作者：王凡，王红英，屠松霞

单位：

223002江苏省淮安市第二人民医院心胸外科

Units：

Cardiothoracic Surgery Department, Huai'an Second People's Hospital, Huaian 223002, China

关键词：

肺癌；快速康复；影响因素分析；列线图

Keywords：

Lung cancer; Rapid recovery; Root cause analysis; Nomograms

CLC：

R 734.2

DOI：

10.12114/j.issn.1008-5971.2024.00.002

Funds：

江苏省卫生健康委2020年度医学科研立项项目（M2020058）

摘要：

目的　探讨肺癌患者术后未快速康复的影响因素，并构建其风险预测列线图模型。方法　选取2020 年5月—2023年5月在淮安市第二人民医院接受手术治疗的 183例肺癌患者为研究对象，根据患者术后住院时间将其分 为未快速康复组（术后住院时间＞7 d， n=68）和快速康复组（术后住院时间 ≤7 d，n=115）。比较两组一般资料、 术前实验室检查指标、诊疗情况。肺癌患者术后未快速康复的影响因素分析采用多因素Logistic回归分析；采用R软件 构建肺癌患者术后未快速康复的风险预测列线图模型，采用ROC曲线分析列线图模型预测肺癌患者术后未快速康复的 区分度，采用H-L拟合优度检验评估列线图模型的拟合情况，采用校准曲线分析列线图模型预测肺癌患者术后未快速 康复的校准度。结果　未快速康复组与快速康复组年龄、合并慢性呼吸系统疾病者占比、术前开展肺功能训练者占 比、手术方式、手术时间、术中失血量比较，差异有统计学意义（P＜0.05）。多因素Logistic回归分析结果显示，年 龄、合并慢性呼吸系统疾病、术前开展肺功能训练、手术方式、手术时间是肺癌患者术后未快速康复的独立影响因素 （P＜0.05）。基于多因素Logistic回归分析结果构建肺癌患者术后未快速康复风险预测列线图模型。ROC曲线分析结 果显示，列线图模型预测肺癌患者术后未快速康复的AUC为0.758〔95%CI（0.684～0.833）〕。H-L拟合优度检验结果 显示，列线图模型的拟合情况较好（χ2 =6.239，P=0.620）；校准曲线分析结果显示，列线图模型对肺癌患者术后未 快速康复的预测概率与实际概率接近。结论　年龄、合并慢性呼吸系统疾病、术前开展肺功能训练、手术方式、手术 时间是肺癌患者术后未快速康复的影响因素，但基于上述影响因素构建的列线图模型对肺癌患者术后未快速康复的区 分度一般。

Abstract：

Objective To explore the influencing factors of postoperative non-rapid recovery in patients with lung cancer, and construct its risk prediction nomogram model. Methods A total of 183 patients with lung cancer who underwent surgical treatment in Huai'an Second People's Hospital from May 2020 to May 2023 were selected as the research objects. According to the postoperative hospitalization time, the patients were divided into non-rapid recovery group (postoperative hospitalization time > 7 d, n=68) and rapid recovery group (postoperative hospitalization time ≤ 7 d, n=115) . The general data, preoperative laboratory examination indexes, diagnosis and treatment were compared between the two groups. Multivariate Logistic regression analysis was used to analyze the influencing factors of postoperative non-rapid recovery in patients with lung cancer. R software was used to construct the risk prediction nomogram model for postoperative non-rapid recovery in lung cancer patients. The ROC curve was used to analyze the discrimination of the nomogram model in predicting postoperative non-rapid recovery in lung cancer patients. The H-L goodness of fit test was used to evaluate the fitting of the nomogram model. The calibration curve was used to analyze the calibration of the nomogram model in predicting postoperative non-rapid recovery in lung cancer patients. Results There were significant differences in age, proportion of patients combined with chronic respiratory diseases, proportion of patients undergoing preoperative pulmonary function training, operation method, operation time and intraoperative blood loss between the non-rapid recovery group and the rapid recovery group (P < 0.05) . Multivariate Logistic regression analysis results showed that age, chronic respiratory diseases, preoperative pulmonary function training, operation method, and operation time were the influencing factors of postoperative non-rapid recovery in lung cancer patients (P < 0.05) . Based on the results of multivariate Logistic regression analysis, the nomogram model for predicting the risk of postoperative non-rapid recovery in patients with lung cancer was constructed. The results of ROC curve analysis showed that the AUC of the nomogram model for predicting postoperative non-rapid recovery in lung cancer patients was 0.758 [95 %CI (0.684-0.833) ] . The results of H-L goodness of fit test showed that the fitting of the nomogram model was better (χ 2 =6.239, P=0.620) . The results of calibration curve analysis showed that the probability of postoperative non-rapid recovery in lung cancer patients predicted by the nomogram model was close to the actual probability. Conclusion Age, chronic respiratory diseases, preoperative pulmonary function training, operation method and operation time are the influencing factors of postoperative non-rapid recovery in patients with lung cancer. However, the nomogram model constructed based on the above influencing factors has a general degree of discrimination for postoperative non-rapid recovery in patients with lung cancer.

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