A meta-analysis of HIV and heart rate variability in the era of antiretroviral therapy

McIntosh, Roger C.

doi:10.1007/s10286-016-0366-6

Cited by 32 publications

(32 citation statements)

References 47 publications

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“…This search yielded 44 articles. Each article was examined for eligibility, which left 16 meta-analyses that fulfilled eligibility for effect size extraction Amichai & Katz-Leurer, 2014;Chalmers, Quintana, Abbott, & Kemp, 2014;Clamor, Lincoln, Thayer, & Koenig, 2016;Koenig, Falvay et al, 2016;Koenig, Kemp, Beauchaine, Thayer, & Kaess, 2016;Koenig, Kemp, Feeling, Thayer, & Kaess, 2016;Koenig, Rash et al, 2016;Koenig, Williams, Kemp, & Thayer, 2016;Liu, Wang, Yan, & Chen, 2013;Lotufo, Valiengo, Benseñor, & Brunoni, 2012;McIntosh, 2016;Peschel et al, 2016;Quintana, McGregor, Guastella, Malhi, & Kemp, 2013;Tak et al, 2009;Tracy et al, 2016); see Figure 1. A total of 242 effect sizes were extracted from nine meta-analyses that combined vagally mediated HRV measure effect sizes (e.g., root mean square of successive differences, RMSSD; standard deviation of NN intervals, SDNN; high frequency HRV, HF-HRV; respiratory sinus arrhythmia, RSA).…”

Section: Methodsmentioning

confidence: 99%

“…The identification of group differences in HRV has been used to better understand disease etiology, psychological phenomena, and the increased risk of frequently comorbid illnesses, such as cardiovascular disease (Kemp & Quintana, ). Indeed, several meta‐analyses have reported reduced HRV in a range of clinical populations in comparison to control groups (e.g., Alvares, Quintana, Hickie, & Guastella, ; Koenig, Falvay et al, ; McIntosh, ).…”

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Statistical considerations for reporting and planning heart rate variability case‐control studies

2016

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The calculation of heart rate variability (HRV) is a popular tool used to investigate differences in cardiac autonomic control between population samples. When interpreting effect sizes to quantify the magnitude of group differences, researchers typically use Cohen's guidelines of small (0.2), medium (0.5), and large (0.8) effects. However, these guidelines were originally proposed as a fallback for when the effect size distribution (ESD) was unknown. Despite the availability of effect sizes from hundreds of HRV studies, researchers still largely rely on Cohen's guidelines to interpret effect sizes and to perform power analyses to calculate required sample sizes for future research. This article describes an ESD analysis of 297 HRV effect sizes from between-group/case-control studies, revealing that the 25th, 50th, and 75th effect size percentiles correspond with effect sizes of 0.26, 0.51, and 0.88, respectively. The analyses suggest that Cohen's guidelines may underestimate the magnitude of small and large effect sizes and that HRV studies are generally underpowered. Therefore, to better reflect the observed ESD, effect sizes of 0.25, 0.5, and 0.9 should be interpreted as small, medium, and large effects (after rounding to the closest 0.05). Based on power calculations using the ESD, suggested sample sizes are also provided for planning suitably powered studies that are more likely to replicate. Researchers are encouraged to use the ESD data set or their own collected data sets in tandem with the provided analysis script to perform custom ESD and power analyses relevant to their specific research area.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Statistical considerations for reporting and planning heart rate variability case‐control studies

2016

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“…HIV+ patients continue to have higher rates of traditional risk factors for CHF, drug abuse, and CAD. Additionally, HIV+ patients are shown to have higher levels of autonomic dysfunction [8, 9] and inflammation [10–12], which are considered novel risk factors for HF in the general population [13]. Finally, even though HIV+ patients on ART do not express overt cardiomyopathy, studies have shown increased subclinical cardiac mechanical dysfunction (both systolic and diastolic) and presence of myocardial abnormalities (fibrosis and steatosis).…”

Section: Heart Failure and Myocardial Diseases In Hiv-infected Patientsmentioning

confidence: 99%

HIV Infection and Risk of Cardiovascular Diseases Beyond Coronary Artery Disease

Barnes

Lacson

Bahrami

2017

Curr Atheroscler Rep

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Purpose of Review Cardiovascular disease (CVD) increasingly afflicts people living with HIV (PLWH) in the contemporary era of antiretroviral therapy (ART). Coronary artery disease (CAD) is the most widely studied cardiovascular problem in PLWH; however, less is known about other clinically relevant subtypes of CVD such as heart failure (HF), cerebrovascular disease, sudden cardiac death, pericardial diseases, and pulmonary hypertension. This paper reviews evidence of other subtypes of CVD as emerging issues in the post-ART era. Recent Findings Recent studies have shown that PLWH have higher risk of HF as well as subclinical impairment of left ventricular (LV) mechanics (systolic and diastolic dysfunction) and myocardial abnormalities (fibrosis and steatosis). The underlying mechanisms, however, are not well-understood. A few studies have also shown higher rates of atrial fibrillation and sudden cardiac death in PLWH. Ischemic stroke is the most common stroke type in the post-ART era, with underlying mechanisms like those identified in CAD: chronic inflammation and associated vasculopathy. Studies of great vessels (carotid artery and aorta) and peripheral arterial disease show heterogeneous results. Small subclinical pericardial effusions are common in PLWH in post-ART era. Pulmonary hypertension continues to be an underdiagnosed and potentially fatal complication of HIV infection. Summary PLWH remain at higher risk for all types of CVD including heart failure, stroke, and arrhythmias in the post-ART era. Chronic inflammation may play an important role in this increased risk. More studies are needed to further elucidate the extent of non-coronary CVD in PLWH and the underlying mechanisms for them.

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“…The identification of group differences in HRV has been used to better understand disease aetiology, psychological phenomena, and the increased risk of frequently comorbid illnesses, such as cardiovascular disease (Kemp and Quintana, 2013). Indeed, several meta-analyses have reported reduced HRV in a range of clinical populations in comparison to control groups (e.g., Alvares et al, 2016; Koenig et al, 2016; McIntosh, 2016).…”

Section: Introductionmentioning

confidence: 99%

An effect size distribution analysis of heart rate variability studies: Recommendations for reporting the magnitude of group differences

Quintana

2016

Preprint

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A meta-analysis of HIV and heart rate variability in the era of antiretroviral therapy

Cited by 32 publications

References 47 publications

Statistical considerations for reporting and planning heart rate variability case‐control studies

Statistical considerations for reporting and planning heart rate variability case‐control studies

HIV Infection and Risk of Cardiovascular Diseases Beyond Coronary Artery Disease

An effect size distribution analysis of heart rate variability studies: Recommendations for reporting the magnitude of group differences

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