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Peer-reviewedCC BY 4.0Frontiers in Neurology

Development and internal validation of a clinical prediction model for postoperative venous thromboembolism after neurosurgical intervention in patients with traumatic brain injury

  • Dongping Li1
  • Jianhui Chen1
  • Chunfa Weng1
  • Jianhuang Huang2
  1. 1Department of Critical Care Medicine, Affiliated Hospital of Putian University
  2. 2Department of Neurosurgery, Affiliated Hospital of Putian University
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Abstract

Background: Venous thromboembolism (VTE) is a critical complication after traumatic brain injury (TBI) surgery. Conventional risk assessment tools were not developed for neurosurgical trauma populations, limiting their applicability.

Objective: To develop and internally validate an interpretable clinical prediction model for postoperative VTE in TBI surgery patients.

Methods: This retrospective cohort study enrolled 1,450 TBI patients who underwent surgery (2016–2024). Seven predictors were selected using Recursive Feature Elimination. Six machine learning algorithms were compared, with logistic regression (LR) selected as the final model. Performance was evaluated using AUC-ROC, calibration, and decision curve analysis.

Results: Among 1,450 patients, 205 (14.1%) developed postoperative VTE. The LR model achieved AUC-ROC of 0.868 (95% CI: 0.815–0.920) with good calibration (Brier score: 0.085). Seven predictors were identified: age, Barthel index, ASA classification, multiple surgeries, general anesthesia, limb AIS score, and serum magnesium. Compared with Caprini score (AUC: 0.686), the model showed improved discrimination (ΔAUC = 0.182; NRI: 0.559).

Conclusion: An interpretable model based on seven routinely available variables demonstrated promising performance for postoperative VTE risk stratification in TBI surgery patients. A nomogram and web-based calculator are provided for clinical use.

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Cite

BibTeXRIS
BibTeX
@article{li2026development,
  title = {{Development and internal validation of a clinical prediction model for postoperative venous thromboembolism after neurosurgical intervention in patients with traumatic brain injury}},
  author = {Dongping Li and Jianhui Chen and Chunfa Weng and Jianhuang Huang},
  journal = {Frontiers in Neurology},
  year = {2026},
  month = oct,
  doi = {10.3389/fneur.2026.1953953},
  url = {https://www.frontiersin.org/articles/10.3389/fneur.2026.1953953/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Development and internal validation of a clinical prediction model for postoperative venous thromboembolism after neurosurgical intervention in patients with traumatic brain injury
AU  - Dongping Li
AU  - Jianhui Chen
AU  - Chunfa Weng
AU  - Jianhuang Huang
PY  - 2026
DA  - 2026/10/12
JO  - Frontiers in Neurology
DO  - 10.3389/fneur.2026.1953953
UR  - https://www.frontiersin.org/articles/10.3389/fneur.2026.1953953/full
AB  - Background: Venous thromboembolism (VTE) is a critical complication after traumatic brain injury (TBI) surgery. Conventional risk assessment tools were not developed for neurosurgical trauma populations, limiting their applicability.  Objective: To develop and internally validate an interpretable clinical prediction model for postoperative VTE in TBI surgery patients.  Methods: This retrospective cohort study enrolled 1,450 TBI patients who underwent surgery (2016–2024). Seven predictors were selected using Recursive Feature Elimination. Six machine learning algorithms were compared, with logistic regression (LR) selected as the final model. Performance was evaluated using AUC-ROC, calibration, and decision curve analysis.  Results: Among 1,450 patients, 205 (14.1%) developed postoperative VTE. The LR model achieved AUC-ROC of 0.868 (95% CI: 0.815–0.920) with good calibration (Brier score: 0.085). Seven predictors were identified: age, Barthel index, ASA classification, multiple surgeries, general anesthesia, limb AIS score, and serum magnesium. Compared with Caprini score (AUC: 0.686), the model showed improved discrimination (ΔAUC = 0.182; NRI: 0.559).  Conclusion: An interpretable model based on seven routinely available variables demonstrated promising performance for postoperative VTE risk stratification in TBI surgery patients. A nomogram and web-based calculator are provided for clinical use.
SN  - 1664-2295
ER  -