Work

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

Year: 2026

Type: article

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.

Source: Frontiers in Neurology

DOI: 10.3389/fneur.2026.1953953

Authors: Dongping Li, Jianhui Chen, Chunfa Weng, Jianhuang Huang

Institution: Putian University

Language: English

Topic: Venous Thromboembolism Diagnosis and Management

Subfield: Internal Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801