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

Björk, Jonas LU ; Lundager Hansen, Jakob LU ; Ohlsson, Mattias LU ; Edenbrandt, Lars LU ; Öhlin, Hans LU and Ekelund, Ulf LU (2006) In BMC Medical Informatics and Decision Making 6(28).
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
BMC Medical Informatics and Decision Making
volume
6
issue
28
publisher
BioMed Central
external identifiers
  • scopus:33748362861
ISSN
1472-6947
DOI
10.1186/1472-6947-6-28
language
English
LU publication?
yes
id
cee6871f-a294-4af1-9fa3-676c4e46deac (old id 766243)
date added to LUP
2007-12-18 14:33:08
date last changed
2019-08-06 01:28:01
@article{cee6871f-a294-4af1-9fa3-676c4e46deac,
  abstract     = {Background<br/><br>
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.<br/><br>
<br/><br>
Methods <br/><br>
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.<br/><br>
<br/><br>
Results <br/><br>
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%.<br/><br>
<br/><br>
Conclusions <br/><br>
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.},
  author       = {Björk, Jonas and Lundager Hansen, Jakob and Ohlsson, Mattias and Edenbrandt, Lars and Öhlin, Hans and Ekelund, Ulf},
  issn         = {1472-6947},
  language     = {eng},
  number       = {28},
  publisher    = {BioMed Central},
  series       = {BMC Medical Informatics and Decision Making},
  title        = {A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department},
  url          = {http://dx.doi.org/10.1186/1472-6947-6-28},
  volume       = {6},
  year         = {2006},
}