Multi-lag HRV analysis discriminates disease progression of post-infarct people with no diabetes versus diabetes.

Abstract

Diabetes mellitus is associated with multi-organ system dysfunction including the cardiovascular and autonomic nervous system. Although it is well documented that post-infarct patients are at higher risk of sudden cardiac death, diabetes adds an additional risk associated with autonomic neuropathy. However it is not known how the presence of diabetes in post-infarct patients affects cardiac rhythm. The majority of HRV algorithms for determining cardiac inter-beat interval changes describe only beat-to-beat variation determined over the whole heart rate recording and therefore do not consider the ability of a heart beat to influence a train of succeeding beats nor whether or how the temporal dynamics of the inter-beat intervals changes. This study used Poincaré Plot derived features and incorporated increased lag intervals to compare post-infarct patients with no history of prior infarct with or without diabetes and found that for the nondiabetic post-infarct patients only increased lag of short term correlation (SD1) predicted mortality, whereas in the diabetic post-infarct group only long-term correlations (SD2) significantly predicted mortality at a follow-up period of eight years. Temporal dynamics measured as a complex correlation measure (CCM) was also a significant predictor of mortality only in the diabetic post-infarct cohort. This study highlights the different pathophysiological progression and risk profile associated with presence of diabetes in a post-infarct patient population at eight year follow-up.