Literature DB >> 9238383

Validation of a new computer program for Minnesota coding.

J A Kors1, G van Herpen, J Wu, Z Zhang, R J Prineas, J H van Bemmel.   

Abstract

The Minnesota code (MC) is a classification system for electrocardiograms (ECGs) that is used for ECG coding in epidemiologic studies. As the MC measurement procedures and rules are complex, visual coding is time-consuming and error-prone. Automation should reduce measurement and coding errors. The authors developed an MC program, closely adhering to the MC regulations. To validate the program, a test set of 300 ECGs containing a wide variety of codable patterns was collected. The ECGs were coded independently by the program and by an experienced human reader. A reference code ("truth") was established by resolving disagreements through a consensus procedure. If the computer and human agreed, they were considered to be correct. Sensitivity and specificity were computed for each of the nine main code categories of the MC, both for the computer and for visual coding. The results show that the program is as good as or better than the human reader for sensitivity and specificity of all MC categories. Particularly noteworthy is the good program performance for arrhythmia coding. Most coding differences between the program and truth arise from small, borderline measurement differences in combination with the all-or-none character of the coding criteria. In conclusion, computerized Minnesota coding is a valuable alternative or supplement to visual coding.

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Year:  1996        PMID: 9238383     DOI: 10.1016/s0022-0736(96)80025-2

Source DB:  PubMed          Journal:  J Electrocardiol        ISSN: 0022-0736            Impact factor:   1.438


  20 in total

1.  Impaired functional status and echocardiographic abnormalities signifying global dysfunction enhance the prognostic significance of previously unrecognized myocardial infarction detected by electrocardiography.

Authors:  Khawaja Afzal Ammar; Ravindrakumar Makwana; Steven J Jacobsen; Jan A Kors; John C Burnett; Margaret M Redfield; Barbara P Yawn; Richard J Rodeheffer
Journal:  Ann Noninvasive Electrocardiol       Date:  2007-01       Impact factor: 1.468

2.  Methodology of QT-interval measurement in the modular ECG analysis system (MEANS).

Authors:  Jan A Kors; Gerard van Herpen
Journal:  Ann Noninvasive Electrocardiol       Date:  2009-01       Impact factor: 1.468

3.  Identification of optimal electrocardiographic criteria for the diagnosis of unrecognized myocardial infarction: a population-based study.

Authors:  Khawaja Afzal Ammar; Barbara P Yawn; Lynn Urban; Douglas W Mahoney; Jan A Kors; Steven Jacobsen; Richard J Rodeheffer
Journal:  Ann Noninvasive Electrocardiol       Date:  2005-04       Impact factor: 1.468

4.  Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060.

Authors:  Bouwe P Krijthe; Anton Kunst; Emelia J Benjamin; Gregory Y H Lip; Oscar H Franco; Albert Hofman; Jacqueline C M Witteman; Bruno H Stricker; Jan Heeringa
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5.  Computerized ST depression analysis improves prediction of all-cause and cardiovascular mortality: the strong heart study.

Authors:  P M Okin; R B Devereux; J A Kors; G van Herpen; R S Crow; R R Fabsitz; B V Howard
Journal:  Ann Noninvasive Electrocardiol       Date:  2001-04       Impact factor: 1.468

6.  Bidirectional Association Between Kidney Function and Atrial Fibrillation: A Population-Based Cohort Study.

Authors:  Anna C van der Burgh; Sven Geurts; M Arfan Ikram; Ewout J Hoorn; Maryam Kavousi; Layal Chaker
Journal:  J Am Heart Assoc       Date:  2022-05-17       Impact factor: 6.106

7.  Distribution of echocardiographic parameters and their associations with cardiovascular risk factors in the Rotterdam Study.

Authors:  Isabella Kardys; Jaap W Deckers; Bruno H Ch Stricker; Wim B Vletter; Albert Hofman; Jacqueline Witteman
Journal:  Eur J Epidemiol       Date:  2010-05-22       Impact factor: 8.082

8.  The additional value of routine electrocardiograms in cardiovascular risk management of older people.

Authors:  Wouter De Ruijter; Willem J J Assendelft; Peter W Macfarlane; Rudi G J Westendorp; Jacobijn Gussekloo
Journal:  Scand J Prim Health Care       Date:  2008       Impact factor: 2.581

9.  Participation bias and its impact on the assembly of a genetic specimen repository for a myocardial infarction cohort.

Authors:  Adelaide M Arruda-Olson; Susan A Weston; Brooke L Fridley; Jill M Killian; Ellen E Koepsell; Véronique L Roger
Journal:  Mayo Clin Proc       Date:  2007-10       Impact factor: 7.616

10.  Methods of data collection and definitions of cardiac outcomes in the Rotterdam Study.

Authors:  Maarten J G Leening; Maryam Kavousi; Jan Heeringa; Frank J A van Rooij; Jolande Verkroost-van Heemst; Jaap W Deckers; Francesco U S Mattace-Raso; Gijsbertus Ziere; Albert Hofman; Bruno H Ch Stricker; Jacqueline C M Witteman
Journal:  Eur J Epidemiol       Date:  2012-03-03       Impact factor: 8.082

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