Previously submitted to: JMIR Formative Research (no longer under consideration since May 19, 2026)
Date Submitted: Apr 22, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Analysis of risk factors for immune checkpoint inhibitor-related pneumonitis and the establishment of nomogram prediction model
ABSTRACT
Abstract
Objective:
Immune checkpoint inhibitors (ICIs) are widely used in the treatment of tumors, especially in lung cancer, ICIs can improve the overall survival (OS) and progression-free survival (PFS) of lung cancer patients. However, ICIs have unique adverse effects called immune-related adverse events (irAEs). The purpose of this study was to analyze the risk factors for immune-associated pneumonia (ICI-P) in lung cancer patients treated with ICIs, and to establish a risk factor prediction model to help identify patients at high risk of ICI-P.
Methods:
Records of 329 patients with advanced lung cancer who were treated with ICIs between 1 June 2019 and 30 June 2024 in No. 960 Hospital of PLA were reviewed. According to whether ICI-P occurred, patients were divided into ICI-P group and non-ICI-P group. The influencing factors of ICI-P were analyzed by differential analysis and binary logistic regression, constructed a forest plot of the results of multivariate logistic regression analysis. The nomogram prediction model of risk factors was constructed, and the goodness of fit, accuracy, and discrimination of the nomogram were evaluated by calibration curve, receiver operating characteristic curve (ROC) and decision curve analysis (DCA).
Results:
ICI-P was found in 9.73%(n=32) of patients with advanced lung cancer who received ICIs. Difference analysis and binary logistic regression analysis showed that smoking history, preexisting lung diseases, combined targeted therapy, combined radiotherapy, as well as low absolute lymphocyte count (ALC) and high absolute eosinophil count (AEC) in baseline peripheral blood cell count were significantly associated with ICI-P risk. The nomograms were constructed on the above risk factors, The calibration curve of the nomogram prediction model was consistent (P>0.05). And AUC of the nomogram is 0. 840 (95%CI 0.775-0.906) indicates a good predictive value.
Conclusions:
The following factors were significantly associated with an increased risk of ICI-P: smoking history, preexisting lung diseases, combined targeted therapy, combined radiotherapy, low ALC and high AEC. We established a valid nomogram for predicting ICI-P in lung cancer patients treated with ICIs, and the accuracy of the prediction nomogram is evaluated.
Citation
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