Prediction of mortality among severely injured trauma patients A comparison between TRISS and machine learning-based predictive models.

BACKGROUND: Given the huge impact of trauma on hospital systems around the world, several attempts have been made to develop predictive models for the outcomes of trauma victims. The most used, and in many studies most accurate predictive model, is the "Trauma Score and Injury Severity Score" (TRISS). Although it has proven to be fairly accurate and is widely used, it has faced criticism for its inability to classify more complex cases. In this study, we aimed to develop machine learning models that better than TRISS could predict mortality among severely injured trauma patients, something that has not been studied using data from a nationwide register before. METHODS: Patient data was collected from the national trauma register in Sweden, SweTrau. The studied period was from the 1st of January 2015 to 31st of December 2019. After feature selection and multiple imputation of missing data three machine learning (ML) methods (Random Forest, eXtreme Gradient Boosting, and a Generalized Linear Model) were used to create predictive models. The ML models and TRISS were then tested on predictive ability for 30-day mortality. RESULTS: The ML models were well-calibrated and outperformed TRISS in all the tested measurements. Among the ML models, the eXtreme Gradient Boosting model performed best with an AUC of 0.91 (0.88-0.93). CONCLUSION: This study showed that all the developed ML-based prediction models were superior to TRISS for the prediction of trauma mortality.

Performance of trauma scoring systems in predicting mortality in geriatric trauma patients: comparison of the ISS, TRISS, and GTOS based on a systemic review and meta-analysis.

PURPOSE: This meta-analysis aimed to evaluate the performance of the Injury Severity Score (ISS), Trauma and Injury Severity Score (TRISS), and the Geriatric Trauma Outcome Score (GTOS) in predicting mortality in geriatric trauma patients. METHODS: The MEDLINE, Web of Science, and EMBASE databases were searched for studies published from January 2008 to October 2023. Studies assessing the performance of the ISS, TRISS, or GTOS in predicting mortality in geriatric trauma patients (over 60 years old) and reporting data for the analysis of the pooled area under the receiver operating characteristic curve (AUROC) and the hierarchical summary receiver operating characteristic curve (HSROC) were included. Studies that were not conducted in a group of geriatric patients, did not consider mortality as the outcome variable, or had incomplete data were excluded. The Critical Appraisal Skills Programme (CASP) Clinical Prediction Rule Checklist was utilized to assess the risk of bias in included studies. STATA 16.0. was used for the AUROC analysis and HSROC analysis. RESULTS: Nineteen studies involving 118,761 geriatric trauma patients were included. The pooled AUROC of the TRISS (AUC = 0.82, 95% CI: 0.77-0.87) was higher than ISS (AUC = 0.74, 95% CI: 0.71-0.79) and GTOS (AUC = 0.80, 95%CI: 0.77-0.83). The diagnostic odds ratio (DOR) calculated from HSROC curves also suggested that the TRISS (DOR = 21.5) had a better performance in predicting mortality in geriatric trauma patients than the ISS (DOR = 6.27) and GTOS (DOR = 4.76). CONCLUSION: This meta-analysis suggested that the TRISS showed better accuracy and performance in predicting mortality in geriatric trauma patients than the ISS and GTOS.

Impact of COVID-19 pandemic on trauma mortality patients: A retrospective observational study in an Iranian level 1 trauma center.

Background and Aims: The COVID-19 pandemic has reshaped the epidemiology of various clinical conditions, including trauma which is closely tied to social policies. This study examines and compares the characteristics of trauma mortality patients, and their initial prognostic trauma scores, in the pre-pandemic and pandemic periods. Methods: We conducted a retrospective observational study involving patients who passed away at a level 1 trauma center from July 23, 2018, to February 19, 2020 (prepandemic), and from February 20, 2020, to September 22, 2021 (pandemic). A subgroup analysis that matched 12 of the same months of the year in the two periods was also done. Patients who arrived deceased or passed away immediately upon arrival were excluded from data analysis. We collected and analyzed demographic and clinical data, employing the Abbreviated Injury Score (AIS), Injury Severity Score (ISS), Revised Trauma Score (RTS), and Trauma and ISS (TRISS) to compare initial prognoses. Results: Our study encompassed 1128 patients, with 529 in the prepandemic group and 599 in the pandemic group. Demographic characteristics showed no significant differences in the number of patients in the two periods. Motor vehicle accidents remained the predominant injury mechanism in both periods. While the mean ISS increased insignificantly (22.80 vs. 22.91, p = 0.902), the mean RTS decreased (6.32 vs. 5.82), and TRISS increased (23.97% vs. 28.93%) during the pandemic (p < 0.05). Hospital length of stay decreased in the pandemic period (15.57 vs. 12.54 days, p < 0.05). Subgroup analysis revealed increased ISS, decreased RTS, and increased TRISS during the pandemic (p < 0.05). Conclusion: In conclusion, while overall demographics and injury mechanisms remained virtually unchanged, trauma patients during the pandemic displayed worse estimated clinical prognoses, particularly in physiological trauma scores. The heightened mortality rate was attributed to poorer clinical conditions of patients.

Use of the Trauma and Injury Severity Score (TRISS) as a Predictor of Patient Outcome in Cases of Trauma Presenting in the Trauma and Emergency Department of a Tertiary Care Institute.

BACKGROUND: In this study, we used the anatomic scoring system Abbreviated Injury Scale (AIS) to calculate the Injury Severity Score (ISS) and the physiological scoring system for the Revised Trauma Score (RTS) on the arrival of patients. Both scores were used to calculate the Trauma and Injury Severity Score (TRISS) for predicting the patient outcome in a case of trauma. METHODS: This prospective, cross-sectional, observational study was carried out at the trauma centre of a tertiary care institute and included patients of either sex, age ≥18 years, and ISS ≥15. A total of 2084 cases of trauma over a period of 18 months were assessed, and 96 cases of blunt trauma meeting the inclusion criteria were studied. RESULTS: Patients injured in road traffic accidents constituted the maximum caseload. Out of a sample size of 96 patients with ISS ≥15, 77 died during the treatment and 19 survived. The ISS ranged from 15 to 66, with a mean ± SD score of 27.48 ± 8.79. Non-survivors had a statistically higher significant ISS than survivors (p<0.001). The RTS ranged from <1 to 7.84, with a mean ± SD score of 4.52 ± 2.08. Non-survivors had low RTS (RTS <5, n=52) compared to survivors, and the difference was statistically significant (p<0.001). The mean ± SD TRISS (Ps) score was 0.69 ± 2.288. In the non-survivor (NS) group, 15 patients had TRISS (Ps) between 0.26-0.50, followed by 0.51-0.75 (n=18), 0.76-0.90 (n=12), and 0.90-0.95 (n=11). While 16 survivors had TRISS (Ps) between 0.96 and 1, a statistically significant association was found between TRISS and patient outcome (p-value <0.001). On the receiver operating characteristic (ROC) curve analysis, the sensitivity of TRISS (94.7%) and RTS was found to be comparable (94.7%), whereas ISS was less sensitive (36.8%) in predicting the patient outcome. RTS (79.2%) and TRISS (76.6%) scores were more specific than ISS (5.2%) for outcome analysis. CONCLUSION: The TRISS score is useful in the management of trauma patients as it can satisfactorily predict mortality in a case of trauma. The trauma scores are of immense help in determining the nature of injury in medicolegal cases.

Modification of the TRISS: simple and practical mortality prediction after trauma in an all-inclusive registry.

PURPOSE: Numerous studies have modified the Trauma Injury and Severity Score (TRISS) to improve its predictive accuracy for specific trauma populations. The aim of this study was to develop and validate a simple and practical prediction model that accurately predicts mortality for all acute trauma admissions. METHODS: This retrospective study used Dutch National Trauma Registry data recorded between 2015 and 2018. New models were developed based on nonlinear transformations of TRISS variables (age, systolic blood pressure (SBP), Glasgow Coma Score (GCS) and Injury Severity Score (ISS)), the New Injury Severity Score (NISS), the sex-age interaction, the best motor response (BMR) and the American Society of Anesthesiologists (ASA) physical status classification. The models were validated in 2018 data and for specific patient subgroups. The models' performance was assessed based on discrimination (areas under the curve (AUCs)) and by calibration plots. Multiple imputation was applied to account for missing values. RESULTS: The mortality rates in the development and validation datasets were 2.3% (5709/245363) and 2.5% (1959/77343), respectively. A model with sex, ASA class, and nonlinear transformations of age, SBP, the ISS and the BMR showed significantly better discrimination than the TRISS (AUC 0.915 vs. 0.861). This model was well calibrated and demonstrated good discrimination in different subsets of patients, including isolated hip fractures patients (AUC: 0.796), elderly (AUC: 0.835), less severely injured (ISS16) (AUC: 878), severely injured (ISS ≥ 16) (AUC: 0.889), traumatic brain injury (AUC: 0.910). Moreover, discrimination for patients admitted to the intensive care (AUC: s0.846), and for both non-major and major trauma center patients was excellent, with AUCs of 0.940 and 0.895, respectively. CONCLUSION: This study presents a simple and practical mortality prediction model that performed well for important subgroups of patients as well as for the heterogeneous population of all acute trauma admissions in the Netherlands. Because this model includes widely available predictors, it can also be used for international evaluations of trauma care within institutions and trauma systems.

Survival factors in patients of high fall - A 10-year level-I multi-trauma center study.

OBJECTIVES: This study aims to investigate the characteristics of patients after free falls at the Level-I trauma centers. The factors associated with survival were differentiated. METHODS: This retrospective study was conducted at the National Taiwan University Hospital, the Hsin-Chu branch, and the Yun-Lin branch, all accredited as Level-I trauma centers between January 2010 and September 2020. Adult patients with falls from height of more than one story (i.e. 3.6 m) were included. Clinical data were obtained from electronic medical records. Odds ratios (OR) were computed with 95% confidence intervals (CIs) for significant parameters for survival. RESULTS: A total of 371 patients were included. Only 2 survived to discharge with poor neurologic outcomes in 101 patients with OHCA. The overall mortality rate was 98% and 11% in patients with and without OHCA. A higher falling height with a one-meter increase (OR, 1.14, 95% CI, 1.10-1.19) was significantly related to OHCA, especially the height over 6 m (OR, 3.07, 95% CI, 1.19-7.94). A higher trauma injury severity score (TRISS) was significantly related to survival among patients without OHCA (OR, 1.07, 95% CI, 1.04-1.11), especially TRISS≧0.945 (OR, 5.21, 95% CI, 1.28-21.24). Patients without severe head/neck injury of Abbreviated Injury Scale (AIS)≧3 (OR, 0.17, 95% CI, 0.07-0.42) were positively associated with survivors among patients without OHCA. CONCLUSION: Patients with traumatic OHCA following falls had a high mortality rate of 98% and dismal outcomes, compared with non-traumatic OHCA. Falling heights, especially over 6 m was associated with OHCA. Patients without OHCA had a mortality rate of 11%. Patients with a higher TRISS, especially more than 0.945, or without severe head injury had more chances to survive in the non-OHCA group. The study provided the evidence to guide termination of high futility resuscitation for traumatic OHCA secondary to falls to conserve the clinical resources.

RISC II is superior to TRISS in predicting 30-day mortality in blunt major trauma patients in Hong Kong.

PURPOSE: Hong Kong (HK) trauma registries have been using the Trauma and Injury Severity Score (TRISS) for audit and benchmarking since their introduction in 2000. We compare the mortality prediction model using TRISS and Revised Injury Severity Classification, version II (RISC II) for trauma centre patients in HK. METHODS: This was a retrospective cohort study with all five trauma centres in HK. Adult trauma patients with Injury Severity Score (ISS) > 15 suffering from blunt injuries from January 2013 to December 2015 were included. TRISS models using the US and local coefficients were compared with the RISC II model. The primary outcome was 30-day mortality and the area under the receiver operating characteristic curve (AUC) for tested models. RESULTS: 1840 patients were included, of whom 1236/1840 (67%) were male. Median age was 59 years and median ISS was 25. Low falls were the most common mechanism of injury. The 30-day mortality was 23%. RISC II yielded a superior AUC of 0.896, compared with the TRISS models (MTOS: 0.848; PATOS: 0.839; HK: 0.858). Prespecified subgroup analyses showed that all the models performed worse for age ≥ 70, ASA ≥ III, and low falls. RISC II had a higher AUC compared with the TRISS models in all subgroups, although not statistically significant. CONCLUSION: RISC II was superior to TRISS in predicting the 30-day mortality for Hong Kong adult blunt major trauma patients. RISC II may be useful when performing future audit or benchmarking exercises for trauma in Hong Kong.

Preventable death in trauma: A systematic review on definition and classification.

PURPOSE: Trauma-related preventable death (TRPD) has been used to assess the management and quality of trauma care worldwide. However, due to differences in terminology and application, the definition of TRPD lacks validity. The aim of this systematic review is to present an overview of current literature and establish a designated definition of TRPD to improve the assessment of quality of trauma care. METHODS: A search was conducted in PubMed, Embase, the Cochrane Library and the Web of Science Core Collection. Including studies regarding TRPD, published between January 1, 1990, and April 6, 2021. Studies were assessed on the use of a definition of TRPD, injury severity scoring tool and panel review. RESULTS: In total, 3,614 articles were identified, 68 were selected for analysis. The definition of TRPD was divided in four categories: I. Clinical definition based on panel review or expert opinion (TRPD, trauma-related potentially preventable death, trauma-related non-preventable death), II. An algorithm (injury severity score (ISS), trauma and injury severity score (TRISS), probability of survival (Ps)), III. Clinical definition completed with an algorithm, IV. Other. Almost 85% of the articles used a clinical definition in some extend; solely clinical up to an additional algorithm. A total of 27 studies used injury severity scoring tools of which the ISS and TRISS were the most frequently reported algorithms. Over 77% of the panels included trauma surgeons, 90% included other specialist; 61% emergency medicine physicians, 46% forensic pathologists and 43% nurses. CONCLUSION: The definition of TRPD is not unambiguous in literature and should be based on a clinical definition completed with a trauma prediction algorithm such as the TRISS. TRPD panels should include a trauma surgeon, anesthesiologist, emergency physician, neurologist, and forensic pathologist.

Validation of a Visual-Based Analytics Tool for Outcome Prediction in Polytrauma Patients (WATSON Trauma Pathway Explorer) and Comparison with the Predictive Values of TRISS.

Introduction: Big data-based artificial intelligence (AI) has become increasingly important in medicine and may be helpful in the future to predict diseases and outcomes. For severely injured patients, a new analytics tool has recently been developed (WATSON Trauma Pathway Explorer) to assess individual risk profiles early after trauma. We performed a validation of this tool and a comparison with the Trauma and Injury Severity Score (TRISS), an established trauma survival estimation score. Methods: Prospective data collection, level I trauma centre, 1 January 2018-31 December 2019. INCLUSION CRITERIA: Primary admission for trauma, injury severity score (ISS) ≥ 16, age ≥ 16. PARAMETERS: Age, ISS, temperature, presence of head injury by the Glasgow Coma Scale (GCS). OUTCOMES: SIRS and sepsis within 21 days and early death within 72 h after hospitalisation. STATISTICS: Area under the receiver operating characteristic (ROC) curve for predictive quality, calibration plots for graphical goodness of fit, Brier score for overall performance of WATSON and TRISS. Results: Between 2018 and 2019, 107 patients were included (33 female, 74 male; mean age 48.3 ± 19.7; mean temperature 35.9 ± 1.3; median ISS 30, IQR 23-36). The area under the curve (AUC) is 0.77 (95% CI 0.68-0.85) for SIRS and 0.71 (95% CI 0.58-0.83) for sepsis. WATSON and TRISS showed similar AUCs to predict early death (AUC 0.90, 95% CI 0.79-0.99 vs. AUC 0.88, 95% CI 0.77-0.97; p = 0.75). The goodness of fit of WATSON (X2 = 8.19, Hosmer-Lemeshow p = 0.42) was superior to that of TRISS (X2 = 31.93, Hosmer-Lemeshow p < 0.05), as was the overall performance based on Brier score (0.06 vs. 0.11 points). Discussion: The validation supports previous reports in terms of feasibility of the WATSON Trauma Pathway Explorer and emphasises its relevance to predict SIRS, sepsis, and early death when compared with the TRISS method.

Prognosis of the Trauma Patients According to the Trauma and Injury Severity Score (TRISS); A Diagnostic Accuracy Study.

OBJECTIVE: To investigate the prognosis and survival rates of a group of Iranian patients with traumatic injuries using the trauma and injury severity score (TRISS) model. METHODS: In this prospective cohort study, all the patients with multi-trauma referring to the Yasuj Shahid Beheshti hospital during 2018 were included. The patients' demographic information, trauma and history of previous illness were recorded. Vital symptoms including respiratory rate, heart rate, hypertension, pulse rate and Glasgow coma scale (GCS) score were assessed. The injury severity score (ISS) was calculated based on the type and location of the injuries and according to the abbreviated injury scale (AIS) classification. The survival probability of the patients was assessed according to the TRISS model. RESULTS: Overall, 252 trauma patients were evaluated out of whom, 195 (77.4%) were men and 57 (22.6%) women. If we consider the TRISS score probability above 0.5 as the chance of being alive, the mortality rate was 6.75%, that was lower than our series (7.1%). The ISS score and GCS had a positive significant relationship with other variables except respiratory rate, body temperature and hospitalization. Revised trauma score (RTS) was significantly associated with other variables including age, GCS, hemoglobin, systolic blood pressure and respiratory rate. TRISS had an area under curve (AUC) of 0.988 indicating a high prognostic accuracy. CONCLUSION: The mortality rate was lower than that of being predicted by TRISS. This might be due to treatment effectiveness and care for traumatic patients leading to decreased mortality. TRISS had high prognostic accuracy in trauma patients. We also reported an association between hemoglobin and survival rate. Therefore, it seems that considering the laboratory parameters can be useful in patients with trauma.

Assessment of the Prognostic Accuracy of Scores in Different Ways on the Example of Assessing the Validity of the Prognostic Model ISS-RTS-TRISS.

The aim of the present study was a comparison of prognostic accuracy assessment results for scores in different ways on the example of validity analysis of the prognostic model ISS-RTS-TRISS for assessing the severity of the condition in children with trauma. The prospective study was conducted using clinical and physiological data collected at the admission and during the first 24 hours of hospitalization from 414 children with trauma. We had three groups of patients, common group children with traumatic brain injuries, and two groups of patients were divided into the two following ways: 141 (34%) patients with isolated traumatic brain injuries and 273 (66%) patients with combined injuries with traumatic brain injuries The validity and prognostic accuracy of prognostic scores were assessed by determining there discrimination and calibration ability. Analysis of the discrimination ability of score was carried out by assessment the areas under the ROC curves. For analysis of the calibration ability of score was used three methods of the Hosmer-Lemeshow test (H-criterion, C-criterion in two variants). The ISS-RTS-TRISS score showed significantly outstanding predictive accuracy in studied groups (AUROC >=0.9) .However, estimation the calibration ability of score using the C-criterion, by dividing into groups with the same number of cases of patients (recommended for abnormal distribution) did not show an unambiguous results. It was shown that to obtain an unambiguous correct result, it was necessary to use the C-criterion method using the division of cases into groups with the same number of patients with the lethal outcome. When using this method, satisfactory results of study of calibration ability were shown for all the studied groups (p<0.05).

Evaluation of quality of care in trauma patients using international scoring systems. / Valoración de la calidad asistencial al traumatismo grave mediante comparación con estándares internacionales.

OBJECTIVE: To evaluate the ability of the TRISS and PS14 models to predict mortality rates in our medical system and population. DESIGN: A retrospective observational study was carried out over a 66-month period. BACKGROUND: The study was conducted in the Trauma Intensive Care Unit (ICU) of a third level hospital. PATIENTS: All severe trauma patients (Injury Severity Score≥16 and/or Revised Trauma Score <12) aged> 14 years were included. VARIABLES OF INTEREST: Medical care data were prospectively recorded. The "W" statistic (difference between expected and observed mortality for every 100 patients) and its significance were calculated for each model. Discrimination and calibration were evaluated by means of receiver operating characteristic (ROC) curves, and the Hosmer-Lemeshow test and GiViTI calibration belt, respectively. RESULTS: A total of 1240 patients were included. Survival at hospital discharge was 81.9%. The "W" scores for the TRISS, TRISS 2010 and PS14 models were+6.72 (P<.01), +1.48 (P=.08) and +2.74 (P<.01) respectively. Subgroup analysis revealed significant favorable results for some populations. The areas under the ROC curve for the TRISS, TRISS 2010 and PS14 models were 0.915, 0.919 and 0.914, respectively. There were no significant differences among them (P>.05). Both the Hosmer-Lemeshow test and GiViTI calibration belt demonstrated poor calibration for the three models. CONCLUSIONS: These models are suitable tools for assessing quality of care in a Trauma ICU, affording excellent discrimination but poor calibration. In our institution, survival rates higher than expected were observed.

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.

Recognizing Preventable Death: Is There a Role of Survival Prediction Algorithms?

Monitoring the quality of trauma care is important but particularly challenging. Preventable death assessment aims to identify those cases where the patient's death would have not occurred if the patient had been treated differently. Determination of preventable death in trauma care is often based on calculated probability of survival, commonly by using the Trauma and Injury Severity Score (TRISS). TRISS is not suited for identifying all cases with opportunities for improvement. Combined with other methods such as morbidity and mortality conferences, however, it might be a valid approach if a complete review of all trauma deaths is not feasible at an institution.

External validation of a modified trauma and injury severity score model in major trauma injury.

BACKGROUND: The establishment of an accurate prognostic model in major trauma patients is important mainly because this group of patients will benefit the most. Clinical prediction models must be validated internally and externally on a regular basis to ensure the prediction is accurate and current. This study aims to externally validate two prediction models, the Trauma and Injury Severity Score model developed using the Major Trauma Outcome Study in North America (MTOS-TRISS model), and the NTrD-TRISS model, which is a refined MTOS-TRISS model with coefficients derived from the Malaysian National Trauma Database (NTrD), by regarding mortality as the outcome measurement. METHOD: This retrospective study included patients with major trauma injuries reported to a trauma centre of Hospital Sultanah Aminah over a 6-year period from 2011 and 2017. Model validation was examined using the measures of discrimination and calibration. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC) and 95% confidence interval (CI). The Hosmer-Lemeshow (H-L) goodness-of-fit test was used to examine calibration capabilities. The predictive validity of both MTOS-TRISS and NTrD-TRISS models were further evaluated by incorporating parameters such as the New Injury Severity Scale and the Injury Severity Score. RESULTS: Total patients of 3788 (3434 blunt and 354 penetrating injuries) with average age of 37 years (standard deviation of 16 years) were included in this study. All MTOS-TRISS and NTrD-TRISS models examined in this study showed adequate discriminative ability with AUCs ranged from 0.86 to 0.89 for patients with blunt trauma mechanism and 0.89 to 0.99 for patients with penetrating trauma mechanism. The H-L goodness-of-fit test indicated the NTrD-TRISS model calibrated as good as the MTOS-TRISS model for patients with blunt trauma mechanism. CONCLUSION: For patients with blunt trauma mechanism, both the MTOS-TRISS and NTrD-TRISS models showed good discrimination and calibration performances. Discrimination performance for the NTrD-TRISS model was revealed to be as good as the MTOS-TRISS model specifically for patients with penetrating trauma mechanism. Overall, this validation study has ascertained the discrimination and calibration performances of the NTrD-TRISS model to be as good as the MTOS-TRISS model particularly for patients with blunt trauma mechanism.

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.

Analysis of preventable deaths and errors in trauma care in a Scandinavian trauma level-I centre.

BACKGROUND: The wide disparity in the methodology of preventable death analysis has created a lack of comparability among previous studies. The guidelines for the peer review (PR) procedure suggest the inclusion of risk-adjustment methods to identify patients to review, that is, exclude non-preventable deaths (probability of survival [Ps] < 25%) or focus on preventable deaths (Ps > 50%). We aimed to, through PR process, (1) identify preventable death and errors committed in a level-I trauma centre, and (2) explore the use of different risk-adjustment methods as a complement. METHODS: A multidisciplinary committee reviewed all trauma patients, which died a trauma-related death, within 30 days of admission to Karolinska University Hospital, Stockholm, in the period of 2012-2016. Ps was calculated according to TRISS and NORMIT and their accuracy where compared. RESULTS: Two hundred and ninety-eight deaths were identified and 252 were reviewed. The majority of deaths occurred between 1 and 7 days. Ten deaths (4.0%) were classified as preventable. Sixty-seven errors were identified in 53 (21.0%) deaths. The most common error was inappropriate treatment in all deaths (21 of 67) and in preventable deaths (5 of 13). Median Ps in non-preventable deaths was higher than the cut-off (<25%) and Ps-TRISS was almost twice as high as Ps-NORMIT (65% vs 33%, P < .001). Two clinically judged preventable deaths with Ps <25% would have been missed with both models. Median Ps in preventable deaths was above the cut-off (>50%) and higher with Ps-TRISS vs Ps-NORMIT (75% vs 58%, P < .001). Three and 4 clinically judged preventable deaths would have been missed, respectively, for TRISS and NORMIT, if using this cut-off. CONCLUSION: Preventable deaths were commonly caused by clinical judgment errors in the early phases but death occurred late. Ps calculated with NORMIT was more accurate than TRISS in predicting mortality, but both perform poorly in identifying preventable and non-preventable deaths when applying the cut-offs. PR of all trauma death is still the golden standard in preventability analysis.

Estimation of the Accuracy of Prognostic Scores for the Treatment of Children with Severe Trauma in a Specialized Trauma Hospital.

The aim of the present work was to study the validity and prognostic accuracy of scores for assessing the severity of the condition in children with severe trauma, located in the Department of Anesthesiology and Resuscitation in the Clinical and Research Institute of Urgent Pediatric Surgery and Trauma. The prospective study was conducted using clinical and physiological data collected at the admission and during the first 24 hours of hospitalization from 474 patients. The validity and prognostic accuracy of prognostic scores were assessed by determining their discrimination and calibration ability. A comparison of the discriminatory ability of scores was carried out by comparing the areas under the ROC curves with the z-criterion. Four prognostic scores were included into the study: PRISM, APACHE II, ISS-RTS-TRISS, which were used for calculating the severity of injury and for prognosis of death. Score PTS was used for evaluating the severity index only. Our results indicate that only score ISS-RTS-TRISS may be useful in practice (has excellent discrimination ability and significant calibration ability). The other lack either discrimination ability (PRISM) or calibration ability (PTS, APACHE II). The result of the study has shown that only one of the four prognostic scores, ISS-RTS-TRISS, can be successfully used in everyday practice in the department of anesthesiology and resuscitation in the specialized hospital of children's traumatology to assess the severity of the condition, with the possibility of predicting the likelihood of a lethal outcome.

Trauma Early Mortality Prediction Tool (TEMPT) for assessing 28-day mortality.

BACKGROUND: Prior mortality prediction models have incorporated severity of anatomic injury quantified by Abbreviated Injury Severity Score (AIS). Using a prospective cohort, a new score independent of AIS was developed using clinical and laboratory markers present on emergency department presentation to predict 28-day mortality. METHODS: All patients (n=1427) enrolled in an ongoing prospective cohort study were included. Demographic, laboratory, and clinical data were recorded on admission. True random number generator technique divided the cohort into derivation (n=707) and validation groups (n=720). Using Youden indices, threshold values were selected for each potential predictor in the derivation cohort. Logistic regression was used to identify independent predictors. Significant variables were equally weighted to create a new mortality prediction score, the Trauma Early Mortality Prediction Tool (TEMPT) score. Area under the curve (AUC) was tested in the validation group. Pairwise comparison of Trauma Injury Severity Score (TRISS), Revised Trauma Score, Glasgow Coma Scale, and Injury Severity Score were tested against the TEMPT score. RESULTS: There was no difference between baseline characteristics between derivation and validation groups. In multiple logistic regression, a model with presence of traumatic brain injury, increased age, elevated systolic blood pressure, decreased base excess, prolonged partial thromboplastin time, increased international normalized ratio (INR), and decreased temperature accurately predicted mortality at 28 days (AUC 0.93, 95% CI 0.90 to 0.96, P<0.001). In the validation cohort, this score, termed TEMPT, predicted 28-day mortality with an AUC 0.94 (95% CI 0.92 to 0.97). The TEMPT score preformed similarly to the revised TRISS score for severely injured patients and was highly predictive in those having mild to moderate injury. DISCUSSION: TEMPT is a simple AIS-independent mortality prediction tool applicable very early following injury. TEMPT provides an AIS-independent score that could be used for early identification of those at risk of doing poorly following even minor injury. LEVEL OF EVIDENCE: Level II.