Yang Yaning, Zhang Chengzhen, Chi Wenying, Zheng Bin, Yu Xiaoqian, Zhang Kaiyun, Junzuo Guo, Meng Fanjun
School of Anesthesiology, Shandong Second Medical University, Weifang, China.
Department of Anesthesiology, Central Hospital Affiliated to Shandong First Medical University, Shandong, PR China.
Medicine (Baltimore). 2025 Feb 7;104(6):e41398. doi: 10.1097/MD.0000000000041398.
The primary goal of this study was to identify the risk factors contributing to moderate-to-severe postoperative pain in patients undergoing laparoscopic sleeve gastrectomy (LSG) and to create a predictive model for these risk factors. A retrospective analysis was performed on a cohort of 375 patients who underwent LSG at Jinan Central Hospital from January 2017 to June 2023. Data for this study was extracted using medical databases. Patients were classified into 2 groups based on their postoperative pain levels: those experiencing moderate-to-severe pain and those not experiencing moderate-to-severe pain. Univariate and multivariate logistic regression analyses were employed to determine which variables were significantly associated with moderate-to-severe pain. Receiver operating characteristic curves were utilized to assess the diagnostic efficacy of different indicators. Additionally, calibration curves and clinical decision curves were applied for model validation. Multifactorial logistic regression analysis identified age, body mass index (BMI), and the modified frailty index (mFI) as independent risk factors for moderate-to-severe postoperative pain in LSG patients. Based on the regression analysis, a predictive model was constructed. The receiver operating characteristic curve for this model demonstrated an area under the curve of 0.96 (95% CI: 0.94-0.97), indicating excellent discriminatory ability between patients likely and unlikely to experience moderate-to-severe pain post-surgery. A scoring system was developed from the predictive model, assigning points to each risk factor. BMI was the most significant predictor (100 points), followed by mFI (30 points) and age (15 points). Calibration analysis showed that the predicted values closely matched the actual values, with a mean error of 0.008, indicating high accuracy of the model. Clinical decision analysis demonstrated a positive net benefit when the threshold probability ranged from 0.001 to 0.999, suggesting broad applicability of the model in clinical decision-making. Age, BMI, and mFI are significant predictors of moderate-to-severe postoperative pain in patients undergoing LSG.
本研究的主要目标是确定导致接受腹腔镜袖状胃切除术（LSG）的患者发生中重度术后疼痛的风险因素，并为这些风险因素创建一个预测模型。对2017年1月至2023年6月在济南中心医院接受LSG的375例患者进行了回顾性分析。本研究的数据通过医学数据库提取。根据患者术后疼痛程度将其分为两组：经历中重度疼痛的患者和未经历中重度疼痛的患者。采用单因素和多因素逻辑回归分析来确定哪些变量与中重度疼痛显著相关。利用受试者工作特征曲线评估不同指标的诊断效能。此外，应用校准曲线和临床决策曲线进行模型验证。多因素逻辑回归分析确定年龄、体重指数（BMI）和改良虚弱指数（mFI）是LSG患者中重度术后疼痛的独立风险因素。基于回归分析，构建了一个预测模型。该模型的受试者工作特征曲线显示曲线下面积为0.96（95%CI：0.94 - 0.97），表明该模型在区分术后可能和不太可能经历中重度疼痛的患者方面具有出色的鉴别能力。根据预测模型开发了一个评分系统，为每个风险因素分配分数。BMI是最显著的预测因素（100分），其次是mFI（30分）和年龄（15分）。校准分析表明预测值与实际值密切匹配，平均误差为0.008，表明模型具有较高的准确性。临床决策分析表明，当阈值概率范围为0.001至0.999时，净效益为正，表明该模型在临床决策中具有广泛的适用性。年龄、BMI和mFI是接受LSG患者中重度术后疼痛的重要预测因素。
