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| 髋或膝关节置换术后急性疼痛的影响因素和预测模型 |
| Influcing factors and prediction model of acute postoperative pain after total hip and total knee arthroplasty |
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| DOI：10.12089/jca.2024.03.001 |
| 中文关键词: 关节置换术 术后急性疼痛 影响因素 预测模型 |
| 英文关键词: Arthroplasty Acute postoperative pain Influcing factor Prediction model |
| 基金项目:国家自然科学基金（31400940） |
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| 摘要点击次数: 427 |
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| 中文摘要: |
目的：探讨髋或膝关节置换术患者术后急性疼痛（APP）的影响因素，建立并验证预测模型。
方法：收集行髋或膝关节置换术患者316例的临床资料，男111例，女205例，年龄≥18岁，ASA Ⅰ—Ⅲ级。根据是否发生APP将患者分为两组：非APP组和APP组。将患者按7∶3的比例随机分成训练集和验证集。通过LASSO回归和多因素Logistic回归分析训练集，筛选危险因素并构建预测模型，绘制受试者工作特征（ROC）曲线并计算曲线下面积（AUC）评估模型的预测效率。在验证集中，采用Bootstrap方法进行内部验证，采用临床决策曲线分析（DCA）评价预测模型的临床价值。
结果：有137例（43.4%）患者发生APP。多因素Logistic回归分析显示，高BMI、糖尿病史、手术时间延长、疼痛灾难化评分≥27分是髋或膝关节置换术患者发生APP的独立危险因素（P＜0.05），放置引流管、术前药物预防性镇痛、术后使用镇痛泵、术后行神经阻滞镇痛是髋或膝关节置换术患者发生APP的保护因素（P＜0.05）。纳入BMI、糖尿病史、放置引流管、手术时间、疼痛灾难化评分构建预测模型，训练集的AUC为0.879（95%CI 0.836～0.922），验证集的AUC为0.819（95%CI 0.730～0.907）。Bootstrap内部验证显示，校准曲线在预测值和实测值之间有很好的一致性；DCA曲线显示，该预测模型具有较高的临床应用价值。
结论：髋或膝关节置换术患者发生APP的危险因素为高BMI、糖尿病史、手术时间延长、疼痛灾难化评分≥27分，保护因素为放置引流管、术前药物预防性镇痛、术后使用镇痛泵、术后行神经阻滞。基于以上因素构建的预测模型具有良好的判别能力与临床应用性，可为预测髋或膝关节置换术患者APP提供参考。 |
| 英文摘要: |
Objective: To investigate the influcing factors of acute postoperative pain (APP) in patients undergoing total hip and total knee arthroplasty, and to establish and verify the prediction model.
Methods: The clinical data of 316 patients, 111 males and 205 females, aged ≥ 18 years, ASA physical status Ⅰ to Ⅲ, who underwent hip and knee arthroplasty were collected. The patients were divided into two groups according to the presence of APP: no APP group and APP group. The patients were randomly divided into a training set and a validation set at a ratio of 7∶3. LASSO regression and multivariate Logistic analysis based on training set were used to screen risk factors to construct a clinical prediction model. The receiver operating characteristic (ROC) curve was drawn and the area under the curve (AUC) was calculated to evaluate the prediction efficiency of the model. In the validation set, the Bootstrap method was used for internal validation, and the clinical decision curve analysis (DCA) was used to evaluate the clinical value of the predictive model.
Results: APP occurred in 137 patients (43.4%). Multivariate Logistic analysis showed that high BMI, history of diabetes, long operation time, and high pain catastrophizing score ≥ 27 points were independent risk factors for APP in patients undergoing total hip and total knee arthroplasty (P < 0.05), and drainage tube placement, preoperative drug preventive analgesia, postoperative use of analgesic pump and postoperative nerve block analgesia were the protective factors for APP (P < 0.05). Using BMI, diabetes history, drainage tube placement, operation duration, and pain catastrophization score to establish prediction model. The AUC of the training set and validation set were 0.879 (95% CI 0.836-0.922) and 0.819 (95% CI 0.730-0.907), respectively. The Bootstrap internal validation showed that the calibration curve had a good agreement between the predicted and measured values, and the DCA curve showed that the prediction model had high clinical application value.
Conclusion: The risk factors of APP in patients undergoing hip and knee arthroplasty are high BMI, history of diabetes, long operation time, and pain catastrophization score ≥ 27 points, and the protective factors are drainage tube placement, preoperative drug preventive analgesia, postoperative use of analgesic pump, and postoperative nerve block. The prediction model based on the above factors has good discrimination ability and clinical application. It can provide a scientific reference for predicting the occurrence of APP in patients undergoing hip and knee arthroplasty. |
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