Han Kunyu, Liu Hui, Bai Ruiping, Li Jiarui, Zhang Linjuan, An Rui, Peng Di, Zhao Jiamin, Xue Mengwen, Shen Xin
Department of Anaesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Shaanxi, China.
Department of Biobank, The First Affiliated Hospital of Xi'an Jiaotong University, Shaanxi, China.
Indian J Anaesth. 2025 Feb;69(2):225-235. doi: 10.4103/ija.ija_885_24. Epub 2025 Jan 29.
Hepatectomy is currently the most effective way to treat liver diseases, and its safety has observably improved. However, the incidence of postoperative complications (POCs) remains high. Therefore, exploring the related influencing factors helps identify high-risk groups early and improve patient prognosis.
Clinical data were retrospectively collected from a real-world setting. Patients were divided into two groups based on the incidence of postoperative pulmonary complications (PPCs). Univariate analysis, LASSO regression, and logistic regression were applied to analyse the correlation between PPCs and perioperative indicators. A nomogram prediction model was constructed, whose discrimination, accuracy, and clinical effectiveness were evaluated.
The incidence of PPCs was 36.33% among the 1244 patients in this study. The total length of hospital stay and perioperative mortality in the PPCs group were markedly higher ( < 0.001) than in the non-PPCs group. Logistic regression showed that surgical method [odds ratio (OR) =2.469 (95% CI: 1.665, 3.748); < 0.001], duration of surgery [OR = 1.003 (95% CI: 1.002, 1.005); < 0.001], postoperative patient destination [OR = 1.453 (95% CI: 1.115, 1.893); = 0.006], and postoperative international normalised ratio (INR) [OR = 2.245 (95% CI: 1.287, 4.120); = 0.007] were independent risk factors of PPCs; the number of clamping [OR = 0.988 (95% CI: 0.980, 0.995); = 0.001] was an independent protective factor of PPCs. The area under the receiver operating characteristic (ROC) curve was 0.675 (95% CI: 0.638, 0.703), the consistency index of the calibration curve was 0.675 (95% CI: 0.641, 0.703), and the Hosmer-Lemeshow goodness-of-fit test yielded = 0.327.
In this study, the incidence of PPCs after hepatectomy was the highest. Our nomogram model can predict the probability of PPCs after hepatectomy.
肝切除术是目前治疗肝脏疾病最有效的方法,其安全性已显著提高。然而,术后并发症(POC)的发生率仍然很高。因此,探索相关影响因素有助于早期识别高危人群并改善患者预后。
从实际临床环境中回顾性收集临床数据。根据术后肺部并发症(PPC)的发生率将患者分为两组。采用单因素分析、LASSO回归和逻辑回归分析PPC与围手术期指标之间的相关性。构建列线图预测模型,并评估其区分度、准确性和临床有效性。
本研究中1244例患者的PPC发生率为36.33%。PPC组的总住院时间和围手术期死亡率显著高于非PPC组(<0.001)。逻辑回归显示,手术方式[比值比(OR)=2.469(95%置信区间:1.665,3.748);<0.001]、手术时长[OR = 1.003(95%置信区间:1.002,1.005);<0.001]、术后患者去向[OR = 1.453(95%置信区间:1.115,1.893);=0.006]和术后国际标准化比值(INR)[OR = 2.245(95%置信区间:1.287,4.120);=0.007]是PPC的独立危险因素;夹闭次数[OR = 0.988(95%置信区间:0.980,0.995);=0.001]是PPC的独立保护因素。受试者工作特征(ROC)曲线下面积为0.675(95%置信区间:0.638,0.703),校准曲线一致性指数为0.675(95%置信区间:0.641,0.703),Hosmer-Lemeshow拟合优度检验结果为=0.327。
在本研究中,肝切除术后PPC的发生率最高。我们的列线图模型可以预测肝切除术后发生PPC的概率。