• Hai Zhou, Yutong Zhao, Hui Zheng, Changcun Chen, and Zongyi Xie.
• Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China.
• World Neurosurg. 2024 Sep 13.

ObjectiveThis study aimed to characterize long-term cerebral perfusion pressure (CPP) trajectory in traumatic brain injury (TBI) patients and construct an interpretable prediction model to assess the risk of unfavorable CPP evolution patterns.MethodsTBI patients with CPP records were identified from the Medical Information Mart for Intensive Care (MIMIC)-IV 2.1, eICU Collaborative Research Database (eICU-CRD) 2.0, and HiRID dataset 1.1.1. The research process consisted of 2 stages. First, group-based trajectory modeling (GBTM) was used to identify different CPP trajectories. Second, different artificial neural network (ANN) algorithms were used to predict the trajectories of CPP.ResultsA total of 331 eligible patients' records from MIMIC-IV 2.1 and eICU-CRD 2.0 were used for trajectory analysis and model development. Additionally, 310 patients' data from HiRID were used for external validation. The GBTM identified 5 CPP trajectory groups, group 1 and group 5 were merged into class 1 based on unfavorable in-hospital mortality. The best 6 predictors were invasive systolic blood pressure coefficient of variation, venous blood chloride ion concentration, PaCO2, prothrombin time, CPP coefficient of variation, and mean CPP. Compared with other algorithms, Scaled Conjugate Gradient performed relatively better in identifying class 1.ConclusionsThis study identified 2 CPP trajectory groups associated with elevated risk and 3 with reduced risk. PaCO2 might be a strong predictor for the unfavorable CPP class. The ANN model achieved the primary goal of risk stratification, which is conducive to early intervention and individualized treatment.Copyright © 2024. Published by Elsevier Inc.

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