Journal Article

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

Fadlalla, Adam, Mollazehi, Monira, El-Menyar, Ayman, Abujaber, Ahmad, Gammoh, Diala, Abdelrahman, Husham

2020

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Adult / Age / Artificial neural networks / Artificial respiration / Blood transfusion / Brain / Brain Injuries, Traumatic - diagnosis / Brain Injuries, Traumatic - mortality / Brain Injuries, Traumatic - therapy / Brain research / Clinical decision-making / Cohort Studies / Data dictionaries / Data mining / Decision making / Disease prevention / Emergency medical care / Emergency medical services / Evaluation / Forecasting / Head injuries / Health Informatics / Heart rate / Hospital Mortality / Humans / Information Systems and Communication Service / Injuries / Injury analysis / Injury Severity Score / knowledge support systems / Learning algorithms / Learning theory / Machine Learning / Machine learning predictive model / Management of Computing and Information Systems / Mechanical ventilation / Medical imaging / Medical prognosis / Medicine / Medicine & Public Health / Middle Aged / Models, Statistical / Mortality / Neural networks / Patient outcomes / Patients / Pediatrics / Performance prediction / Power / Prognosis / Qatar / Regression analysis / Research Article / Respiration, Artificial - adverse effects / Retrospective Studies / theory / Thromboembolism / Transfusion / Trauma / Traumatic brain injury / Treatment Outcome / Ventilation / Ventilators / Vital signs