MGAP Score Research Articles

Machine learning classifier is associated with mortality in interstitial lung disease: a retrospective validation study leveraging registry data

BackgroundMortality prediction in interstitial lung disease (ILD) poses a significant challenge to clinicians due to heterogeneity across disease subtypes. Currently, forced vital capacity (FVC) and Gender, Age, and Physiology (GAP) score are the two most utilized metrics in prognostication. Recently, a machine learning classifier system, Fibresolve, designed to identify a variety of computed tomography (CT) patterns associated with idiopathic pulmonary fibrosis (IPF), was demonstrated to have a significant association with mortality across multiple subtypes of ILD. The purpose of this follow-up study was to retrospectively validate these findings in a large, external cohort of patients with ILD.MethodsIn this multi-center validation study, Fibresolve was applied to chest CT scans of patients with confirmed ILD that had available follow-up data. Fibresolve scores categorized by tertile were analyzed using Cox regression analysis adjusted for tobacco use and modified GAP (mGAP) score.ResultsOf 643 patients included, 446 (69.3%) died over a median follow-up time of 144 [1-821] weeks. The median [range] mGAP score was 5 [3–7]. In multivariable analysis, Fibresolve score categorized by tertile was significantly associated with mortality (Tertile 2 HR 1.47, 95% CI 0.82–2.37, p = 0.11; Tertile 3 HR 3.12, 95% CI 1.98–4.90, p < 0.001). Subgroup analyses revealed significant associations amongst those with non-IPF ILDs (Tertile 2 HR 1.95, 95% CI 1.28–2.97, Tertile 3 HR 4.66, 95% CI 2.94–7.38) and severe disease, defined by a FVC ≤ 75% (Tertile 2 HR 2.29, 95% CI 1.43–3.67, Tertile 3 HR 4.80, 95% CI 2.93–7.86).ConclusionsFibresolve is independently associated with mortality in ILD, particularly amongst patients with non-IPF ILDs and in those with severe disease.

Accuracy and external validation of the modified rapid emergency medicine score in road traffic injuries in a Bangkok level I trauma center

BackgroundTrauma is a significant public health problem. Therefore, many injury scores have been created to predict mortality and triage patients. This study aims to validate the modified Rapid Emergency Medicine Score (mREMS) for in-hospital mortality prediction in road traffic injuries and compare the mREMS with the revised trauma score (RTS) and the mechanisms, Glasgow Coma Scale (GCS), age, and arterial pressure (MGAP) score. MethodsData were retrospectively collected from the Vajira Hospital (1,033 cases). The mREMS was calculated from six predictors: age, systolic blood pressure, heart rate, respiratory rate, pulse oxygen saturation, and GCS. The receiver operating characteristic curve was plotted, and the area under the curve (AUC) was calculated. The AUC and 95% confidence interval (CI) of the mREMS were compared with the AUCs of other scores. Model calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test. ResultsThe mREMS was significantly better than the RTS at predicting death in road traffic injury patients [mREMS: AUCs, 0.909 (95% CI, 0.866–0.951); RTS: AUCs, 0.859 (95% CI, 0.791–0.927] (p = 0.023). However, the difference between the AUCs of the mREMS and MGAP score was not statistically significant (p = 0.150). The mREMS’ calibration performance was also satisfactory in this dataset based on the Hosmer–Lemeshow goodness-of-fit test (p = 0.277). ConclusionIn the road traffic injury population, the mREMS is an excellent predictor of in-hospital mortality. These results can be applied to improve triage. However, this score should be further validated in other trauma centers before nationwide implementation.

Serum levels of copeptin predict adverse outcomes and improve risk prediction of TRISS and MGAP scores in patients with multiple trauma: A single-center prospective cohort study.

Multiple trauma deserves early prognostication and stratification. Copeptin, a precursor of vasopressin, is produced in response to stress. We examined the association between serum levels of copeptin and mortality risk in patients with multiple trauma. We aimed to also enhance the previously established Trauma-Related Injury Severity Score (TRISS) and Mechanism, GCS, Age, and Arterial Pressure (MGAP) score with the additional consideration of copeptin levels. This single-center prospective cohort study enrolled patients who presented to the emergency department with potential major injuries. The serum levels of copeptin were measured, and the correlation to clinical severity in terms of 30-day mortality and requirement of intensive care management was analyzed. By combining copeptin levels with TRISS or MGAP, comparison between performance of the original models with the copeptin-enhanced models was performed via discrimination, calibration, and reclassification analyses. There was a significant increase in copeptin levels in patients who died within 30 days (median 644.4 pg/L, interquartile range [472.5, 785.9]) or were admitted to intensive care units (233.8 pg/L, [105.7, 366.4]), compared with those who survived (37.49 pg/L, [17.88, 77.68]). Adding the natural log of copeptin levels to the established TRISS and MGAP models improved the AUC of TRISS from 0.89 to 0.96, and that of MGAP from 0.82 to 0.95. Both calibrations as measured by Brier's scores and reclassification as measured by net reclassification improvement or integrated discrimination improvement demonstrated significant improvements. A Web-based calculator was built to generate predicted mortality rates of various models for convenient clinical use. Admission serum copeptin levels were correlated with clinical severity in multiple trauma. Coupling copeptin with preexisting trauma severity scores improved prediction accuracy. Copeptin shows promise as a novel biomarker for the prediction of trauma outcome. Prognostic and Epidemiologic; Level III.

Frequency and Predictors of Trauma Transfer Futility to a Rural Level I Trauma Center

IntroductionTransfer of trauma patients whose injuries are deemed unsurvivable, often results in early death or transition to comfort care and could be considered misuse of health care resources. This is particularly true where tertiary care resources are limited. Identifying riskfactors for and predicting futile transfers could reduce this impact and help to optimize triage and management. MethodsA retrospective study of interfacility trauma transfers to a single rural Level I rauma center from 2014 to 2019. Futility was defined as death, hospice, or declaration of comfort measures within 48 h of transfer without procedural or radiographic intervention at the accepting center. Multiple logistic regressions identified independent predictors of futile transfers. The predictive power of Mechanism,Glasgow coma scale, Age, and Arterial pressure (MGAP), an injury severity score based on Mechanism, Glasgow coma scale, Age, and systolic blood Pressure, were evaluated. ResultsOf the 3368 trauma transfers, 37 (1.1%) met criteria as futile. Futile transfers occurred among patients who were significantly older with falls as the most common mechanism. Age, Glasgow coma scale, systolic blood Pressure and Injury Severity Score were significant (P < 0.05) independent predictors of futile transfer. MGAP had a high predictive power area under the receiver operating characteristic (AUROC 0.864, 95% confidence interval 0.803-0.925) for futility. ConclusionsA small proportion (1.1%) of transfers to a rural Level I trauma center met criteria for futility. Predictive tools, such as MGAP scoring, can provide objective criteria for evaluation of transfer necessity and prompt care pathways that involve pre-transfer communications, telemedicine, and/or patient centered goals of care discussions. Such tools could be used in conjunction with a more granular assessment regarding potential operational barriers to reduce futile transfers and to enhance optimization of resource utilization in low-resource service areas.

Interest of the MGAP score on in-hospital trauma patients: Comparison with TRISS, ISS and NISS scores

Trauma scoring systems were created to predict mortality and enhance triage capabilities. However, efficacy of scoring systems to predict mortality and accuracy of originally reported severity thresholds remains uncertain. A single-center, retrospective study was conducted at University of Virginia (UVA), an American College of Surgeons verified Level I trauma center. We compared four scoring systems: MGAP (Mechanism, Glasgow Coma Scale, Age, and arterial pressure), Injury Severity Score (ISS), New Injury Severity Score (NISS), and Trauma Related Injury Severity Score (TRISS) to predict in-hospital mortality and disposition from the emergency department to higher acuity level of care including mortality (i.e. operating room, intensive care unit, morgue) versus standard floor admission using area under the curve (AUC) for receiver operating characteristic analysis. Second, we examined sensitivity of these scores at standard thresholds to determine if adjustments were needed to minimize under-triage (sensitivity ≥95%). TRISS was the best predictor of mortality in a cohort of n=16,265 with AUC of 0.920 (95% CI: 0.911-0.929, p<0.0001), followed by MGAP with AUC of 0.900 (95% CI: 0.889-0.911, p<0.0001), and finally ISS and NISS (0.830 (95% CI: 0.814-0.847) and 0.827 (95% CI: 0.809-0.844) respectively). NISS was the best predictor of high acuity disposition with an AUC of 0.729 (95% CI: 0.721-0.736, p<0.0001), followed by ISS with AUC of 0.714 (95% CI: 0.707-0.722, p<0.0001), and finally TRISS and MGAP (0.673 (95% CI: 0.665-0.682) and 0.613 (95% CI: 0.604-0.621) respectively (p<0.0001). At historic thresholds, no scoring system displayed adequate sensitivity to predict mortality, with values ranging from 73% for ISS to 80% for NISS. In conclusion, in the reported study cohort, TRISS was the best predictor of mortality while NISS was the best predictor of high acuity disposition. We also stress updating scoring system thresholds to achieve ideal sensitivity, and investigating how scoring systems derived to predict mortality perform when predicting indicators of morbidity such as disposition from the emergency department.

A retrospective study on evaluating GAP, MGAP, RTS and ISS trauma scoring system for the prediction of mortality among multiple trauma patients

ObjectivesPredicting the outcome of trauma helps clinician to prioritize patients and provide timely and effective treatment. Several scoring systems are implemented to predict prognosis and mortality among these patients. Our study aims to use four scoring systems to predict mortality among multiple trauma patients. MethodsIn retrospective descriptive study, the data was collected from records of (XXX) of multiple trauma patients referred to the hospital from June 2019–January 2020. The patients were scored using four scoring systems: MGAP (mechanism, Glasgow coma scale, age, and arterial pressure), GAP (Glasgow coma scale, age, and arterial pressure), ISS (injury severity score) and RTS (revised trauma score). ResultsThe mean age of the patients was 37.4 ± 4.2 years and of 112 patients, 92 patients (82.1%) were males. Sensitivity of GAP, RTS and ISS was 100% in predicting mortality where MGAP had highest specificity, 97.2%. All four scoring systems significantly predicted mortality, p < 0.001, respectively and the highest area under the curve was for RTS criteria, 0.969. ConclusionMGAP, GAP, RTS and ISS were all effective in predicting mortality among multiple trauma patients whereas MGAP had both, highest sensitivity and specificity. Scoring trauma for mortality can be achieved by using any of the systems, provided the information required for score can be obtained.

Mortality of Traumatic Injuries in Traumatological Emergencies of the Yalgado Ouedraogo University Hospital Center in Ouagadougou (Burkina Faso)

Background: Serious trauma is the main cause of death for people under 40 years old. According to the WHO, in 2002, nearly 1.2 million people died worldwide from road traffic injuries. The vast majority (90%) are from low and middle-income countries. The diagnostic performance, the quality of the treatments offered, and the optimization of trauma care channels, make it possible to improve the management of serious trauma. This is not always the case in our context of a country with limited resources. Objectives: The objective is o describe the epidemiological, diagnostic and therapeutic aspects of premortal death in patients with traumatic injuries. Methods: This was a descriptive study with retrospective collection in the trauma emergency department of Yalgado Ouedraogo Teaching Hospital (Ouagadougou, Burkina Faso). All patients who died on the ward while in the hospital or on arrival were included. Results: 192 deaths were listed. The annual mortality was 1.6%, the sex ratio was 6.1 and the average age was 36 years. Road traffic accidents were the main etiology: 163 deaths (85%). Head injuries were the most common injuries: 45.3% of deceased patients had a Glasgow score ≤ 8. There was no pre-hospital care in 63% of the patients who died. The first six hours, on-call periods and the weekend were correlated with the death rate. Discussion: the high mortality in road traffic accident injuries in Africa is due to the weakness of pre-hospital care, the non-medicalization of the transport of the injured and also the non-wearing of helmets by motorcyclists. The MGAP (Mechanism, Glasgow coma scale, Age, arterial Pressure) score is reliable in predicting the mortality of serious injuries.

CT quantification of the heterogeneity of fibrosis boundaries in idiopathic pulmonary fibrosis.

ObjectivesTo quantify the heterogeneity of fibrosis boundaries in idiopathic pulmonary fibrosis (IPF) using the Gaussian curvature analysis for evaluating disease severity and predicting survival.MethodsWe retrospectively included 104 IPF patients and 52 controls who underwent baseline chest CT scans. Normal lungs below − 500 HU were segmented, and the boundary was three-dimensionally reconstructed using in-house software. Gaussian curvature analysis provided histogram features on the heterogeneity of the fibrosis boundary. We analyzed the correlations between histogram features and the gender-age-physiology (GAP) and CT fibrosis scores. We built a regression model to predict diffusing capacity of carbon monoxide (DLCO) using the histogram features and calculated the modified GAP (mGAP) score by replacing DLCO with the predicted DLCO. The performances of the GAP, CT-GAP, and mGAP scores were compared using 100 repeated random-split sets.ResultsPatients with moderate-to-severe IPF had more numerous Gaussian curvatures at the fibrosis boundary, lower uniformity, and lower 10th to 30th percentiles of Gaussian curvature than controls or patients with mild IPF (all p < 0.0033). The 20th percentile was most significantly correlated with the GAP score (r = − 0.357; p < 0.001) and the CT fibrosis score (r = − 0.343; p = 0.001). More numerous Gaussian curvatures, higher entropy, lower uniformity, and 10th to 30th percentiles (p < 0.001–0.041) were associated with mortality. The mGAP score was comparable to the GAP and CT-GAP scores for survival prediction (mean C-indices, 0.76 vs. 0.79 vs. 0.77, respectively).ConclusionsGaussian curvatures of fibrosis boundaries became more heterogeneous as the disease progressed, and heterogeneity was negatively associated with survival in IPF.Key Points • Gaussian curvature of the fibrotic lung boundary was more heterogeneous in patients with moderate-to-severe IPF than those with mild IPF or normal controls. • The 20th percentile of the Gaussian curvature of the fibrosis boundary was linearly correlated with the GAP score and the CT fibrosis score. • A modified GAP score that replaced the diffusing capacity of carbon monoxide with a composite measure using histogram features of the Gaussian curvature of the fibrosis boundary showed a comparable ability to predict survival to both the GAP and the CT-GAP score. Supplementary InformationThe online version contains supplementary material available at 10.1007/s00330-020-07594-y.

Derivation and validation of an easy-to-compute trauma score that improves prognostication of mortality or the Trauma Rating Index in Age, Glasgow Coma Scale, Respiratory rate and Systolic blood pressure (TRIAGES) score

BackgroundMultiple trauma scores have been developed and validated, including the Revised Trauma Score (RTS) and the Mechanism, Glasgow Coma Scale, Age, and Arterial Pressure (MGAP) score. However, these scores are complex to calculate or have low prognostic abilities for trauma mortality. Therefore, we aimed to develop and validate a trauma score that is easier to calculate and more accurate than the RTS and the MGAP score.MethodsThe study was a retrospective prognostic study. Data from patients registered in the Japan Trauma Databank (JTDB) were dichotomized into derivation and validation cohorts. Patients’ data from the Clinical Randomisation of an Antifibrinolytic in Significant Haemorrhage-2 (CRASH-2) trial were assigned to another validation cohort. We obtained age and physiological variables at baseline, created ordinal variables from continuous variables, and defined integer weighting coefficients. Score performance to predict all-cause in-hospital death was assessed using the area under the curve in receiver operating characteristics (AUROC) analyses.ResultsBased on the JTDB derivation cohort (n = 99,867 with 12.5% mortality), the novel score ranged from 0 to 14 points, including 0–2 points for age, 0–6 points for the Glasgow Coma Scale, 0–4 points for systolic blood pressure, and 0–2 points for respiratory rate. The AUROC of the novel score was 0.932 for the JTDB validation cohort (n = 76,762 with 10.1% mortality) and 0.814 for the CRASH-2 cohort (n = 19,740 with 14.6% mortality), which was superior to RTS (0.907 and 0.808, respectively) and MGAP score (0.918 and 0.774, respectively) results.ConclusionsWe report an easy-to-use trauma score with better prognostication ability for in-hospital mortality compared to the RTS and MGAP score. Further studies to test clinical applicability of the novel score are warranted.

Predictores de mortalidad Predictores de mortalidad en atención pre hospitalaria para accidentes de tránsito

Introducción: En Tungurahua se registraron 93 fallecimientos 4.73% del total a nivel nacional durante el año 2016, debido a accidentes de tránsito, siendo esta una de las primeras causas de muerte en Ecuador según el Instituto Nacional de Estadísticas y Censos.&#x0D; Objetivo: Correlacionar las escalas M.G.A.P, G.A.P y R.T.S como predictores de mortalidad en los pacientes con trauma.&#x0D; Material y Métodos: Es un estudio descriptivo retrospectivo, tomando como datos los registros médicos de los pacientes atendidos por traumatismo por accidente de tránsito; se aplicó las escalas de pronóstico de mortalidad para trauma: MGAP, GAP y RTS y se midió la fuerza de la asociación con la mortalidad.&#x0D; Resultados: 1105 pacientes fueron incluidos en el estudio. En riesgo bajo, la escala de RTS presentó un 0,24%, MGAP 0,23% y GAP 0,22%. En el riesgo medio RTS mostró 29,63%, MGAP 8% y GAP 28,6%. Para el riesgo alto RTS 66,7%, MGAP 61,5% y GAP 60,0%. El análisis estadístico de dichos resultados demostró que no existen diferencias significativas en la valoración de mortalidad entre las tres escalas.&#x0D; Conclusión: La mortalidad estimada en el riesgo bajo, medio y alto, fue similar en las tres escalas, no se encontró diferencia estadísticamente significativa entre los sistemas de puntuación MGAP, GAP y RTS.

Development and validation of a new simplified anatomic trauma mortality score

BackgroundMost trauma mortality prediction scores are complex in nature. GAP (Glasgow Coma Scale, Age, Systolic blood pressure) and mGAP (mechanism, Glasgow Coma Scale, Age, Systolic blood pressure) scores are relatively simple scoring tools. However, these scores were not validated in low and middle income countries including Malaysia and its accuracies are influenced by the fluctuating physiologic parameters. This study aims to develop a relevant simplified anatomic trauma scoring system for the local trauma patients in Malaysia. MethodA total of 3825 trauma patients from 2011 to 2016 were extracted from the Hospital Sultanah Aminah Trauma Surgery Registry. Patients were split into a development sample (n = 2683) and a validation sample (n = 1142). Univariate analysis is applied to identify significant anatomic predictors. These predictors were further analyzed using multivariable logistic regression to develop the new score and compared to existing score systems. The quality of prediction was determined regarding discrimination using sensitivity, specificity and receiver operating characteristic [ROC] curve. ResultsExisting simplified score systems (GAP & mGAP) revealed areas under the ROC curve of 0.825 and 0.806. The newly developed HeCLLiP (Head, cervical spine, lung, liver, pelvic fracture) score combines only five anatomic components: injury involving head, cervical spine, lung, liver and pelvic bone. The probabilities of mortality can be estimated by charting the total score points onto a graph chart or using the cut-off value of (>2) with a sensitivity of 79.2 and specificity of 70.6% on the validation dataset. The HeCLLiP score achieved comparable values of 0.802 for the area under the ROC curve in validation samples. ConclusionHeCLLiP Score is a simplified anatomic score suited to the local Malaysian population with a good predictive ability for trauma mortality.

Evaluation and Comparison of Different Prehospital Triage Scores of Trauma Patients on In-Hospital Mortality

Introduction: Several prehospital major trauma patient triage scores have been developed, the triage revised trauma score (T-RTS), Vittel criteria, Mechanism/Glasgow Coma Scale/Age/Systolic blood pressure score (MGAP), and the new trauma score (NTS). These scoring schemes allow a rapid and accurate prognostic assessment of the severity of potential lesions. The aim of our study was to compare these scores with in-hospital mortality predictions in a cohort of consecutive trauma patients admitted in a Level 1 trauma center. Materials: Between 2013 and 2016, 1,112 patients were admitted to the "major trauma" spinneret of a Level 1 trauma center in the south of France. All prehospital data needed to calculate the T-RTS, Vittel criteria, the MGAP score, and the NTS were collected. The main evaluation criterion was in-hospital mortality at 30 days for all causes. The predictive performances of these scores were evaluated and compared with each other using the analysis of the receiver operating curves. Results: A total of 1,001 patients were included in the analysis, 238 (24%) females, aged 43 ± 19 years with ISS 15 ± 13. The area under the curve was for each score: T-RTS, AUC = 0.84, [0.82–0.87]; Vittel criteria, AUC = 0.87 [0.85–0.89]; MGAP score, AUC = 0.91 [0.89–0.92] and NTS, AUC = 0.90 [0.88–0.92]. By comparing the ROC curves of these scores, the MGAP and NTS scores were statistically higher than the T-RTS. With the current thresholds, the sensitivity, specificity, positive and negative predictive values of these scores were 91%, 35%, 10%, 98% for T-RTS, 100%, 2%, 8%, 100% for Vittel criteria, 91%, 71%, 24%, 99% for MGAP score, 82%, 86%, 33%, 98% for NTS. Only Vittel's criteria allowed undertriage below 5% as recommended by the American College of Surgeons Committee on Trauma (ACSCOT). Conclusion: The comparison of these different triage scores concluded with a superiority of the MGAP and NTS scores compared with the T-RTS. Including the calculation of MGAP or NTS scores with the Vittel criteria would reduce the risk of overtriage in the Level 1 trauma centers by further directing patients at low risk of death to a lower-level trauma facility.

Correlation Between the Revised Trauma Score and Injury Severity Score: Implications for Prehospital Trauma Triage

Objective: Prehospital triage of the seriously injured patient is fraught with challenges, and trauma scoring systems in current triage guidelines warrant further investigation. The primary objective of this study was to assess the correlation of the physiologically based Revised Trauma Score (RTS) and MGAP score (mechanism of injury, Glasgow Coma Scale, age, blood pressure) with the anatomically based Injury Severity Score (ISS). The secondary objectives for this study were to compare the accuracy of the MGAP score and the RTS for the prediction of in-hospital mortality for trauma patients. Methods: This study was a retrospective cohort including 10 years of patient data in a large single-center trauma registry at a primary adult resource center (Level I) for trauma patients. Participants included adults (age ≥18 years). The primary outcome measure was injury severity (measured by ISS) and a secondary analysis compared the RTS and MGAP for the prediction of patient mortality. Descriptive statistics were used to describe the cohort and correlation methods were employed. Each score’s accuracy for the prediction of mortality was calculated using the area under receiver operating characteristic (AUROC) curves. Results: In total, 43,082 trauma patient records were reviewed; 32,798 patients had complete RTS data available and 32,371 patients had complete data available for MGAP analyses. The correlation between scene RTS and ISS was poor (−.29), as was the correlation between MGAP and ISS (−.28). For the prediction of mortality, admission MGAP demonstrated the highest sensitivity and specificity for mortality (AUROC 0.96; 95% CI, 0.95–0.96). Conclusions: While elements of the RTS remain the first criterion recommended to quantify the totality of physiological injury severity, the composite RTS score derived from this system correlates poorly with actual anatomical injury severity. The MGAP scoring system demonstrated higher sensitivity and specificity for mortality but was not superior to the RTS for predicting anatomical injury severity. In the future development of national and international field triage guidelines for trauma patients, the findings from this study may be considered in order to improve the accuracy of prehospital triage. The findings in this analysis complement a growing body of evidence that suggests that MGAP may be a superior and more easily calculable prehospital scoring system for the prediction of mortality in trauma patients.

Prognostic value of 2-hour lactate level and lactate clearance for 30-day mortality and comparison with trauma scores in multi-trauma patients.

Objective:Trauma scores are prone to misreading. Therefore, a readily available, objective way to estimate the mortality of the trauma patients is needed. We aimed to evaluate the prognostic utility of lactate levels, and clearance for 30-days mortality, and compare with the physiological trauma scores.Methods:All adult trauma patients (two hundred) admitted to ED were enrolled. Initial and 2-hour serum lactate levels were measured and components of GAP, MGAP, RTS, VIEWS and VIEWS-L trauma scores were calculated.Results:Final study population was 200 patients with a median age of 33 years. Mortality was 7/200 (3.5%) in 30-days. Both initial (2.3 vs. 7.7 mmol/L) and 2h-lactate (1.7 vs. 8.4 mmol/L) levels were significantly lower, and lactate clearance was significantly higher (23.8% vs. -12.0%) in survivors. Also, the change in the lactate level from 0h to 2h (2.3 vs. 1.7mmol/L) was significant in survivors, contrary to non-survivors (7.7 vs. 8.4mmol/L). VIEWS-L, VIEWS, two hour-lactate level and EMTRAS showed high specificity at the 100% sensitivity cut-offs, therefore, were the most valuable prognostic parameters in this study.Conclusion:Calculation of 2h-lactate clearance and evaluation of a 2h-lactate level may not be needed to predict long-term mortality if the initial lactate level is below 2.8mmol/L.

Evaluation of MGAP and GAP Trauma Scores to Predict Prognosis of Multiple-trauma Patients

Background: Early diagnosis of major trauma and rapid transmission of patients to appropriate therapeutic centers have always been issues of concern. Several prognostic models for rapid clinical decision-making and estimating the mortality rate of multipletrauma patients have been presented previously. Objectives: The current study aimed to evaluate the GCS, Age, and systolic blood pressure (GAP) and mechanism, GCS, age, and systolic blood pressure (MGAP) scores of patients with multiple trauma and determine the cut-off points of these scores for predicting mortality rates. Patients and Methods: This cross-sectional descriptive study was included 374 patients with multiple trauma. Data regarding age, mechanism of injury, systolic blood pressure, and Glasgow coma score were collected. GAP and MGAP scores were calculated, and their relationship with the need for surgery, mortality in the ED, and mortality in the hospital ward were investigated. Results: Mean± SD of the MGAP and GAP scores of patients were 24.36± 5.04 and 20.53± 5.08, respectively. For no need for surgery, survival in the ED, and survival in the hospital ward, areas under the Roc curves for MGAP were 0.75, 0.93, and 0.99, respectively, and for GAP, were 0.74, 0.80, and 0.99. Conclusions: MGAP and GAP scores were used to accurately predict outcomes for patients with multiple traumas. We recommend these simple triage tools for use by emergency medical technicians in pre-hospital settings to refer patients to appropriate trauma centers

Tree-based algorithm for prehospital triage of polytrauma patients

BackgroundThere is a need for better allocation of medical resources in polytrauma, by optimizing both the over and undertriage rates. The goal of this study is to provide a new working definition for polytrauma based on the prediction of the need for specialized trauma care. MethodsThis is a prospective, observational study, performed in a specialized trauma center in Paris. All consecutive patients admitted for a trauma at a major trauma center in Paris were included in the study. The primary outcome was the need for specialized trauma care as defined by the North American consensus. The explanatory variables included basic variables collected on scene. The modeling approach relied on recursive partitioning based decision trees. Its prediction performance was evaluated both internally and externally on a validation cohort, and compared to the MGAP (Mechanism, Glasgow coma scale, Age and Arterial pressure) score. Measurements and main results1160 patients were included in the analysis over a 3-year period (2012–2014), out of which 41% needed specialized trauma care as defined by the recent US guidelines. The decision tree outperformed the MGAP and reached an area under the receiver operating characteristic curve of 0.82 [0.79–0.84]. This optimal decision rule was associated with a sensitivity of 0.94 [0.92–0.96], a specificity of 0.48 [0.44–0.52]. A conservative decision rule (refer to a trauma center all patient with a predicted probability ≥0.34) would result in an undertriage rate of 5.7% and an overtriage of 52.3% (respectively 7% and 64% in the validation cohort). ConclusionsOur tree-based decision algorithm is a user-friendly and reliable alternative to the preexisting scores, which offers good performance to predict the need for specialized trauma care.

Prediction of intra-hospital mortality after severe trauma: which pre-hospital score is the most accurate?

Computing trauma scores in the field allows immediate severity assessment for appropriate triage. Two pre-hospital scores can be useful in this context: the Triage-Revised Trauma Score (T-RTS) and the Mechanism, Glasgow, Age and arterial Pressure (MGAP) score. The Trauma Revised Injury Severity Score (TRISS), not applicable in the pre-hospital setting, is the reference score to predict in-hospital mortality after severe trauma. The aim of this study was to compare T-RTS, MGAP and TRISS in a cohort of consecutive patients admitted in the Trauma system of the Northern French Alps(TRENAU). From 2009 to 2011, 3260 patients with suspected severe trauma according to the Vittel criteria were included in the TRENAU registry. All data necessary to compute T-RTS, MGAP and TRISS were collected in patients admitted to one level-I, two level-II and ten level-III trauma centers. The primary endpoint was death from any cause during hospital stay. Discriminative power of each score to predict mortality was measured using receiver operating curve (ROC) analysis. To test the relevancy of each score for triage, we also tested their sensitivity at usual cut-offs. We expected a sensitivity higher than 95% to limit undertriage. The TRISS score showed the highest area under the ROC curve (0.95 [CI 95% 0.94-0.97], p<0.01). Pre-hospital MGAP score had significantly higher AUC compared to T-RTS (0.93 [CI 95% 0.91-0.95] vs 0.86 [CI 95% 0.83-0.89], respectively, p<0.01). MGAP score<23 had a sensitivity of 88% to detect mortality. Sensitivities of T-RTS<12 and TRISS<0.91 were 79% and 87%, respectively. Pre-hospital calculation of the MGAP score appeared superior to T-RTS score in predicting intra-hospital mortality in a cohort of trauma patients. Although TRISS had the highest AUC, this score can only be available after hospital admission. These findings suggest that the MGAP score could be of interest in the pre-hospital setting to assess patients' severity. However, its lack of sensitivity indicates that MGAP should not replace the decision scheme to direct the most severe patients to level-I trauma center.

Mechanism of Injury, Glasgow Coma Scale, Age, and Systolic Blood Pressure: A New Trauma Scoring System to Predict Mortality in Trauma Patients.

Background:Trauma is the most common cause of death in people aged 1 - 44 years and the third leading cause of death regardless of age. Early diagnosis can expedite emergency care and thus patients can be transferred more quickly to a treatment center.Objectives:The purpose of this study was to evaluate the mechanism of injury, Glasgow coma scale, age, and arterial pressure (MGAP) scoring system in predicting mortality in trauma patients.Patients and Methods:In this cross-sectional study, 5,484 victims over 12 years of age referred to a trauma referral hospital and were evaluated. The MGAP score was assessed based on type of injury, Glasgow coma scale (GCS), systolic blood pressure (BP) and patient’s age. The area under the receiver operating characteristic (AUROC) curve was used as a measure of predictive performance. Data were analyzed using SPSS software version 16.Results:Patients were divided into three groups : scores of less than 18, 18 - 22 and greater than 22; in which the mortality rates were 75.2%, 9.5% and 0.1%, respectively (P < 0.0001). The best cut-off point was 22 in our study, and the MGAP scoring system had 93.7% sensitivity and 91.3% specificity.Conclusions:The MGAP scoring system can be used as an appropriate scoring system to predict mortality in triage trauma patients.

Choice of injury scoring system in low- and middle-income countries: Lessons from Mumbai.

Injury is a major cause of morbidity and mortality in low- and middle-income countries. Effective trauma surveillance is imperative to guide research and quality improvement interventions, so an accurate metric for quantifying injury severity is crucial. The objectives of this study are (1) to assess the feasibility of calculating five injury scoring systems--ISS (injury severity score), RTS (revised trauma score), KTS (Kampala trauma score), MGAP (mechanism, GCS (Glasgow coma score), age, pressure) and GAP (GCS, age, pressure)--with data from a trauma registry in a lower middle-income country and (2) to determine which of these scoring systems most accurately predicts in-hospital mortality in this setting. This is a retrospective analysis of data from an institutional trauma registry in Mumbai, India. Values for each score were calculated when sufficient data were available. Logistic regression was used to compare the correlation between each score and in-hospital mortality. There were sufficient data recorded to calculate ISS in 73% of patients, RTS in 35%, KTS in 35%, MGAP in 88% and GAP in 92%. ISS was the weakest predictor of in-hospital mortality, while RTS, KTS, MGAP and GAP scores all correlated well with in-hospital mortality (area under ROC (receiver operating characteristic) curve 0.69 for ISS, 0.85 for RTS, 0.86 for KTS, 0.84 for MGAP, 0.85 for GAP). Respiratory rate measurements, missing in 63% of patients, were a major barrier to calculating RTS and KTS. Given the realities of medical practice in low- and middle-income countries, it is reasonable to modify the approach to characterising injury severity to favour simplified injury scoring systems that accurately predict in-hospital mortality despite limitations in trauma registry datasets.

Mortality Prediction of MGAP Scoring System (type of injury, GCS, age, and systolic blood pressure) in Trauma Patients

1 Assistant professor, General surgery Department, Guilan University of Medical Sciences 2 General Surgeon, General surgery Department, Guilan University of Medical Sciences 3 Assistant professor, General surgery Department, Guilan University of Medical Sciences 4 Assistant professor, Biostatistics Department, Guilan University of Medical Sciences 5 Associate Professor, Department of Preventive and Community Medicine, Medical Faculty, Guilan University of Medical Sciences, postal code 4163545851, Rasht, Iran Corresponding Author: Z Mohtasham-Amiri, MD, MPH; Associate Professor in Preventive and Social Medicine; Fellowship in Geriatrics from Sydney University; Member of Guilan Road Trauma Research Center; Medical Faculty of Guilan University of Medical Sciences, Rasht/ Iran. Tel: +98 131 6690006, Fax: +98 131 7753836. Mobile: +98 9111329799. Address: postal code 4163545851 Rasht, Iran. E-mail: mohtashamaz@yahoo.com; mohtasham@gums.ac.ir