Risk stratification after acute myocardial infarction by amplitude–frequency mapping of cyclic variation of heart rate

Abstract

Background: Bluntedcyclicvariationofheartrate(CVHR),measuredasadecrease in CVHR amplitude (Acv), predicts mortality risk after acute myocardial infarction (AMI).However,AcvalsocanbereducedinmildsleepapneawithmildO 2 desaturation.WeinvestigatedwhetherAcv'spredictivepowerforpost-AMImortalitycould be improved by considering the effect of sleep apnea severity. Methods: In24-hrECGin265,291participantsoftheAllostaticStateMappingbyAmbulatoryECGRepositoryproject,sleepapneaseveritywasestimatedbythefre-quencyofCVHR(Fcv)measuredbyanautomatedalgorithmforauto-correlatedwave detectionbyadaptivethreshold(ACAT).ThedistributionofAcvontheAcv-Fcvrelation map was modeled by percentile regression, and a function converting Acv into percentile value was developed. In the retrospective cohort of the Enhancing RecoveryinCoronaryHeartDisease(ENRICHD)study,consistingof673survivors and 44 non-survivors after AMI, the mortality predictive power of percentile Acv calculatedbythefunctionwascomparedwiththatofunadjustedAcv.Results: AmongtheALLSTARECGdata,lowAcvvaluesappearedmorelikelywhen Fcvwaslow.ThelogisticregressionanalysisformortalityintheENRICHDcohort showed c-statistics of 0.667 (SE, 0.041), 0.817 (0.035), and 0.843 (0.030) for Fcv, unadjusted Acv, and the percentile Acv, respectively. Compared with unadjusted Acv,thepercentileAcvshowedasignificantnetreclassificationimprovementof0.90 (95%CI,0.51-1.42).

Night‐to‐night variability of sleep apnea detected by cyclic variation of heart rate during long‐term continuous ECG monitoring

“…Also, while the average Fcv was higher in the supine position than in the left lateral and prone positions, the night-to-night In this study, an algorithm of the auto-correlated wave detection with adaptive threshold was used to detect CVHR and Fcv was used to estimate AHI. The usefulness of the CVHR as a maker of sleep apnea has been reported by many researchers (Arikawa et al, 2020;Guilleminault et al, 1984;Hayano et al, 2011;Hsu et al, 2020;Magnusdottir & Hilmisson, 2018;Shimizu et al, 2015;Yatsu et al, 2021), and the accuracy of AHI estimation by Fcv has been confirmed by several earlier studies (Hayano et al, 2011(Hayano et al, , 2013(Hayano et al, , 2020(Hayano et al, , 2021. In a study of 864 polysomnographic subjects with suspected sleep apnea, patients with AHI > 15 were detected with a sensitivity of 83% and specificity of 88% by using Fcv > 15 cph as a criterion (Hayano et al, 2011).…”

“…These studies also reported that the classification performance of Fcv was unaffected by older age (≥ 65 years), obesity, hypertension, diabetes mellitus, dyslipidemia, or the type of sleep apnea (obstructive or central) (Hayano et al, 2011(Hayano et al, , 2013. The merit of this method is that it requires only a single-lead ECG (Hayano et al, 2021;Hsu et al, 2020;Magnusdottir & Hilmisson, 2018), and in the present study, by applying this method to long-term ECG data, we were able to delineate the night-to-night dynamics of sleep apnea.…”

“…R‐R interval time series obtained from valid sleep ECG data were analyzed by the automated CVHR detection algorism named Auto‐Correlated Wave Detection with Adaptive Threshold (ACAT). The detail of this algorithm has been reported previously (Hayano et al, 2011, 2013, 2021). Briefly, the ACAT algorithm is a time‐domain analysis that detects cyclic, autocorrelated dips in R‐R interval time series, evaluates the features of the dips meet the criteria for CVHR, and calculates the hourly frequency of the dips that fit as CVHR.…”