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

Abstract

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.

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“…This study proved that MP had superior discrimination ability than GCS in predicting mortality. Furthermore, this finding also confirms that GCS can be affected by various factors such as ventilator settings that can negatively impact the accuracy and discrimination of the prediction model [28].…”

Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients

“…Machine learning models have been developed to predict mortality in patients undergoing CRRT [ 27 ], critical trauma patients [ 28 , 29 ], and patients in the ICU [ 30 ]. Other studies have also used these models to predict in-hospital cardiac arrest [ 22 ] and real-time mortality [ 31 , 32 ].…”

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

“…We identified 59 studies for the topic of in-hospital mortality. 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 While the majority of studies used a retrospective cohort design, 11 used a prospective approach, 120 122 129 140 143 166 169 170 172 174 175 and two were meta-analysis studies. 154 …”

“…The majority of studies used a regression model including Cox's proportional hazards 131 167 and mixed effect models. 141 158 More contemporary techniques included neural networks, 117 118 125 126 127 134 139 142 165 171 173 random forests, 124 126 133 135 139 142 144 gradient boosting, 127 128 129 130 134 135 139 140 144 168 and NLP. 127 Four studies 137 149 159 173 leveraged unsupervised methods, with or without supervised methods.…”