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Can functional cardiac age be predicted from the ECG in a normal healthy population?

Starc, Vito; Leban, Manj A.; Sinigoj, Petra; Vrhovec, Milos; Potocnik, Nejka; I. Fernlund, Eva LU ; Liuba, Petru LU and Schlegel, Todd T (2012) 39th Computing in Cardiology Conference, CinC 2012 In Computing in Cardiology 2012, CinC 2012 39. p.101-104
Abstract

We hypothesized that in a normal healthy population changes in several ECG parameters together might reliably characterize the functional age of the heart. Data from 377 healthy subjects (209 men, 168 women, aged 4 to 75 years) were included in the study. In all subjects, ECG recordings (resting 5-minute 12-lead high fidelity ECG) were evaluated via custom software programs to calculate up to 120 different conventional and advanced ECG parameters. Using factor analysis, those 5 parameters that exhibited the highest linear correlations with age and that were mutually the least correlated were evaluated by multiple linear regression analysis to predict the functional electrical age of the heart. Ignoring small differences between males... (More)

We hypothesized that in a normal healthy population changes in several ECG parameters together might reliably characterize the functional age of the heart. Data from 377 healthy subjects (209 men, 168 women, aged 4 to 75 years) were included in the study. In all subjects, ECG recordings (resting 5-minute 12-lead high fidelity ECG) were evaluated via custom software programs to calculate up to 120 different conventional and advanced ECG parameters. Using factor analysis, those 5 parameters that exhibited the highest linear correlations with age and that were mutually the least correlated were evaluated by multiple linear regression analysis to predict the functional electrical age of the heart. Ignoring small differences between males and females, functional electrical age was best predicted (R2 of 0.76, P < 0.001) by multiple linear regression analysis incorporating the RR-interval normalized high frequency variability of RRV; the RR-interval normalized value of a QT variability parameter called QTcor; the mean high frequency QRS (150-250 Hz) amplitude; the mean ST segment level at the J point; and the body mass index. In apparently healthy subjects, functional cardiac age can be estimated by multiple linear regression analysis of mostly advanced ECG parameters.

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author
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type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Computing in Cardiology 2012, CinC 2012
volume
39
pages
4 pages
conference name
39th Computing in Cardiology Conference, CinC 2012
external identifiers
  • scopus:84875644556
ISBN
9781467320740
language
English
LU publication?
yes
id
fe69489f-7f9b-4775-b4d4-162e08533265
date added to LUP
2017-07-21 10:21:42
date last changed
2017-09-15 11:29:36
@inproceedings{fe69489f-7f9b-4775-b4d4-162e08533265,
  abstract     = {<p>We hypothesized that in a normal healthy population changes in several ECG parameters together might reliably characterize the functional age of the heart. Data from 377 healthy subjects (209 men, 168 women, aged 4 to 75 years) were included in the study. In all subjects, ECG recordings (resting 5-minute 12-lead high fidelity ECG) were evaluated via custom software programs to calculate up to 120 different conventional and advanced ECG parameters. Using factor analysis, those 5 parameters that exhibited the highest linear correlations with age and that were mutually the least correlated were evaluated by multiple linear regression analysis to predict the functional electrical age of the heart. Ignoring small differences between males and females, functional electrical age was best predicted (R<sup>2</sup> of 0.76, P &lt; 0.001) by multiple linear regression analysis incorporating the RR-interval normalized high frequency variability of RRV; the RR-interval normalized value of a QT variability parameter called QTcor; the mean high frequency QRS (150-250 Hz) amplitude; the mean ST segment level at the J point; and the body mass index. In apparently healthy subjects, functional cardiac age can be estimated by multiple linear regression analysis of mostly advanced ECG parameters.</p>},
  author       = {Starc, Vito and Leban, Manj A. and Sinigoj, Petra and Vrhovec, Milos and Potocnik, Nejka and I. Fernlund, Eva and Liuba, Petru and Schlegel, Todd T},
  booktitle    = {Computing in Cardiology 2012, CinC 2012},
  isbn         = {9781467320740},
  language     = {eng},
  pages        = {101--104},
  title        = {Can functional cardiac age be predicted from the ECG in a normal healthy population?},
  volume       = {39},
  year         = {2012},
}