Literature DB >> 32986084

Accuracy of Physicians' Electrocardiogram Interpretations: A Systematic Review and Meta-analysis.

David A Cook1, So-Young Oh2, Martin V Pusic3.   

Abstract

Importance: The electrocardiogram (ECG) is the most common cardiovascular diagnostic test. Physicians' skill in ECG interpretation is incompletely understood.
Objectives: To identify and summarize published research on the accuracy of physicians' ECG interpretations. Data Sources: A search of PubMed/MEDLINE, Embase, Cochrane CENTRAL (Central Register of Controlled Trials), PsycINFO, CINAHL (Cumulative Index to Nursing and Allied Health), ERIC (Education Resources Information Center), and Web of Science was conducted for articles published from database inception to February 21, 2020. Study Selection: Of 1138 articles initially identified, 78 studies that assessed the accuracy of physicians' or medical students' ECG interpretations in a test setting were selected. Data Extraction and Synthesis: Data on study purpose, participants, assessment features, and outcomes were abstracted, and methodological quality was appraised with the Medical Education Research Study Quality Instrument. Results were pooled using random-effects meta-analysis. Main Outcomes and Measures: Accuracy of ECG interpretation.
Results: Of 1138 studies initially identified, 78 assessed the accuracy of ECG interpretation. Across all training levels, the median accuracy was 54% (interquartile range [IQR], 40%-66%; n = 62 studies) on pretraining assessments and 67% (IQR, 55%-77%; n = 47 studies) on posttraining assessments. Accuracy varied widely across studies. The pooled accuracy for pretraining assessments was 42.0% (95% CI, 34.3%-49.6%; n = 24 studies; I2 = 99%) for medical students, 55.8% (95% CI, 48.1%-63.6%; n = 37 studies; I2 = 96%) for residents, 68.5% (95% CI, 57.6%-79.5%; n = 10 studies; I2 = 86%) for practicing physicians, and 74.9% (95% CI, 63.2%-86.7%; n = 8 studies; I2 = 22%) for cardiologists. Conclusions and Relevance: Physicians at all training levels had deficiencies in ECG interpretation, even after educational interventions. Improved education across the practice continuum appears warranted. Wide variation in outcomes could reflect real differences in training or skill or differences in assessment design.

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Mesh:

Year:  2020        PMID: 32986084      PMCID: PMC7522782          DOI: 10.1001/jamainternmed.2020.3989

Source DB:  PubMed          Journal:  JAMA Intern Med        ISSN: 2168-6106            Impact factor:   21.873


  4 in total

1.  Transfer learning enables prediction of myocardial injury from continuous single-lead electrocardiography.

Authors:  Boyang Tom Jin; Raj Palleti; Siyu Shi; Andrew Y Ng; James V Quinn; Pranav Rajpurkar; David Kim
Journal:  J Am Med Inform Assoc       Date:  2022-10-07       Impact factor: 7.942

2.  Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning.

Authors:  R R van de Leur; H Bleijendaal; K Taha; T Mast; J M I H Gho; M Linschoten; B van Rees; M T H M Henkens; S Heymans; N Sturkenboom; R A Tio; J A Offerhaus; W L Bor; M Maarse; H E Haerkens-Arends; M Z H Kolk; A C J van der Lingen; J J Selder; E E Wierda; P F M M van Bergen; M M Winter; A H Zwinderman; P A Doevendans; P van der Harst; Y M Pinto; F W Asselbergs; R van Es; F V Y Tjong
Journal:  Neth Heart J       Date:  2022-03-17       Impact factor: 2.854

Review 3.  State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.

Authors:  Georgios Petmezas; Leandros Stefanopoulos; Vassilis Kilintzis; Andreas Tzavelis; John A Rogers; Aggelos K Katsaggelos; Nicos Maglaveras
Journal:  JMIR Med Inform       Date:  2022-08-15

Review 4.  [Detection of ECG alterations typical for myocardial ischemia : New methods 2021].

Authors:  Sascha Beck; Valeria Martínez Pereyra; Andreas Seitz; Raffi Bekeredjian; Udo Sechtem; Peter Ong
Journal:  Internist (Berl)       Date:  2021-05-26       Impact factor: 0.743

  4 in total

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