Prediction and Analysis of Risk Factors for Lower Extremity Deep Vein Thrombosis After Craniotomy in Patients with Primary Brain Tumors: A Machine Learning Approach.

AIM

To explore the risk factors associated with the occurrence of lower extremity deep vein thrombosis (DVT) after craniotomy in patients with primary brain tumors, and to develop a predictive model using machine learning.

MATERIAL AND METHODS

A prospective cohort study was conducted on 140 patients with primary brain tumors who underwent neurosurgical treatment at our hospital between March 2021 and September 2022. A logistic regression analysis was performed to identify independent risk factors associated with postoperative DVT. Additionally, multiple machine learning models were developed and evaluated to determine their predictive performance.

RESULTS

The incidence of lower extremity DVT after craniotomy was 27.9%. Logistic regression identified age [OR=1.07, 95% CI (1.03-1.11)], GCS score [OR=0.88, 95% CI (0.78-0.98)], D-dimer level [OR=1.08, 95% CI (1.02-1.15)], and mechanical ventilation (≥48 hours) [OR=3.83, 95% CI (1.21-12.15)] as independent risk factors (P < 0.05). The Gradient Boosting Machine (GBM) had the highest prediction accuracy among the assessed machine learning models, achieving an area under the curve (AUC) of 0.850, with a sensitivity of 56.44% and a specificity of 90.09%.

CONCLUSION

Age, D-dimer, and mechanical ventilation (≥48 hours) are independent risk factors for the development of lower extremity DVT after craniotomy in patients with primary brain tumors. The GCS score serves as a potential protective risk factor. The GBM model, with its high AUC and specificity, offers a promising tool for early identification of high-risk patients, potentially informing clinical decision-making and targeted interventions.