Ahmad Abujaber1, Adam Fadlalla2, Diala Gammoh3, Hassan Al-Thani4, Ayman El-Menyar5,6. 1. Assistant Executive Director of Nursing, Hamad Medical Corporation, Doha, Qatar. 2. Management Information Systems, Business, and Economics Faculty, Qatar University, Doha, Qatar. 3. Industrial Engineering, University of Central Florida- USA. 4. Department of Surgery, Trauma Surgery, Hamad Medical Corporation, Doha, Qatar. 5. Department of Surgery, Trauma Surgery, Clinical Research, Hamad Medical Corporation, Doha, Qatar. 6. Department of Clinical Medicine, Weill Cornell Medical College, Doha, Qatar.
Abstract
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.
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.