Heart Rate Variability in Hyperthyroidism: A Systematic Review and Meta-Analysis

2. Methods

2.1. Literature Search

We reviewed all studies measuring HRV in patients with untreated hyperthyroidism and healthy controls. We searched the main article databases (PubMed, Cochrane Library, Embase and Google Scholar) with the following keywords: (“hyperthyroidism” OR “hyperthyroid”) AND (“heart rate variability” OR “HRV”) until 20 August 2021. All articles compatible with our inclusions criteria were included, independently of article language and years of publication. To be included, studies had to describe our main primary outcome i.e., the measurement of HRV parameters in untreated hyperthyroid patients and healthy controls. We imposed no limitation on the regional origin or the nature of the control group. We excluded studies that assessed the effects of treated hyperthyroidism in adults on HRV parameters, animal studies, studies in children, conferences, congresses, seminars and studies without frequency or time domains for HRV parameters or without controls. Studies needed to be primary research. In addition, reference lists from all publications meeting the inclusion criteria were manually searched to identify any further studies that were not found with the electronic search. Ancestry searches were also completed on previous reviews to locate other potentially eligible primary studies. The search strategy is presented in Figure 1 and Figure S1. Two authors (VB and RB) conducted the literature searches, reviewed the abstracts and articles independently, checked suitability for inclusion, and extracted the data. When necessary, disagreements were solved with a third author (FD).

2.2. Data Extraction

The primary endpoint was the analysis of HRV parameters in untreated hyperthyroid patients and in healthy controls. Traditionally, HRV is measured by linear methods [12], and most studies dealing HRV and dysthyroidism used linear HRV measurement methods. In the time domain, we analyzed RR intervals (or normal-to-normal intervals—NNs), standard deviation of RR intervals (SDNN), percentage of adjacent NN intervals differing by more than 50 ms (pNN50) and the square root of the mean squared difference of successive RR intervals (RMSSD). The time domain of HRV can be decomposed into its frequency components by the spectral analysis technique, either with the fast Fourier transform algorithm or with autoregressive modeling [12]. This is analogous to a prism that refracts light into its wavelength components [12]. In the spectral domain, we analyzed the total power (TP), low frequency (LF, 0.04 ± 0.15 Hz), high frequency (HF, 0.15 ± 0.4 Hz) and very low frequency (VLF, 0.003 ± 0.04 Hz), and the LF/HF ratio. Power is the energy found in a frequency band [23]. LF and HF powers are absolute powers, reported in units of ms2 (square milliseconds). LFnu and HFnu are normalized powers, called relative powers, in the LF and HF bands, comprising a derived index that is calculated by dividing LF or HF by an appropriate denominator representing the relevant total power: LFnu = LF/(LF + HF) and HFnu = HF/(LF + HF). These normalized powers allow direct comparison of the frequency domain measurements of two patients despite a large variation in specific band power and total power [24]. LF power represents both sympathetic and parasympathetic activity and is associated with SDNN, but LFnu emphasizes the control and balance of cardiac sympathetic behavior [12]. HF power and HFnu represent the most efferent parasympathetic activity [25] and are associated with RMSSD and pNN50 [23]. As for SDNN, both sympathetic and parasympathetic activities contribute to VLF with uncertainty about the physiological mechanisms responsible for activity in this band [26]. The LF/HF ratio is the most sensitive indicator of sympathovagal balance [12], which was also calculated and reported in this meta-analysis. Secondary outcomes included hyperthyroidism characteristics (duration and etiology of hyperthyroidism, free thyroxine—fT4, free triiodothyronine—fT3, thyroid-stimulating hormone—TSH), clinical parameters (BMI, blood pressure, treatments, other diseases), electrical measures such as heart rate, and sociodemographic parameters (age, sex, smoking).

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2.3. Quality of Assessment

We used the Scottish Intercollegiate Guidelines Network (SIGN) criteria to check the quality of included articles with the dedicated evaluation grids. For clinical trials, checklists consist of 10 items if randomized and 7 items if non-randomized, based on the main causes of bias [27]. We also used the SIGN score for cohort and cross-sectional studies, in two sections: design of the study (14 items), and overall evaluation (3 items). There were 4 possibilities of answers (yes, no, can’t say or not applicable) (Figure S2). We also used the “STrengthening the Reporting of OBservational studies in Epidemiology” (STROBE—32 items/sub-items) for cohort and cross-sectional studies [28] and the Consolidated Standards of Reporting Trials (CONSORT—37 items/subitems) for randomized trials [29]. We attributed one point per item or sub-item, to achieve a maximal score of 32 or 37, respectively, then converted this into a percentage.

2.4. Statistical Considerations

We used Stata software (v16, StataCorp, College Station, TX, USA) for the statistical analysis [30,31,32,33,34]. Main characteristics were synthetized for each study population and reported as mean ± standard deviation (SD) for continuous variables and number (%) for categorical variables. When data could be pooled, we conducted random effects meta-analyses (DerSimonian and Laird approach) for each HRV parameter comparing patients with untreated hyperthyroidism with healthy controls [35]. A negative effect size (ES, standardized mean differences—SMD) [36] denoted lower HRV in patients than in controls. An ES is a unitless measure, centered at zero if the HRV parameter did not differ between hyperthyroidism patients and controls. An ES of −0.8 reflects a large effect i.e., a large HRV decrease in patients compared to controls, −0.5 a moderate effect, and −0.2 a small effect. Then, we conducted meta-analyses stratified on biochemical status of hyperthyroidism, subclinical or overt. We evaluated heterogeneity in the study results by examining forest plots, confidence intervals (CI) and I-squared (I2). I2 is the most common metric to measure heterogeneity between studies, ranging from 0 to 100%. Heterogeneity is considered low for I2 < 25%, modest for 25 < I2 < 50%, and high for I2 > 50%. We also searched for potential publication bias by examining funnel plots of these meta-analyses. We verified the strength of our results by conducting further meta-analyses after exclusion of studies that were not evenly distributed around the base of the funnel. When possible (sufficient sample size), meta-regressions were proposed to study the relationship between each HRV parameter, and clinically relevant parameters (age, sex, blood pressure, BMI), hyperthyroidism status (subclinical or overt) and biological relevant parameters (fT3, fT4, TSH). Results are expressed as regression coefficients and 95% CI. p-Values less than 0.05 were considered statistically significant.

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About the Author: Tung Chi