Development and validation of a nomogram for differentiating immune checkpoint inhibitor-related pneumonitis from pneumonia in patients undergoing immunochemotherapy: a multicenter, real-world, retrospective study.

Background: Immune Checkpoint Inhibitor-related Pneumonitis (CIP) exhibits high morbidity and mortality rates in the real world, often coexisting with pneumonia, particularly after immunochemotherapy. We aimed to develop and validate a non-invasive nomogram for differentiating CIP from pneumonia in patients undergoing immunochemotherapy. Methods: This study encompassed 237 patients from three hospitals. A multivariate logistic regression analysis was conducted to identify risk factors for CIP. Utilizing the random forest machine learning method, optimal development and validation cohort allocation ratios (in a ratio of 8:2) were determined for the predictive model. The performance of the nomogram was evaluated using calibration, the area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA). Subsequently respiratory pathogens, management, and outcomes were compared between CIP and No CIP cases. Results: Among the 237 patients, 104 were diagnosed with CIP, and 133 were no CIP but pneumonia(No CIP). Smoking status, prior chronic obstructive pulmonary disease (COPD), ground glass opacities, non-specific interstitial pneumonitis, Neutrophil to Lymphocyte Ratio (NLR), pleural effusions, and Oxygen Partial Pressure (PaO Conclusion: The nomogram offers a non-invasive approach to differentiate CIP from pneumonia associated with immunochemotherapy, potentially facilitating early intervention and informed treatment decisions.