A Revision of the Trauma Score

Champion, Howard R.; Sacco, William J.; Copes, Wayne S.; Gann, Donald S.; Gennarelli, Thomas A.; Flanagan, Maureen E.

doi:10.1097/00005373-198905000-00017

Cited by 1,767 publications

(1,100 citation statements)

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“…The most common is the Injury Severity Score (ISS), an anatomic score that incorporates multiple Abbreviated Injury Scores (AIS), which reflect the severity of injuries to different body regions [11]. A popular alternative is the Revised Trauma Score (RTS), a physiologic score that reflects a patient's systemic response to injury measured through Glasgow Coma Score (GCS), systolic blood pressure and respiratory rate [12]. It is the current standard physiologic scoring system used in trauma research and quality improvement in both high-income countries and low-and middle-income countries [13].…”

mentioning

confidence: 99%

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

Laytin

Kumar

Juillard

et al. 2015

Injury

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mentioning

confidence: 99%

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

Laytin

Kumar

Juillard

et al. 2015

Injury

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“…Age, the ISS, the GCS, SBP, RR, and their coded values (cAGE, cISS, cSBP, cGCS, cRR), defined in Table 1, were used as predictor variables. The GCS, SBP, RR, and age were coded according to the RTS [4] and the TRISS method [1,2]. For ISS categorization to cISS, recursive partitioning, which is an exploratory technique to split a dataset into increasingly homogeneous subgroups having the greatest difference between the groups at each stage, was conducted with reference to previous literature [7,8].…”

Section: Methodsmentioning

confidence: 99%

“…However, it requires the Injury Severity Score (ISS) [3], the Revised Trauma Score (RTS) [4] calculated based on the Glasgow Coma Scale score (GCS), the systolic blood pressure (SBP), the respiratory rate (RR), and the categorically coded value of age (cAGE). The formulas are:…”

Section: Introductionmentioning

confidence: 99%

The development of simple survival prediction models for blunt trauma victims treated at Asian emergency centers

Kimura

Nakahara

Chadbunchachai

2012

Scand J Trauma Resusc Emerg Med

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BackgroundFor real-time assessment of the probability of survival (Ps) of blunt trauma victims at emergency centers, this study aimed to establish regression models for estimating Ps using simplified coefficients.MethodsThe data of 10,210 blunt trauma patients not missing both the binary outcome data about survival and the data necessary for Ps calculation by The Trauma and Injury Severity Score (TRISS) method were extracted from the Japan Trauma Data Bank (2004-2007) and analyzed. Half (5,113) of the data was allocated to a derivation data set, with the other half (5,097) allocated to a validation data set. The data of 6,407 blunt trauma victims from the trauma registry of Khon Kaen Regional Hospital in Thailand were analyzed for validation. The logistic regression models included age, the Injury Severity Score (ISS), the Glasgow Coma Scale score (GCS), systolic blood pressure (SBP), respiratory rate (RR), and their coded values (cAGE, 0-1; cISS, 0-4; cSBP, 0-4; cGCS, 0-4; cRR, 0-4) as predictor variables. The coefficients were simplified by rounding off after the decimal point or choosing 0.5 if the coefficients varied across 0.5. The area under the receiver-operating characteristic curve (AUROCC) was calculated for each model to measure discriminant ability.ResultsA group of formulas (log (Ps/1-Ps) = logit (Ps) = -9 + cISS - cAGE + cSBP + cGCS + cRR/2, where -9 becomes -7 if the predictor variable of cRR or cISS is missing) was developed. Using these formulas, the AUROCCs were between 0.950 and 0.964. When these models were applied to the Khon Kean data, their AUROCCs were greater than 0.91. Conclusion: These equations allow physicians to perform real-time assessments of survival by easy mental calculations at Asian emergency centers, which are overcrowded with blunt injury victims of traffic accidents.

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“…A week-long induction period included the training workshop, hands-on practice and real-time data entry. Three-day training workshops included fundamental elements of TRs, injury data abstraction from medical charts, data entry on a mobile device, injury coding such as e-codes, ICD-10-CM, and basic principles for various scaling and scoring systems including the Revised Trauma Score (RTS) and Injury Severity Score (ISS) [24,25]. The workshop was formatted to include lectures, workbooks, case scenarios, quizzes, online injury scoring calculators, and demonstrations to learn and practice new concepts.…”

Section: Ict Implementation Frameworkmentioning

confidence: 99%

Development of an mHealth trauma registry in the Middle East using an implementation science framework

Mehmood

Chan

Allen

et al. 2017

Global Health Action

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Background: Trauma registries (TRs) play a vital role in the assessment of trauma care, but are often underutilized in countries with a high burden of injuries. Objectives: We investigated whether information and communications technology (ICT) such as mobile health (mHealth) could enable the design of a tablet-based application for healthcare professionals. This would be used to inform trauma care and acquire surveillance data for injury control and prevention in Oman. This paper focuses on documenting the implementation process in a healthcare setting. Methods: The study was conducted using an ICT implementation framework consisting of multistep assessment, development and pilot testing of an electronic tablet-based TR. The pilot study was conducted at two large hospitals in Oman, followed by detailed evaluation of the process, system and impact of implementation. Results: The registry was designed to provide comprehensive information on each trauma case from the location of injury until hospital discharge, with variables organized to cover 11 domains of demographic and clinical information. The pilot study demonstrated that the registry was user friendly and reliable, and the implementation framework was useful in planning for the Omani hospital setting. Data collection by trained and dedicated nurses proved to be more feasible, efficient and reliable than real-time data entry by care providers. Conclusions: The initial results show the promising potential of a user-friendly, comprehensive electronic TR through the use of mHealth tools. The pilot test in two hospitals indicates that the registry can be used to create a multicenter trauma database.

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A Revision of the Trauma Score

Cited by 1,767 publications

References 0 publications

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

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

The development of simple survival prediction models for blunt trauma victims treated at Asian emergency centers

Development of an mHealth trauma registry in the Middle East using an implementation science framework

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