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Crit Care. 2015 Dec 10;19:425. doi: 10.1186/s13054-015-1142-z.

Combining multiple ECG features does not improve prediction of defibrillation outcome compared to single features in a large population of out-of-hospital cardiac arrests.

Author information

1
School of Biomedical Engineering, Third Military Medical University and Chongqing University, 30 Gaotanyan Main Street, Chongqing, 400038, China. hmcherry@126.com.
2
School of Biomedical Engineering, Third Military Medical University and Chongqing University, 30 Gaotanyan Main Street, Chongqing, 400038, China. johnsongong@163.com.
3
School of Biomedical Engineering, Third Military Medical University and Chongqing University, 30 Gaotanyan Main Street, Chongqing, 400038, China. leeoken@gmail.com.
4
Department of Anesthesia, Critical Care and Emergency, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy. tommymauri@gmail.com.
5
IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri", Via Privata Giuseppe La Masa, 19, 20156, Milan, Italy. francesca.fumagalli@marionegri.it.
6
Azienda Regionale Emergenza Urgenza (AREU), Via Alfredo Campanini, 6, 20124, Milan, Italy. m.bozzola@areu.lombardia.it.
7
Research Centre on Public Health, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo, 1, 20126, Milan, Italy. giancarlo.cesana@unimib.it.
8
IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri", Via Privata Giuseppe La Masa, 19, 20156, Milan, Italy. roberto.latini@marionegri.it.
9
Department of Anesthesia, Critical Care and Emergency, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy. antonio.pesenti@unimib.it.
10
Azienda Regionale Emergenza Urgenza (AREU), Via Alfredo Campanini, 6, 20124, Milan, Italy. antonio.pesenti@unimib.it.
11
IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri", Via Privata Giuseppe La Masa, 19, 20156, Milan, Italy. gristag@gmail.com.

Abstract

INTRODUCTION:

Quantitative electrocardiographic (ECG) waveform analysis provides a noninvasive reflection of the metabolic milieu of the myocardium during resuscitation and is a potentially useful tool to optimize the defibrillation strategy. However, whether combining multiple ECG features can improve the capability of defibrillation outcome prediction in comparison to single feature analysis is still uncertain.

METHODS:

A total of 3828 defibrillations from 1617 patients who experienced out-of-hospital cardiac arrest were analyzed. A 2.048-s ECG trace prior to each defibrillation without chest compressions was used for the analysis. Sixteen predictive features were optimized through the training dataset that included 2447 shocks from 1050 patients. Logistic regression, neural network and support vector machine were used to combine multiple features for the prediction of defibrillation outcome. Performance between single and combined predictive features were compared by area under receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and prediction accuracy (PA) on a validation dataset that consisted of 1381 shocks from 567 patients.

RESULTS:

Among the single features, mean slope (MS) outperformed other methods with an AUC of 0.876. Combination of complementary features using neural network resulted in the highest AUC of 0.874 among the multifeature-based methods. Compared to MS, no statistical difference was observed in AUC, sensitivity, specificity, PPV, NPV and PA when multiple features were considered.

CONCLUSIONS:

In this large dataset, the amplitude-related features achieved better defibrillation outcome prediction capability than other features. Combinations of multiple electrical features did not further improve prediction performance.

PMID:
26652159
PMCID:
PMC4674958
DOI:
10.1186/s13054-015-1142-z
[Indexed for MEDLINE]
Free PMC Article

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