基于肺部超声评分构建腹部手术患者术后肺部并发症的列线图预测模型

Development of a nomogram prediction model for postoperative pulmonary complications based on lung ultrasound score in patients undergoing abdominal surgery

中文关键词: 肺部超声评分  列线图  预测模型  术后肺部并发症

英文关键词: Lung ultrasound score  Nomograms  Prediction model  Postoperative pulmonary complications

中文摘要:

      
目的：探讨腹部手术患者肺部超声评分（LUS）和术后肺部并发症（PPCs）的关系，构建腹部手术患者PPCs列线图预测模型。
方法：选择2024年1—6月行腹部手术的患者446例为训练集。分别在入手术室、术后1、24 h行床旁肺部超声检查并记录LUS，记录术后7 d内PPCs发生情况。采用多因素Logistic回归分析腹部手术患者PPCs的危险因素并建立列线图预测模型。另选择2024年7—9月行腹部手术的患者193例为验证集，用以评估模型性能。
结果：训练集有66例（14.8%）患者发生PPCs，验证集有29例（15.0%）患者发生PPCs。与非PPCs患者比较，术后1 h、术后24 h PPCs患者LUS明显升高(P<0.05)。多因素Logistic回归分析显示，术后24 h LUS是腹部手术患者PPCs的危险因素（OR=1.166，95%CI 1.091～1.247，P＜0.001）。最终筛选出的危险因素有术后24 h LUS、年龄、手术时间、手术部位、白蛋白水平，并根据以上因素构建列线图预测模型。在训练集和验证集中预测腹部手术PPCs的曲线下面积(AUC)分别为0.844（95%CI 0.792～0.895）和0.860（95%CI 0.792～0.929）；校准曲线显示，在训练集和验证集中实际曲线和理想曲线重合度较好；决策曲线分析（DCA）显示，该模型具有较好的临床实用性。
结论：术后24 h LUS是腹部手术患者PPCs的危险因素，基于术后24 h LUS、年龄、手术时间、手术部位、白蛋白水平构建的列线图模型可以较好地预测腹部手术患者PPCs的发生。

英文摘要:

      
Objective: To investigate the association between lung ultrasound score (LUS) and postoperative pulmonary complications (PPCs) in patients undergoing abdominal surgery and to develop a nomogram prediction model for predicting PPCs.
Methods: A total of 446 patients who underwent abdominal surgery from January to June 2024 were enrolled as the training set. Bedside lung ultrasound examinations were performed and LUS were recorded at admission to the operating room, 1 hour, and 24 hours after surgery. Patients were observed for one week after surgery to identify PPCs. Multivariate logistic regression analysis was used to identify the risk factors of PPCs in patients undergoing abdominal surgery, and a nomogram model of PPCs was constructed. Additionally, a total of 193 patients who underwent abdominal surgery from July to September 2024 were enrolled as the validation set to evaluate the model performance.
Results: In the training set, 66 patients (14.8%) developed PPCs, while 29 patients (15.0%) developed PPCs in the validation set. Compared with patients without PPCs, those with PPCs exhibited significantly higher LUS values at 1 hour and 24 hours postoperatively (P < 0.05). Multivariate logistic regression analysis revealed that LUS at 24 hours postoperatively was identified a risk factor for PPCs (OR = 1.166, 95% CI 1.091-1.247, P < 0.001). The results of multivariate logistic regression analysis showed that LUS at 24 hours postoperatively, age, surgical duration, surgical site and albumin levels were factors influencing PPCs in patients undergoing abdominal surgery. A nomogram model for predicting PPCs was developed based on the results of the multivariate logistic regression analysis. The area under the curve (AUC) of the nomogram predictive model was 0.844 (95% CI 0.792-0.895) in the training set, and the AUC in the validation set was 0.860 (95% CI 0.792-0.929). The calibration curve results showed that the prediction curves of the training and validation sets were generally fitted to the standard curve. The decision curve analysis (DCA) demonstrated that the model had good clinical utility.
Conclusion: LUS at 24 hours postoperatively is a risk factor of PPCs in patients undergoing abdominal surgery. The nomogram prediction model constructed by the LUS at 24 hours after operation, age, operation time, operation site, and albumin level can better predict the occurrence of PPCs in patients undergoing abdominal surgery.