• Rui Guo, Siyu Yan, Yansheng Li, Kejia Liu, Fatian Wu, Tianyu Feng, Ruiqi Chen, Yi Liu, Chao You, and Rui Tian.
• Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
• World Neurosurg. 2024 Sep 1; 189: e141e152e141-e152.

BackgroundPneumonia is one of the most common complications after spontaneous intracerebral hemorrhage (sICH), i.e., stroke-associated pneumonia (SAP). Timely identification of targeted patients is beneficial to reduce poor prognosis. So far, there is no consensus on SAP prediction, and application of existing predictors is limited. The aim of this study was to develop a machine learning model to predict SAP after sICH.MethodsWe retrospectively reviewed 748 patients diagnosed with sICH and collected data from 4 dimensions-demographic features, clinical features, medical history, and laboratory tests. Five machine learning algorithms-logistic regression, gradient boosting decision tree, random forest, extreme gradient boosting, and category boosting-were used to build and validate the predictive model. We also applied recursive feature elimination with cross-validation to obtain the best feature combination for each model. Predictive performance was evaluated by area under the receiver operating characteristic curve.ResultsSAP was diagnosed in 237 patients. The model developed by category boosting yielded the most satisfactory outcomes overall with area under the receiver operating characteristic curves in the training set and test set of 0.8307 and 0.8178, respectively.ConclusionsThe incidence of SAP after sICH in our center was 31.68%. Machine learning could potentially provide assistance in the prediction of SAP after sICH.Copyright © 2024 Elsevier Inc. All rights reserved.

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