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Neurological Sciences

09-05-2024 | Central Nervous System Trauma | Original Article

Development and validation of a nomogram for predicting mortality in patients with acute severe traumatic brain injury: A retrospective analysis

Authors: Haosheng Wang, Yehong Liu, Jun Yuan, Yuhai Wang, Ying Yuan, Yuanyuan Liu, Xu Ren, Jinxu Zhou

Published in: Neurological Sciences

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Abstract

Background

Recent evidence links the prognosis of traumatic brain injury (TBI) to various factors, including baseline clinical characteristics, TBI specifics, and neuroimaging outcomes. This study focuses on identifying risk factors for short-term survival in severe traumatic brain injury (sTBI) cases and developing a prognostic model.

Methods

Analyzing 430 acute sTBI patients from January 2018 to December 2023 at the 904th Hospital's Neurosurgery Department, this retrospective case–control study separated patients into survival outcomes: 288 deceased and 142 survivors. It evaluated baseline, clinical, hematological, and radiological data to identify risk and protective factors through univariate and Lasso regression. A multivariate model was then formulated to pinpoint independent prognostic factors, assessing their relationships via Spearman's correlation. The model's accuracy was gauged using the Receiver Operating Characteristic (ROC) curve, with additional statistical analyses for quantitative factors and model effectiveness. Internal validation employed ROC, calibration curves, Decision Curve Analysis (DCA), and Clinical Impact Curves (CIC) to assess model discrimination, utility, and accuracy. The International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) and Corticosteroid Randomization After Significant Head injury (CRASH) models were also compared through multivariate regression.

Results

Factors like unilateral and bilateral pupillary non-reactivity at admission, the derived neutrophil to lymphocyte ratio (dNLR), platelet to lymphocyte ratio (PLR), D-dimer to fibrinogen ratio (DFR), infratentorial hematoma, and Helsinki CT score were identified as independent risk factors (OR > 1), whereas serum albumin emerged as a protective factor (OR < 1). The model showed superior predictive performance with an AUC of 0.955 and surpassed both IMPACT and CRASH models in predictive accuracy. Internal validation confirmed the model's high discriminative capability, clinical relevance, and effectiveness.

Conclusions

Short-term survival in sTBI is significantly influenced by factors such as pupillary response, dNLR, PLR, DFR, serum albumin levels, infratentorial hematoma occurrence, and Helsinki CT scores at admission. The developed nomogram accurately predicts sTBI outcomes, offering significant clinical utility.
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Metadata
Title
Development and validation of a nomogram for predicting mortality in patients with acute severe traumatic brain injury: A retrospective analysis
Authors
Haosheng Wang
Yehong Liu
Jun Yuan
Yuhai Wang
Ying Yuan
Yuanyuan Liu
Xu Ren
Jinxu Zhou
Publication date
09-05-2024
Publisher
Springer International Publishing
Published in
Neurological Sciences
Print ISSN: 1590-1874
Electronic ISSN: 1590-3478
DOI
https://doi.org/10.1007/s10072-024-07572-y