Latent Trajectories of Cerebral Perfusion Pressure and Risk Prediction Models Among Patients with Traumatic Brain Injury: Based on an Interpretable Artificial Neural Network.

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  • Additional Information
    • Source:
      Publisher: Elsevier Country of Publication: United States NLM ID: 101528275 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-8769 (Electronic) Linking ISSN: 18788750 NLM ISO Abbreviation: World Neurosurg Subsets: MEDLINE
    • Publication Information:
      Original Publication: New York : Elsevier
    • Subject Terms:
      Brain Injuries, Traumatic*/physiopathology ; Neural Networks, Computer* ; Cerebrovascular Circulation*/physiology; Humans ; Male ; Female ; Middle Aged ; Adult ; Intracranial Pressure/physiology ; Aged ; Risk Assessment/methods ; Algorithms ; Hospital Mortality ; Blood Pressure/physiology
    • Abstract:
      Objective: This 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.
      Methods: TBI 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.
      Results: A 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, PaCO 2 , prothrombin time, CPP coefficient of variation, and mean CPP. Compared with other algorithms, Scaled Conjugate Gradient performed relatively better in identifying class 1.
      Conclusions: This study identified 2 CPP trajectory groups associated with elevated risk and 3 with reduced risk. PaCO 2 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 Elsevier Inc. All rights reserved.)
    • Contributed Indexing:
      Keywords: Artificial neural network; Cerebral perfusion pressure trajectories; Group-based trajectory modeling; Traumatic brain injury
    • Publication Date:
      Date Created: 20240915 Date Completed: 20241203 Latest Revision: 20241203
    • Publication Date:
      20241204
    • Accession Number:
      10.1016/j.wneu.2024.09.045
    • Accession Number:
      39278542