Clinician vs mathematical statistical models: which is better at predicting an abnormal chest radiograph finding in injured patients?
OBJECTIVE
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).


METHODS
A prospective observational study was done during a 12-month period.


METHODS
The study was conducted in a level I trauma center.


METHODS
Injured patients meeting trauma team activation criteria were enrolled to the study.


METHODS
Physical examination findings by a clinician were interpreted and CXR was performed.


METHODS
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.


RESULTS
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.


CONCLUSIONS
Clinician judgment was superior to mathematical decision aids for predicting an abnormal CXR finding in injured patients with chest trauma.
Dillard
Elizabeth
E
Stritch School of Medicine, Loyola University Medical Center, Maywood, IL 60157, USA.
Luchette
Fred A
FA
Sears
Benjamin W
BW
Norton
John
J
Schermer
Carol R
CR
Reed
R Lawrence
RL
Gamelli
Richard L
RL
Esposito
Thomas J
TJ
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D002983:Clinical Competence; D003663:Decision Trees; D005260:Female; D006801:Humans; D007223:Infant; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011446:Prospective Studies; D011859:Radiography; D013898:Thoracic Injuries / Q000150:complications / Q000000981:diagnostic imaging*; D015599:Trauma Severity Indices