A neural network aid for the early diagnosis of cardiac ischemia in patients presenting to the emergency department with chest pain.
OBJECTIVE
Chest pain is the second most common chief complaint presented to the emergency department. Although the causes of chest pain span the clinical spectrum from the trivial to the life threatening, it is often difficult to identify which patients have the most common life-threatening cause, cardiac ischemia. Because of the potential for poor outcome if this diagnosis is missed, physicians have had a low threshold for admitting patients with chest pain to the hospital, the vast majority of whom are found not to have cardiac ischemia. In an earlier study with a large chest pain patient registry, an artificial neural network was shown to be able to identify the subset of patients who present to the ED with chest pain who have sustained acute myocardial infarction. The objective of this study was to use the same registry to determine whether a network could be trained accurately to identify the larger subset of patients who have cardiac ischemia.


METHODS
Two thousand two hundred four adult patients presenting to the ED with chest pain who received an ECG were used to train and test an artificial neural network to recognize the presence of cardiac ischemia. Only the data available at the time of initial patient contact were used to replicate the conditions of real-time evaluation. Forty variables from patient history, physical examination, ECG, and the first set of chemical cardiac marker determinations were used to train and subsequently test the network. The network was trained and tested by using the jackknife variance technique to allow for the network to be trained on as many of the features of the small subset of ischemic patients as possible. Network accuracy was compared with 2 existing aids to the diagnosis of cardiac ischemia, as well as a derived regression model.


RESULTS
The network had a sensitivity of 88.1% (95% confidence interval [CI] 84.8% to 91.4%) and a specificity of 86.2% (95% CI 84.6% to 87.7%) for cardiac ischemia despite the fact that a mean of 5% of all required network input data and 41% of cardiac chemical marker data were missing. The network also performed more accurately than the 3 other tested approaches.


CONCLUSIONS
These data suggest that an artificial neural network might be able to identify which patients who present to the ED with chest pain have cardiac ischemia with useful sensitivities and specificities.
Baxt
William G
WG
Department of Emergency Medicine, Hospital of the University of Pennsylvania, Philadelphia 19104-4283, USA. baxt@mail.med.upenn.edu
Shofer
Frances S
FS
Sites
Frank D
FD
Hollander
Judd E
JE
D002637:Chest Pain / Q000175:diagnosis*; D003951:Diagnostic Errors; D004562:Electrocardiography; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D017202:Myocardial Ischemia / Q000175:diagnosis*; D016571:Neural Networks, Computer; D011379:Prognosis; D012372:ROC Curve; D012042:Registries