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Identifying frailty in trials: an analysis of individual participant data from trials of novel pharmacological interventions.

ABSTRACT: BACKGROUND:Frailty is common in clinical practice, but trials rarely report on participant frailty. Consequently, clinicians and guideline-developers assume frailty is largely absent from trials and have questioned the relevance of trial findings to frail people. Therefore, we examined frailty in phase 3/4 industry-sponsored clinical trials of pharmacological interventions for three exemplar conditions: type 2 diabetes mellitus (T2DM), rheumatoid arthritis (RA), and chronic obstructive pulmonary disease (COPD). METHODS:We constructed a 40-item frailty index (FI) in 19 clinical trials (7 T2DM, 8 RA, 4 COPD, mean age 42-65?years) using individual-level participant data. Participants with a FI >?0.24 were considered 'frail'. Baseline disease severity was assessed using HbA1c for T2DM, Disease Activity Score-28 (DAS28) for RA, and % predicted FEV1 for COPD. Using generalised gamma regression, we modelled FI on age, sex, and disease severity. In negative binomial regression, we modelled serious adverse event rates on FI and combined results for each index condition in a random-effects meta-analysis. RESULTS:All trials included frail participants: prevalence 7-21% in T2DM trials, 33-73% in RA trials, and 15-22% in COPD trials. The 99th centile of the FI ranged between 0.35 and 0.45. Female sex was associated with higher FI in all trials. Increased disease severity was associated with higher FI in RA and COPD, but not T2DM. Frailty was associated with age in T2DM and RA trials, but not in COPD. Across all trials, and after adjusting for age, sex, and disease severity, higher FI predicted increased risk of serious adverse events; the pooled incidence rate ratios (per 0.1-point increase in FI scale) were 1.46 (95% CI 1.21-1.75), 1.45 (1.13-1.87), and 1.99 (1.43-2.76) for T2DM, RA, and COPD, respectively. CONCLUSION:The upper limit of frailty in trials is lower than has been described in the general population. However, mild to moderate frailty was common, suggesting trial data may be harnessed to inform disease management in people living with frailty. Participants with higher FI experienced more serious adverse events, suggesting screening for frailty in trial participants would enable identification of those that merit closer monitoring. Frailty is identifiable and prevalent among middle-aged and older participants in phase 3/4 drug trials and has clinically important safety implications.

SUBMITTER: Hanlon P

PROVIDER: S-EPMC7579922 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Identifying frailty in trials: an analysis of individual participant data from trials of novel pharmacological interventions.

Hanlon Peter P Butterly Elaine E Lewsey Jim J Siebert Stefan S Mair Frances S FS McAllister David A DA

BMC medicine 20201022 1

<h4>Background</h4>Frailty is common in clinical practice, but trials rarely report on participant frailty. Consequently, clinicians and guideline-developers assume frailty is largely absent from trials and have questioned the relevance of trial findings to frail people. Therefore, we examined frailty in phase 3/4 industry-sponsored clinical trials of pharmacological interventions for three exemplar conditions: type 2 diabetes mellitus (T2DM), rheumatoid arthritis (RA), and chronic obstructive p ...[more]

PMID: 33087107

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Project description:IntroductionSeveral evidence-based treatments are effective for post-traumatic stress disorder (PTSD), yet a substantial proportion of patients do not respond or dropout of treatment. We describe the protocol for a systematic review and individual participant data meta-analysis (IPD-MA) aimed at assessing the effectiveness and adverse effects of psychotherapy and pharmacotherapy interventions for treating PTSD. Additionally, we seek to examine moderators and predictors of treatment outcomes.Method and analysisThis IPD-MA includes randomised controlled trials comparing psychotherapy and pharmacotherapy interventions for PTSD. PubMed, Embase, PsycINFO, PTSDpubs and CENTRAL will be screened up till the 11th of January 2021. The target population is adults with above-threshold baseline PTSD symptoms on any standardised self-report measure. Trials will only be eligible if at least 70% of the study sample have been diagnosed with PTSD by means of a structured clinical interview. The primary outcomes of this IPD-MA are PTSD symptom severity, and response rate. Secondary outcomes include treatment dropout and adverse effects. Two independent reviewers will screen major bibliographic databases and past reviews. Authors will be contacted to contribute their participant-level datasets. Datasets will be merged into a master dataset. A one-stage IPD-MA will be conducted focusing on the effects of psychological and pharmacological interventions on PTSD symptom severity, response rate, treatment dropout and adverse effects. Subsequent analyses will focus on examining the effect of moderators and predictors of treatment outcomes. These will include sociodemographic, treatment-related, symptom-related, resilience, intervention, trauma and combat-related characteristics. By determining the individual factors that influence the effectiveness of specific PTSD treatments, we will gain insight into personalised treatment options for PTSD.Ethics and disseminationSpecific ethics approval for an IPD-MA is not required as this study entails secondary analysis of existing anonymised data. The results of this study will be published in peer-reviewed scientific journals and presentations.

Project description:BackgroundClinicians are less likely to prescribe guideline-recommended treatments to people with multimorbidity than to people with a single condition. Doubts as to the applicability of clinical trials of drug treatments (the gold standard for evidence-based medicine) when people have co-existing diseases (comorbidity) may underlie this apparent reluctance. Therefore, for a range of index conditions, we measured the comorbidity among participants in clinical trials of novel drug therapies and compared this to the comorbidity among patients in the community.MethodsData from industry-sponsored phase 3/4 multicentre trials of novel drug therapies for chronic medical conditions were identified from two repositories: Clinical Study Data Request and the Yale University Open Data Access project. We identified 116 trials (n =?122,969 participants) for 22 index conditions. Community patients were identified from a nationally representative sample of 2.3 million patients in Wales, UK. Twenty-one comorbidities were identified from medication use based on pre-specified definitions. We assessed the prevalence of each comorbidity and the total number of comorbidities (level of multimorbidity), for each trial and in community patients.ResultsIn the trials, the commonest comorbidities in order of declining prevalence were chronic pain, cardiovascular disease, arthritis, affective disorders, acid-related disorders, asthma/COPD and diabetes. These conditions were also common in community-based patients. Mean comorbidity count for trial participants was approximately half that seen in community-based patients. Nonetheless, a substantial proportion of trial participants had a high degree of multimorbidity. For example, in asthma and psoriasis trials, 10-15% of participants had ??3 conditions overall, while in osteoporosis and chronic obstructive pulmonary disease trials 40-60% of participants had ??3 conditions overall.ConclusionsComorbidity and multimorbidity are less common in trials than in community populations with the same index condition. Comorbidity and multimorbidity are, nevertheless, common in trials. This suggests that standard, industry-funded clinical trials are an underused resource for investigating treatment effects in people with comorbidity and multimorbidity.

Project description:BackgroundIncreasingly in network meta-analysis (NMA), there is a need to incorporate non-randomised evidence to estimate relative treatment effects, and in particular in cases with limited randomised evidence, sometimes resulting in disconnected networks of treatments. When combining different sources of data, complex NMA methods are required to address issues associated with participant selection bias, incorporating single-arm trials (SATs), and synthesising a mixture of individual participant data (IPD) and aggregate data (AD). We develop NMA methods which synthesise data from SATs and randomised controlled trials (RCTs), using a mixture of IPD and AD, for a dichotomous outcome.MethodsWe propose methods under both contrast-based (CB) and arm-based (AB) parametrisations, and extend the methods to allow for both within- and across-trial adjustments for covariate effects. To illustrate the methods, we use an applied example investigating the effectiveness of biologic disease-modifying anti-rheumatic drugs for rheumatoid arthritis (RA). We applied the methods to a dataset obtained from a literature review consisting of 14 RCTs and an artificial dataset consisting of IPD from two SATs and AD from 12 RCTs, where the artificial dataset was created by removing the control arms from the only two trials assessing tocilizumab in the original dataset.ResultsWithout adjustment for covariates, the CB method with independent baseline response parameters (CBunadjInd) underestimated the effectiveness of tocilizumab when applied to the artificial dataset compared to the original dataset, albeit with significant overlap in posterior distributions for treatment effect parameters. The CB method with exchangeable baseline response parameters produced effectiveness estimates in agreement with CBunadjInd, when the predicted baseline response estimates were similar to the observed baseline response. After adjustment for RA duration, there was a reduction in across-trial heterogeneity in baseline response but little change in treatment effect estimates.ConclusionsOur findings suggest incorporating SATs in NMA may be useful in some situations where a treatment is disconnected from a network of comparator treatments, due to a lack of comparative evidence, to estimate relative treatment effects. The reliability of effect estimates based on data from SATs may depend on adjustment for covariate effects, although further research is required to understand this in more detail.

Dataset Information

Identifying frailty in trials: an analysis of individual participant data from trials of novel pharmacological interventions.

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Identifying frailty in trials: an analysis of individual participant data from trials of novel pharmacological interventions.

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