Qin Mei-Lan, Dai Xuan, Yang Chao, Su Wan-Ying
Logistics Department, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, China.
Nursing Department, The First Affiliated Hospital of Hunan Normal University (Hunan Provincial People's Hospital), Changsha, Hunan, China.
J Arthroplasty. 2025 Mar;40(3):602-610. doi: 10.1016/j.arth.2024.09.011. Epub 2024 Sep 14.
It is clinically important to anticipate the likelihood of pain catastrophizing in patients who undergo total knee arthroplasty (TKA). Persistent pain and diminished physical function following TKA are independently associated with preoperative pain catastrophizing. The purpose of this study was to develop and validate a nomogram model to predict pain catastrophizing in patients who have severe osteoarthritis undergoing primary TKA.
Data were collected from patients who have severe osteoarthritis undergoing primary TKA at four tertiary general hospitals in Changsha, China, from September to December 2023. The study cohort was randomly divided into a training group and a validation group in the proportion of 70 to 30%. Least absolute shrinkage and selection operator regression analysis was utilized to select the optimal predictive variables for the model. A nomogram model was created using independent risk factors that were identified through multivariate regression analysis. Their performance was assessed using the concordance index and calibration curves, and their clinical utility was analyzed using decision curve analysis.
A total of 416 patients were included, 291 in the training group and 125 in the validation group. There were 115 (27.6%) who had pain catastrophizing. The predictors contained in the nomogram were pain intensity during activity, anxiety and depression, body mass index, social support, and household. The area under the curve of the nomogram was 0.976 (95% confidence interval = 0.96 to 0.99) for the training group and 0.917 (95% confidence interval = 0.88 to 0.96) for the validation group. The calibration curves confirmed the nomogram's accuracy, and decision curve analysis showed its strong predictive performance.
The comprehensive nomogram generated in this study was a valid and easy-to-use tool for assessing the risk of pain catastrophizing in preoperative TKA patients, and helped healthcare professionals to screen the high-risk population.
预测接受全膝关节置换术(TKA)患者发生疼痛灾难化的可能性在临床上具有重要意义。TKA术后持续疼痛和身体功能下降与术前疼痛灾难化独立相关。本研究的目的是开发并验证一种列线图模型,以预测重度骨关节炎患者接受初次TKA时的疼痛灾难化情况。
收集2023年9月至12月在中国长沙四家三级综合医院接受初次TKA的重度骨关节炎患者的数据。研究队列按70%至30%的比例随机分为训练组和验证组。采用最小绝对收缩和选择算子回归分析为模型选择最佳预测变量。使用通过多变量回归分析确定的独立危险因素创建列线图模型。使用一致性指数和校准曲线评估其性能,并使用决策曲线分析分析其临床效用。
共纳入416例患者,训练组291例,验证组125例。其中115例(27.6%)发生疼痛灾难化。列线图中的预测因素包括活动时的疼痛强度、焦虑和抑郁、体重指数、社会支持和家庭情况。训练组列线图的曲线下面积为0.976(95%置信区间=0.96至0.99),验证组为0.917(95%置信区间=0.88至0.96)。校准曲线证实了列线图的准确性,决策曲线分析显示了其强大的预测性能。
本研究生成的综合列线图是评估术前TKA患者疼痛灾难化风险的有效且易于使用的工具,有助于医疗保健专业人员筛查高危人群。