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

Tomas Novotny, Raymond Bond, Irena Andrsova, Lumir Koc, Martina Sisakova, Dewar Finlay, Daniel Guldenring, Jindrich Spinar, Marek Malik

Research output: Contribution to journalArticle

10 Citations (Scopus)

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.
LanguageEnglish
Pages988-994
JournalJournal of Electrocardiology
Volume48
Issue number6
DOIs
Publication statusPublished - 1 Nov 2015

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Cardiology
Electrocardiography
Logistic Models
Odds Ratio
Lead
Internal Medicine
Cardiovascular System
Medical Education
Confidence Intervals
Education

Keywords

  • ECG
  • Health informatics
  • decision making

Cite this

Novotny, Tomas ; Bond, Raymond ; Andrsova, Irena ; Koc, Lumir ; Sisakova, Martina ; Finlay, Dewar ; Guldenring, Daniel ; Spinar, Jindrich ; Malik, Marek. / Data analysis of diagnostic accuracies in 12-lead electrocardiogram interpretation by junior medical fellows. In: Journal of Electrocardiology. 2015 ; Vol. 48, No. 6. pp. 988-994.
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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.",
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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; Malik, Marek.

In: Journal of Electrocardiology, Vol. 48, No. 6, 01.11.2015, p. 988-994.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Data analysis of diagnostic accuracies in 12-lead electrocardiogram interpretation by junior medical fellows

AU - Novotny, Tomas

AU - Bond, Raymond

AU - Andrsova, Irena

AU - Koc, Lumir

AU - Sisakova, Martina

AU - Finlay, Dewar

AU - Guldenring, Daniel

AU - Spinar, Jindrich

AU - Malik, Marek

PY - 2015/11/1

Y1 - 2015/11/1

N2 - 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.

AB - 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.

KW - ECG

KW - Health informatics

KW - decision making

U2 - 10.1016/j.jelectrocard.2015.08.023

DO - 10.1016/j.jelectrocard.2015.08.023

M3 - Article

VL - 48

SP - 988

EP - 994

JO - Journal of Electrocardiology

T2 - Journal of Electrocardiology

JF - Journal of Electrocardiology

SN - 0022-0736

IS - 6

ER -