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Predicting mortality with the international classification of disease injury severity score using survival risk ratios derived from an Indian trauma population: A cohort study.

ABSTRACT: BACKGROUND:Trauma is predicted to become the third leading cause of death in India by 2020, which indicate the need for urgent action. Trauma scores such as the international classification of diseases injury severity score (ICISS) have been used with great success in trauma research and in quality programmes to improve trauma care. To this date no valid trauma score has been developed for the Indian population. STUDY DESIGN:This retrospective cohort study used a dataset of 16047 trauma-patients from four public university hospitals in urban India, which was divided into derivation and validation subsets. All injuries in the dataset were assigned an international classification of disease (ICD) code. Survival Risk Ratios (SRRs), for mortality within 24 hours and 30 days were then calculated for each ICD-code and used to calculate the corresponding ICISS. Score performance was measured using discrimination by calculating the area under the receiver operating characteristics curve (AUROCC) and calibration by calculating the calibration slope and intercept to plot a calibration curve. RESULTS:Predictions of 30-day mortality showed an AUROCC of 0.618, calibration slope of 0.269 and calibration intercept of 0.071. Estimates of 24-hour mortality consistently showed low AUROCCs and negative calibration slopes. CONCLUSIONS:We attempted to derive and validate a version of the ICISS using SRRs calculated from an Indian population. However, the developed ICISS-scores overestimate mortality and implementing these scores in clinical or policy contexts is not recommended. This study, as well as previous reports, suggest that other scoring systems might be better suited for India and other Low- and middle-income countries until more data are available.

SUBMITTER: Attergrim J

PROVIDER: S-EPMC6021077 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Predicting mortality with the international classification of disease injury severity score using survival risk ratios derived from an Indian trauma population: A cohort study.

Attergrim Jonatan J Sterner Mattias M Claeson Alice A Dharap Satish S Gupta Amit A Khajanchi Monty M Kumar Vineet V Gerdin Wärnberg Martin M

PloS one 20180627 6

<h4>Background</h4>Trauma is predicted to become the third leading cause of death in India by 2020, which indicate the need for urgent action. Trauma scores such as the international classification of diseases injury severity score (ICISS) have been used with great success in trauma research and in quality programmes to improve trauma care. To this date no valid trauma score has been developed for the Indian population.<h4>Study design</h4>This retrospective cohort study used a dataset of 16047 ...[more]

PMID: 29949624

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Project description:BackgroundPrecise models are necessary to estimate mortality risk following traumatic injury to inform clinical decision making or quantify hospital performance. The Trauma and Injury Severity Score (TRISS) has been the historical gold standard in survival prediction but its limitations are well-characterized. The present study used International Classification of Diseases 10th Revision (ICD-10) injury codes with machine learning approaches to develop models whose performance was compared to that of TRISS.MethodsThe 2015-2017 National Trauma Data Bank was used to identify patients following trauma-related admission. Injury codes from ICD-10 were grouped by clinical relevance into 1,495 variables. The TRISS score, which comprises the Injury Severity Score, age, mechanism (blunt vs penetrating) as well as highest 24-hour values for systolic blood pressure (SBP), respiratory rate (RR) and Glasgow Coma Scale (GCS) was calculated for each patient. A base eXtreme gradient boosting model (XGBoost), a machine learning technique, was developed using injury variables as well as age, SBP, RR, mechanism and GCS. Prediction of in-hospital survival and other in-hospital complications were compared between both models using receiver operating characteristic (ROC) and reliability plots. A complete XGBoost model, containing injury variables, vitals, demographic information and comorbidities, was additionally developed.ResultsOf 1,380,740 patients, 1,338,417 (96.9%) survived to discharge. Compared to survivors, those who died were older and had a greater prevalence of penetrating injuries (18.0% vs 9.44%). The base XGBoost model demonstrated a greater receiver-operating characteristic (ROC) than TRISS (0.950 vs 0.907) which persisted across sub-populations and secondary endpoints. Furthermore, it exhibited high calibration across all risk levels (R2 = 0.998 vs 0.816). The complete XGBoost model had an exceptional ROC of 0.960.ConclusionsWe report improved performance of machine learning models over TRISS. Our model may improve stratification of injury severity in clinical and quality improvement settings.

Project description:BACKGROUND:Children suffering nonaccidental trauma (NAT) are at high risk of death. It is unclear whether markers of injury severity for trauma center/system benchmarking such as Injury Severity Score (ISS) adequately characterize this. Our objective was to evaluate mortality prediction of ISS in children with NAT compared with accidental trauma (AT). METHODS:Pediatric patients younger than 16 years from the Pennsylvania state trauma registry 2000 to 2013 were included. Logistic regression predicted mortality from ISS for NAT and AT patients. Multilevel logistic regression determined the association between mortality and ISS while adjusting for age, vital signs, and injury pattern in NAT and AT patients. Similar models were performed for head Abbreviated Injury Scale (AIS). Sensitivity analysis examined impaired functional independence at discharge as an alternate outcome. RESULTS:Fifty thousand five hundred seventy-nine patients were included with 1,866 (3.7%) NAT patients. Nonaccidental trauma patients had a similar rate of mortality at an ISS of 13 as an ISS of 25 for AT patients. Nonaccidental trauma patients also have higher mortality for a given head AIS level (range, 1.2-fold to 5.9-fold higher). Injury Severity Score was a significantly greater predictor of mortality in AT patients (adjusted odds rations [AOR], 1.14; 95% confidence interval [CI], 1.13-1.15; p < 0.01) than NAT patients (AOR, 1.09; 95% CI, 1.07-1.12; p < 0.01) per 1-point ISS increase, while head injury was a significantly greater predictor of mortality in NAT patients (AOR, 3.48; 95% CI, 1.54-8.32; p < 0.01) than AT patients (AOR, 1.21; 95% CI, 0.95-1.45; p = 0.12). Nonaccidental trauma patients had a higher rate of impaired functional independence at any given ISS or head AIS level than AT patients. CONCLUSION:Nonaccidental trauma patients have higher mortality and impaired function at a given ISS/head AIS than AT patients. Conventional ISS thresholds may underestimate risk and head injury is a more important predictor of mortality in the NAT population. These findings should be considered in system performance improvement and benchmarking efforts that rely on ISS for injury characterization. LEVEL OF EVIDENCE:Epidemiologic study, level III.

Project description:BackgroundNumerous trauma scoring systems have been developed in an attempt to accurately and efficiently predict the prognosis of emergent trauma cases. However, it has been questioned as to whether the accuracy and pragmatism of such systems still hold in lower-resource settings that exist in many hospitals in lower- and middle-income countries (LMICs). In this study, it was hypothesized that the physiologically-based Revised Trauma Score (RTS), Mechanism/Glasgow Coma Scale/Age/Pressure (MGAP) score, and Glasgow Coma Scale/Age/Pressure (GAP) score would be effective at predicting mortality outcomes using clinical data at presentation in a representative LMIC hospital in Upper Egypt.MethodsThis was a retrospective analysis of the medical records of trauma patients at Beni-Suef University Hospital. Medical records of all trauma patients admitted to the hospital over the 8-month period from January to August 2016 were reviewed. For each case, the RTS, MGAP, and GAP scores were calculated using clinical data at presentation, and mortality prediction was correlated to the actual in-hospital outcome.ResultsThe Area Under the Receiver Operating Characteristic (AUROC) was calculated to be 0.879, 0.890, and 0.881 for the MGAP, GAP, and RTS respectively, with all three scores showing good discriminatory ability. With regards to prevalence-dependent statistics, all three scores demonstrated efficacy in ruling out mortality upon presentation with negative predictive values > 95%, while the MGAP score best captured the mortality subgroup with a sensitivity of 94%. Adjustment of cutoff scores showed a steep trade-off between optimizing the positive predictive values versus the sensitivities.ConclusionThe RTS, MGAP, and GAP all showed good discriminatory capabilities per AUROC. Given the relative simplicity and potentially added clinical benefit in capturing critically ill patients, the MGAP score should be further studied for stratifying risk of incoming trauma patients to the emergency department, allowing for more efficacious triage of patients in lower-resource healthcare settings.

Project description:BackgroundWith an increasing number of motor vehicle crashes, there is an urgent need in emergency departments (EDs) to assess patients with multiple trauma quickly, easily, and reliably. Trauma severity can range from a minor to major threats to life or bodily function. In-hospital mortality and trauma severity prediction in such cases is crucial in the ED for the management of multiple trauma and improvement of the outcome of these patients. Previous studies have examined the performance of Modified Early Warning Score (MEWS) or Circulation, Respiration, Abdomen, Motor, and Speech (CRAMS) score based solely on mortality prediction or injury severity prediction. However, to the best of our knowledge, the performances of both scoring systems on in-hospital mortality and trauma severity prediction have not been compared previously. This retrospective study evaluated the value of MEWS and CRAMS score to predict in-hospital mortality and trauma severity in patients presenting to the ED with multiple traumatic injuries.MethodsAll study subjects were multiple trauma patients. Medical data of 1,127 patients were analyzed between January 2014 and April 2018. The MEWS and CRAMS score were calculated, and logistic regression and receiver operating characteristic curve analysis were conducted to investigate their performances regarding in-hospital mortality and trauma severity prediction.ResultsFor in-hospital mortality prediction, the areas under the receiver operating characteristic curve (AUROCs) for MEWS and CRAMS score were 0.90 and 0.91, respectively, indicating that both of them were good in-hospital mortality predictors. Further, our study indicated that the CRAMS score performed better in trauma severity prediction, with an AUROC value of 0.84, which was higher than that of MEWS (AUROC = 0.77). For trauma severity prediction, the optimal cut-off value for MEWS was 2, while that of the CRAMS score was 8.ConclusionsWe found that both MEWS and CRAMS score can be used as predictors for trauma severity and in-hospital mortality for multiple trauma patients, but that CRAMS score was superior to MEWS for trauma severity prediction. CRAMS score should be prioritized in the prediction of trauma severity due to its excellence as a multiple trauma triage tool and potential contribution to rapid emergency rescue decisions.

Project description:BackgroundIn-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.MethodsData 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.ResultsIn 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.ConclusionsThe 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.

Dataset Information

Predicting mortality with the international classification of disease injury severity score using survival risk ratios derived from an Indian trauma population: A cohort study.

Publications

Predicting mortality with the international classification of disease injury severity score using survival risk ratios derived from an Indian trauma population: A cohort study.

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