Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: An individual participant data meta-analysis.
Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: An individual participant data meta-analysis.
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DOI：
10.1371/journal.pmed.1004154
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发表时间：
2023-01
期刊：
影响因子：
15.8
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中科院分区：
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与健康相关的生活质量指标以对患者重要的方式评估治疗，因此通常包含在随机临床试验（以下简称试验）中。多重疾病（即个体患有两种或两种以上疾病）与生活质量呈负相关。然而，多重发病是否会预测随时间的变化或改变治疗效果对生活质量的影响尚不清楚。因此，临床医生和指南制定者不确定试验结果对患有多种疾病的人是否适用。我们检查了合并症计数（计数越高表明多重发病率越高）是否与基线生活质量相关； (ii) 预测生活质量随时间的变化；和/或 (iii) 改变治疗对生活质量的影响。纳入的试验在美国试验登记处注册，针对选定的指标医疗状况和药物类别，2/3、3或4期，有≥300名受试者，无年龄上限限制，并分别于2016年11月21日和2018年5月18日在2个试验存储库中的1个上提供。在符合这些标准的 124 项试验中，有 56 项试验（33,421 名受试者、16 种指标条件和 23 种药物类别）收集了一般生活质量结果测量（35 项 EuroQol-5 维度 (EQ-5D)、31 项 36 项简短调查 (SF-36)，其中 10 项同时收集两者）。每项试验均检查了盲法和随访的完整性。使用可从 2 个存储库获得个体参与者数据 (IPD) 的试验，根据病史和/或处方数据计算合并症计数。对合并症计数与（i）基线生活质量之间的关联进行线性回归拟合； (ii) 试验随访期间生活质量的变化； (iii) 治疗对生活质量的影响。然后将这些结果合并到贝叶斯线性模型中。后样本通过平均值、第 2.5 个和 97.5 个百分位总结为可信区间 (95% CI)，并通过值小于 0 的比例作为负关联的概率 (PBayes)。所有结果均采用标准化单位（通过将 EQ-5D/SF-36 估计值除以已发布的总体标准差获得）。根据额外的合并症，调整年龄和性别，在所有指标条件和治疗比较中，合并症计数与基线生活质量较低以及随着时间的推移生活质量下降相关（EQ-5D -0.02 [95% CI -0.03 至 -0.01]，PBayes > 0.999）。相关性相似，但 SF-36-PCS 和 SF-36-MCS 的 95% CI 跨越零值（分别为 −0.05 [−0.10 至 0.01]，PBayes = 0.956 和 −0.05 [−0.10 至 0.01]，PBayes = 0.966）。重要的是，没有证据表明 EQ-5D 或 SF-36 的合并症计数与治疗效果之间存在任何相互作用（EQ-5D -0.0035 [95% CI -0.0153 至 -0.0065]，PBayes = 0.746；SF-36-MCS（-0.0111 [95% CI -0.0647 至 -0.0065]） 0.0416]，PBayes = 0.70 和 SF-36-PCS -0.0092 [95% CI -0.0758 至 0.0476]，PBayes = 0.631。基线时，对于所研究的医疗状况、合并症的类型和严重程度以及生活质量水平，治疗对生活质量的影响并不因基线时的多重发病率（通过合并症计数测量）而有所不同。 基线，表明临床试验的证据可能适用于（至少适度）合并症水平较高的环境。预先指定的协议已在 PROSPERO 上注册 (CRD42018048202)。 ➢ EuroQol-5 维度 (EQ-5D) 和 SF-36 是基于问卷的工具，将身心健康指标结合到总体生活质量评分中。这些分数用于 药物治疗的随机对照试验，以评估治疗如何影响生活质量。然后，这些估计可以帮助决定应该向患有特定疾病的人提供哪些治疗。 ➢ 多重发病，即存在 2 种或更多病症，使得诊断和治疗更加复杂，并且在某些情况下与较差的生活质量相关。 ➢ 患有多重疾病的人在临床试验中代表性不足，并且对于多重疾病是否以及如何改变生活质量知之甚少。 临床试验。这使得临床医生和临床指南制定者很难确定如何将临床试验的结果应用于患有多种疾病的人。 ➢ 为了解决这种不确定性，我们重新分析了现有临床试验的数据。在 33,421 名参与者中，针对 16 种不同的医疗状况进行了 56 项新疗法试验，我们使用药物使用情况和病史数据来生成合并症计数（计数越高表明合并症越多）。 每个人的多重发病率）。 ➢ 然后，我们使用统计模型来检查合并症计数的关联，发现较高的合并症计数与试验开始时较差的生活质量相关，并预测在试验过程中生活质量会更快下降。然而，较高的合并症计数并没有改变治疗对生活质量的影响。 ➢ 这些发现表明，如果治疗改善了参与者的整体生活质量，那么他们也同样可能对人们产生同样的影响。 患有多种疾病。 ➢ 对于合并症较多的个体或我们分析中未包含的病症和治疗的个体来说，这是否属实仍不确定。尽管如此，我们的研究结果有助于为临床决策提供信息，让临床医生、健康经济学家和指南制定者放心，在考虑如何最好地管理许多患有多种疾病的人时，可以使用总体试验结果。
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. 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. 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. A prespecified protocol was registered on PROSPERO (CRD42018048202). ➢ The EuroQol-5 dimension (EQ-5D) and SF-36 are questionnaire-based tools that combine measures of physical and mental health into overall quality of life scores. These scores are used in randomised controlled trials of drug treatments to estimate how treatments affect quality of life. These estimates then inform decisions about which treatments should be offered to people with specific conditions. ➢ Multimorbidity, the presence of 2 or more conditions, makes diagnosis and treatment more complex and is associated with worse quality of life in some settings. ➢ People with multimorbidity are underrepresented in clinical trials, and little is known about whether and how multimorbidity changes quality of life in clinical trials. This makes it difficult for clinicians and clinical guideline developers to determine how results from clinical trials should be applied to people with multimorbidity. ➢ To address this uncertainty, we re-analysed data from existing clinical trials. Among 33,421 participants in 56 trials of new treatments for 16 different medical conditions, we used data on medication usage and medical histories to produce a comorbidity count (higher counts indicating more multimorbidity) for each individual. ➢ We then used statistical models to examine associations for comorbidity counts finding that higher comorbidity counts were associated with worse quality of life at trial entry and predicted a more rapid decline in quality of life over the course of the trial. However, having a higher comorbidity count did not change the effect of treatment on quality of life. ➢ These findings suggest that where treatments improve quality of life for participants overall, they are similarly likely to do so for people with multimorbidity. ➢ Whether this is true for individuals with higher numbers of comorbidities or with conditions and treatments not included in our analyses remains uncertain. ➢ Nonetheless, our findings help inform clinical decision-making by reassuring clinicians, health economists, and guideline developers that overall trial results can be used when considering how best to manage many people with multimorbidity.
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Taal, Maarten W.
DOI：
10.3390/medicina58010126
发表时间：
2022-01-14
期刊：
Medicina (Kaunas, Lithuania)
影响因子：
--
作者：
Kim SK;Choe JY
通讯作者：
Choe JY