Literature DB >> 32194027

Determination of Sensitivity and Specificity of Electrocardiography for Left Ventricular Hypertrophy in a Large, Diverse Patient Population.

Maxwell Bressman1, Alon Y Mazori2, Eric Shulman3, Jay J Chudow3, Ythan Goldberg3, John D Fisher3, Kevin J Ferrick3, Mario Garcia3, Luigi Di Biase3, Andrew Krumerman3.   

Abstract

BACKGROUND: Electrocardiography (ECG) is poorly sensitive, but highly specific for the diagnosis of left ventricular hypertrophy. However, previous studies documenting this were small and lacked patient diversity. Furthermore, little is known about the impact of patient characteristics on the sensitivity and specificity of ECG for left ventricular hypertrophy. To address this issue, the present study was conducted to ascertain the sensitivity and specificity of ECG for left ventricular hypertrophy in a large, diverse patient population.
METHODS: We performed a retrospective cohort study using ECG and echocardiography (ECHO) data from a large metropolitan health system. All patients had one ECG and ECHO on file, obtained within 1 week of each other. Sensitivity and specificity of ECG for left ventricular hypertrophy were determined by comparing results from the MUSE® 12-SL (GE Healthcare, Chicago, IL) computer-generated algorithm for ECG to ECHO left ventricular mass index. Subgroup analyses of individual patient characteristics were performed with corresponding chi-squared analyses to determine significance.
RESULTS: A total of 13,960 subjects were included in the study. The typical subject was 60 years of age or older, female, overweight, and hypertensive, and demonstrated low socioeconomic status. The sensitivity and specificity of ECG for left ventricular hypertrophy in the overall cohort were 30.7% and 84.4%, respectively, with multiple patient characteristics influencing these results.
CONCLUSIONS: This is the first study to confirm the sensitivity and specificity of ECG for left ventricular hypertrophy in a large, diverse patient population with significant minority representation. Furthermore, although these statistical measures are influenced by patient characteristics, such differences are likely not clinically significant.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Electrocardiogram, Echocardiogram; Left ventricular hypertrophy; Sensitivity; Specificity

Year:  2020        PMID: 32194027     DOI: 10.1016/j.amjmed.2020.01.042

Source DB:  PubMed          Journal:  Am J Med        ISSN: 0002-9343            Impact factor:   4.965


  4 in total

1.  Optimizing ECG to detect echocardiographic left ventricular hypertrophy with computer-based ECG data and machine learning.

Authors:  Fernando De la Garza Salazar; Maria Elena Romero Ibarguengoitia; José Ramón Azpiri López; Arnulfo González Cantú
Journal:  PLoS One       Date:  2021-11-30       Impact factor: 3.240

2.  Deep learning assessment of left ventricular hypertrophy based on electrocardiogram.

Authors:  Xiaoli Zhao; Guifang Huang; Lin Wu; Min Wang; Xuemin He; Jyun-Rong Wang; Bin Zhou; Yong Liu; Yesheng Lin; Dinghui Liu; Xianguan Yu; Suzhen Liang; Borui Tian; Linxiao Liu; Yanming Chen; Shuhong Qiu; Xujing Xie; Lanqing Han; Xiaoxian Qian
Journal:  Front Cardiovasc Med       Date:  2022-08-11

3.  Electrocardiographic Characteristics and Their Correlation with Echocardiographic Alterations in Fabry Disease.

Authors:  Matthew Zada; Queenie Lo; Siddharth J Trivedi; Mehmet Harapoz; Anita C Boyd; Kerry Devine; Norman Sadick; Michel C Tchan; Liza Thomas
Journal:  J Cardiovasc Dev Dis       Date:  2022-01-03

4.  Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach.

Authors:  Eleni Angelaki; Maria E Marketou; Georgios D Barmparis; Alexandros Patrianakos; Panos E Vardas; Fragiskos Parthenakis; Giorgos P Tsironis
Journal:  J Clin Hypertens (Greenwich)       Date:  2021-01-28       Impact factor: 3.738

  4 in total

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