Including patients with diabetes mellitus or coronary artery bypass grafting decreases the association between heart rate variability and mortality after myocardial infarction

Background

Decreased heart rate variability (HRV) is often assumed to be associated with mortality in all patients after myocardial infarction (MI), independent of clinical factors or time after MI.

Method

HRV was determined from Holter tapes in the Cardiac Arrhythmia Suppression Trial (CAST). Patients were 71 ± 120 days after MI. A total of 735 pre-therapy tapes were analyzed in patients who had ventricular premature contractions (VPCs) suppressed on the first treatment. The period of follow-up was 362 ± 243 days (69 deaths). The association of clinical and demographic factors and 24-hour, daytime, and nighttime HRV to mortality in all patients, patients without coronary artery bypass graft (CABG) surgery between the qualifying MI and the Holter monitoring, and patients with neither CABG nor diabetes mellitus was determined with univariate Cox regression analysis.

Results

For the entire group and the subgroup without CABG, the strongest association was with increased daytime normalized high frequency power (NHF day). Further excluding patients with diabetes mellitus strengthened the association of HRV with mortality rate. Decreased natural logarithm (ln) 24-hour total and ultra low frequency (ULF) power were the strongest predictors of mortality. The best cutoff point for ln ULF for separating survivors and non-survivors was determined. After including a history of MI, congestive heart failure, or both as co-factors, ln ULF ≤7.85 identified patients at approximately 4-times the relative risk of mortality, but did not risk-stratify patients without prior MI or history of congestive heart failure.

Conclusions

HRV predicts mortality rate in a broad range of times after MI. Excluding patients with CABG after MI or with diabetes mellitus significantly strengthens the association of HRV with mortality. HRV measures beyond the peri-infarction period, with clinical factors, can identify subgroups at an elevated risk of mortality.