刘盼盼, 陈永学, 李炜, 侯俊德, 赵广平, 程少飞, 王志刚. 老年患者膝骨性关节炎术后急性疼痛的危险因素及预测模型[J]. 实用临床医药杂志, 2023, 27(6): 76-80. DOI: 10.7619/jcmp.20222461
引用本文: 刘盼盼, 陈永学, 李炜, 侯俊德, 赵广平, 程少飞, 王志刚. 老年患者膝骨性关节炎术后急性疼痛的危险因素及预测模型[J]. 实用临床医药杂志, 2023, 27(6): 76-80. DOI: 10.7619/jcmp.20222461
LIU Panpan, CHEN Yongxue, LI Wei, HOU Junde, ZHAO Guangping, CHENG Shaofei, WANG Zhigang. Risk factors and prediction models of acute pain in elderly patients with knee osteoarthritis after surgery[J]. Journal of Clinical Medicine in Practice, 2023, 27(6): 76-80. DOI: 10.7619/jcmp.20222461
Citation: LIU Panpan, CHEN Yongxue, LI Wei, HOU Junde, ZHAO Guangping, CHENG Shaofei, WANG Zhigang. Risk factors and prediction models of acute pain in elderly patients with knee osteoarthritis after surgery[J]. Journal of Clinical Medicine in Practice, 2023, 27(6): 76-80. DOI: 10.7619/jcmp.20222461

老年患者膝骨性关节炎术后急性疼痛的危险因素及预测模型

Risk factors and prediction models of acute pain in elderly patients with knee osteoarthritis after surgery

  • 摘要:
    目的 探讨老年膝骨性关节炎(KOA)患者行全膝关节置换术(TKA)术后急性疼痛(APSP)的危险因素, 并进一步构建预测模型。
    方法 回顾性纳入行TKA治疗的236例老年KOA患者。术后72 h, 采用疼痛数字评分法(NRS)评定疼痛, 并分为APSP组94例(39.83%)和非APSP组142例(60.17%)。收集2组患者的临床资料, 采用多因素Logistic回归分析筛选APSP发生的危险因素, 并以此为基础构建预测模型; 采用受试者工作特征(ROC)曲线对预测模型的性能进行评定, 结果以曲线下面积(AUC)、敏感性和特异性表示。
    结果 单因素分析显示, APSP组年龄>75岁、糖尿病、术前睡眠障碍、焦虑及术后并发症的比例以及术前视觉模拟评分法(VAS)、术前西安大略和麦克马斯特大学骨关节炎指数量表(WOMAC)及止血带使用时间均高于或长于非APSP组, 差异有统计学意义(P < 0.05)。多因素Logistic回归分析结果提示, 年龄>75岁(OR=1.318, 95%CI: 1.030~1.686)、糖尿病(OR=1.489, 95%CI: 1.134~1.954)、术前VAS评分>4分(OR=1.551, 95%CI: 1.095~2.197)、睡眠障碍(OR=1.398, 95%CI: 1.093~1.789)、焦虑(OR=1.709, 95%CI: 1.247~2.343)是KOA患者TKA术后发生APSP的独立危险因素。采用ROC曲线结果显示, 内部验证时, 模型预测APSP的AUC为0.894(95%CI: 0.843~0.945), 敏感性为82.98, 特异性为85.92%, 准确率为84.75%; 外部验证时AUC为0.858(95%CI: 0.797~0.919), 敏感性为78.22%, 特异性为81.36%, 准确率为80.22%。
    结论 高龄、合并糖尿病、术前疼痛、睡眠障碍、焦虑是行TKA治疗的老年KOA患者发生APSP的独立危险因素, 以此构建的模型可预测APSP发生风险。

     

    Abstract:
    Objective To investigate the risk factors of acute post-surgical pain (APSP) after total knee arthroplasty (TKA) in elderly patients with knee osteoarthritis (KOA), and to construct a prediction model.
    Methods A total of 236 elderly KOA patients treated with TKA were retrospectively included. At 72 hours after operation, pain was assessed by numerical rating scales (NRS), and the patients were divided into APSP group (n=94, 39.83%) and non-APSP group (n=142, 60.17%). The clinical data of the two groups were collected, the risk factors of APSP were screened by multivariate Logistic regression analysis, and the prediction model was built on this basis; the performance of the prediction model was evaluated using receiver operating characteristic (ROC) curves, and the results were expressed by area under the curve (AUC), sensitivity and specificity.
    Results Univariate analysis showed that age older than 75 years, diabetes, preoperative sleep disturbance, anxiety and postoperative complications, preoperative Visual Analogue Scale (VAS), preoperative Western Ontario and McMahon according to Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), tourniquet usage time in the APSP group were significantly higher than those of the non-APSP group (P < 0.05). Multivariate Logistic regression analysis showed that age >75 years old (OR=1.318, 95%CI, 1.030 to 1.686), diabetes mellitus (OR=1.489, 95%CI, 1.134 to 1.954), preoperative VAS score >4 points (OR=1.551, 95%CI, 1.095 to 2.197), sleep disturbance (OR=1.398, 95%CI, 1.093 to 1.789), anxiety (OR=1.709, 95%CI, 1.247 to 2.343) were independent risk factors for APSP after TKA in KOA patients. ROC curve results showed that the AUC of APSP predicted by the model was 0.894(95%CI, 0.843 to 0.945), the sensitivity was 82.98, the specificity was 85.92%, and the accuracy was 84.75% during internal validation; in external validation, the AUC was 0.858(95%CI, 0.797 to 0.919), the sensitivity was 78.22%, the specificity was 81.36% and the accuracy was 80.22%.
    Conclusion Advanced age, diabetes mellitus, preoperative pain, sleep disorders, and anxiety are independent risk factors for APSP in elderly KOA patients treated with TKA, and the model could predict the risk of APSP.

     

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