Jakob L. Forberg, Jonas Björk, Michael Green, Mattias Ohlsson, Lars Edenbrandt, Hans Öhlin and Ulf Ekelund
In search of the best method to predict acute coronary syndrome using only the ECG from the emergency department
To appear in Journal of Electrocardiology (2008)
Abstract:
The aim of this study was to compare different
methods to predict acute coronary syndrome (ACS)
using only data from a single ECG in the emergency
department (ED). Method We compared the ACS
prediction abilities of classical ECG criteria,
human expert ECG interpretation, a logistic
regression model and an artificial neural network
ensemble (ANN). The ED ECG and discharge diagnoses
were retrieved for 861 patient visits to the ED for
chest pain. Cross-validation was used to estimate
the generalization performance of the logistic
regression and the ANN model. Results The logistic
regression model had the overall best performance in
predicting ACS with an area under the receiver
operating characteristic curve of 0.88. The
sensitivities of logistic regression, ANN, expert
physicians and classical ECG criteria were 95, 95,
82 and 75% respectively, and the specificities were
54, 44, 63 and 69%. Conclusion Our logistic
regression model was the best overall method to
predict ACS, followed by our ANN. Decision support
models have the potential to improve even
experienced ECG readers’ ability to predict ACS in
the ED.
LU TP 07-42