Effects of pressure-controlled and volume-controlled ventilation on respiratory mechanics and systemic stress response during laparoscopic cholecystectomy.

BACKGROUND

Timely identification of the need for surgical intervention in traumatic epidural hematoma (tEDH) is critical to optimizing outcomes. This retrospective study aimed to identify predictive factors for surgical intervention in tEDH using machine learning and develop a nomogram to support clinical decision-making.

METHODS

In this retrospective study, data from 147 patients with tEDH at a major trauma center in western Iran (2023-2024) were analyzed. Demographic, Clinical, and CT scan data were extracted from medical records. Four machine learning models (Logistic Regression (LR)/ Support Vector Machine (SVM)/ Naive Bayes (NB)/Neural Network (NN)), were developed to predict surgical need. A Random Forest (RF) algorithm identified key predictors, and a nomogram was constructed from the LR model to facilitate individualized risk assessment. Statistical analyses were conducted using R software (version 4.3.2).

RESULTS

In this study, 131 (89.1%) of 147 patients with tEDH were male. Of these, 72 (49%) underwent surgery. The cause of brain trauma was a Motor Vehicle Accident (MVA) in 76 (51.7%) of patients and a fall in 50 (34%) of patients. The mean (±Standard Deviation) age of the patients was 31.47 (±18.27). The initial hematoma volume demonstrated the highest discriminatory power, with an AUC of 0.92 (95% CI: 0.83-1.00) and an accuracy of 0.89 (95% CI: 0.76-0.96). The Glasgow Coma Scale (GCS) score also exhibited strong predictive performance, with an AUC of 0.76 (95% CI: 0.62-0.89) and an accuracy of 0.71 (95% CI: 0.56-0.84). The SVM model demonstrated the highest AUC of 0.96 (95% CI: 0.91-1.00), with sensitivity and specificity values above 90%.

CONCLUSIONS

In this study, the novel integration of machine learning with a nomogram offers clinicians a precise, user-friendly tool for rapid decision-making, potentially reducing complications. These findings help surgeons to make more informed clinical decisions by accurately assessing these parameters in the early stages and to identify patients at higher risk for surgical intervention more quickly.