Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury

Abstract

Background Rapid referral of traumatic brain injury (TBI) patients requiring emergency neurosurgery to a specialized trauma center can significantly reduce morbidity and mortality. Currently, no model has been reported to predict the need for acute neurosurgery in severe to moderate TBI patients. This study aims to evaluate the performance of Machine Learning-based models to establish to predict the need for neurosurgery procedure within 24 h after moderate to severe TBI. Methods Retrospective multicenter cohort study using data from a national trauma registry (Traumabase®) from November 2011 to December 2020. Inclusion criteria correspond to patients over 18 years old with moderate or severe TBI (Glasgow coma score ≤ 12) during prehospital assessment. Patients who died within the first 24 h after hospital admission and secondary transfers were excluded. The population was divided into a train set (80% of patients) and a test set (20% of patients). Several approaches were used to define the best prognostic model (linear nearest neighbor or ensemble model). The Shapley Value was used to identify the most relevant pre-hospital variables for prediction. Results 2159 patients were included in the study. 914 patients (42%) required neurosurgical intervention within 24 h. The population was predominantly male (77%), young (median age 35 years [IQR 24–52]) with severe head injury (median GCS 6 [3–9]). Based on the evaluation of the predictive model on the test set, the logistic regression model had an AUC of 0.76. The best predictive model was obtained with the CatBoost technique (AUC 0.81). According to the Shapley values method, the most predictive variables in the CatBoost were a low initial Glasgow coma score, the regression of pupillary abnormality after osmotherapy, a high blood pressure and a low heart rate. Conclusion Machine learning-based models could predict the need for emergency neurosurgery within 24 h after moderate and severe head injury. Potential clinical benefits of such models as a decision-making tool deserve further assessment. The performance in real-life setting and the impact on clinical decision-making of the model requires workflow integration and prospective assessment.

Development and validation of a Bayesian network predicting neurosurgical intervention after injury in children and adolescents

“…Our model had excellent discrimination and was well calibrated. The incorporation of additional examination findings and injury characteristics (pupillary response, mechanism of injury, and prehospital CPR) improved model performance compared with using the GCS score alone, a finding consistent with other studies related to timing to neurosurgical intervention and outcomes 10,12 . The model performed similarly among patients with blunt and penetrating injuries.…”

“…The SITI scoring system is a scale validated at a binary threshold to identify injured children and adults who may receive a craniotomy or craniectomy within 24 hours of arrival 10 . The cutoff values for the SITI scale were performed using an AUROC analysis to identify the optimal sensitivity and specificity 10 . Like NINJA, the number of children who received an intervention was small compared with the population used for validation.…”

“…Criteria used to trigger this activation were developed using domain knowledge instead of a data-driven approach. Only one data-driven triage tool has been validated to identify injured children and adolescents at risk for requiring a craniotomy or craniectomy, the Surgical Intervention for Traumatic Injury (SITI) score 8–10 . Using SITI in practice has had several barriers, including the inability to generate a score with missing data, delays that may occur when obtaining radiographic findings needed for the calculation, and exclusion of several categories of children, including those with mild TBI, penetrating head injuries, and skull fractures.…”

Machine learning‐based models to predict the need for neurosurgical intervention after moderate traumatic brain injury

“…52,53 In various medical situations, including TBI, ML has shown promise in predicting patient outcomes and determining the likelihood of deterioration or the need for intervention. 54 Our models achieved balanced prediction by balancing specificity and sensitivity, yielding fair predictions for both groups. Furthermore,…”

“…This provides valuable insight into the resuscitation of TBI patients 52,53 . In various medical situations, including TBI, ML has shown promise in predicting patient outcomes and determining the likelihood of deterioration or the need for intervention 54 …”