Abstract Introduction Heart rate variability (HRV) measures the variation in time intervals between successive heartbeats (RR intervals) and is considered a measure of a healthy regulatory system. It is influenced by a variety of factors, including age and comorbidities. We estimate temporal HRV metrics, such as the standard deviation of RR intervals (SDNN), and frequency metrics, such as the normalized high frequency component of the RR intervals (HFNU), to identify which were best for characterizing inter-individual differences by age, gender, and health status. Purpose Understand inter-individual differences in HRV and the influence of age, gender, and available comorbidities. Methods Data from at least one 2-week single-lead ECG were obtained from the 1,742 participants of the trial. Inclusion criteria for the study are defined as follows: age of 75 years or older, or a male older than age 55 years or female older than 65 years with 1 or more comorbidities that are risk factors for cardiovascular diseases. We adopted a linear regression method, where each HRV measure constitutes the outcome of a separate model, while the demographic characteristics (age and gender), the heart rate (HR), and the presence of comorbidities (yes/no for each comorbidity) are used as predictor variables. Age has been evaluated as a quadratic term to assess non-linearity of the association, and an age-gender interaction term has also been evaluated. Coefficients are expressed in terms of standard deviations, allowing for a direct comparison between metrics with different scales and variability. The HRV metrics are evaluated every 5 minutes, then the average value over a 1-day period is considered as input for the model. Results Temporal HRV metrics were lower among women compared with men and increased with age. They were higher in the presence of many disease states, including chronic heart failure (CHF), obesity (OBE), chronic obstructive pulmonary disease (COP), and sleep apnea (SA), but were lower among those with history of myocardial infarction (MI), chronic renal failure (CRF), and use of beta blocker medications (BB). There was no significant association of these metrics with hypertension or diabetes. For frequency metrics, analysis was limited to HFNU due to the high correlations among these measures. HFNU had strong positive associations with all co-morbidities except stroke, with particularly large coefficients for CHF, COP, and MI. HFNU values were also higher among subjects taking beta blockers. (Figure) Conclusion HRV can be an indicator of autonomic nervous system (ANS) dysfunction, which is often present in many diseases. By measuring HRV, clinicians can gain insights into the state of the ANS and potentially identify early warning signs of disease progression or complications.HRV and comorbidities
Impact of comorbidities on heart rate variability in patients with high cardiovascular risk
F Mason;G Quer
2023
Abstract
Abstract Introduction Heart rate variability (HRV) measures the variation in time intervals between successive heartbeats (RR intervals) and is considered a measure of a healthy regulatory system. It is influenced by a variety of factors, including age and comorbidities. We estimate temporal HRV metrics, such as the standard deviation of RR intervals (SDNN), and frequency metrics, such as the normalized high frequency component of the RR intervals (HFNU), to identify which were best for characterizing inter-individual differences by age, gender, and health status. Purpose Understand inter-individual differences in HRV and the influence of age, gender, and available comorbidities. Methods Data from at least one 2-week single-lead ECG were obtained from the 1,742 participants of the trial. Inclusion criteria for the study are defined as follows: age of 75 years or older, or a male older than age 55 years or female older than 65 years with 1 or more comorbidities that are risk factors for cardiovascular diseases. We adopted a linear regression method, where each HRV measure constitutes the outcome of a separate model, while the demographic characteristics (age and gender), the heart rate (HR), and the presence of comorbidities (yes/no for each comorbidity) are used as predictor variables. Age has been evaluated as a quadratic term to assess non-linearity of the association, and an age-gender interaction term has also been evaluated. Coefficients are expressed in terms of standard deviations, allowing for a direct comparison between metrics with different scales and variability. The HRV metrics are evaluated every 5 minutes, then the average value over a 1-day period is considered as input for the model. Results Temporal HRV metrics were lower among women compared with men and increased with age. They were higher in the presence of many disease states, including chronic heart failure (CHF), obesity (OBE), chronic obstructive pulmonary disease (COP), and sleep apnea (SA), but were lower among those with history of myocardial infarction (MI), chronic renal failure (CRF), and use of beta blocker medications (BB). There was no significant association of these metrics with hypertension or diabetes. For frequency metrics, analysis was limited to HFNU due to the high correlations among these measures. HFNU had strong positive associations with all co-morbidities except stroke, with particularly large coefficients for CHF, COP, and MI. HFNU values were also higher among subjects taking beta blockers. (Figure) Conclusion HRV can be an indicator of autonomic nervous system (ANS) dysfunction, which is often present in many diseases. By measuring HRV, clinicians can gain insights into the state of the ANS and potentially identify early warning signs of disease progression or complications.HRV and comorbiditiesPubblicazioni consigliate
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