Literature DB >> 16003708

Diagnostic performance of a computer-based ECG rhythm algorithm.

Kimble Poon1, Peter M Okin, Paul Kligfield.   

Abstract

We examined the accuracy of computer-based rhythm interpretation from one electrocardiograph manufacturer (GE Healthcare Technologies MUSE software 005C) in 4297 consecutive recordings in a university hospital setting. Overreading was performed by either of 2 experienced cardiologists, and all disagreements with the initial computer rhythm statement were reviewed by the second cardiologist to achieve physician consensus used as the "gold standard" for rhythm diagnosis. Overall, 13.2% (565/4297) of computer-based rhythm statements required revision, but excluding tracings with pacemakers, the revision rate was 7.8% (307/3954), including 3.8% involving the primary rhythm diagnosis and 3.9% involving definition of ectopic complexes. The false-negative rate for sinus rhythm was only 1.3%, but a computer diagnosis of sinus rhythm was incorrect in 9.9% of other rhythms. The false-negative rate for atrial fibrillation was 9.2%, whereas a computer diagnosis of atrial fibrillation was incorrect in 1.1% of other rhythms, including sinus. Computer diagnosis of paced rhythms remains problematic, and physician overreading to correct computer-based electrocardiogram rhythm diagnoses remains mandatory.

Entities:  

Mesh:

Year:  2005        PMID: 16003708     DOI: 10.1016/j.jelectrocard.2005.01.008

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


  18 in total

1.  Accurate ECG diagnosis of atrial tachyarrhythmias using quantitative analysis: a prospective diagnostic and cost-effectiveness study.

Authors:  David E Krummen; Mitul Patel; Hong Nguyen; Gordon Ho; Dhruv S Kazi; Paul Clopton; Marian C Holland; Scott L Greenberg; Gregory K Feld; Mitchell N Faddis; Sanjiv M Narayan
Journal:  J Cardiovasc Electrophysiol       Date:  2010-11

2.  Socioeconomic status and the development of atrial fibrillation in Hispanics, African Americans and non-Hispanic whites.

Authors:  Eric Shulman; Faraj Kargoli; Philip Aagaard; Ethan Hoch; Luigi Di Biase; John Fisher; Jay Gross; Soo Kim; Kevin J Ferrick; Andrew Krumerman
Journal:  Clin Cardiol       Date:  2017-06-09       Impact factor: 2.882

3.  Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: A pilot usability study.

Authors:  Meghan Brennan; Sahil Puri; Tezcan Ozrazgat-Baslanti; Zheng Feng; Matthew Ruppert; Haleh Hashemighouchani; Petar Momcilovic; Xiaolin Li; Daisy Zhe Wang; Azra Bihorac
Journal:  Surgery       Date:  2019-02-18       Impact factor: 3.982

4.  Risk factors for QTc interval prolongation.

Authors:  Charlotte P M Heemskerk; Marieke Pereboom; Karlijn van Stralen; Florine A Berger; Patricia M L A van den Bemt; Aaf F M Kuijper; Ruud T M van der Hoeven; Aukje K Mantel-Teeuwisse; Matthijs L Becker
Journal:  Eur J Clin Pharmacol       Date:  2017-11-22       Impact factor: 2.953

5.  Troponin is superior to electrocardiogram and creatinine kinase MB for predicting clinically significant myocardial injury after coronary artery bypass grafting.

Authors:  Jochen D Muehlschlegel; Tjörvi E Perry; Kuang-Yu Liu; Luigino Nascimben; Amanda A Fox; Charles D Collard; Edwin G Avery; Sary F Aranki; Michael N D'Ambra; Stanton K Shernan; Simon C Body
Journal:  Eur Heart J       Date:  2009-04-30       Impact factor: 29.983

6.  Electrocardiography screening for cardiotoxicity after modified Vaccinia Ankara vaccination.

Authors:  Junko Sano; Bernard R Chaitman; Jason Swindle; Sharon E Frey
Journal:  Am J Med       Date:  2009-01       Impact factor: 4.965

7.  Accuracy of diagnosing atrial fibrillation on electrocardiogram by primary care practitioners and interpretative diagnostic software: analysis of data from screening for atrial fibrillation in the elderly (SAFE) trial.

Authors:  Jonathan Mant; David A Fitzmaurice; F D Richard Hobbs; Sue Jowett; Ellen T Murray; Roger Holder; Michael Davies; Gregory Y H Lip
Journal:  BMJ       Date:  2007-06-29

8.  Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.

Authors:  Awni Y Hannun; Pranav Rajpurkar; Masoumeh Haghpanahi; Geoffrey H Tison; Codie Bourn; Mintu P Turakhia; Andrew Y Ng
Journal:  Nat Med       Date:  2019-01-07       Impact factor: 53.440

9.  Estimating the prevalence of atrial fibrillation in a general population using validated electronic health data.

Authors:  Johannes Norberg; Svante Bäckström; Jan-Håkan Jansson; Lars Johansson
Journal:  Clin Epidemiol       Date:  2013-12-09       Impact factor: 4.790

Review 10.  Deep learning for comprehensive ECG annotation.

Authors:  Benjamin A Teplitzky; Michael McRoberts; Hamid Ghanbari
Journal:  Heart Rhythm       Date:  2020-05       Impact factor: 6.779

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.