BackgroundThe purpose of this study was to build a model of machine learning (ML) for the prediction of mortality in patients with isolated moderate and severe traumatic brain injury (TBI).MethodsHospitalized adult patients registered in the Trauma Registry System between January 2009 and December 2015 were enrolled in this study. Only patients with an Abbreviated Injury Scale (AIS) score ≥ 3 points related to head injuries were included in this study. A total of 1734 (1564 survival and 170 non-survival) and 325 (293 survival and 32 non-survival) patients were included in the training and test sets, respectively.ResultsUsing demographics and injury characteristics, as well as patient laboratory data, predictive tools (e.g., logistic regression [LR], support vector machine [SVM], decision tree [DT], naive Bayes [NB], and artificial neural networks [ANN]) were used to determine the mortality of individual patients. The predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operator characteristic curves. In the training set, all five ML models had a specificity of more than 90% and all ML models (except the NB) achieved an accuracy of more than 90%. Among them, the ANN had the highest sensitivity (80.59%) in mortality prediction. Regarding performance, the ANN had the highest AUC (0.968), followed by the LR (0.942), SVM (0.935), NB (0.908), and DT (0.872). In the test set, the ANN had the highest sensitivity (84.38%) in mortality prediction, followed by the SVM (65.63%), LR (59.38%), NB (59.38%), and DT (43.75%).ConclusionsThe ANN model provided the best prediction of mortality for patients with isolated moderate and severe TBI.
BackgroundPatients with polytrauma are expected to have a higher risk of mortality than the summation of expected mortality for their individual injuries. This study was designed to investigate the outcome of polytrauma patients, diagnosed by abbreviated injury scale (AIS) ≥ 3 for at least two body regions, at a level I trauma center.MethodsDetailed data of 694 polytrauma patients and 2104 non-polytrauma patients with an overall Injury Severity Score (ISS) ≥ 16 and hospitalized between January 1, 2009, and December 31, 2014 for treatment of all traumatic injuries, were retrieved from the Trauma Registry System. Two-sided Fisher exact or Pearson chi-square tests were used to compare categorical data. The unpaired Student t-test was used to analyze normally distributed continuous data, and the Mann–Whitney U-test was used to compare non-normally distributed data. Propensity-score matching in a 1:1 ratio was performed using NCSS software with logistic regression to evaluate the effect of polytrauma on in-hospital mortality.ResultsThere was no significant difference in short-term mortality between polytrauma and non-polytrauma patients, regardless of whether the comparison was made among the total patients (11.4% vs. 11.0%, respectively; p = 0.795) or among the selected propensity score-matched groups of patients following controlled covariates including sex, age, systolic blood pressure, co-morbidities, Glasgow Coma Scale scores, injury region based on AIS.ConclusionsPolytrauma defined by AIS ≥3 for at least two body regions failed to recognize a significant difference in short-term mortality among trauma patients.
The reverse shock index (rSI) multiplied by Glasgow Coma Scale (GCS) score (rSIG), calculated by multiplying the GCS score with systolic blood pressure (SBP)/hear rate (HR), was proposed to be a reliable triage tool for identifying risk of in-hospital mortality in trauma patients. This study was designed to externally validate the accuracy of the rSIG in the prediction of mortality in our cohort of trauma patients, in comparison with those that were predicted by the Revised Trauma Score (RTS), shock index (SI), and Trauma and Injury Severity Score (TRISS). Adult trauma patients aged ≥20 years who were admitted to the hospital from 1 January 2009 to 31 December 2017, were included in this study. The rSIG, RTS, and SI were calculated according to the initial vital signs and GCS scores of patients upon arrival at the emergency department (ED). The end-point of primary outcome is in-hospital mortality. Discriminative power of each score to predict mortality was measured using area under the curve (AUC) by plotting the receiver operating characteristic (ROC) curve for 18,750 adult trauma patients, comprising 2438 patients with isolated head injury (only head Abbreviated Injury Scale (AIS) ≥ 2) and 16,312 without head injury (head AIS ≤ 1). The predictive accuracy of rSIG was significantly lower than that of RTS in all trauma patients (AUC 0.83 vs. AUC 0.85, p = 0.02) and in patients with isolated head injury (AUC 0.82 vs. AUC 0.85, p = 0.02). For patients without head injury, no difference was observed in the predictive accuracy between rSIG and RTS (AUC 0.83 vs. AUC 0.83, p = 0.97). Based on the cutoff value of 14.0, the rSIG can predict the probability of dying in trauma patients without head injury with a sensitivity of 61.5% and specificity of 94.5%. The predictive accuracy of both rSIG and RTS is significantly poorer than that of TRISS, in all trauma patients (AUC 0.93) or in patients with (AUC 0.89) and without head injury (AUC 0.92). In addition, SI had the significantly worse predictive accuracy than all of the other three models in all trauma patients (AUC 0.57), and the patients with (AUC 0.53) or without (AUC 0.63) head injury. This study revealed that rSIG had a significantly higher predictive accuracy of mortality than SI in all of the studied population but a lower predictive accuracy of mortality than RTS in all adult trauma patients and in adult patients with isolated head injury. In addition, in the adult patients without head injury, rSIG had a similar performance as RTS to the predictive risk of mortality of the patients.
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