Ulster University Logo

Data analysis of diagnostic accuracies in 12-lead electrocardiogram interpretation by junior medical fellows

Novotny, Tomas, Bond, Raymond, Andrsova, Irena, Koc, Lumir, Sisakova, Martina, Finlay, Dewar, Guldenring, Daniel, Spinar, Jindrich and Malik, Marek (2015) Data analysis of diagnostic accuracies in 12-lead electrocardiogram interpretation by junior medical fellows. Journal of Electrocardiology, 48 (6). pp. 988-994. [Journal article]

Full text not available from this repository.

URL: http://www.sciencedirect.com/science/article/pii/S0022073615002654

DOI: doi:10.1016/j.jelectrocard.2015.08.023

Abstract

BackgroundThe electrocardiogram (ECG) is the most commonly used diagnostic procedure for assessing the cardiovascular system. The aim of this study was to compare ECG diagnostic skill among fellows of cardiology and of other internal medicine specialties (non-cardiology fellows).MethodsA total of 2900 ECG interpretations were collected. A set of 100 clinical 12-lead ECG tracings were selected and classified into 12 diagnostic categories. The ECGs were evaluated by 15 cardiology fellows and of 14 non-cardiology fellows. Diagnostic interpretations were classified as (1) correct, (2) almost correct, (3) incorrect, and (4) dangerously incorrect. Multivariate logistic regression was used to assess confounding factors and to determine the odds ratios for the months of experience, age, sex, and the distinction between cardiology and non-cardiology fellows.ResultsThe mean rate of correct diagnoses by cardiology vs. non-cardiology fellows was 48.9 ± 8.9% vs. 35.9 ± 8.0% (p = 0.001; 70.1% vs. 55.0% for the aggregate of ‘correct’ and ‘almost correct’ diagnoses). There were 10.2 ± 5.6% of interpretations classified as ‘dangerously incorrect’ by cardiology fellows vs. 16.3 ± 5.0% by non-cardiology fellows (p = 0.008). The cardiology fellows achieved statistically significantly greater diagnostic accuracy in 7 out of the 12 diagnostic classes. In multivariable logistic regression, the distinction between cardiology and non-cardiology fellows was the only independent statistically significant (p < 0.001) predictor of whether the reader is likely correct or incorrect. Being a non-cardiology fellow reduced the probability of correct classification by 42% (odds ratio [95% confidence interval]: 0.58 [0.50; 0.68]).ConclusionsAlthough cardiology fellows out-performed the others, skills in ECG interpretation were found not adequately proficient. A comprehensive approach to ECG education is necessary. Further studies are needed to evaluate proper methods of training, testing, and continuous medical education in ECG interpretation.

Item Type:Journal article
Keywords:ECG, Health informatics, decision making
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Computing & Engineering > School of Engineering
Research Institutes and Groups:Engineering Research Institute
Engineering Research Institute > Nanotechnology & Integrated BioEngineering Centre (NIBEC)
Computer Science Research Institute > Smart Environments
Computer Science Research Institute
ID Code:32529
Deposited By: Dr Raymond Bond
Deposited On:04 Nov 2015 11:19
Last Modified:04 Nov 2015 11:19

Repository Staff Only: item control page