A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department.
BACKGROUND
Several models for prediction of acute coronary syndrome (ACS) among chest pain patients in the emergency department (ED) have been presented, but many models predict only the likelihood of acute myocardial infarction, or include a large number of variables, which make them less than optimal for implementation at a busy ED. We report here a simple statistical model for ACS prediction that could be used in routine care at a busy ED.


METHODS
Multivariable analysis and logistic regression were used on data from 634 ED visits for chest pain. Only data immediately available at patient presentation were used. To make ACS prediction stable and the model useful for personnel inexperienced in electrocardiogram (ECG) reading, simple ECG data suitable for computerized reading were included.


RESULTS
Besides ECG, eight variables were found to be important for ACS prediction, and included in the model: age, chest discomfort at presentation, symptom duration and previous hypertension, angina pectoris, AMI, congestive heart failure or PCI/CABG. At an ACS prevalence of 21% and a set sensitivity of 95%, the negative predictive value of the model was 96%.


CONCLUSIONS
The present prediction model, combined with the clinical judgment of ED personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.
Björk
Jonas
J
Competence Center for Clinical Research, Lund University Hospital, Lund, Sweden. jonas.bjork@skane.se
Forberg
Jakob L
JL
Ohlsson
Mattias
M
Edenbrandt
Lars
L
Ohlin
Hans
H
Ekelund
Ulf
U
D000208:Acute Disease; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002637:Chest Pain / Q000175:diagnosis* / Q000209:etiology; D003955:Diagnostic Tests, Routine; D004562:Electrocardiography; D004636:Emergency Service, Hospital; D005260:Female; D005544:Forecasting; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000503:physiopathology; D011859:Radiography; D012307:Risk Factors