A computer protocol to predict myocardial infarction in emergency department patients with chest pain.

To achieve more appropriate triage to the coronary care unit of patients presenting with acute chest pain, we used clinical data on 1379 patients at two hospitals to construct a simple computer protocol to predict the presence of myocardial infarction. When we tested this protocol prospectively in 4770 patients at two university hospitals and four community hospitals, the computer-derived protocol had a significantly higher specificity (74 vs. 71 percent) in predicting the absence of infarction than physicians deciding whether to admit patients to the coronary care unit, and it had a similar sensitivity in detecting the presence of infarction (88.0 vs. 87.8 percent). Decisions based solely on the computer protocol would have reduced the admission of patients without infarction to the coronary care unit by 11.5 percent without adversely affecting the admission of patients in whom emergent complications developed that required intensive care. Although this protocol should not be used to override careful clinical judgment in individual cases, the computer protocol for the most part yields accurate estimates of the probability of myocardial infarction. Decisions about admission to the coronary care unit based on the protocol would have been as effective as those actually made by the unaided physicians who cared for the patients, and less costly. Whether physicians who are aided by the protocol perform better than unaided physicians cannot be determined without further study.

[1]  E. Cook,et al.  The decline in ischemic heart disease mortality rates. An analysis of the comparative effects of medical interventions and changes in lifestyle. , 1984, Annals of internal medicine.

[2]  L. Goldman,et al.  Care of patients with a low probability of acute myocardial infarction. Cost effectiveness of alternatives to coronary-care-unit admission. , 1984, The New England journal of medicine.

[3]  F. T. de Dombal,et al.  Human and Computer-aided Diagnosis of Abdominal Pain: Further Report with Emphasis on Performance of Clinicians , 1974, British medical journal.

[4]  E. Cook,et al.  Diagnostic implications for myocardial ischemia of the circadian variation of the onset of chest pain. , 1987, The American journal of cardiology.

[5]  H. Sox,et al.  Clinical prediction rules. Applications and methodological standards. , 1985, The New England journal of medicine.

[6]  Frans J. Th. Wackers,et al.  Use of the initial electrocardiogram to predict in-hospital complications of acute myocardial infarction. , 1985, The New England journal of medicine.

[7]  R. Olshen,et al.  Consistent nonparametric regression from recursive partitioning schemes , 1980 .

[8]  R B D'Agostino,et al.  The usefulness of a predictive instrument to reduce inappropriate admissions to the coronary care unit. , 1980, Annals of internal medicine.

[9]  Q. Mcnemar Note on the sampling error of the difference between correlated proportions or percentages , 1947, Psychometrika.

[10]  E F Cook,et al.  Sensitivity of routine clinical criteria for diagnosing myocardial infarction within 24 hours of hospitalization. , 1987, Annals of internal medicine.

[11]  G W Rouan,et al.  Clinical characteristics and natural history of patients with acute myocardial infarction sent home from the emergency room. , 1987, The American journal of cardiology.

[12]  W. Kannel,et al.  Incidence and prognosis of unrecognized myocardial infarction. An update on the Framingham study. , 1984, The New England journal of medicine.

[13]  Jeffrey A. Stem,et al.  A computer-derived protocol to aid in the diagnosis of emergency room patients with acute chest pain. , 1982, The New England journal of medicine.

[14]  B. Modan,et al.  Disposition of presumed coronary patients from an emergency room. A follow-up study. , 1976, JAMA.

[15]  E F Cook,et al.  Acute chest pain in the emergency room. Identification and examination of low-risk patients. , 1985, Archives of internal medicine.

[16]  E F Cook,et al.  Impact of a cardiology data bank on physicians' prognostic estimates. Evidence that cardiology fellows change their estimates to become as accurate as the faculty. , 1981, Archives of internal medicine.

[17]  R D Cebul,et al.  The importance of disease prevalence in transporting clinical prediction rules. The case of streptococcal pharyngitis. , 1986, Annals of internal medicine.

[18]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[19]  B. Psaty,et al.  Predictors of myocardial infarction in emergency room patients , 1985, Critical care medicine.

[20]  E. Cook,et al.  Evaluation of creatine kinase and creatine kinase-MB for diagnosing myocardial infarction. Clinical impact in the emergency room. , 1987, Archives of internal medicine.

[21]  A. Dannenberg,et al.  Enhancement of Clinical Predictive Ability by Computer Consultation) , 1979, Methods of Information in Medicine.

[22]  R B D'Agostino,et al.  A predictive instrument to improve coronary-care-unit admission practices in acute ischemic heart disease. A prospective multicenter clinical trial. , 1984, The New England journal of medicine.

[23]  S. Yusuf,et al.  The entry ECG in the early diagnosis and prognostic stratification of patients with suspected acute myocardial infarction. , 1984, European heart journal.

[24]  T Takamatsu,et al.  Aneurysms of the Coronary Arteries in Kawasaki Disease An Angiographic Study of 30 Cases , 1982, Circulation.