Thoracic injury rule out criteria and NEXUS chest in predicting the risk of traumatic intra-thoracic injuries: A diagnostic accuracy study

被引:11
作者
Safari, Saeed [1 ]
Radfar, Fatemeh [1 ]
Baratloo, Alireza [2 ]
机构
[1] Shahid Beheshti Univ Med Sci, Shohadaye Tajrish Hosp, Emergency Dept, Tehran, Iran
[2] Univ Tehran Med Sci, Sina Hosp, Emergency Dept, Tehran, Iran
来源
INJURY-INTERNATIONAL JOURNAL OF THE CARE OF THE INJURED | 2018年 / 49卷 / 05期
关键词
Decision support techniques; Multiple trauma; Thoracic injuries; Radiography thoracic; Diagnostic imaging; X-RAY; BLUNT TRAUMA; RADIOGRAPHY; ULTRASONOGRAPHY; METAANALYSIS; CT;
D O I
10.1016/j.injury.2018.01.031
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
Introduction: This study aimed to compare the diagnostic accuracy of NEXUS chest and Thoracic Injury Rule out criteria (TIRC) models in predicting the risk of intra-thoracic injuries following blunt multiple trauma. Methods: In this diagnostic accuracy study, using the 2 mentioned models, blunt multiple trauma patients over the age of 15 years presenting to emergency department were screened regarding the presence of intra-thoracic injuries that are detectable via chest x-ray and screening performance characteristics of the models were compared. Results: In this study, 3118 patients with the mean (SD) age of 37.4 (16.9) years were studied (57.4% male). Based on TIRC and NEXUS chest, respectively, 1340 (43%) and 1417 (45.4%) patients were deemed in need of radiography performance. Sensitivity, specificity, and positive and negative predictive values of TIRC were 98.95%, 62.70%, 21.19% and 99.83%. These values were 98.61%, 59.94%, 19.97% and 99.76%, for NEXUS chest, respectively. Accuracy of TIRC and NEXUS chest models were 66.04 (95% CI: 64.34-67.70) and 63.50 (95% CI: 61.78-65.19), respectively. Conclusion: TIRC and NEXUS chest models have proper and similar sensitivity in prediction of blunt traumatic intra-thoracic injuries that are detectable via chest x-ray. However, TIRC had a significantly higher specificity in this regard. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:959 / 962
页数:4
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