实用医学杂志2026，Vol.42Issue(1)：1-11,11.DOI:10.3969/j.issn.1006-5725.2026.01.001
Ⅲ—Ⅳ期肺癌免疫检查点抑制剂治疗30天内非计划再入院风险预测模型
Predictive model for unplanned 30-day readmission in stage Ⅲ—Ⅳ lung cancer patients receiving immune checkpoint inhibitors
摘要
Abstract
Objective To identify risk factors for unplanned 30-day readmission(UPR)following immune checkpoint inhibitors(ICIs)treatment in stage Ⅲ—Ⅳ lung cancer patients and to develop/validate a predictive model.Methods We retrospectively analyzed clinical data from stage Ⅲ—Ⅳ lung cancer patients treated with ICIs at our institution(January 2023-May 2024).Risk factors were preliminarily screened using the Boruta algorithm;independent predictors were identified via logistic regression.A nomogram prediction model was subsequently developed.Model performance was evaluated by:discrimination(receiver operating characteristic curves,ROC),calibration(calibration plots),and clinical utility(decision curve analysis,DCA).Restricted cubic spline(RCS)regression combined with SHapley Additive exPlanations(SHAP)analysis further explored dose-response relationships and threshold effects of key risk factors on UPR.Results Among 284 included patients,the UPR incidence was 30.63%.Independent risk factors identified by logistic regression were:hospital length of stay,Nutritional Risk Screening 2002(NRS 2002)score,invasive procedures,and Karnofsky Performance Status(KPS)score(all P<0.05).The model showed strong discrimination:training set AUC=0.88(95%CI:0.84～0.93),sensitivity 84%,specificity 80%;validation set AUC=0.87(95%CI:0.79～0.95),sensitivity 82%,specificity 70%.Calibration curves indicated good model fit.Decision curve analysis demonstrated positive net benefit at threshold probabilities of 10%～90%.SHAP analysis prioritized length of stay as the most influential predictor;SHAP-RCS analysis revealed increased UPR risk when:hospital stay>6.43 days,NRS 2002>2.05,KPS<79.01,or prior invasive procedures.Conclusion The nomogram model incorporating four key risk factors effectively pre-dicts 30-day unplanned readmission risk in stage Ⅲ—Ⅳ lung cancer patients receiving ICI therapy.With robust performance and clinical utility,it may facilitate early identification and intervention for high-risk individuals.关键词
肺癌/免疫检查点抑制剂治疗/非计划再入院/Boruta算法/预测模型Key words
lung neoplasms/immune checkpoint inhibitors/unplanned readmission/boruta algo-rithm/predictive model分类
医药卫生引用本文复制引用
邓波,彭曹霞,熊启连,辇伟奇,刘影..Ⅲ—Ⅳ期肺癌免疫检查点抑制剂治疗30天内非计划再入院风险预测模型[J].实用医学杂志,2026,42(1):1-11,11.基金项目
国家自然科学基金青年项目(编号:82204911) （编号:82204911）
重庆市科卫联合课题(编号:2022QNXM070) （编号:2022QNXM070）
重庆市中医肿瘤防治公共卫生重点专科项目(编号:重庆市卫生健康委员会2022.11.2) （编号:重庆市卫生健康委员会2022.11.2）
