The average heart rate contribution to the prognostic power of heart rate variability in non-diabetics
and diabetics after myocardial infarction.
Jerzy Sacha1, Szymon Barabach1, Gabriela Statkiewicz-Barabach2, Krzysztof Sacha3, Georg Schmidt4, Alexander Muller4
1
Department of Cardiology - Regional Medical Center, Opole, Poland; 2Institute of Physics - Wroclaw University of Technology, Wroclaw, Poland; 3Atom Optics Department, Institute of Physics Jagiellonian University, Krakow, Poland; 41. Medizinische Klinik und Deutsches Herzzentrum Munchen der Technischen Universitat Munchen, Munchen, Germany
INTRODUCTION
RESULTS
Heart rate variability (HRV) is associated with an average heart rate (HR). However, this
relationship is partly physiologically and partly mathematically determined. The latter is due
to the mathematical non-linear relationship between R-R interval (RR) and HR (Fig. 1A). By
the mathematical correction one may strengthen or weaken the relationship between HRV
and an average HR (Fig. 1B-H) – this enables us to explore the contribution of HR to the
prognostic power of HRV.
The figure exhibits prognostic power of each HRV index depending on the type of outcome
(p-values refer to the Friedman test for AUC differences between classes). If HRV is getting
more dependent on HR its prediction power increases for all-cause and cardiac death in nDb
and Db and for sudden cardiac death in Db, but it decreases for non-cardiac death in nDb and
it behaves equivocally for sudden cardiac death in nDb and non-cardiac death in Db.
Figure 1. In panel A the mathematical non-linear relationship between R-R interval (RR) and heart rate (HR) is shown. In other panels,
Spearman correlations between total powers (TP) of respective hrv spectra and average HR are depicted for: B - hrv1 calculated by division
of standard HRV spectrum by the average RR (avRR) to the power 4; C - hrv2 achieved by division of standard HRV spectrum by avRR
squared; D - hrv3 which is a standard HRV spectrum; E - hrv4 obtained by multiplication of standard HRV spectrum by avRR squared; F hrv5 calculated by multiplication of standard HRV spectrum by avRR to the power 4; G - hrv6 achieved by multiplication of standard HRV
spectrum by avRR to the power 8; H - hrv7 obtained by multiplication of standard HRV spectrum by avRR to the power 16.
PURPOSE
To explore HR contribution to the prognostic power of HRV in non-diabetics (nDb) and
diabetics (Db) after myocardial infarction.
METHODS
Prognostic powers of HRV indices were tested on 1455 post-infarction patients (1213 nDb
and 242 Db followed up for 5 years) by calculation of areas under receiver-operator
characteristic curves (AUC). Seven classes of spectral HRV indices with different
associations with HR were obtained: hrv1, hrv2, hrv3, hrv4, hrv5, hrv6, hrv7 (Fig. 1B-H) their respective average Spearman correlation coefficients with HR were: -0.001, -0.4, -0.64,
-0.78, -0.85, -0.93, -0.97 (insignificant for hrv1). Class hrv3 consisted of standard HRV
indices – in classes hrv1, hrv2 the association between HRV and HR was weakened but it
was strengthened in classes hrv4, hrv5, hrv6, hrv7.
Figure 2. AUC’s for different types of outcomes for different classes of spectral HRV indices in non-diabetics and diabetics (p-values refer
to Friedman ANOVA test for differences between classes).
CONCLUSIONS
The mathematical strengthening of the association between HRV and HR may improve the
prognostic power of HRV for all-cause death and cardiac death in nDb and Db and sudden
cardiac death in Db while the weakening improves the prediction power of non-cardiac
death in nDb.
Address for correspondence: Jerzy Sacha, Department of Cardiology, Regional Medical Center in Opole, Al. Witosa 26, 45-418 Opole, Poland e-mail: [email protected]