Comparison of nine trauma scoring systems in prediction of inhospital outcomes of pediatric trauma patients: a multicenter study.

Hereby, we aimed to comprehensively compare different scoring systems for pediatric trauma and their ability to predict in-hospital mortality and intensive care unit (ICU) admission. The current registry-based multicenter study encompassed a comprehensive dataset of 6709 pediatric trauma patients aged ≤ 18 years from July 2016 to September 2023. To ascertain the predictive efficacy of the scoring systems, the area under the receiver operating characteristic curve (AUC) was calculated. A total of 720 individuals (10.7%) required admission to the ICU. The mortality rate was 1.1% (n = 72). The most predictive scoring system for in-hospital mortality was the adjusted trauma and injury severity score (aTRISS) (AUC = 0.982), followed by trauma and injury severity score (TRISS) (AUC = 0.980), new trauma and injury severity score (NTRISS) (AUC = 0.972), Glasgow coma scale (GCS) (AUC = 0.9546), revised trauma score (RTS) (AUC = 0.944), pre-hospital index (PHI) (AUC = 0.936), injury severity score (ISS) (AUC = 0.901), new injury severity score (NISS) (AUC = 0.900), and abbreviated injury scale (AIS) (AUC = 0.734). Given the predictive performance of the scoring systems for ICU admission, NTRISS had the highest predictive performance (AUC = 0.837), followed by aTRISS (AUC = 0.836), TRISS (AUC = 0.823), ISS (AUC = 0.807), NISS (AUC = 0.805), GCS (AUC = 0.735), RTS (AUC = 0.698), PHI (AUC = 0.662), and AIS (AUC = 0.651). In the present study, we concluded the superiority of the TRISS and its two derived counterparts, aTRISS and NTRISS, compared to other scoring systems, to efficiently discerning individuals who possess a heightened susceptibility to unfavorable consequences. The significance of these findings underscores the necessity of incorporating these metrics into the realm of clinical practice.

Validation of the Conventional Trauma and Injury Severity Score and a Newly Developed Survival Predictive Model in Pediatric Patients with Blunt Trauma: A Nationwide Observation Study.

To date, there is no clinically useful prediction model that is suitable for Japanese pediatric trauma patients. Herein, this study aimed to developed a model for predicting the survival of Japanese pediatric patients with blunt trauma and compare its validity with that of the conventional TRISS model. Patients registered in the Japan Trauma Data Bank were grouped into a derivation cohort (2009-2013) and validation cohort (2014-2018). Logistic regression analysis was performed using the derivation dataset to establish prediction models using age, injury severity, and physiology. The validity of the modified model was evaluated by the area under the receiver operating characteristic curve (AUC). Among 11 predictor models, Model 1 and Model 11 had the best performance (AUC = 0.980). The AUC of all models was lower in patients with survival probability Ps < 0.5 than in patients with Ps ≥ 0.5. The AUC of all models was lower in neonates/infants than in other age categories. Model 11 also had the best performance (AUC = 0.762 and 0.909, respectively) in patients with Ps < 0.5 and neonates/infants. The predictive ability of the newly modified models was not superior to that of the current TRISS model. Our results may be useful to develop a highly accurate prediction model based on the new predictive variables and cutoff values associated with the survival mortality of injured Japanese pediatric patients who are younger and more severely injured by using a nationwide dataset with fewer missing data and added valuables, which can be used to evaluate the age-related physiological and anatomical severity of injured patients.

Predicting the Future in Trauma: Trauma and Injury Severity Score Loses Accuracy and Validity for Late Deaths After Injury.

BACKGROUND: The Trauma and Injury Severity Score (TRISS) is widely used to predict mortality in trauma patients, but its performance metrics have not been analyzed for early vs later deaths. Therefore, we aimed to investigate the impact of time to death on the accuracy of TRISS. METHODS: Patients from 2013 to 2018 American College of Surgeons Trauma Quality Improvement Program database were included. We compared predicted survival by TRISS using the areas under receiver operating characteristic curves (AUCs) and calibration curves between different cut-off times and subgroups. We further compared early (≤72 hr) and late (>72 hr) deaths based on mechanisms and severity. RESULTS: Among the 1,180,745 patients, the total mortality rate was 6.4%, with 59% early deaths and 41% late deaths. The AUC of TRISS for all patients was .919 (95% CI: .918-.921) for ≤72 hr survival and .845 (95% CI: .843-.848) for >72 hr survival. Significant discrepancies in AUCs between the early and late death groups existed in all cohorts based on blunt/penetrating mechanisms and severity. TRISS predicted well in early survival of penetrating injury but was less reliable in late survival of penetrating injury and all blunt injury. TRISS tended to underestimate survival, particularly for patients with lower probability of survival, with increased discrepancies seen for predicting late deaths. CONCLUSIONS: The predictive ability of TRISS varies significantly based on the timing of deaths and key injury factors. TRISS may be best utilized in predicting early survival in penetrating injury, but its reliability and accuracy diminish when predicting late deaths for all kinds of injury.

Comparison of National Surgical Quality Improvement Program Surgical Risk Calculator and Trauma and Injury Severity Score Risk Assessment Tools in Predicting Outcomes in High-Risk Operative Trauma Patients.

BACKGROUND: The Trauma and Injury Severity Score (TRISS) uses anatomic/physiologic variables to predict outcomes. The National Surgical Quality Improvement Program Surgical Risk Calculator (NSQIP-SRC) includes functional status and comorbidities. It is unclear which of these tools is superior for high-risk trauma patients (American Society of Anesthesiologists Physical Status (ASA-PS) class IV or V). This study compares risk prediction of TRISS and NSQIP-SRC for mortality, length of stay (LOS), and complications for high-risk operative trauma patients. METHODS: This is a prospective study of high-risk (ASA-PS IV or V) trauma patients (≥18 years-old) undergoing surgery at 4 trauma centers. We compared TRISS vs NSQIP-SRC vs NSQIP-SRC + TRISS for ability to predict mortality, LOS, and complications using linear, logistic, and negative binomial regression. RESULTS: Of 284 patients, 48 (16.9%) died. The median LOS was 16 days and number of complications was 1. TRISS + NSQIP-SRC best predicted mortality (AUROC: .877 vs .723 vs .843, P = .0018) and number of complications (pseudo-R2/median error (ME) 5.26%/1.15 vs 3.39%/1.33 vs 2.07%/1.41, P < .001) compared to NSQIP-SRC or TRISS, but there was no difference between TRISS + NSQIP-SRC and NSQIP-SRC with LOS prediction (P = .43). DISCUSSION: For high-risk operative trauma patients, TRISS + NSQIP-SRC performed better at predicting mortality and number of complications compared to NSQIP-SRC or TRISS alone but similar to NSQIP-SRC alone for LOS. Thus, future risk prediction and comparisons across trauma centers for high-risk operative trauma patients should include a combination of anatomic/physiologic data, comorbidities, and functional status.

Impact of AIS 2015 versus 1998 on injury severity scoring and mortality prediction - single centre retrospective comparison study.

BACKGROUND: A key component of trauma system evaluation is the Injury Severity Score (ISS). The ISS is dependent on the AIS, and as AIS versions are updated this effects the number of patients within a health system which are considered severely injured (ISS >15). This study aims to analyse the changes comparing AIS1998 and AIS2015, and its impact on injury severity scoring and survival prediction model in a major trauma centre. METHODS: This retrospective study reviewed all blunt trauma admissions from 1 January 2020 to 31 December 2020 from the trauma registry of Prince of Wales Hospital, Hong Kong. Patients were manually double coded with AIS1998 and AIS2015 by the same experienced trauma nurse who have completed both AIS 1998 and AIS 2015 Courses. AIS patterns and Injury Severity Scores (ISS) derived from AIS 1998 and 2015 were compared using the Wilcoxon Signed Rank Test. The area under the receiving operator curve (AUROC) was compared based on the Trauma and Injury Severity Score (TRISS) model using AIS 1998 and AIS 2015. RESULTS: 739 patients were included. There were 34 deaths within 30 days (30-day mortality rate 4.6%). Patients coded with AIS2015 compared with AIS1998 had significant reductions in the classification of serious, severe and critical categories of AIS, with a substantial increase in the mild and moderate categories. The largest reduction was observed in the head and neck region (Z = -11.018, p < 0.001), followed by the chest (Z = -6.110, p < 0.001), abdomen (Z = -4.221, p < 0.001) and extremity regions (Z = -4.252, p < 0.001). There was a 27% reduction in number of cases with ISS >15 in AIS2015 compared with AIS1998. Rates of 30-day mortality, ICU admission, emergency operation and trauma team activation of ISS > 15 using AIS 1998 were similar to the cut off for New Injury Severity Score (NISS) >12 using AIS 2015. The AUROC from the TRISS (AIS2015) was 0.942, and not different from the AUROC for TRISS (AIS1998) of 0.936. The sensitivity and specificity were 93.9% and 82.1% for TRISS (AIS2015), and 93.9% and 76.0% for TRISS (AIS1998). CONCLUSION: Trauma centres should be aware of the impact of the AIS2015 update on the benchmarking of trauma care, and consider the need for updating the ISS cut off for major trauma definitions.

Injury scoring systems for blast injuries: a narrative review.

Injury scoring systems can be used for triaging, predicting morbidity and mortality, and prognosis in mass casualty incidents. Recent conflicts and civilian incidents have highlighted the unique nature of blast injuries, exposing deficiencies in current scoring systems. Here, we classify and describe deficiencies with current systems used for blast injury. Although current scoring systems highlight survival trends for populations, there are several major limitations. The reliable prediction of mortality on an individual basis is inaccurate. Other limitations include the saturation effect (where scoring systems are unable to discriminate between high injury score individuals), the effect of the overall injury burden, lack of precision in discriminating between mechanisms of injury, and a lack of data underpinning scoring system coefficients. Other factors influence outcomes, including the level of healthcare and the delay between injury and presentation. We recommend that a new score incorporates the severity of injuries with the mechanism of blast injury. This may include refined or additional codes, severity scores, or both, being added to the Abbreviated Injury Scale for high-frequency, blast-specific injuries; weighting for body regions associated with a higher risk for death; and blast-specific trauma coefficients. Finally, the saturation effect (maximum value) should be removed, which would enable the classification of more severe constellations of injury. An early accurate assessment of blast injury may improve management of mass casualty incidents.

Predicting Mortality of Korean Geriatric Trauma Patients: A Comparison between Geriatric Trauma Outcome Score and Trauma and Injury Severity Score.

PURPOSE: The Geriatric Trauma Outcome Score (GTOS) is a new prognostic tool used to predict mortality of geriatric trauma patients. We aimed to apply this model to Korean geriatric trauma patients and compare it with the Trauma and Injury Severity Score (TRISS) method. MATERIALS AND METHODS: Patients aged ≥65 years who were admitted to a level 1 trauma center from 2014 to 2018 were included in this study. Data on age, Injury Severity Score (ISS), packed red blood cell transfusion within 24 h, TRISS, admission disposition, mortality, and discharge disposition were collected. We analyzed the validity of GTOS and TRISS by comparing the area under the survival curve. Subgroup analysis for age, admission disposition, and ISS was performed. RESULTS: Among 2586 participants, the median age was 75 years (interquartile range: 70-81). The median ISS was 9 (interquartile range: 4-12), with a transfusion rate (within 24 h) of 15.9% and mortality rate of 6.1%. The areas under the curve (AUCs) were 0.832 [95% confidence interval (CI), 0.817-0.846] and 0.800 (95% CI, 0.784-0.815) for GTOS and TRISS, respectively. On subgroup analysis, patients with ISS ≥9 showed a higher AUC of GTOS compared to the AUC of TRISS (p<0.05). Other subgroup analyses showed equally good power of discrimination for mortality. CONCLUSION: GTOS can be used to predict mortality of severely injured Korean geriatric patients, and also be helpful in deciding whether invasive or aggressive treatments should be administered to them.

Development of a new score for early mortality prediction in trauma ICU patients: RETRASCORE.

BACKGROUND: Severity scores are commonly used for outcome adjustment and benchmarking of trauma care provided. No specific models performed only with critically ill patients are available. Our objective was to develop a new score for early mortality prediction in trauma ICU patients. METHODS: This is a retrospective study using the Spanish Trauma ICU registry (RETRAUCI) 2015-2019. Patients were divided and analysed into the derivation (2015-2017) and validation sets (2018-2019). We used as candidate variables to be associated with mortality those available in RETRAUCI that could be collected in the first 24 h after ICU admission. Using logistic regression methodology, a simple score (RETRASCORE) was created with points assigned to each selected variable. The performance of the model was carried out according to global measures, discrimination and calibration. RESULTS: The analysis included 9465 patients: derivation set 5976 and validation set 3489. Thirty-day mortality was 12.2%. The predicted probability of 30-day mortality was determined by the following equation: 1/(1 + exp (- y)), where y = 0.598 (Age 50-65) + 1.239 (Age 66-75) + 2.198 (Age > 75) + 0.349 (PRECOAG) + 0.336 (Pre-hospital intubation) + 0.662 (High-risk mechanism) + 0.950 (unilateral mydriasis) + 3.217 (bilateral mydriasis) + 0.841 (Glasgow ≤ 8) + 0.495 (MAIS-Head) - 0.271 (MAIS-Thorax) + 1.148 (Haemodynamic failure) + 0.708 (Respiratory failure) + 0.567 (Coagulopathy) + 0.580 (Mechanical ventilation) + 0.452 (Massive haemorrhage) - 5.432. The AUROC was 0.913 (0.903-0.923) in the derivation set and 0.929 (0.918-0.940) in the validation set. CONCLUSIONS: The newly developed RETRASCORE is an early, easy-to-calculate and specific score to predict in-hospital mortality in trauma ICU patients. Although it has achieved adequate internal validation, it must be externally validated.

Prediction of Outcome Based on Trauma and Injury Severity Score, IMPACT and CRASH Prognostic Models in Moderate-to-Severe Traumatic Brain Injury in the Elderly.

OBJECTIVES: This study aimed to evaluate the trauma and injury severity score (TRISS), IMPACT (international mission for prognosis and analysis of clinical trials), and CRASH (corticosteroid randomization after significant head injury) prognostic models for prediction of outcome after moderate-to-severe traumatic brain injury (TBI) in the elderly following road traffic accident. DESIGN: This was a prospective observational study. MATERIALS AND METHODS: This was a prospective observational study on 104 elderly trauma patients who were admitted to tertiary care hospital, over a consecutive period of 18 months from December 2016 to May 2018. On the day of admission, data were collected from each patient to compute the TRISS, IMPACT, and CRASH and outcome evaluation was prospectively done at discharge, 14th day, and 6-month follow-up. RESULTS: This study included 104 TBI patients with a mean age of 66.75 years and with a mortality rate of 32% and 45%, respectively, at discharge and at the end of 6 months. The predictive accuracies of the TRISS, CRASH (computed tomography), and IMPACT (core, extended, laboratory) were calculated using receiver operator characteristic (ROC) curves for the prediction of mortality. Best cutoff point for predicting mortality in elderly TBI patients using TRISS system was a score of ≤88 (sensitivity 94%, specificity of 80%, and area under ROC curve 0.95), similarly cutoff point under the CRASH at 14 days was score of >35 (100%, 80%, 0.958); for CRASH at 6 months, best cutoff point was at >84 (88%, 88%, 0.959); for IMPACT (core), it was >38 (88%, 93%, 0.976); for IMPACT (extended), it was >27 (91%, 89%, 0.968); and for IMPACT (lab), it was >41 (82%, 100%, 0.954). There were statistical differences among TRISS, CRASH (at 14 days and 6 months), and IMPACT (core, extended, lab) in terms of area under the ROC curve (P < 0.0001). CONCLUSION: IMPACT (core, extended) models were the strongest predictors of mortality in moderate-to-severe TBI when compared with the TRISS, CRASH, and IMPACT (lab) models.

Evaluation of ISS, RTS, CASS and TRISS scoring systems for predicting outcomes of blunt trauma abdomen.

PURPOSE: Trauma is the leading cause of mortality in people below the age of 45 years. Abdominal trauma constitutes one-fourth of the trauma burden. Scoring systems in trauma are necessary for grading the severity of the injury and prior mobilization of resources in anticipation. The aim of this study was to evaluate RTS, ISS, CASS and TRISS scoring systems in blunt trauma abdomen. MATERIALS AND METHODS: A prospective single-center study was conducted on 43 patients of blunt trauma abdomen. Revised trauma score (RTS), Injury Severity Score (ISS), Clinical Abdominal Scoring System (CASS) and Trauma and Injury Severity Score (TRISS) were calculated and compared with the outcomes such as need for surgical intervention, post-operative complications and mortality. RESULTS: The majority of the study subjects were males (83.7%). The most common etiology for blunt trauma abdomen as per this study was road traffic accident (72.1%). Spleen was the most commonly injured organ as per the study. CASS and TRISS were significant in predicting the need for operative intervention. Only ISS significantly predicted post-operative complications. All scores except CASS significantly predicted mortality. CONCLUSIONS: Among the scoring systems studied CASS and TRISS predicted the need for operative intervention with good accuracy. For the prediction of post-operative complications, only the ISS score showed statistical significance. ISS, RTS and TRISS predicted mortality with good accuracy but the superiority of one score over the other couldn't be proved.

Spectral analysis of heart rate variability for trauma outcome prediction: an analysis of 210 ICU multiple trauma patients.

PURPOSE: This study aimed to test and compare short-term spectral HRV indices with most used trauma scorings in outcome prediction of multiple trauma, and then to explore the efficacy of their combined application. METHODS: A prospective study was conducted for patients with blunt multiple trauma admitted to an emergency intensive care unit (ICU) between January 2016 and December 2017. Short-term spectral HRV indices on admission were measured, including normalized low-frequency power (nLF), normalized high-frequency power (nHF), and the nLF/nHF ratio. Injury severity score (ISS), new injury severity score (NISS), and revised trauma score (RTS) were evaluated for each patient, as well as probability of survival (Ps) by trauma and injury severity score (TRISS) model. The primary outcome was 30-day mortality and secondary outcomes were incidence of multiple organ dysfunction syndrome (MODS) and length of ICU stay. RESULTS: Two hundred and ten patients were recruited. The nLF/nHF ratio, RTS, and Ps(TRISS) were independent predictors of 30-day mortality, while nLF/nHF, NISS and RTS were independent predictors of MODS. The area under the receiver operating characteristic (ROC) curve (AUC) of nLF/nHF for 30-day mortality prediction was 0.924, comparable to RTS (0.951) and Ps(TRISS) (0.892). AUC of nLF/nHF-RTS combination was 0.979, significantly greater than that of each alone. Combination of nLF/nHF and Ps(TRISS) showed an increased AUC (0.984) compared to each of them. The nLF/nHF ratio presented a similar AUC (0.826) to NISS (0.818) or RTS (0.850) for MODS prediction. AUC of nLF/nHF-RTS combination was 0.884, significantly greater than that of nLF/nHF. Combination of nLF/nHF and NISS showed a greater AUC (0.868) than each alone. The nLF/nHF ratio, NISS, RTS, and Ps(TRISS) were correlated with length of ICU stay for survivors, with correlation coefficients 0.476, 0.617, - 0.588, and - 0.539. CONCLUSIONS: These findings suggest that the short-term spectral analysis of HRV might be a potential early tool to assess injury severity and predict outcome of multiple trauma. Combination of nLF/nHF and conventional trauma scores can provide more accuracy in outcome prediction of multiple trauma.

Validation of age-specific survival prediction in pediatric patients with blunt trauma using trauma and injury severity score methodology: a ten-year Nationwide observational study.

BACKGROUND: In-hospital mortality in trauma patients has decreased recently owing to improved trauma injury prevention systems. However, no study has evaluated the validity of the Trauma and Injury Severity Score (TRISS) in pediatric patients by a detailed classification of patients' age and injury severity in Japan. This retrospective nationwide study evaluated the validity of TRISS in predicting survival in Japanese pediatric patients with blunt trauma by age and injury severity. METHODS: Data were obtained from the Japan Trauma Data Bank during 2009-2018. The outcomes were as follows: (1) patients' characteristics and mortality by age groups (neonates/infants aged 0 years, preschool children aged 1-5 years, schoolchildren aged 6-11 years, and adolescents aged 12-18 years), (2) validity of survival probability (Ps) assessed using the TRISS methodology by the four age groups and six Ps-interval groups (0.00-0.25, 0.26-0.50, 0.51-0.75, 0.76-0.90, 0.91-0.95, and 0.96-1.00), and (3) the observed/expected survivor ratio by age- and Ps-interval groups. The validity of TRISS was evaluated by the predictive ability of the TRISS method using the receiver operating characteristic (ROC) curves that present the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, area under the receiver operator characteristic curve (AUC) of TRISS. RESULTS: In all the age categories considered, the AUC for TRISS demonstrated high performance (0.935, 0.981, 0.979, and 0.977). The AUC for TRISS was 0.865, 0.585, 0.614, 0.585, 0.591, and 0.600 in Ps-interval groups (0.96-1.00), (0.91-0.95), (0.76. - 0.90), (0.51-0.75), (0.26-0.50), and (0.00-0.25), respectively. In all the age categories considered, the observed survivors among patients with Ps interval (0.00-0.25) were 1.5 times or more than the expected survivors calculated using the TRISS method. CONCLUSIONS: The TRISS methodology appears to predict survival accurately in Japanese pediatric patients with blunt trauma; however, there were several problems in adopting the TRISS methodology for younger blunt trauma patients with higher injury severity. In the next step, it may be necessary to develop a simple, high-quality prediction model that is more suitable for pediatric trauma patients than the current TRISS model.

Performance of Prognostic Scoring Systems in Trauma Patients in the Intensive Care Unit of a Trauma Center.

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.

In-hospital mortality and length of hospital stay with craniotomy versus craniectomy for acute subdural hemorrhage: a multicenter, propensity score-matched analysis.

OBJECTIVE: The optimal surgical treatment for acute subdural hemorrhage (ASDH) remains controversial. The purpose of this study was to compare outcomes in patients who underwent craniotomy with those in patients who underwent decompressive craniectomy for the treatment of ASDH. METHODS: Using the Japan Trauma Data Bank, a nationwide trauma registry, the authors identified patients aged ≥ 18 years with ASDH who underwent surgical evacuation after blunt head trauma between 2004 and 2015. Logistic regression analysis was used to estimate a propensity score to predict decompressive craniectomy use. They then used propensity score-matched analysis to compare patients who underwent craniotomy with those who underwent decompressive craniectomy. To identify the potential benefits and disadvantages of decompressive craniectomy among different subgroups, they estimated the interactions between treatment and the subgroups using logistic regression analysis. RESULTS: Of 236,698 patients who were registered in the database, 1788 were eligible for propensity score-matched analysis. The final analysis included 514 patients who underwent craniotomy and 514 patients who underwent decompressive craniectomy. The in-hospital mortality did not differ significantly between the groups (41.6% for the craniotomy group vs 39.1% for the decompressive craniectomy group; absolute difference -2.5%; 95% CI -8.5% to 3.5%). The length of hospital stay was significantly longer in patients who underwent decompressive craniectomy (median 23 days [IQR 4-52 days] vs 30 days [IQR 7-60 days], p = 0.005). Subgroup analyses demonstrated qualitative interactions between decompressive craniectomy and the patient subgroups, suggesting that patients who were more severely injured (Glasgow Coma Scale score < 9 and probability of survival < 0.64) and those involved in high-energy injuries may be good candidates for decompressive craniectomy. CONCLUSIONS: The results of this study showed that overall, decompressive craniectomy did not appear to be superior to craniotomy in ASDH treatment in terms of in-hospital mortality. In contrast, there were significant differences in the effectiveness of decompressive craniectomy between the subgroups. Thus, future studies should prioritize the identification of a subset of patients who will possibly benefit from the performance of each of the procedures.

Machine Learning Models of Survival Prediction in 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.

Comparison of Injury Severity Score, New Injury Severity Score, Revised Trauma Score and Trauma and Injury Severity Score for Mortality Prediction in Elderly Trauma Patients.

OBJECTIVES: This study tests the accuracy of the Injury Severity Score (ISS), New Injury Severity Score (NISS), Revised Trauma Score (RTS) and Trauma and Injury Severity Score (TRISS) in prediction of mortality in cases of geriatric trauma. DESIGN: Prospective observational study. MATERIALS AND METHODS: This was a prospective observational study on two hundred elderly trauma patients who were admitted to JSS Hospital, Mysuru over a consecutive period of 18 months between December 2016 to May 2018. On the day of admission, data were collected from each patient to compute the ISS, NISS, RTS, and TRISS. RESULTS: Mean age of patients was 66.35 years. Most common mechanism of injury was road traffic accident (94.0%) with mortality of 17.0%. The predictive accuracies of the ISS, NISS, RTS and the TRISS were compared using receiver operator characteristic (ROC) curves for the prediction of mortality. Best cutoff points for predicting mortality in elderly trauma patient using TRISS system was a score of 91.6 (sensitivity 97%, specificity of 88%, area under ROC curve 0.972), similarly cutoff point under the NISS was score of 17(91%, 93%, 0.970); for ISS best cutoff point was at 15(91%, 89%, 0.963) and for RTS it was 7.108(97%,80%,0.947). There were statistical differences among ISS, NISS, RTS and TRISS in terms of area under the ROC curve (p <0.0001). CONCLUSION: TRISS was the strongest predictor of mortality in elderly trauma patients when compared to the ISS, NISS and RTS. HOW TO CITE THIS ARTICLE: Javali RH, Krishnamoorthy et al. Comparison of Injury Severity Score, New Injury Severity Score, Revised Trauma Score and Trauma and Injury Severity Score for Mortality Prediction in Elderly Trauma Patients. Indian J of Crit Care Med 2019;23(2):73-77.

Significance of Blood Transfusion Units in Determining the Probability of Mortality among Elderly Trauma Patients Based on the Geriatric Trauma Outcome Scoring System: A Cross-Sectional Analysis Based on Trauma Registered Data.

Background: For elderly trauma patients, a prognostic tool called the Geriatric Trauma Outcome Score (GTOS), where GTOS = (age) + (ISS × 2.5) + (22 if any packed red blood cells (pRBCs) were transfused within 24 h after admission), was developed for predicting mortality. In such calculation, a score of 22 was added in the calculation of GTOS regardless of the transfused units of blood. This study aimed to assess the effect of transfused blood units on the mortality outcomes of the elderly trauma patients who received blood transfusion (BT). Methods: Detailed data of 687 elderly trauma patients aged ≥65 years who were transfused with pRBCs within 24 h after admission into a level I trauma center between 1 January 2009 and 31 December 2016 were retrieved from the Trauma Registry System database. Based on the units of pRBCs transfused, the study population was divided into two groups to compare the mortality outcomes between these groups. Adjusted odds ratios (AORs) with its 95% confidence intervals (CIs) for mortality were calculated by adjusting sex, pre-existing comorbidities, and GTOS. Results: When the cut-off value of BT was set as 3 U of pRBCs, patients who received BT ≥ 3 U had higher odds of mortality than those who received BT < 3 U (OR, 3.0; 95% CI, 1.94⁻4.56; p < 0.001). Patients who received more units of pRBCs still showed higher odds of mortality than their counterparts. After adjusting for sex, pre-existing comorbidities, and GTOS, comparison revealed that the patients who received BT of 3 U to 6 U had a 1.7-fold adjusted odds of mortality than their counterparts. The patients who received BT ≥ 8 U and 10 U had a 2.1-fold (AOR, 2.1; 95% CI, 1.09⁻3.96; p < 0.001) and 4.4-fold (AOR, 4.4; 95% CI, 2.04⁻9.48; p < 0.001) adjusted odds of mortality than those who received BT < 8 U and <10 U, respectively. Conclusions: This study revealed that the units of BT did matter in determining the probability of mortality. For those who received more units of blood, the mortality may be underestimated according to the GTOS.

The Reverse Shock Index Multiplied by Glasgow Coma Scale Score (rSIG) and Prediction of Mortality Outcome in Adult Trauma Patients: A Cross-Sectional Analysis Based on Registered Trauma Data.

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.

A survival prediction logistic regression models for blunt trauma victims in Japan.

Aim: This research aimed to propose a logistic regression model for Japanese blunt trauma victims. Methods: We tested whether the logistic regression model previously created from data registered in the Japan Trauma Data Bank between 2005 and 2008 is still valid for the data from the same data bank between 2009 and 2013. Additionally, we analyzed whether the model would be highly accurate even when its coefficients were rounded off to two decimal places. Results: The model was proved to be highly accurate (94.56%) in the recent data (2009-2013). We also showed that the model remains valid without respiratory rate data and the simplified model would maintain high accuracy. Conclusion: We propose the equation of survival prediction of blunt trauma victims in Japan to be Ps = 1/(1+e-b), where b = -0.76 + 1.03 × Revised Trauma Score - 0.07 × Injury Severity Score - 0.04 × age.

The Revised Trauma Score plus serum albumin level improves the prediction of mortality in trauma patients.

INTRODUCTION: The Revised Trauma Score (RTS) is used worldwide in prehospital practice and in the emergency department (ED) settings to triage trauma patients. The main purpose of this study was to evaluate the value of the RTS plus serum albumin (RTS-A) and to compare it with other existing trauma scores as well as to compare the predictive performance of the Trauma and Injury Severity Score with the RTS-A (TRISS-A) with the original TRISS. METHODS: This was a single center, trauma registry based observational cohort study. Data were collected from consecutive patients with blunt or penetrating injuries who presented to the emergency department of a tertiary referral hospital, between January 2012 and June 2016. 3145 and 2447 patients were assigned to the derivation group and validation group, respectively. Main outcome was in-hospital mortality. RESULTS: Among patients in the derivation group, the median [interquartile range] age was 59 [43-73] years, and 66.7% were male. The area under the receiver operating characteristic curves (AUC) of the RTS-A (0.948; 95% CI: 0.939-0.955) was higher than that of the RTS (0.919; 95% CI: 0.909-0.929). In patients with blunt trauma, the AUC of the TRISS-A (0.960; 95% CI: 0.952-0.967) was significantly higher than that of the original TRISS (0.949; 95% CI: 0.941-0.957). CONCLUSION: The value of the RTS-A predicts the in-hospital mortality of trauma patients better than the RTS, and the TRISS-A is a better mortality predictor compared to the original TRISS in patients with blunt trauma.