The Journal of Practical Medicine >

2026 , Vol. 42 >Issue 1: 1 - 11

DOI: https://doi.org/10.3969/j.issn.1006-5725.2026.01.001

Oncology: Diagnosis, Treatment and Prevention

Predictive model for unplanned 30-day readmission in stage Ⅲ—Ⅳ lung cancer patients receiving immune checkpoint inhibitors

  • Bo DENG ,
  • Caoxia PENG ,
  • Qilian XIONG ,
  • Weiqi NIAN ,
  • Ying LIU
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  • 1. Department of Oncology，Chongqing Hospital of Traditional Chinese Medicine，Chongqing 400021，Chongqing，China
    2. Department of Emergency & ICU，Chongqing Hospital of Traditional Chinese Medicine，Chongqing 400021，Chongqing，China

Received date: 2025-08-03

  Online published: 2026-01-14

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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 predicts 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.

Key words： lung neoplasms; immune checkpoint inhibitors; unplanned readmission; boruta algorithm; predictive model

Cite this article

Bo DENG , Caoxia PENG , Qilian XIONG , Weiqi NIAN , Ying LIU . Predictive model for unplanned 30-day readmission in stage Ⅲ—Ⅳ lung cancer patients receiving immune checkpoint inhibitors[J]. The Journal of Practical Medicine, 2026 , 42(1) : 1 -11 . DOI: 10.3969/j.issn.1006-5725.2026.01.001

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