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Foto på Mattias Ohlsson

Mattias Ohlsson

Professor

Foto på Mattias Ohlsson

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

Författare

  • Jonas Björk
  • Jakob Lundager Hansen
  • Mattias Ohlsson
  • Lars Edenbrandt
  • Hans Öhlin
  • Ulf Ekelund

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.

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

Aktiv

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