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OA

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.