Dillard Elizabeth, Luchette Fred A, Sears Benjamin W, Norton John, Schermer Carol R, Reed R Lawrence, Gamelli Richard L, Esposito Thomas J
Stritch School of Medicine, Loyola University Medical Center, Maywood, IL 60157, USA.
Am J Emerg Med. 2007 Sep;25(7):823-30. doi: 10.1016/j.ajem.2006.12.009.
The purpose of this study was to determine if statistical models for prediction of chest injuries would outperform the clinician's (MD) ability to identify injured patients at risk for a thoracic injury diagnosed by chest radiograph (CXR).
A prospective observational study was done during a 12-month period.
The study was conducted in a level I trauma center.
Injured patients meeting trauma team activation criteria were enrolled to the study.
Physical examination findings by a clinician were interpreted and CXR was performed.
The accuracy of 2 mathematical models is compared against the accuracy of clinician's clinical judgment in predicting an injury by CXR. Two newly constructed multivariate models, binary logistic regression (LR) and classification and regression tree (CaRT) analysis, are compared to previously published data of clinician clinical assessment of probability of thoracic injury identified by CXR.
Data for 757 patients were analyzed. Classification and regression tree analysis developed a stepwise decision tree to determine which signs/symptoms were indicative of an abnormal CXR finding. The sensitivity (CaRT, 36.6%; LR, 36.3%; MD, 58.7%), specificity (CaRT, 98.3%; LR, 98.2%; MD, 96.4%), and error rates (CaRT, 0.93; LR, 0.94; MD, 0.82) show that the mathematical decision aids are less sensitive and risk more misclassification compared to clinician judgment in predicting an injury by CXR.
Clinician judgment was superior to mathematical decision aids for predicting an abnormal CXR finding in injured patients with chest trauma.
本研究旨在确定用于预测胸部损伤的统计模型在识别胸部X线片（CXR）诊断为胸部损伤风险的受伤患者方面是否优于临床医生（MD）的能力。
在12个月期间进行了一项前瞻性观察研究。
该研究在一级创伤中心进行。
符合创伤团队激活标准的受伤患者被纳入研究。
由临床医生对体格检查结果进行解读并进行胸部X线检查。
将两种数学模型的准确性与临床医生在通过胸部X线片预测损伤时的临床判断准确性进行比较。将两个新构建的多变量模型，二元逻辑回归（LR）和分类回归树（CaRT）分析，与先前发表的临床医生对胸部X线片识别的胸部损伤概率的临床评估数据进行比较。
分析了757例患者的数据。分类回归树分析开发了一个逐步决策树，以确定哪些体征/症状表明胸部X线片检查结果异常。敏感性（CaRT为36.6%；LR为36.3%；MD为58.7%）、特异性（CaRT为98.3%；LR为98.2%；MD为96.4%）和错误率（CaRT为0.93；LR为0.94；MD为0.82）表明，在通过胸部X线片预测损伤方面，与临床医生的判断相比，数学决策辅助工具敏感性较低且错误分类风险更高。
在预测胸部创伤受伤患者胸部X线片异常方面，临床医生的判断优于数学决策辅助工具。
