Objectives: The shock index (SI) and its derivations, the modified shock index (MSI) and the age shock index (Age SI), have been used to identify trauma patients with unstable hemodynamic status. The aim of this study was to evaluate their use in predicting the requirement for massive transfusion (MT) in trauma patients upon arrival at the hospital. Participants: A patient receiving transfusion of 10 or more units of packed red blood cells or whole blood within 24 h of arrival at the emergency department was defined as having received MT. Detailed data of 2490 patients hospitalized for trauma between 1 January 2009, and 31 December 2014, who had received blood transfusion within 24 h of arrival at the emergency department, were retrieved from the Trauma Registry System of a level I regional trauma center. These included 99 patients who received MT and 2391 patients who did not. Patients with incomplete registration data were excluded from the study. The two-sided Fisher exact test or Pearson chi-square test 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. Parameters including systolic blood pressure (SBP), heart rate (HR), hemoglobin level (Hb), base deficit (BD), SI, MSI, and Age SI that could provide cut-off points for predicting the patients’ probability of receiving MT were identified by the development of specific receiver operating characteristic (ROC) curves. High accuracy was defined as an area under the curve (AUC) of more than 0.9, moderate accuracy was defined as an AUC between 0.9 and 0.7, and low accuracy was defined as an AUC less than 0.7. Results: In addition to a significantly higher Injury Severity Score (ISS) and worse outcome, the patients requiring MT presented with a significantly higher HR and lower SBP, Hb, and BD, as well as significantly increased SI, MSI, and Age SI. Among these, only four parameters (SBP, BD, SI, and MSI) had a discriminating power of moderate accuracy (AUC > 0.7) as would be expected. A SI of 0.95 and a MSI of 1.15 were identified as the cut-off points for predicting the requirement of MT, with an AUC of 0.760 (sensitivity: 0.563 and specificity: 0.876) and 0.756 (sensitivity: 0.615 and specificity: 0.823), respectively. However, in the groups of patients with comorbidities such as hypertension, diabetes mellitus, or coronary artery disease, the discriminating power of these three indices in predicting the requirement of MT was compromised. Conclusions: This study reveals that the SI is moderately accurate in predicting the need for MT. However, this predictive power may be compromised in patients with HTN, DM or CAD. Moreover, the more complex calculations of MSI and Age SI failed to provide better discriminating power than the SI.

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.

Background: This study was designed to measure the effect of stress-induced hyperglycemia (SIH) and diabetic hyperglycemia (DH) versus non-diabetic normoglycemia (NDN) on the outcomes of trauma patients in the intensive care unit (ICU). Methods: Diabetes mellitus (DM) was determined based on patient history and/or a hemoglobin A1c (HbA1c) level of ≥6.5% at admission. The patients who had serum glucose levels of ≥200 mg/dL in the absence or presence of DM were assigned into the groups SIH and DH, respectively. Diabetic normoglycemia (DN) and NDN were determined based on serum glucose levels of <200 mg/dL in patients with and without DM, respectively. Patients with burn injury or incomplete data were excluded. Detailed data of trauma patients in the ICU of a Level-I trauma center from 1 January 2009 to 31 December 2016 were retrieved from the database of the Trauma Registry System. These patients were classified into four exclusive groups, including NDN (n = 1745), DN (n = 306), SIH (n = 225) and DH (n = 206). The Pearson chi-square test was used to compare categorical data between groups. Continuous variables were compared using one-way analysis of variance along with the Games–Howell post hoc test. To decrease the confounding effect of the differences in sex and age, preexisting comorbidities and injury severity score (ISS) among different groups of patients, 1:1 ratio propensity score-matched cohorts were assigned using the NCSS software. The effect of hyperglycemia on the outcomes of patients with and without DM was assessed with a logistic regression analysis. Results: Among those selected propensity score-matched patient cohorts, the patients with SIH and DH had a 3.88-fold (95% CI, 2.13–7.06; p < 0.001) and 1.83-fold (95% CI, 1.00–3.34; p = 0.048) higher mortality, respectively, than those with NDN. Moreover, the patients in the SIH group (10.0 vs. 7.4 days; p = 0.005) and those in the DH group (10.1 vs. 7.4 days; p = 0.006) who were admitted to the ICU had a significantly longer length of stay than those in the NDN group. In addition, the SIH group had a 2.13-fold (95% CI, 1.04–4.36; p = 0.038) higher adjusted odds ratio for mortality than the DH group. Conclusions: This study revealed significantly worse outcomes in terms of mortality among patients with SIH and DH who were admitted to the ICU after controlling for sex and age, preexisting comorbidities and ISS. In addition, patients who had SIH presented significantly higher adjusted odds for mortality than those DH patients. These results suggest that hyperglycemia is detrimental in patients with or without DM who were admitted to the ICU, and there is a different pathophysiological mechanisms behind the SIH and DH.

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.

The geriatric nutritional risk index (GNRI) is a simple and efficient tool to assess the nutritional status of patients with malignancies or after surgery. Because trauma patients constitute a specific population that generally acquires accidental and acute injury, this study aimed to identify the association between the GNRI at admission and mortality outcomes of older trauma patients in the intensive care unit (ICU). Methods: The study population included 700 older trauma patients admitted to the ICU between 1 January 2009 and 31 December 2019. The collected data included age, sex, body mass index (BMI), albumin level at admission, preexisting comorbidities, injury severity score (ISS), and in-hospital mortality. Multivariate logistic regression analysis was conducted to identify the independent effects of univariate predictive variables resulting in mortality in our study population. The study population was categorized into four nutritional risk groups: a major-risk group (GNRI < 82; n = 128), moderate-risk group (GNRI 82 to 98; n = 245). Results: There was no significant difference in sex predominance, age, and BMI between the mortality (n = 125) and survival (n = 575) groups. The GNRI was significantly lower in the mortality group than in the survival group (89.8 ± 12.9 vs. 94.2 ± 12.0, p < 0.001). Multivariate logistic regression analysis showed that the GNRI (odds ratio—OR, 0.97; 95% confidence interval (CI) 0.95–0.99; p = 0.001), preexisting end-stage renal disease (OR, 3.6; 95% CI, 1.70–7.67; p = 0.001), and ISS (OR, 1.1; 95% CI, 1.05–1.10; p < 0.001) were significant independent risk factors for mortality. Compared to the patients in group of GNRI > 98, those patients in group of GNRI < 82 presented a significantly higher mortality rate (26.6% vs. 13.1%; p < 0.001) and length of stay in hospital (26.5 days vs. 20.9 days; p = 0.016). Conclusions: This study demonstrated that GNRI is a significant independent risk factor and a promising simple screening tool to identify the subjects with malnutrition associated with higher risk for mortality in those ICU elderly trauma patients.

Background: Hyperglycemia at the time of hospital admission is associated with higher morbidity and mortality rates in patients with traumatic brain injury (TBI). Using data from the Chang Gung Research Database (CGRD), this study aimed to compare mortality outcomes between patients with stress-induced hyperglycemia (SIH), diabetic hyperglycemia (DH), and nondiabetic normoglycemia (NDN). The study occurred at Keelung, Linkou, Chiayi, and Kaohsiung Chang Gung Memorial Hospitals (CGMHs). Methods: A total of 1166, 6318, 3622, and 5599 health records from Keelung, Linkou, Chiayi, and Kaohsiung CGMHs, respectively, were retrieved from the CGRD for hospitalized patients with TBI between January 2001 and December 2015. After propensity score matching for sex, age, and Glasgow Coma Scale (GCS) score, the matched cohorts were compared to evaluate differences in the primary outcome between patients with SIH, DH, and NDN. In-hospital mortality was the primary outcome. Results: The analysis of matched patient populations revealed that at the Kaohsiung CGMH, patients with SIH had 1.63-fold (95% CI: 1.09–2.44; p = 0.017) and 1.91-fold (95% CI: 1.12–3.23; p = 0.017) higher odds of mortality than patients with NDN and DH, respectively. Similar patterns were found at the Linkou CGMH; patients with SIH had higher odds of mortality than patients with NDN and DH. In contrast, at the Keelung CGMH, patients with SIH had significantly higher odds of mortality than those with NDN (OR: 3.25; 95% CI: 1.06–9.97; p = 0.039). At the Chiayi CGMH, there were no significant differences in mortality rates among all groups. Conclusions: This study’s results suggest that SIH and DH differ in their effect on the outcomes of patients with TBI. The results were similar between medical centers but not nonmedical centers; in the medical centers, patients with SIH had significantly higher odds of mortality than patients with either NDN or DH.