Zhou Liang, Wu Hong, Chen Xin
Liang Zhou, Department of General Surgery, BenQ Medical Center, Nanjing, Jiangsu Province 210019, P.R. China.
Hong Wu, Department of General Surgery, BenQ Medical Center, Nanjing, Jiangsu Province 210019, P.R. China.
Pak J Med Sci. 2025 May;41(5):1344-1351. doi: 10.12669/pjms.41.5.11650.
To develop and validate a nomogram model for predicting infection after radical resection of gastric cancer (GC).
In this retrospective cohort study clinical data of patients who underwent radical resection of GC in BenQ Medical Center in Nanjing, China from January 2020 to April 2024 was retrospectively selected. Patients were randomly assigned to the training cohort and the validation cohort in a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression analysis were used to analyze the characteristics and screen the independent risk factors of infection after radical resection of GC to construct a predictive nomogram model. The prediction performance and clinical utility of the nomogram model were evaluated by drawing the receiver operating characteristic (ROC) and calculating the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).
Records of 581 patients with GC after radical resection were included in this study. The incidence of postoperative infection was 19.1% (111/581). The nomogram model that included age, hypertension, open surgery, operation duration, lymphocyte count, and prognostic nutritional index (PNI) showed sufficient prediction accuracy, with the AUC of the training set and validation set of 0.833 (95% CI: 0.778-0.888) and 0.859 (0.859; 0.777-0.941), respectively. The calibration curve showed that the model's predicted value is basically consistent with the actual value, and the calibration effect is good. DCA also shows that the predictive model has good clinical utility.
The established nomogram model has a good predictive value in predicting infection after radical resection of GC in this study, which may be a reliable tool for clinicians to identify patients with GC at high risk of infection after radical gastrectomy.
建立并验证用于预测胃癌（GC）根治性切除术后感染的列线图模型。
在这项回顾性队列研究中，回顾性选取了2020年1月至2024年4月在中国南京明基医院接受GC根治性切除术患者的临床资料。患者按7:3的比例随机分为训练队列和验证队列。采用最小绝对收缩和选择算子（LASSO）算法及逻辑回归分析，分析GC根治性切除术后感染的特征并筛选独立危险因素，以构建预测列线图模型。通过绘制受试者工作特征（ROC）曲线并计算曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）来评估列线图模型的预测性能和临床实用性。
本研究纳入了581例GC根治性切除术后患者的记录。术后感染发生率为19.1%（111/581）。包含年龄、高血压、开放手术、手术时长、淋巴细胞计数和预后营养指数（PNI）的列线图模型显示出足够的预测准确性，训练集和验证集的AUC分别为0.833（95%CI：0.778 - 0.888）和0.859（0.859；0.777 - 0.941）。校准曲线表明模型的预测值与实际值基本一致，校准效果良好。DCA也表明该预测模型具有良好的临床实用性。
本研究中建立的列线图模型在预测GC根治性切除术后感染方面具有良好的预测价值，可能是临床医生识别根治性胃切除术后感染高风险GC患者的可靠工具。
