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Ichiro Kukita - One of the best experts on this subject based on the ideXlab platform.
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Revised Trauma scoring system to predict in hospital mortality in the emergency department glasgow coma scale age and systolic blood pressure Score
Critical Care, 2011Co-Authors: Yutaka Kondo, Toshikazu Abe, Kiyotaka Kohshi, Yasuharu Tokuda, Francis E Cook, Ichiro KukitaAbstract:Introduction: Our aim in this study was to assess whether the new Glasgow Coma Scale, Age, and Systolic Blood Pressure (GAP) scoring system, which is a modification of the Mechanism, Glasgow Coma Scale, Age, and Arterial Pressure (MGAP) scoring system, better predicts in-hospital mortality and can be applied more easily than previous Trauma Scores among Trauma patients in the emergency department (ED). Methods: This multicenter, prospective, observational study was conducted to analyze readily available variables in the ED, which are associated with mortality rates among Trauma patients. The data used in this study were derived from the Japan Trauma Data Bank (JTDB), which consists of 114 major emergency hospitals in Japan. A total of 35,732 Trauma patients in the JTDB from 2004 to 2009 who were 15 years of age or older were eligible for inclusion in the study. Of these patients, 27,154 (76%) with complete sets of important data (patient age, Glasgow Coma Scale (GCS) Score, systolic blood pressure (SBP), respiratory rate and Injury Severity Score (ISS)) were included in our analysis. We calculated weight for the predictors of the GAP Scores on the basis of the records of 13,463 Trauma patients in a derivation data set determined by using logistic regression. Scores derived from four existing scoring systems (Revised Trauma Score, Triage Revised Trauma Score, Trauma and Injury Severity Score and MGAP Score) were calibrated using logistic regression models that fit in the derivation set. The GAP scoring system was compared to the calibrated scoring systems with data from a total of 13,691 patients in a validation data set using c-statistics and reclassification tables with three defined risk groups based on a previous publication: low risk (mortality 50%). Results: Calculated GAP Scores involved GCS Score (from three to fifteen points), patient age 120 mmHg, six points; 60 to 120 mmHg, four points). The c-statistics for the GAP Scores (0.933 for long-term mortality and 0.965 for short-term mortality) were better than or comparable to the Trauma Scores calculated using other scales. Compared with existing instruments, our reclassification tables show that the GAP scoring system reclassified all patients except one in the correct direction. In most cases, the observed incidence of death in patients who were reclassified matched what would have been predicted by the GAP scoring system. Conclusions: The GAP scoring system can predict in-hospital mortality more accurately than the previously developed Trauma scoring systems.
Edward E Cornwell - One of the best experts on this subject based on the ideXlab platform.
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multiple imputation in Trauma disparity research
Journal of Surgical Research, 2011Co-Authors: Tolulope A Oyetunji, Edward E Cornwell, Joseph G Crompton, Imudia Ehanire, Kent A Stevens, David T Efron, Elliott R Haut, David C Chang, Marie Crandall, Adil H HaiderAbstract:BACKGROUND: Missing data has remained a major disparity in Trauma outcomes research due to missing race and insurance data. Multiple imputation (M.IMP) has been recommended as a solution to deal with this major drawback. STUDY DESIGN: Using the National Data Trauma Bank (NTDB) as an example, a complete dataset was developed by deleting cases with missing data across variables of interest. An incomplete dataset was then created from the complete set using random deletion to simulate the original NTDB, followed by five M.IMP rounds to generate a final imputed dataset. Identical multivariate analyses were performed to investigate the effect of race and insurance on mortality in both datasets. RESULTS: Missing data proportions for known Trauma mortality covariates were as follows: age-4%, gender-0.4%, race-8%, insurance-17%, injury severity Score-6%, Revised Trauma Score-20%, and Trauma type-3%. The M.IMP dataset results were qualitatively similar to the original dataset. CONCLUSION: M.IMP is a feasible tool in NTDB for handling missing race and insurance data. Language: en
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multiple imputation in Trauma disparity research
Journal of Surgical Research, 2011Co-Authors: Tolulope A Oyetunji, Edward E Cornwell, Joseph G Crompton, Imudia Ehanire, Kent A Stevens, David T Efron, Elliott R Haut, David C Chang, Marie Crandall, Adil H HaiderAbstract:Background Missing data has remained a major disparity in Trauma outcomes research due to missing race and insurance data. Multiple imputation (M.IMP) has been recommended as a solution to deal with this major drawback. Study Design Using the National Data Trauma Bank (NTDB) as an example, a complete dataset was developed by deleting cases with missing data across variables of interest. An incomplete dataset was then created from the complete set using random deletion to simulate the original NTDB, followed by five M.IMP rounds to generate a final imputed dataset. Identical multivariate analyses were performed to investigate the effect of race and insurance on mortality in both datasets. Results Missing data proportions for known Trauma mortality covariates were as follows: age-4%, gender-0.4%, race-8%, insurance-17%, injury severity Score-6%, Revised Trauma Score-20%, and Trauma type-3%. The M.IMP dataset results were qualitatively similar to the original dataset. Conclusion M.IMP is a feasible tool in NTDB for handling missing race and insurance data.
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lethal abdominal gunshot wounds at a level i Trauma center analysis of triss Revised Trauma Score and injury severity Score fallouts
Journal of The American College of Surgeons, 1998Co-Authors: Edward E Cornwell, George C Velmahos, Thomas V Berne, Raymond Tatevossian, Howard Belzberg, Mark Eckstein, James Murray, Juan A Asensio, Demetrios DemetriadesAbstract:Abstract Background: The TRISS methodology (composite index of the Revised Trauma Score and the Injury Severity Score) has become widely used by Trauma centers to assess quality of care. The American College of Surgeons recommends including negative TRISS fallouts (fatally injured patients predicted to survive by the TRISS methodology) as a filter to select patients for peer review. The purpose of this study was to analyze the TRISS fallouts among patients with lethal abdominal gunshot wounds admitted to a level I Trauma center. Study Design: All patients categorized as TRISS fallouts admitted from January 1995 through December 1996 were analyzed. Results: During the study period, 848 patients with abdominal gunshot wounds were admitted. Of the 108 patients with any sign of life on admission who subsequently died, 39 (36%) were TRISS fallouts. The patients were largely young (mean age, 29 years) and male (87%), received rapid transport (mean scene time, 11 minutes), and had an attending-led Trauma-team response ( Conclusions: "TRISS fallouts" were predominantly patients who died despite receiving rapid prehospital transport, rapid senior-level Trauma-team response, and surgical intervention for a serious complex of injuries. We conclude that without regional adjustment of coefficients used to predict the probability of survival, the TRISS methodology is of limited use in patients with abdominal gunshot wounds.
Maulida, Aima Nur - One of the best experts on this subject based on the ideXlab platform.
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ANALISIS PENILAIAN TRIAGE DAN PENILAIAN Revised Trauma Score (RTS) DALAM MEMPREDIKSI MORTALITAS PADA PASIEN Trauma KEPALA DI IGD RSUD JOMBANG
2019Co-Authors: Maulida, Aima NurAbstract:Sistem manajemen penanganan yang kurang tepat dan cepat dapat meningkatkan mortalitas pada cedera kepala. Triage dapat memprediksi tingkat kegawatan pada semua pasien untuk menetapkan prioritas penanganannya. Sedangkan Revised Trauma Score (RTS) adalah merupakan physiologycal scoring systems yang dapat digunakan sebagai prediktor mortality pasien cedera kepala. Triage dan RTS memiliki tujuan yang sama yaitu untuk meminimalkan kejadian mortalitas. Tujuan dari penelitian ini yaitu untuk menganalisis penilaian triage dan penilaian Revised Trauma Score (RTS) dalam memprediksi mortalitas pada pasien Trauma kepala di IGD RSUD Jombang. Desain penelitian adalah observasional analitik dengan pendekatan cross sectional. Populasi adalah seluruh pasien Trauma kepala di IGD RSUD Jombang selama tahun 2018 sejumlah 1121 pasien. Sampel terdiri dari 89 responden di ambil menggunakan Simple Random Sampling. Instrumen penelitian menggunakan lembar observasi. Data dianalisis menggunakan uji Chi-square dengan taraf signifikansi p
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ANALISIS PENILAIAN TRIAGE DAN Revised Trauma Score DALAM MEMPREDIKSI MORTALITAS PADA PASIEN Trauma KEPALA
'Universitas Pesantren Tinggi Darul Ulum (Unipdu)', 2019Co-Authors: Maulida, Aima Nur, Khotimah KhotimahAbstract:ABSTRACTImproper handling of management systems and quickly increase mortality in head injuries. Triage assessment can reduce the risk of death. However, triage is at risk of undertriage which can increase mortality. A more detailed assessment system is needed, so Revised Trauma Score (RTS) can be used to minimize undertriage. The purpose of this study is to analyze the triage assessment and RTS assessment in predicting mortality in cases of head Trauma. The study design was observational analytic with cross sectional approach. The population was all head Trauma patients in the emergency room at Jombang Regional Hospital with 121 patients. The sample consisted of 89 respondents taken using Simple Random Sampling. The research instrument used observation sheets. Data were analyzed using Chi-square test with a significance level of p
Adil H Haider - One of the best experts on this subject based on the ideXlab platform.
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multiple imputation in Trauma disparity research
Journal of Surgical Research, 2011Co-Authors: Tolulope A Oyetunji, Edward E Cornwell, Joseph G Crompton, Imudia Ehanire, Kent A Stevens, David T Efron, Elliott R Haut, David C Chang, Marie Crandall, Adil H HaiderAbstract:Background Missing data has remained a major disparity in Trauma outcomes research due to missing race and insurance data. Multiple imputation (M.IMP) has been recommended as a solution to deal with this major drawback. Study Design Using the National Data Trauma Bank (NTDB) as an example, a complete dataset was developed by deleting cases with missing data across variables of interest. An incomplete dataset was then created from the complete set using random deletion to simulate the original NTDB, followed by five M.IMP rounds to generate a final imputed dataset. Identical multivariate analyses were performed to investigate the effect of race and insurance on mortality in both datasets. Results Missing data proportions for known Trauma mortality covariates were as follows: age-4%, gender-0.4%, race-8%, insurance-17%, injury severity Score-6%, Revised Trauma Score-20%, and Trauma type-3%. The M.IMP dataset results were qualitatively similar to the original dataset. Conclusion M.IMP is a feasible tool in NTDB for handling missing race and insurance data.
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multiple imputation in Trauma disparity research
Journal of Surgical Research, 2011Co-Authors: Tolulope A Oyetunji, Edward E Cornwell, Joseph G Crompton, Imudia Ehanire, Kent A Stevens, David T Efron, Elliott R Haut, David C Chang, Marie Crandall, Adil H HaiderAbstract:BACKGROUND: Missing data has remained a major disparity in Trauma outcomes research due to missing race and insurance data. Multiple imputation (M.IMP) has been recommended as a solution to deal with this major drawback. STUDY DESIGN: Using the National Data Trauma Bank (NTDB) as an example, a complete dataset was developed by deleting cases with missing data across variables of interest. An incomplete dataset was then created from the complete set using random deletion to simulate the original NTDB, followed by five M.IMP rounds to generate a final imputed dataset. Identical multivariate analyses were performed to investigate the effect of race and insurance on mortality in both datasets. RESULTS: Missing data proportions for known Trauma mortality covariates were as follows: age-4%, gender-0.4%, race-8%, insurance-17%, injury severity Score-6%, Revised Trauma Score-20%, and Trauma type-3%. The M.IMP dataset results were qualitatively similar to the original dataset. CONCLUSION: M.IMP is a feasible tool in NTDB for handling missing race and insurance data. Language: en
Yutaka Kondo - One of the best experts on this subject based on the ideXlab platform.
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Revised Trauma scoring system to predict in hospital mortality in the emergency department glasgow coma scale age and systolic blood pressure Score
Critical Care, 2011Co-Authors: Yutaka Kondo, Toshikazu Abe, Kiyotaka Kohshi, Yasuharu Tokuda, Francis E Cook, Ichiro KukitaAbstract:Introduction: Our aim in this study was to assess whether the new Glasgow Coma Scale, Age, and Systolic Blood Pressure (GAP) scoring system, which is a modification of the Mechanism, Glasgow Coma Scale, Age, and Arterial Pressure (MGAP) scoring system, better predicts in-hospital mortality and can be applied more easily than previous Trauma Scores among Trauma patients in the emergency department (ED). Methods: This multicenter, prospective, observational study was conducted to analyze readily available variables in the ED, which are associated with mortality rates among Trauma patients. The data used in this study were derived from the Japan Trauma Data Bank (JTDB), which consists of 114 major emergency hospitals in Japan. A total of 35,732 Trauma patients in the JTDB from 2004 to 2009 who were 15 years of age or older were eligible for inclusion in the study. Of these patients, 27,154 (76%) with complete sets of important data (patient age, Glasgow Coma Scale (GCS) Score, systolic blood pressure (SBP), respiratory rate and Injury Severity Score (ISS)) were included in our analysis. We calculated weight for the predictors of the GAP Scores on the basis of the records of 13,463 Trauma patients in a derivation data set determined by using logistic regression. Scores derived from four existing scoring systems (Revised Trauma Score, Triage Revised Trauma Score, Trauma and Injury Severity Score and MGAP Score) were calibrated using logistic regression models that fit in the derivation set. The GAP scoring system was compared to the calibrated scoring systems with data from a total of 13,691 patients in a validation data set using c-statistics and reclassification tables with three defined risk groups based on a previous publication: low risk (mortality 50%). Results: Calculated GAP Scores involved GCS Score (from three to fifteen points), patient age 120 mmHg, six points; 60 to 120 mmHg, four points). The c-statistics for the GAP Scores (0.933 for long-term mortality and 0.965 for short-term mortality) were better than or comparable to the Trauma Scores calculated using other scales. Compared with existing instruments, our reclassification tables show that the GAP scoring system reclassified all patients except one in the correct direction. In most cases, the observed incidence of death in patients who were reclassified matched what would have been predicted by the GAP scoring system. Conclusions: The GAP scoring system can predict in-hospital mortality more accurately than the previously developed Trauma scoring systems.
