It is recognized that the trabecular bone score (TBS) provides skeletal information, and frailty measurement is significantly associated with increased risks of adverse health outcomes. Given the suboptimal predictive power in fracture risk assessment tools, we aimed to evaluate the combination of frailty and TBS regarding predictive accuracy for risk of major osteoporotic fracture (MOF). Data from the prospective longitudinal study of CaMos (Canadian Multicentre Osteoporosis Study) were used for this study. TBS values were estimated using lumbar spine (L1 to L4) dual‐energy X‐ray absorptiometry (DXA) images; frailty was evaluated by a frailty index (FI) of deficit accumulation. Outcome was time to first incident MOF during the follow‐up. We used the Harrell's C‐index to compare the model predictive accuracy. The Akaike information criterion, likelihood ratio test, and net reclassification improvement (NRI) were used to compare model performances between the model combining frailty and TBS (subsequently called “FI + TBS”), FI‐alone, and TBS‐alone models. We included 2730 participants (mean age 69 years; 70% women) for analyses (mean follow‐up 7.5 years). There were 243 (8.90%) MOFs observed during follow‐up. Participants with MOF had significantly higher FI (0.24 versus 0.20) and lower TBS (1.231 versus 1.285) than those without MOF. FI and TBS were significantly related with MOF risk in the model adjusted for FRAX with bone mineral density (BMD) and other covariates: hazard ratio (HR) = 1.26 (95% confidence interval [CI] 1.11–1.43) for per‐SD increase in FI; HR = 1.38 (95% CI 1.21–1.59) for per‐SD decrease in TBS; and these associations showed negligible attenuation (HR = 1.24 for per‐SD increase in FI, and 1.35 for per‐SD decrease in TBS) when combined in the same model. Although the model FI + TBS was a better fit to the data than FI‐alone and TBS‐alone, only minimal and nonsignificant enhancement of discrimination and NRI were observed in FI + TBS. To conclude, frailty and TBS are significantly and independently related to MOF risk. Larger studies are warranted to determine whether combining frailty and TBS can yield improved predictive accuracy for MOF risk. © 2020 American Society for Bone and Mineral Research.

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IntroductionAlthough interest in including non-randomised studies of interventions (NRSIs) in meta-analysis of randomised controlled trials (RCTs) is growing, estimates of effectiveness obtained from NRSIs are vulnerable to greater bias than RCTs. The objectives of this study are to: (1) explore how NRSIs can be integrated into a meta-analysis of RCTs; (2) assess concordance of the evidence from non-randomised and randomised trials and explore factors associated with agreement; and (3) investigate the impact on estimates of pooled bodies of evidence when NRSIs are included.Methods and analysisWe will conduct a systematic survey of 210 systematic reviews that include both RCTs and NRSIs, published from 2017 to 2022. We will randomly select reviews, stratified in a 1:1 ratio by Core vs non-Core clinical journals, as defined by the National Library of Medicine. Teams of paired reviewers will independently determine eligibility and abstract data using standardised, pilot-tested forms. The concordance of the evidence will be assessed by exploring agreement in the relative effect reported by NRSIs and RCT addressing the same clinical question, defined as similarity of the population, intervention/exposure, control and outcomes. We will conduct univariable and multivariable logistic regression analyses to examine the association of prespecified study characteristics with agreement in the estimates between NRSIs and RCTs. We will calculate the ratio of the relative effect estimate from NRSIs over that from RCTs, along with the corresponding 95% CI. We will use a bias-corrected meta-analysis model to investigate the influence on pooled estimates when NRSIs are included in the evidence synthesis.Ethics and disseminationEthics approval is not required. The findings of this study will be disseminated through peer-reviewed publications, conference presentations and condensed summaries for clinicians, health policymakers and guideline developers regarding the design, conduct, analysis, and interpretation of meta-analysis that integrate RCTs and NRSIs.