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Deceleration Capacity of Heart Rate Predicts Arrhythmic and Total Mortality in Heart Failure Patients
Petros Arsenos1, George Manis2, Konstantinos A Gatzoulis1
1First Division of Cardiology, Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Insights
A new method for measuring heart rate deceleration capacity (DC) predicts sudden cardiac death and total mortality in heart failure patients. This improved DCsgn method avoids negative values, enhancing its clinical utility.
Area of Science:
- Cardiology
- Biomedical Engineering
- Data Science
Background:
- Deceleration capacity (DC) of heart rate is an established mortality predictor in post-myocardial infarction patients.
- The original DC measurement method (DCorig) can yield negative values, limiting its application.
- Investigating DC's predictive capability for arrhythmic mortality is crucial.
Purpose of the Study:
- To refine the DC measurement method to overcome limitations of negative values.
- To evaluate the improved DC method's (DCsgn) ability to predict sudden cardiac death (SCD) and total mortality (TM).
Main Methods:
- Analysis of time-series data from 221 heart failure patients.
- Comparison of the original DC (DCorig) with a novel variant, DCsgn, which uses four-beat windows.
- Cox regression analysis incorporating clinical variables and DC indices to assess predictive power for SCD and TM.
Main Results:
- The DCsgn method demonstrated a significant association with reduced risk for SCD (HR: 0.742, P < 0.001) and TM (HR: 0.686, P = 0.001).
- DCsgn also independently predicted SCD when dichotomized (HR: 1.815, P = 0.024) and TM (HR: 2.443, P = 0.008).
- Both DCorig and DCsgn showed predictive value for mortality, with DCsgn exhibiting stronger statistical significance.
Conclusions:
- Deceleration capacity effectively predicts both sudden cardiac death and total mortality in heart failure patients.
- The improved DCsgn method offers a more robust and reliable measure by avoiding negative values.
- DCsgn enhances the predictive accuracy and clinical applicability of heart rate variability analysis in cardiovascular risk assessment.
Background:
Deceleration capacity (DC) of heart rate proved an independent mortality predictor in postmyocardial infarction patients. The original method (DCorig) may produce negative values (9% in our analyzed sample). We aimed to improve the method and to investigate if DC also predicts the arrhythmic mortality.
Methods:
Time series from 221 heart failure patients was analyzed with DCorig and a new variant, the DCsgn, in which decelerations are characterized based on windows of four consecutive beats and not on anchors. After 41.2 months, 69 patients experienced sudden cardiac death (SCD) surrogate end points, while 61 died.
Results:
(SCD+ vs SCD-group) DCorig: 3.7 ± 1.6 ms versus 4.6 ± 2.6 ms (P = 0.020) and DCsgn: 4.9 ± 1.7 ms versus 6.1 ± 2.2 ms (P < 0.001). After Cox regression (gender, age, left ventricular ejection fraction, filtered QRS, NSVT≥1/24h, VPBs≥240/24h, mean 24-h QTc, and each DC index added on the model separately), DCsgn (continuous) was an independent SCD predictor (hazard ratio [H.R.]: 0.742, 95% confidence intervals (C.I.): 0.631-0.871, P < 0.001). DCsgn ≤ 5.373 (dichotomous) presented 1.815 H.R. for SCD (95% C.I.: 1.080-3.049, P = 0.024), areas under curves (AUC)/receiver operator characteristic (ROC): 0.62 (DCorig) and 0.66 (DCsgn), P = 0.190 (chi-square). Results for deceased versus alive group: DCorig: 3.2 ± 2.0 ms versus 4.8 ± 2.4 ms (P < 0.001) and DCsgn: 4.6 ± 1.4 ms versus 6.2 ± 2.2 ms (P < 0.001). In Cox regression, DCsgn (continuous) presented H.R.: 0.686 (95% C.I. 0.546-0.862, P = 0.001) and DCsgn ≤ 5.373 (dichotomous) presented an H.R.: 2.443 for total mortality (TM) (95% C.I. 1.269-4.703, P = 0.008).
Auc/Roc:
0.71 (DCorig) and 0.73 (DCsgn), P = 0.402.
Conclusions:
DC predicts both SCD and TM. DCsgn avoids the negative values, improving the method in a nonstatistical important level.
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