Artificial Intelligence for Early Prediction of Intracranial Hypertension: From Single-Centre Development to Multicentre Validation

BACKGROUND: Raised intracranial pressure is one of the most severe secondary insults after traumatic brain injury. Recognising physiological patterns before they progress to dangerous intracranial pressure elevations could enable preventive rather than reactive interventions. In our previous Uppsala single-centre study, an XGBoost machine learning model showed high discriminative performance for prediction of imminent intracranial pressure insults. The model identifies short-term physiological trajectories associated with imminent threshold crossing rather than predicting independent future clinical events. Whether these results are generalisable across centres is unknown. The aim was therefore to evaluate whether the XGBoost model trained on an Uppsala dataset maintains similar performance when applied to a multicentre TBI cohort - The CENTER-TBI High-Resolution ICU cohort. METHODS: The XGBoost model trained on the Uppsala high frequency traumatic brain injury cohort was externally validated using data from the multicentre CENTER-TBI cohort. Minute-level averages of intracranial pressure and invasive arterial blood pressure were used to predict the onset of an intracranial pressure insult within 30 min on the basis of a 4h historical window. A bidirectional validation design was applied and performance was assessed using multiple metrics. RESULTS: The cohorts comprised 602 patients from Uppsala and 255 patients from the CENTER-TBI database. When trained on the Uppsala cohort and tested on CENTER-TBI, the model achieved an area under the receiver operating characteristic curve (AUROC) of 97.4%, sensitivity of 93.8%, and specificity of 98.6%. Training on CENTER-TBI and testing on the Uppsala dataset yielded an AUROC of 96.1%, sensitivity of 81.2%, and specificity of 98.7%. Balanced accuracy exceeded 88% in both validation directions. CONCLUSIONS: The XGBoost model retained high performance under external multicentre validation, indicating that pre-insult physiological patterns preceding increased intracranial pressure are shared across cohorts. Differences in sensitivity between validation directions were observed and may reflect cohort characteristics or dataset size. These findings support the need for local evaluation before clinical implementation.

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Publication Details

Journal
Neurocritical Care
Published
2026-10-14
DOI
https://doi.org/10.1007/s12028-026-02633-w
Primary Topic
Traumatic Brain Injury and Neurovascular Disturbances
Type
article
Field-Weighted Citation Impact
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article

Artificial Intelligence for Early Prediction of Intracranial Hypertension: From Single-Centre Development to Multicentre Validation

Endre Czeiter, Rahul Raj, David W. Nelson, Manuel Teixeira Cabeleira et al.
Neurocritical Care
Traumatic Brain Injury and Neurovascular Disturbances
article

Artificial Intelligence for Early Prediction of Intracranial Hypertension: From Single-Centre Development to Multicentre Validation

Endre Czeiter, Rahul Raj, David W. Nelson, Manuel Teixeira Cabeleira, Saulius Ročka, Ludvig Hult, Ronny Beer, Raimund Helbok, Arturo Chieregato, Anders Lewén, Jonathan R. Rhodes, Erta Beqiri, Anders Hånell, Ari Ercole, Bo‐Michael Bellander, Riikka S. K. Takala, Shirin Kordasti Frisvold, Lars‐Owe Koskinen, Anna Piippo-Karjalainen, Marco Carbonara, Nino Stocchetti, Geert Meyfroidt, Oliver Werner Sakowitz, Arminas Ragauskas, Andreea Rădoi, Peter Galos, Tomas Tamošuitis, Peter S. Smielewski, Frederick Adam Zeiler, Daniel Kondziella, Olli S. Tenovuo, Hans Clusmann, Stefan Jankowski, Audny Anke, Rimantas Vilcinis, Per Enblad, Thomas B. Schön, Marek Czosnyka, Ana Kowark, Giuseppe Citerio, Nina Sundström, Stefan Wolf, Andras Buki, Kirsten Moeller, Rolf Rossaint, Alessia Vargiolu, Peter Vajkoczy, Bart Depreitere, Andreas Unterberg, Alexander Younsi, Juan Sahuquillo, David K. Menon
article en

Abstract

BACKGROUND: Raised intracranial pressure is one of the most severe secondary insults after traumatic brain injury. Recognising physiological patterns before they progress to dangerous intracranial pressure elevations could enable preventive rather than reactive interventions. In our previous Uppsala single-centre study, an XGBoost machine learning model showed high discriminative performance for prediction of imminent intracranial pressure insults. The model identifies short-term physiological trajectories associated with imminent threshold crossing rather than predicting independent future clinical events. Whether these results are generalisable across centres is unknown. The aim was therefore to evaluate whether the XGBoost model trained on an Uppsala dataset maintains similar performance when applied to a multicentre TBI cohort - The CENTER-TBI High-Resolution ICU cohort. METHODS: The XGBoost model trained on the Uppsala high frequency traumatic brain injury cohort was externally validated using data from the multicentre CENTER-TBI cohort. Minute-level averages of intracranial pressure and invasive arterial blood pressure were used to predict the onset of an intracranial pressure insult within 30 min on the basis of a 4h historical window. A bidirectional validation design was applied and performance was assessed using multiple metrics. RESULTS: The cohorts comprised 602 patients from Uppsala and 255 patients from the CENTER-TBI database. When trained on the Uppsala cohort and tested on CENTER-TBI, the model achieved an area under the receiver operating characteristic curve (AUROC) of 97.4%, sensitivity of 93.8%, and specificity of 98.6%. Training on CENTER-TBI and testing on the Uppsala dataset yielded an AUROC of 96.1%, sensitivity of 81.2%, and specificity of 98.7%. Balanced accuracy exceeded 88% in both validation directions. CONCLUSIONS: The XGBoost model retained high performance under external multicentre validation, indicating that pre-insult physiological patterns preceding increased intracranial pressure are shared across cohorts. Differences in sensitivity between validation directions were observed and may reflect cohort characteristics or dataset size. These findings support the need for local evaluation before clinical implementation.

Neurocritical Care
Uppsala University (SE), Uppsala University Hospital (SE), Science for Life Laboratory (SE), Department of Medical Sciences (RU), University of Milano-Bicocca (IT)
Vetenskapsrådet, Kjell och Märta Beijers Stiftelse, FP7 Ideas: European Research Council
Good health and well-being
Openalex Percentile: Top 14%
Traumatic Brain Injury and Neurovascular Disturbances
DOIOpen Access PDFOpenAlex
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