Mattias Ohlsson
Professor
A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department
Författare
Summary, in English
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.
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.
Avdelning/ar
- Centrum för ekonomisk demografi
- Avdelningen för arbets- och miljömedicin
- Medicin/akutsjukvård, Lund
- Beräkningsbiologi och biologisk fysik - Har omorganiserats
- Nuklearmedicin, Malmö
- Kardiologi
Publiceringsår
2006
Språk
Engelska
Publikation/Tidskrift/Serie
BMC Medical Informatics and Decision Making
Volym
6
Issue
28
Dokumenttyp
Artikel i tidskrift
Förlag
BioMed Central (BMC)
Ämne
- Other Health Sciences
Status
Published
Projekt
- AIR Lund Chest pain - More efficient and equal emergency care with advanced medical decision support tools
Forskningsgrupp
- Nuclear medicine, Malmö
ISBN/ISSN/Övrigt
- ISSN: 1472-6947