Identification and prediction of immune checkpoint inhibitors-related pneumonitis by machine learning - Gong, Li; Gong, Jun; Sun, Xin; Yu, Lin; Liao, Bin; Chen, Xia; Li, Yong-sheng (Frontiers in Immunology, 2023)
Background: Immune checkpoint inhibitors (ICIs)-related pneumonitis (IRP) is a clinically common and potentially fatal adverse event. Identification and prediction the risk of ICIs-related IRP was a major clinical issue. The objective of this study was to apply machine learning method to explore risk factors and establish prediction model. Methods: By screening the electronic medical records of hospitalized patients, we conducted a retrospective analysis of 48 patients with IRP (IRP group) and 142 patients without IRP (control group) who were treated with ICIs drugs. . An Elastic Net model was constructed under a repeated kfold cross-validation framework (repeat =10, k=3). The prediction models were validated internally and final prediction model was built on the whole training set using hyperparameters with best interval validation performance. We assessed the generalizability of the final prediction model by applying it to an independent test set. We evaluated the overall performance, discrimination, and calibration of our prediction model.Eleven predictors were included in the final predictive model: sindillizumab, number of underlying diseases>=2, history of lung diseases, tirelizumab, non-small cell lung cancer (NSCLC), percentage of CD4 + lymphocyte, body temperature, KPS score <=70, hemoglobin, cancer stage = IV, and history of antitumor therapy. The external validation of the risk prediction model on an independent test set of 37 patients and showed good discrimination and acceptable calibration ability: with AUC of 0.81 (95%CI 0.58-0.90), AP of 0.76, scaled Brier score of 0.31, and Spiegelhalter-z of -0.29 (P-value:0.77). We also designed an online IRP risk calculator for clinical practice.The prediction model of ICIs-related IRP provides a tool to accurately predict IRP occurrence in cancer patients who received ICIs.