探讨老年急性胆囊炎（AC）患者术后住院时间（POHS）的主要影响因素及预测指标，对比机器学习算法（MLA）与多元线性回归模型（MLR）建立其预测模型的优缺点。

回顾性分析2013年8月~2022年7月北京电力医院普外科手术治疗的287例老年AC患者的临床资料，将POHS分为正常住院时间（ND）组（POHS≤6 d）和长住院时间（LD）组（POHS>6 d），应用MLA与MLR构建预测模型，探讨围手术期变量与POHS的关系，绘制受试者工作特征（ROC）曲线。

287例老年AC手术患者，根据MLA的逻辑回归（LR）、决策树（DT）、朴素贝叶斯（NB）、随机森林（RF）、K最邻近（KNN）算法构建POHS预测模型，绘制ROC曲线，准确率分别为87.9%、84.4%、86.2%、91.3%、74.1%，AUC分别为0.964、0.707、0.973、0.978、0.816，表明上述5种MLA预测模型均具有较好的POHS预测能力。MLR提示合并糖尿病、术前血清白蛋白（ALB）降低、术中出血量多、术后病理报告胆囊化脓或坏疽、术后并发症为老年AC患者POHS的独立危险因素，ROC曲线显示术前ALB的AUC为0.726、术中出血量AUC为0.778，二者的截断值分别是37.35 g/L、12.50 ml。对比两个预测模型，结果发现MLA在预测POHS准确性上优势明显，尤其是其随机森林（RF）算法的准确率最高，而MLR可更为直观地展现预测模型的独立危险因素。

MLA的随机森林算法能更准确预测老年AC患者POHS，MLR提示合并糖尿病、术前ALB降低、术中出血量多、术后病理报告胆囊化脓或坏疽、术后并发症是POHS延长的独立预测因素，据此及时采取有效防治措施，可以缩短POHS，提高医疗质量和服务效率，因此具有临床指导意义。

To explore the main influencing factors and predictors of postoperative hospital stay (POHS) in elderly patients with acute cholecystitis (AC), and compare the advantages and disadvantages of machine learning algorithms (MLAs) and multiple linear regression (MLR) in establishing prediction models for POHS.

The clinical data of 287 elderly AC patients treated by general surgery at Beijing Electric Power Hospital from August 2013 to July 2022 were retrospectively analyzed. Based on the duration of POHS, the patients were divided into a normal duration (ND) group (POHS≤6 days) and a long duration (LD) group (POHS>6 days). Prediction models were built using MLAs and MLR to explore the relationship between perioperative variables and POHS, and receiver operating characteristic (ROC) curve analysis was performed to assess the prediction performance of the models.

Based on the clinical data of 287 elderly patients with AC surgery, POHS prediction models were established using the MLAs logistic regression (LR), decision tree (DT), naive Bayes (NB), random forest (RF), and K nearest neighbor (KNN), and ROC curves were plotted. The accuracy rates of these models were 87.9%, 84.4%, 86.2%, 91.3%, and 74.1%, and their AUC (area under the curve) values were 0.964, 0.707, 0.973, 0.978, and 0.816, respectively, indicating that these five MLA prediction models all had good prediction performance for POHS. MLR suggested that the combination of diabetes, decreased preoperative serum albumin (ALB), high intraoperative blood loss, postoperative pathological report of suppuration or gangrene of the gallbladder, and postoperative complications were independent risk factors for POHS in elderly patients with AC after surgery. ROC curve analysis showed that the AUC values of preoperative ALB and intraoperative blood loss for POHS prediction were 0.726 and 0.778, with the cut-off values of 37.35 g/L and 12.50 ml, respectively. Comparing the prediction models developed based on MLAs and MLR, it was found that MLAs had obvious advantages in the predictive accuracy for POHS, with the RF algorithm having the highest accuracy. MLR can more intuitively display the independent risk factors of the prediction model.

The RF algorithm can more accurately predict POHS in elderly AC patients. MLR suggests that diabetes, preoperative ALB reduction, high intraoperative blood loss, postoperative pathological reports of gallbladder suppuration or gangrene, and postoperative complications are independent predictors of POHS prolongation. Therefore, timely and effective prevention and treatment measures can shorten POHS, improve medical quality and service efficiency, and are of great clinical significance.