Performance evaluation and validation of the animal trauma triage score and modified Glasgow Coma Scale with suggested category adjustment in dogs: A VetCOT registry study

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

<div class="section"> <a class="named-anchor" id="S1">  </a> <h5 class="section-title" id="d6218919e121">Objective:</h5> <p id="P1">To examine the animal trauma triage (ATT) and modified Glasgow coma scare (mGCS) scores as predictors of mortality outcome (death or euthanasia) in injured dogs. </p> </div><div class="section"> <a class="named-anchor" id="S2">  </a> <h5 class="section-title" id="d6218919e126">Design:</h5> <p id="P2">Observational cohort study conducted September 2013 to March 2015 with follow-up until death or hospital discharge </p> </div><div class="section"> <a class="named-anchor" id="S3">  </a> <h5 class="section-title" id="d6218919e131">Setting:</h5> <p id="P3">9 veterinary hospitals including private referral and veterinary teaching hospitals</p> </div><div class="section"> <a class="named-anchor" id="S4">  </a> <h5 class="section-title" id="d6218919e136">Animals:</h5> <p id="P4">Consecutive sample of 3,599 dogs with complete data entries recruited into the Veterinary Committee on Trauma (VetCOT) patient registry. </p> </div><div class="section"> <a class="named-anchor" id="S5">  </a> <h5 class="section-title" id="d6218919e141">Interventions:</h5> <p id="P5">None</p> </div><div class="section"> <a class="named-anchor" id="S6">  </a> <h5 class="section-title" id="d6218919e146">Measurements and Main Results:</h5> <p id="P6">We compared the predictive power (area under receiver operating characteristic AUROC) and calibration of the ATT and mGCS scores to their components. Overall mortality risk was 7.3% (n=264). Incidence of head trauma was 9.5% (n=341). The ATT score showed a linear relationship with mortality risk. Discriminatory performance of the ATT score was excellent with AUROC=0.92 (95% CI 0.91-0.94), pseudo R <sup>2</sup>=0.42. Each ATT score increase of 1 point was associated with an increase in mortality odds of 2.07 (95% CI=1.94-2.21 <i>P<0.001</i>). The ‘Eye/Muscle/Integument’ category of the ATT showed poor discrimination (AUROC=0.55). When this component together with the skeletal and cardiac components were omitted from calculation of the overall score, there was no loss in discriminatory capacity (AUROC=0.92 vs 0.91), <i>P=0.09</i>) compared with the full score. The mGCS showed good performance overall, but performance improved when restricted to head trauma patients (AUROC=0.84, 95% CI=0.79-0.90, n=341 vs 0.82 95% CI=0.79-0.85, n=3599). The motor component of the mGCS showed the best predictive performance (AUROC=0.79 vs 0.66/0.69), however the full score performed better than the motor component alone ( <i>P=0.002</i>). When assessment was restricted to patients with head injury (n=341), the ATT score still performed better than the mGCS (AUROC=0.90 vs 0.84, <i>P=0.04</i>). </p> </div><div class="section"> <a class="named-anchor" id="S7">  </a> <h5 class="section-title" id="d6218919e166">Conclusions:</h5> <p id="P7">In external validation on a large, multi-center dataset, the ATT score showed excellent discrimination and calibration, however a more parsimonious score calculated on only the perfusion, respiratory, and neurological categories showed equivalent performance. </p> </div>

Related collections

Most cited references 22

Record: found
Abstract: found
Article: not found

The meaning and use of the area under a receiver operating characteristic (ROC) curve.

J A Hanley, B J McNeil, Marnix van Holsbeeck (1982)

A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.

0 comments Cited 3901 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

ASSESSMENT OF OUTCOME AFTER SEVERE BRAIN DAMAGE A Practical Scale

B Jennett (1975)

0 comments Cited 425 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Update of the trauma risk adjustment model of the TraumaRegister DGU™: the Revised Injury Severity Classification, version II

Rolf Lefering, Stefan Huber-Wagner, Ulrike Nienaber … (2014)

Introduction The TraumaRegister DGU™ (TR-DGU) has used the Revised Injury Severity Classification (RISC) score for outcome adjustment since 2003. In recent years, however, the observed mortality rate has fallen to about 2% below the prognosis, and it was felt that further prognostic factors, like pupil size and reaction, should be included as well. Finally, an increasing number of cases did not receive a RISC prognosis due to the missing values. Therefore, there was a need for an updated model for risk of death prediction in severely injured patients to be developed and validated using the most recent data. Methods The TR-DGU has been collecting data from severely injured patients since 1993. All injuries are coded according to the Abbreviated Injury Scale (AIS, version 2008). Severely injured patients from Europe (ISS ≥4) documented between 2010 and 2011 were selected for developing the new score (n = 30,866), and 21,918 patients from 2012 were used for validation. Age and injury codes were required, and transferred patients were excluded. Logistic regression analysis was applied with hospital mortality as the dependent variable. Results were evaluated in terms of discrimination (area under the receiver operating characteristic curve, AUC), precision (observed versus predicted mortality), and calibration (Hosmer-Lemeshow goodness-of-fit statistic). Results The mean age of the development population was 47.3 years; 71.6% were males, and the average ISS was 19.3 points. Hospital mortality rate was 11.5% in this group. The new RISC II model consists of the following predictors: worst and second-worst injury (AIS severity level), head injury, age, sex, pupil reactivity and size, pre-injury health status, blood pressure, acidosis (base deficit), coagulation, haemoglobin, and cardiopulmonary resuscitation. Missing values are included as a separate category for every variable. In the development and the validation dataset, the new RISC II outperformed the original RISC score, for example AUC in the development dataset 0.953 versus 0.939. Conclusions The updated RISC II prognostic score has several advantages over the previous RISC model. Discrimination, precision and calibration are improved, and patients with partial missing values could now be included. Results were confirmed in a validation dataset.