Bedside autonomic risk stratification after myocardial infarction by means of short-term deceleration capacity of heart rate

Aims Twenty-four-hour deceleration capacity (DC 24h) of heart rate is a strong predictor of mortality after myocardial infarction (MI). Assessment of DC from short-term recordings (DC st) would be of practical use in everyday clinical practice but its predictive value is unknown. Here, we test the usefulness of DC st for autonomic bedside risk stratification after MI. Methods and results We included 908 patients after acute MI enrolled in Munich and 478 patients with acute (n = 232) and chronic MI (n = 246) enrolled in Tuebingen, both in Germany. We assessed DC st from high-resolution resting electrocardiogram (ECG) recordings (<30 min) performed under standardized conditions in supine position. In the Munich cohort, we also assessed DC 24h from 24-h Holter recordings. Deceleration capacity was dichotomized at the established cut-off value of ≤ 2.5 ms. Primary endpoint was 3-year mortality. Secondary endpoint was 3-year cardiovascular mortality. In addition to DC, multivariable analyses included the Global Registry of Acute Coronary Events score >140 and left ventricular ejection fraction ≤ 35%. During follow-up, 48 (5.3%) and 48 (10.0%) patients died in the Munich and Tuebingen cohorts, respectively. On multivariable analyses, DC st ≤ 2.5 ms was the strongest predictor of mortality, yielding hazard ratios of 5.04 (2.68-9.49; P < 0.001) and 3.19 (1.70-6.02; P < 0.001) in the Munich and Tuebingen cohorts, respectively. Deceleration capacity assessed from short-term recordings ≤ 2.5 ms was also an independent predictor of cardiovascular mortality in both cohorts. Implementation of DC st ≤ 2.5 ms into the multivariable models led to a significant increase of C-statistics and integrated discrimination improvement score. Conclusion Deceleration capacity assessed from short-term recordings is a strong and independent predictor of mortality and cardiovascular mortality after MI, which is complementary to existing risk stratification strategies.