TY  - JOUR
PY  - 2021//
TI  - Predicting outcomes after trauma: prognostic model development based on admission features through machine learning
JO  - Medicine (Baltimore)
A1  - Lee, Kuo-Chang
A1  - Lin, Tzu-Chieh
A1  - Chiang, Hsiu-Fen
A1  - Horng, Gwo-Jiun
A1  - Hsu, Chien-Chin
A1  - Wu, Nan-Chun
A1  - Su, Hsiu-Chen
A1  - Chen, Kuo-Tai
SP  - e27753
EP  - e27753
VL  - 100
IS  - 49
N2  - In an overcrowded emergency department (ED), trauma surgeons and emergency physicians need an accurate prognostic predictor for critical decision-making involving patients with severe trauma. We aimed to develope a machine learning-based early prognostic model based on admission features and initial ED management.We only recruited patients with severe trauma (defined as an injury severity score >15) as the study cohort and excluded children (defined as patients <16 years old) from a 4-years database (Chi-Mei Medical Center, from January 2015, to December 2018) recording the clinical features of all admitted trauma patients. We considered only patient features that could be determined within the first 2 hours after arrival to the ED. These variables included Glasgow Coma Scale (GCS) score; heart rate; respiratory rate; mean arterial pressure (MAP); prehospital cardiac arrest; abbreviated injury scales (AIS) of head and neck, thorax, and abdomen; and ED interventions (tracheal intubation/tracheostomy, blood product transfusion, thoracostomy, and cardiopulmonary resuscitation). The endpoint for prognostic analyses was mortality within 7 days of admission.We divided the study cohort into the early death group (149 patients who died within 7 days of admission) and non-early death group (2083 patients who survived at >7 days of admission). The extreme Gradient Boosting (XGBoost) machine learning model provided mortality prediction with higher accuracy (94.0%), higher sensitivity (98.0%), moderate specificity (54.8%), higher positive predict value (PPV) (95.4%), and moderate negative predictive value (NPV) (74.2%).We developed a machine learning-based prognostic model that showed high accuracy, high sensitivity, and high PPV for predicting the mortality of patients with severe trauma.<p />  <p>Language: en</p>
LA  - en
SN  - 0025-7974
UR  - http://dx.doi.org/10.1097/MD.0000000000027753
ID  - ref1
ER  -