A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department

被引：29

作者：

Björk J. ^{[1
]}

Forberg J.L. ^{[2
]}

Ohlsson M. ^{[3
]}

Edenbrandt L. ^{[4
,5
]}

Öhlin H. ^{[6
]}

Ekelund U. ^{[2
]}

机构：

[1] Competence Center for Clinical Research, Lund University Hospital, Lund

[2] Department of Clinical Sciences, Section for Emergency Medicine, Lund University Hospital, Lund

[3] Department of Theoretical Physics, Lund University, Lund

[4] Department of Clinical Physiology, Malmö University Hospital, Malmö

[5] Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg

[6] Department of Cardiology, Lund University Hospital, Lund

来源：

BMC Medical Informatics and Decision Making | / 6卷 / 1期

关键词：

Emergency Department; Acute Coronary Syndrome; Positive Predictive Value; Acute Myocardial Infarction; Negative Predictive Value;

D O I：

10.1186/1472-6947-6-28

中图分类号：

学科分类号：

摘要：

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%. Conclusion: 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. © 2006 Björk et al; licensee BioMed Central Ltd.

引用

共 38 条

[1]

Pope J.H., Ruthazer R., Beshansky J.R., Griffith J.L., Selker H.P., Clinical Features of Emergency Department Patients Presenting with Symptoms Suggestive of Acute Cardiac Ischemia: A Multicenter Study, J Thromb Thrombolysis, 6, pp. 63-74, (1998)

[2]

Ekelund U., Nilsson H.J., Frigyesi A., Torffvit O., Patients with suspected acute coronary syndrome in a university hospital emergency department: An observational study, BMC Emerg Med, 2, pp. 1-7, (2002)

[3]

Forberg J.L., Henriksen L.S., Edenbrandt L., Ekelund U., Direct hospital costs of chest pain patients attending the emergency department: A retrospective study, BMC Emerg Med, 6, (2006)

[4]

Kontos M.C., Schmidt K.L., McCue M., Rossiter L.F., Jurgensen M., Nicholson C.S., Jesse R.L., Ornato J.P., Tatum J.L., A comprehensive strategy for the evaluation and triage of the chest pain patient: A cost comparison study, J Nucl Cardiol, 10, pp. 284-290, (2003)

[5]

Chandra A., Rudraiah L., Zalenski R.J., Stress testing for risk stratification of patients with low to moderate probability of acute cardiac ischemia, Emerg Med Clin North Am, 19, pp. 87-103, (2001)

[6]

Udelson J.E., Beshansky J.R., Ballin D.S., Feldman J.A., Griffith J.L., Handler J., Heller G.V., Hendel R.C., Pope J.H., Ruthazer R., Spiegler E.J., Woolard R.H., Selker H.P., Myocardial perfusion imaging for evaluation and triage of patients with suspected acute cardiac ischemia: A randomized controlled trial, JAMA, 288, pp. 2693-2700, (2002)

[7]

Kontos M.C., Role of Echocardiography in the Emergency Department for Identifying Patients with Myocardial Infarction and Ischemia, Echocardiography, 16, pp. 193-205, (1999)

[8]

Baxt W.G., Shofer F.S., Sites F.D., Hollander J.E., A neural network aid for the early diagnosis of cardiac ischemia in patients presenting to the emergency department with chest pain, Ann Emerg Med, 40, pp. 575-583, (2002)

[9]

Baxt W.G., Shofer F.S., Sites F.D., Hollander J.E., A neural computational aid to the diagnosis of acute myocardial infarction, Ann Emerg Med, 39, pp. 366-373, (2002)

[10]

Kennedy R.L., Burton A.M., Fraser H.S., McStay L.N., Harrison R.F., Early diagnosis of acute myocardial infarction using clinical and electrocardiographic data at presentation: Derivation and evaluation of logistic regression models, Eur Heart J, 17, pp. 1181-1191, (1996)

← 1 2 3 4 →