readmission, and 1-year mortality rates for the overall and the CCI ≥ 3 group were 1.92% and 4.69%, 0.85% and 2.01%, and 0.43% and 1.34%, respectively, with no mortality in the other groups.ConclusionPatients with higher CCI were more susceptible to physical function and pain outcome deterioration, experienced longer waiting time before surgery, took longer to recover, and had higher rates of complications, 30-day readmission, and mortality after THA. Older age in the group led to a greater impact.

show abstract

“…19 The CCI score was calculated for each patient and categorized based on the degree of comorbidity burden as none (0), medium (1 or 2), or high (>3). 20 , 21 We also explored the inclusion of DEXA results and had insufficient data available.…”

Section: Methodsmentioning

confidence: 99%