Centre for Habilitation and Rehabilitation, Haukeland University Hospital, Bergen, Norway.
Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
Eur J Pain. 2022 May;26(5):1123-1134. doi: 10.1002/ejp.1937. Epub 2022 Mar 15.
The objective of this study was to develop prediction models and explore the external validity of the models in a large sample of patients with chronic widespread pain (CWP) and fibromyalgia (FM).
Patients with CWP and FM referred to rehabilitation services in Norway (n = 986) self-reported data on potential predictors prior to entering rehabilitation, and self-reported outcomes at one-year follow-up. Logistic regression models of improvement, worsening and work status, and a linear regression model of health-related quality of life (HRQoL), were developed using lasso regression. Externally validated estimates of model performance were obtained from the validation set.
The number of participants in the development and the validation sets was 771 and 215 respectively; only participants with outcome data (n = 519-532 and 185, respectively) were included in the analyses. On average, HRQoL and work status changed little over one year. The prediction models included 10-11 predictors. Discrimination (AUC statistic) for prediction of outcome at follow-up was 0.71 for improvement, 0.67 for worsening, and 0.87 for working. The median absolute error of predictions of HRQoL was 0.36 (0.22-0.51). Reasonably good predictions of working at follow-up and HRQoL could be obtained using only the baseline scores as predictors.
Moderately complex prediction models (10-11 predictors) generated poor to excellent predictions of patient-relevant outcomes. Simple prediction models of working and HRQoL at follow-up may be nearly as accurate and more practical.
Prediction modelling of outcome in rehabilitation has been sparsely explored. Such models may guide clinical decision-making. This study developed and externally validated prediction models for outcomes of people with chronic widespread pain and fibromyalgia in a rehabilitation setting. Multivariable prediction models generated poor to excellent predictions of patient-relevant outcomes, but the complexity of these models may reduce their clinical utility. Simple univariable prediction models were nearly as accurate and may have more potential for use in clinical practice.
本研究的目的是在患有慢性广泛性疼痛（CWP）和纤维肌痛（FM）的大量患者中建立预测模型，并探讨模型的外部有效性。
挪威康复服务机构转介的 CWP 和 FM 患者（n=986）在进入康复治疗前自行报告潜在预测因素的数据，在一年随访时自行报告结果。使用套索回归法建立了改善、恶化和工作状态的逻辑回归模型，以及健康相关生活质量（HRQoL）的线性回归模型。使用验证集中的外部验证估计模型性能。
开发和验证集的参与者人数分别为 771 人和 215 人；仅对具有结局数据的参与者（分别为 519-532 人和 185 人）进行了分析。平均而言，HRQoL 和工作状态在一年中变化不大。预测模型包括 10-11 个预测因素。对随访时结局预测的判别能力（AUC 统计量）为改善为 0.71，恶化为 0.67，工作为 0.87。HRQoL 预测的中位数绝对误差为 0.36（0.22-0.51）。仅使用基线评分作为预测因子，即可对随访时的工作和 HRQoL 进行合理的准确预测。
中等复杂的预测模型（10-11 个预测因素）对患者相关结局的预测结果为差到良好。随访时工作和 HRQoL 的简单预测模型可能同样准确且更实用。
康复结局的预测模型研究较少。此类模型可指导临床决策。本研究为康复环境中慢性广泛性疼痛和纤维肌痛患者的结局开发并验证了预测模型。多变量预测模型对患者相关结局的预测结果为差到良好，但模型的复杂性可能会降低其临床实用性。简单的单变量预测模型几乎同样准确，可能更有潜力应用于临床实践。
