Development of a Statistical Model for Prediction of Acute Myocardial Infarction by Biochemical Markers
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Development of a Statistical Model for Prediction of Acute Myocardial Infarction by Biochemical Markers Tiepu Liu, Qiong Wang, M. Stephen Baxter, Michael R. Sayre and W. Brian Gibler Drug Information Journal 1999 33: 141 DOI: 10.1177/009286159903300116 The online version of this article can be found at: http://dij.sagepub.com/content/33/1/141
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Drug Information J o u m l , Vol. 33, pp. 141-148, 1999 Printed in the USA. All rights reserved.
0092-8615199 Copyright 0 1999 Drug Information Association Inc.
DEVELOPMENT OF A STATISTICAL MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION BY BIOCHEMICAL MARKERS TIEPULIU, MD, DRPH, QIONGWANG,MD, MS, M. STEPHEN BAXTER,MD, MICHAELR. SAYRE,MD, AND W. BRIANGIBLER, MD Department of Emergency Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio
Diagnosis and management of potential acute myocardial infarction (AMI)patients presenting to the hospital emergency department (ED) with chest pain are dificult. To aid in clinical decision making, a multivariate logistic regression model was developed using the patient S presenting history, electrocardiogram (ECG),and biochemical marker (cardiac Troponin z cTnT) to predict the probability of AMI. Data from 102 AM1 and 619 non-AMIpatientsfrom a multicenter clinical trial were used for model building. The World Health Organization criteria were used for AM1 diagnosis. Univariate analyses were pegormed to assess the effect of individual factors. Based on the significance of univariate effect and clinical importance, the following variables were included in a risk factor only model: age, sex, chest pain, systolic blood pressure, smoking, and history of myocardial infarction and hypertension with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.66 (95% confidence interval [CI]:0.60-0.71) and an k of 0.09. The final model, which included the risk factors and results of ECG and cTnT increased the AUC and k values to 0.93 (95% CI: 0.90-0.96) and 0.83, respectively. At the probability level of 0.10 for AMl, the model showed a sensitivity of 78.4% and a specificity of 92.6%. Statistical modeling is a useful tool in predicting risk of chest pain patients in the ED. Key Words: Statistical model; Logistic regression; Acute Myocardial Infarction; Troponin T
INTRODUCTION THE OF who present to the emergency department with chest pain can be very challenging for the emergency physician. Acute coronary syndromes (ACS) are a leadi
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