Background: Polytrauma patients are expected to have a higher risk of mortality than that obtained by the summation of expected mortality owing to their individual injuries. This study was designed to investigate the outcome of patients with polytrauma, which was defined using the new Berlin definition, as cases with an Abbreviated Injury Scale (AIS) ≥ 3 for two or more different body regions and one or more additional variables from five physiologic parameters (hypotension [systolic blood pressure ≤ 90 mmHg], unconsciousness [Glasgow Coma Scale score ≤ 8], acidosis [base excess ≤ −6.0], coagulopathy [partial thromboplastin time ≥ 40 s or international normalized ratio ≥ 1.4], and age [≥70 years]). Methods: We retrieved detailed data on 369 polytrauma patients and 1260 non-polytrauma patients with an overall Injury Severity Score (ISS) ≥ 18 who were hospitalized between 1 January 2009 and 31 December 2015 for the treatment of all traumatic injuries, from the Trauma Registry System at a level I trauma center. Patients with burn injury or incomplete registered data were excluded. Categorical data were compared with two-sided Fisher exact or Pearson chi-square tests. The unpaired Student t-test and the Mann–Whitney U-test was used to analyze normally distributed continuous data and non-normally distributed data, respectively. Propensity-score matched cohort in a 1:1 ratio was allocated using the NCSS software with logistic regression to evaluate the effect of polytrauma on patient outcomes. Results: The polytrauma patients had a significantly higher ISS than non-polytrauma patients (median (interquartile range Q1–Q3), 29 (22–36) vs. 24 (20–25), respectively; p < 0.001). Polytrauma patients had a 1.9-fold higher odds of mortality than non-polytrauma patients (95% CI 1.38–2.49; p < 0.001). Compared to non-polytrauma patients, polytrauma patients had a substantially longer hospital length of stay (LOS). In addition, a higher proportion of polytrauma patients were admitted to the intensive care unit (ICU), spent longer LOS in the ICU, and had significantly higher total medical expenses. Among 201 selected propensity score-matched pairs of polytrauma and non-polytrauma patients who showed no significant difference in sex, age, co-morbidity, AIS ≥ 3, and Injury Severity Score (ISS), the polytrauma patients had a significantly higher mortality rate (OR 17.5, 95% CI 4.21–72.76; p < 0.001), and a higher proportion of patients admitted to the ICU (84.1% vs. 74.1%, respectively; p = 0.013) with longer stays in the ICU (10.3 days vs. 7.5 days, respectively; p = 0.003). The total medical expenses for polytrauma patients were 35.1% higher than those of non-polytrauma patients. However, there was no significant difference in the LOS between polytrauma and non-polytrauma patients (21.1 days vs. 19.8 days, respectively; p = 0.399). Conclusions: The findings of this propensity-score matching study suggest that the new Berlin definition of polytrauma is feasible and applicable for trauma patients.

Background: We aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS). Methods: Enrolled hospitalized trauma patients from 2009 to 2016 were divided into a training dataset (70% of the original data set) for generation of a plausible model under supervised classification, and a test dataset (30% of the original data set) to test the performance of the model. The training and test datasets comprised 13,208 (12,871 survival and 337 mortality) and 5603 (5473 survival and 130 mortality) patients, respectively. With the provision of additional information such as pre-existing comorbidity status or laboratory data, logistic regression (LR), support vector machine (SVM), and neural network (NN) (with the Stuttgart Neural Network Simulator (RSNNS)) were used to build models of survival prediction and compared to the predictive performance of TRISS. Predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operating characteristic curves. Results: In the validation dataset, NN and the TRISS presented the highest score (82.0%) for balanced accuracy, followed by SVM (75.2%) and LR (71.8%) models. In the test dataset, NN had the highest balanced accuracy (75.1%), followed by the TRISS (70.2%), SVM (70.6%), and LR (68.9%) models. All four models (LR, SVM, NN, and TRISS) exhibited a high accuracy of more than 97.5% and a sensitivity of more than 98.6%. However, NN exhibited the highest specificity (51.5%), followed by the TRISS (41.5%), SVM (40.8%), and LR (38.5%) models. Conclusions: These four models (LR, SVM, NN, and TRISS) exhibited a similar high accuracy and sensitivity in predicting the survival of the trauma patients. In the test dataset, the NN model had the highest balanced accuracy and predictive specificity.

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.

Background: Admission hyperglycemia is associated with higher morbidity and mortality in patients with traumatic brain injury (TBI). Stress-induced hyperglycemia (SIH), a form of hyperglycemia induced by the stress response, is associated with increased patient mortality following TBI. However, admission hyperglycemia occurs not only in SIH but also in patients with diabetic hyperglycemia (DH). Current information regarding whether trauma patients with SIH represent a distinct group with differential outcomes compared to those with DH remains limited. Methods: Serum glucose concentration ≥200 mg/dL upon arrival at the emergency department was defined as hyperglycemia. Presence of diabetes mellitus (DM) was determined by patient history and/or admission glycated hemoglobin (HbA1c) level ≥6.5%. In the present study, the patient cohort included those with moderate and severe TBI, as defined by an Abbreviated Injury Scale (AIS) score ≥3 points in the head, and excluded those who had additional AIS scores ≥3 points in any other region of the body. A total of 1798 adult patients with isolated moderate to severe TBI were allocated into four groups: SIH (n = 140), DH (n = 187), diabetic normoglycemia (DN, n = 186), and non-diabetic normoglycemia (NDN, n = 1285). Detailed patient information was retrieved from the Trauma Registry System at a level I trauma center between 1 January 2009, and 31 December 2015. Unpaired Student’s t- and Mann–Whitney U-tests were used to analyze normally and non-normally distributed continuous data, respectively. Categorical data were compared using the Pearson chi-square or two-sided Fisher’s exact tests. Matched patient populations were allocated in a 1:1 ratio according to propensity scores calculated by NCSS software. Logistic regression was used to evaluate the effect of SIH and DH on the adjusted mortality outcome. Results: In patients with isolated moderate to severe TBI, the presence of SIH and DH led to 9.1-fold and 2.3-fold higher odds of mortality, respectively, than patients with NDN. After adjusting for confounding factors, including sex and age, pre-existing co-morbidities, existence of different kinds of intracerebral hemorrhage, and injury severity, patients with SIH still had 6.6-fold higher odds of mortality than those with NDN; however, DH did not present significantly higher adjusted mortality odds. SIH and DH presented different effects on outcomes after TBI. The results also suggested that the pathophysiological effect associated with SIH was different from that of DH. Conclusions: This study demonstrated that patients with SIH and DH had significantly higher mortality than patients with NDN. However, the adjusted mortality was significantly higher only in the selected propensity score-matched patients with SIH and not in those with DH.

Background: Stress-induced hyperglycemia (SIH) is a form of hyperglycemia secondary to stress and commonly occurs in patients with trauma. Trauma patients with SIH have been reported to have an increased risk of mortality. However, information regarding whether these trauma patients with SIH represent a distinct group with differential outcomes when compared to those with diabetic hyperglycemia (DH) remains limited. Methods: Diabetes mellitus (DM) was determined by patient history and/or admission glycated hemoglobin (HbA1c) ≥6.5%. Non-diabetic normoglycemia (NDN) was determined by a serum glucose level <200 mg/dL in the patients without DM. Diabetic normoglycemia (DN) was determined by a serum glucose level <200 mg/dL in the patients with DM. DH and SIH was diagnosed by a serum glucose level ≥200 mg/dL in the patients with and without DM, respectively. Detailed data of these four groups of hospitalized patients, which included NDN (n = 7806), DN (n = 950), SIH (n = 493), and DH (n = 897), were retrieved from the Trauma Registry System at a level I trauma center between 1 January 2009 and 31 December 2015. Patients with incomplete registered data were excluded. Categorical data were compared with Pearson chi-square tests or two-sided Fisher exact tests. The unpaired Student’s t-test and the Mann–Whitney U-test were used to analyze normally distributed continuous data and non-normally distributed data, respectively. Propensity-score-matched cohorts in a 1:1 ratio were allocated using NCSS software with logistic regression to evaluate the effect of SIH and DH on the outcomes of patients. Results: The SIH (median [interquartile range: Q1–Q3], 13 [9–24]) demonstrated a significantly higher Injury Severity Score (ISS) than NDN (9 [4–10]), DN (9 [4–9]), and DH (9 [5–13]). SIH and DH had a 12.3-fold (95% confidence interval [CI] 9.31–16.14; p < 0.001) and 2.4-fold (95% CI 1.71–3.45; p < 0.001) higher odds of mortality, respectively, when compared to NDN. However, in the selected propensity-score-matched patient population, SIH had a 3.0-fold higher odd ratio of mortality (95% CI 1.96–4.49; p < 0.001) than NDN, but DH did not have a significantly higher mortality (odds ratio 1.2, 95% CI 0.99–1.38; p = 0.065). In addition, SIH had 2.4-fold higher odds of mortality (95% CI 1.46–4.04; p = 0.001) than DH. These results suggest that the characteristics and injury severity of the trauma patients contributed to the higher mortality of these patients with hyperglycemia upon admission, and that the pathophysiological effect of SIH was different from that of DH. Conclusions: Although there were worse mortality outcomes among trauma patients presenting with hyperglycemia, this effect was only seen in patients with SIH, but not DH when controlling for age, sex, pre-existed co-morbidities, and ISS.

Background: Prediction of mortality outcomes in trauma patients in the intensive care unit (ICU) is important for patient care and quality improvement. We aimed to measure the performance of 11 prognostic scoring systems for predicting mortality outcomes in trauma patients in the ICU. Methods: Prospectively registered data in the Trauma Registry System from 1 January 2016 to 31 December 2018 were used to extract scores from prognostic scoring systems for 1554 trauma patients in the ICU. The following systems were used: the Trauma and Injury Severity Score (TRISS); the Acute Physiology and Chronic Health Evaluation (APACHE II); the Simplified Acute Physiology Score (SAPS II); mortality prediction models (MPM II) at admission, 24, 48, and 72 h; the Multiple Organ Dysfunction Score (MODS); the Sequential Organ Failure Assessment (SOFA); the Logistic Organ Dysfunction Score (LODS); and the Three Days Recalibrated ICU Outcome Score (TRIOS). Predictive performance was determined according to the area under the receiver operator characteristic curve (AUC). Results: MPM II at 24 h had the highest AUC (0.9213), followed by MPM II at 48 h (AUC: 0.9105). MPM II at 24, 48, and 72 h (0.8956) had a significantly higher AUC than the TRISS (AUC: 0.8814), APACHE II (AUC: 0.8923), SAPS II (AUC: 0.9044), MPM II at admission (AUC: 0.9063), MODS (AUC: 0.8179), SOFA (AUC: 0.7073), LODS (AUC: 0.9013), and TRIOS (AUC: 0.8701). There was no significant difference in the predictive performance of MPM II at 24 and 48 h (p = 0.37) or at 72 h (p = 0.10). Conclusions: We compared 11 prognostic scoring systems and demonstrated that MPM II at 24 h had the best predictive performance for 1554 trauma patients in the ICU.