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

Overview

Authors: Dongping Li1, Jianhui Chen1, Chunfa Weng1, Jianhuang Huang2
  1. Department of Critical Care Medicine, Affiliated Hospital of Putian University, Putian, Fujian, China
  2. Department of Neurosurgery, Affiliated Hospital of Putian University, Putian, Fujian, China
Journal: Frontiers in neurology, volume 17, article 1953953
Dates: received 30 July 2026; accepted 30 September 2026; published online 12 October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI · PMCID PMC13701100
Status: code on request (how the code is found and verified)
Categories: human (organism), traumatic brain injury (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Machine learning
Keywords: clinical prediction model, internal validation, interpretability, logistic regression, postoperative complications, traumatic brain injury, venous thromboembolism
Citations: not cited yet (Europe PMC); 25 references in the paper

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

Tracing map

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Data

No dataset and no data link were found in the paper.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 October 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 7 keywords, 25 references.

Cite

This paper

Li, D., Chen, J., Weng, C., & Huang, J. (2026). Development and internal validation of a clinical prediction model for postoperative venous thromboembolism after neurosurgical intervention in patients with traumatic brain injury. Frontiers in neurology, 17, 1953953.

BibTeX

@article{li2026development,
author = {Li, Dongping and Chen, Jianhui and Weng, Chunfa and Huang, Jianhuang},
title = {{Development and internal validation of a clinical prediction model for postoperative venous thromboembolism after neurosurgical intervention in patients with traumatic brain injury}},
journal = {Frontiers in neurology},
year = {2026},
month = oct,
volume = {17},
pages = {1953953},
publisher = {Frontiers Media SA},
issn = {1664-2295},
pmcid = {PMC13701100}
}

RIS

TY - JOUR
AU - Li, Dongping
AU - Chen, Jianhui
AU - Weng, Chunfa
AU - Huang, Jianhuang
TI - Development and internal validation of a clinical prediction model for postoperative venous thromboembolism after neurosurgical intervention in patients with traumatic brain injury
T2 - Frontiers in neurology
J2 - Front Neurol
PY - 2026
DA - 2026/10/27
VL - 17
SP - 1953953
SN - 1664-2295
PB - Frontiers Media SA
LA - en
ER -

CSL-JSON

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