Machine Learning Model to Predict Ventilator Associated Pneumonia in patients with Traumatic Brain Injury: The C.5 Decision Tree Approach.

BACKGROUND: There is paucity in the literature to predict the occurrence of Ventilator Associated Pneumonia (VAP) in patients with Traumatic Brain Injury (TBI). We aimed to build a C.5. Decision Tree (C.5 DT) machine learning model to predict VAP in patients with moderate to severe TBI. METHODS: This was a retrospective study including all adult patients who were hospitalized with TBI plus head abbreviated injury scale (AIS) ≥ 3 and were mechanically ventilated in a level 1 trauma center between 2014 and 2019. RESULTS: A total of 772 eligible patients were enrolled, of them 169 had VAP (22%). The C.5 DT model achieved moderate performance with 83.5% accuracy, 80.5% area under the curve, 71% precision, 86% negative predictive value, 43% sensitivity, 95% specificity and 54% F-score. Out of 24 predictors, C.5 DT identified 5 variables predicting occurrence of VAP post-moderate to severe TBI (Time from injury to emergency department arrival, blood transfusion during resuscitation, comorbidities, Injury Severity Score and pneumothorax). CONCLUSIONS: This study could serve as baseline for the quest of predicting VAP in patients with TBI through the utilization of C.5. DT machine learning approach. This model helps provide timely decision support to caregivers to improve patient's outcomes.

Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach.

BACKGROUND: The study aimed to introduce a machine learning model that predicts in-hospital mortality in patients on mechanical ventilation (MV) following moderate to severe traumatic brain injury (TBI). METHODS: A retrospective analysis was conducted for all adult patients who sustained TBI and were hospitalized at the trauma center from January 2014 to February 2019 with an abbreviated injury severity score for head region (HAIS) ≥ 3. We used the demographic characteristics, injuries and CT findings as predictors. Logistic regression (LR) and Artificial neural networks (ANN) were used to predict the in-hospital mortality. Accuracy, area under the receiver operating characteristics curve (AUROC), precision, negative predictive value (NPV), sensitivity, specificity and F-score were used to compare the models` performance. RESULTS: Across the study duration; 785 patients met the inclusion criteria (581 survived and 204 deceased). The two models (LR and ANN) achieved good performance with an accuracy over 80% and AUROC over 87%. However, when taking the other performance measures into account, LR achieved higher overall performance than the ANN with an accuracy and AUROC of 87% and 90.5%, respectively compared to 80.9% and 87.5%, respectively. Venous thromboembolism prophylaxis, severity of TBI as measured by abbreviated injury score, TBI diagnosis, the need for blood transfusion, heart rate upon admission to the emergency room and patient age were found to be the significant predictors of in-hospital mortality for TBI patients on MV. CONCLUSIONS: Machine learning based LR achieved good predictive performance for the prognosis in mechanically ventilated TBI patients. This study presents an opportunity to integrate machine learning methods in the trauma registry to provide instant clinical decision-making support.

Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach.

OBJECTIVES: We aimed to build a machine learning predictive model to predict the risk of prolonged mechanical ventilation (PMV) for patients with Traumatic Brain Injury (TBI). METHODS: This study included TBI patients who were hospitalized in a level 1 trauma center between January 2014 and February 2019. Data were analyzed for all adult patients who received mechanical ventilation following TBI with abbreviated injury severity (AIS) score for the head region of ≥ 3. This study designed three sets of machine learning models: set A defined PMV to be greater than 7 days, set B (PMV > 10 days) and set C (PMV >14 days) to determine the optimal model for deployment. Patients' demographics, injury characteristics and CT findings were used as predictors. Logistic regression (LR), Artificial neural networks (ANN) Support vector machines (SVM), Random Forest (RF) and C.5 Decision Tree (C.5 DT) were used to predict the PMV. RESULTS: The number of eligible patients that were included in the study were 674, 643 and 622 patients in sets A, B and C respectively. In set A, LR achieved the optimal performance with accuracy 0.75 and Area under the curve (AUC) 0.83. SVM achieved the optimal performance among other models in sets B with accuracy/AUC of 0.79/0.84 respectively. ANNs achieved the optimal performance in set C with accuracy/AUC of 0.76/0.72 respectively. Machine learning models in set B demonstrated more stable performance with higher prediction success and discrimination power. CONCLUSION: This study not only provides evidence that machine learning methods outperform the traditional multivariate analytical methods, but also provides a perspective to reach a consensual definition of PMV.

Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach.

BACKGROUND: The use of machine learning techniques to predict diseases outcomes has grown significantly in the last decade. Several studies prove that the machine learning predictive techniques outperform the classical multivariate techniques. We aimed to build a machine learning predictive model to predict the in-hospital mortality for patients who sustained Traumatic Brain Injury (TBI). METHODS: Adult patients with TBI who were hospitalized in the level 1 trauma center in the period from January 2014 to February 2019 were included in this study. Patients' demographics, injury characteristics and CT findings were used as predictors. The predictive performance of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) was evaluated in terms of accuracy, Area Under the Curve (AUC), sensitivity, precision, Negative Predictive Value (NPV), specificity and F-score. RESULTS: A total of 1620 eligible patients were included in the study (1417 survival and 203 non-survivals). Both models achieved accuracy over 91% and AUC over 93%. SVM achieved the optimal performance with accuracy 95.6% and AUC 96%. CONCLUSIONS: for prediction of mortality in patients with TBI, SVM outperformed the well-known classical models that utilized the conventional multivariate analytical techniques.