dc.contributor.author | Nakhjavan-Shahraki, B | |
dc.contributor.author | Baikpour, M | |
dc.contributor.author | Yousefifard, M | |
dc.contributor.author | Nikseresht, ZS | |
dc.contributor.author | Abiri, S | |
dc.contributor.author | Razaz, JM | |
dc.contributor.author | Faridaalaee, G | |
dc.contributor.author | Pouraghae, M | |
dc.contributor.author | Shirzadegan, S | |
dc.contributor.author | Hosseini, M | |
dc.date.accessioned | 2018-08-26T09:33:19Z | |
dc.date.available | 2018-08-26T09:33:19Z | |
dc.date.issued | 2017 | |
dc.identifier.uri | http://dspace.tbzmed.ac.ir:8080/xmlui/handle/123456789/57526 | |
dc.description.abstract | Introduction: Rapid acute physiology score (RAPS) and rapid emergency medicine score (REMS) are two physiologic models for measuring injury severity in emergency settings. The present study was designed to compare the two models in outcome prediction of trauma patients presenting to emergency department (ED). Methods: In this cross-sectional study, the two models of RAPS and REMS were compared regarding prediction of mortality and poor outcome (severe disability) of trauma patients presenting to the EDs of 5 educational hospitals. The discriminatory power and calibration of themodelswere calculated and compared using STATA 11. Results: 2148 patients with the mean age of 39.50أ¯?آ½17.27 years were studied (75.56% males). The area under the curve of REMS and RAPS in predicting in-hospital mortality were 0.93 (95% CI: 0.92-0.95) and 0.899 (95% CI: 0.86-0.93), respectively (p=0.02). Thesemeasureswere 0.92 (95%CI: 0.90-0.94) and 0.86 (95% CI: 0.83-0.90), respectively, regarding poor outcome (p=0.001). The optimumcut-off point in predicting outcome was found to be 3 for REMS model and 2 for RAPS model. The sensitivity and specificity of REMS and RAPS in the mentioned cut offs were 95.93 vs. 85.37 and 77.63 vs. 83.51, respectively, in predicting mortality. Calibration and overall performance of the two models were acceptable. Conclusion: The present study showed that adding age and level of arterial oxygen saturation to the variables included in RAPS model can increase its predictive value. Therefore, it seems that REMS could be used for predictingmortality and poor outcome of trauma patients in emergency settings. أ¯?آ½ (2016) Shahid Beheshti University ofMedical Sciences. | |
dc.language.iso | English | |
dc.relation.ispartof | Emergency | |
dc.subject | adult | |
dc.subject | area under the curve | |
dc.subject | arterial oxygen saturation | |
dc.subject | Article | |
dc.subject | comparative study | |
dc.subject | consciousness level | |
dc.subject | controlled study | |
dc.subject | cross-sectional study | |
dc.subject | diagnostic test accuracy study | |
dc.subject | disability | |
dc.subject | emergency ward | |
dc.subject | female | |
dc.subject | human | |
dc.subject | injury scale | |
dc.subject | injury severity | |
dc.subject | male | |
dc.subject | mathematical model | |
dc.subject | outcome assessment | |
dc.subject | predictive value | |
dc.subject | rapid acute physiology score | |
dc.subject | rapid emergency medicine score | |
dc.subject | sensitivity and specificity | |
dc.subject | traffic accident | |
dc.title | Rapid acute physiology score versus rapid emergency medicine score in traumaoutcomeprediction; acomparative study | |
dc.type | Article | |
dc.citation.volume | 5 | |
dc.citation.issue | 1 | |
dc.citation.spage | 165 | |
dc.citation.epage | 172 | |
dc.citation.index | Scopus | |