Good reproducibility of heart rate variability after orthostatic challenge in patients with a history of acute coronary syndrome

Schäfer, Daniela; Nil, Markus; Herzig, David; Eser, Prisca; Saner, Hugo; Wilhelm, Matthias

doi:10.1016/j.jelectrocard.2015.04.004

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…This is, to the best of our knowledge, the first study to quantify variance components of HRV parameters as well as reproducibility in different sleep stages. Reproducibility of HRV measurements in an awake state between different days has been extensively studied (Schroeder et al, 2004 ; Pinna et al, 2007 ; Sacre et al, 2012 ; Schafer et al, 2015 ; Silva et al, 2017 ). During sleep, reproducibility of mean heart rate of a whole night's sleep has been investigated (Waldeck and Lambert, 2003 ).…”

Section: Discussionmentioning

confidence: 99%

Reproducibility of Heart Rate Variability Is Parameter and Sleep Stage Dependent

et al. 2018

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Objective: Measurements of heart rate variability (HRV) during sleep have become increasingly popular as sleep could provide an optimal state for HRV assessments. While sleep stages have been reported to affect HRV, the effect of sleep stages on the variance of HRV parameters were hardly investigated. We aimed to assess the variance of HRV parameters during the different sleep stages. Further, we tested the accuracy of an algorithm using HRV to identify a 5-min segment within an episode of slow wave sleep (SWS, deep sleep).Methods: Polysomnographic (PSG) sleep recordings of 3 nights of 15 healthy young males were analyzed. Sleep was scored according to conventional criteria. HRV parameters of consecutive 5-min segments were analyzed within the different sleep stages. The total variance of HRV parameters was partitioned into between-subjects variance, between-nights variance, and between-segments variance and compared between the different sleep stages. Intra-class correlation coefficients of all HRV parameters were calculated for all sleep stages. To identify an SWS segment based on HRV, Pearson correlation coefficients of consecutive R-R intervals (rRR) of moving 5-min windows (20-s steps). The linear trend was removed from the rRR time series and the first segment with rRR values 0.1 units below the mean rRR for at least 10 min was identified. A 5-min segment was placed in the middle of such an identified segment and the corresponding sleep stage was used to assess the accuracy of the algorithm.Results: Good reproducibility within and across nights was found for heart rate in all sleep stages and for high frequency (HF) power in SWS. Reproducibility of low frequency (LF) power and of LF/HF was poor in all sleep stages. Of all the 5-min segments selected based on HRV data, 87% were accurately located within SWS.Conclusions: SWS, a stable state that, in contrast to waking, is unaffected by internal and external factors, is a reproducible state that allows reliable determination of heart rate, and HF power, and can satisfactorily be detected based on R-R intervals, without the need of full PSG. Sleep may not be an optimal condition to assess LF power and LF/HF power ratio.

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Section: Discussionmentioning

confidence: 99%

Reproducibility of Heart Rate Variability Is Parameter and Sleep Stage Dependent

et al. 2018

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“…Furthermore, HRV is heavily influenced by a participant’s physical position during data collection (D’Silva, Davies, Emery, & Lewis, 2014). The best position to obtain the most accurate HRV reading is supine rest because all other positions (e.g., supine standing) result in significant fluctuations in HRV that may not be representative of the true value (D’Silva et al, 2014; Lowenstein et al, 2014; Schäfer et al, 2015). Subsequently, the two studies in this review controlled for the diurnal rhythm of HRV by the investigators collecting baseline HRV data with a wave pulse ear clip as well as by requiring participants to sit upright and rest for approximately 10 minutes before HRV analysis (Kudo et al, 2014; Siepmann et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

Reducing Psychological Stress in Peripartum Women With Heart Rate Variability Biofeedback: A Systematic Review

Herbell

Zauszniewski

2018

J Holist Nurs

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Peripartum women are exposed to a variety of stressors that have adverse health consequences for the maternal-child dyad (e.g., impaired bonding). To combat these adverse health consequences, heart rate variability biofeedback (HRVBF) may be implemented by holistic nurses to aid peripartum women experiencing a high level of stress. A systematic review was completed using the guidelines established in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. To be included in the review, studies had to meet the following criteria: (a) published scientific articles, (b) studies published in English, (c) experimental, quasi-experimental, or case reports, (d) use of HRVBF as the main treatment, (e) use of psychological stress as a dependent variable, and (f) studies published until December 2017. The major findings of this review can be described as follows: (a) HRVBF and psychological stress in peripartum women are related concepts, (b) peripartum women who completed HRVBF report a reduction in stress compared with participants who did not receive HRVBF, and (c) there is currently no information on the effectiveness of HRVBF on psychological stress in the first and early second trimester of pregnancy. Overall, this systematic review of the literature provides objective evidence that HRVBF may be a potential beneficial adjuvant treatment for stress management in peripartum women.

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“…Physiologically, the ANS plays a crucial role in maintaining and promoting cardiac physiological function ( 37 , 38 ). Pathologically, increasing research has confirmed that the context of acute myocardial ischemia could trigger an organismal stress response, induce cardiac sympathetic hyperactivity and suppress vagal activity, subsequently leading to coronary constriction, especially culprit vessel vasoconstriction, thus accelerating focal ischemia and hypoxia and causing the deterioration of myocardial ischemia ( 34 , 39 ). In addition, previous clinical and basic research has shown that the vagus nerve of the ANS is involved in the regulation of the inflammatory response ( 40 , 41 ), and the potential link among the ANS, inflammation and coronary artery physiology was confirmed by our previous studies ( 42 , 43 ).…”

Section: Discussionmentioning

confidence: 99%

Enrichment of the Postdischarge GRACE Score With Deceleration Capacity Enhances the Prediction Accuracy of the Long-Term Prognosis After Acute Coronary Syndrome

Duan

Wang

et al. 2022

Front. Cardiovasc. Med.

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BackgroundCardiac autonomic nerve imbalance has been well documented to provide a critical foundation for the development of acute coronary syndrome (ACS) but is not included in the postdischarge GRACE score. We investigated whether capturing cardiac autonomic nervous system (ANS)-related modulations by 24-h deceleration capacity (DC) could improve the capability of existing prognostic models, including the postdischarge Global Registry of Acute Coronary Events (GRACE) score, to predict prognosis after ACS.MethodPatients with ACS were assessed with 24-h Holter monitoring in our department from June 2017 through June 2019. The GRACE score was calculated for postdischarge 6-month mortality. The patients were followed longitudinally for the incidence of major adverse cardiac events (MACEs), set as a composite of non-fatal myocardial infarction and death. To evaluate the improvement in its discriminative and reclassification capabilities, the GRACE score with DC model was compared with a model using the GRACE score only, using area under the receiver-operator characteristic curve (AUC), Akaike's information criteria, the likelihood ratio test, category-free integrated discrimination index (IDI) and continuous net reclassification improvement (NRI).ResultsOverall, 323 patients were enrolled consecutively. After the follow-up period (mean, 43.78 months), 41 patients were found to have developed MACEs, which were more frequent among patients with DC <2.5 ms. DC adjusted for the GRACE score independently predicted the occurrence of MACEs with an adjusted hazard ratio (HR) of 0.885 and 95% CI of 0.831–0.943 (p < 0.001). Moreover, adding DC to the GRACE score only model increased the discriminatory ability for MACEs, as indicated by the likelihood ratio test (χ2 = 9.277, 1 df; p < 0.001). The model including the GRACE score combined with DC yielded a lower corrected Akaike's information criterion compared to that with the GRACE score alone. Incorporation of the DC into the existing model that uses the GRACE score enriched the net reclassification indices (NRIe>0 7.3%, NRIne>0 12.8%, NRI>0 0.200; p = 0.003). Entering the DC into the GRACE score model enhanced discrimination (IDI of 1.04%, p < 0.001).ConclusionDC serves as an independent and effective predictor of long-term adverse outcomes after ACS. Integration of DC and the postdischarge GRACE score significantly enhanced the discriminatory capability and precision in the prediction of poor long-term follow-up prognosis.

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Good reproducibility of heart rate variability after orthostatic challenge in patients with a history of acute coronary syndrome

Cited by 6 publications

References 28 publications

Reproducibility of Heart Rate Variability Is Parameter and Sleep Stage Dependent

Reproducibility of Heart Rate Variability Is Parameter and Sleep Stage Dependent

Reducing Psychological Stress in Peripartum Women With Heart Rate Variability Biofeedback: A Systematic Review

Enrichment of the Postdischarge GRACE Score With Deceleration Capacity Enhances the Prediction Accuracy of the Long-Term Prognosis After Acute Coronary Syndrome

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