Reduced heart-rate variability and increased risk of hypertension-a prospective study of the ELSA-Brasil.

Although autonomic disturbances are known to precede hypertension onset, the risks underlying different ranges of blood pressure and impaired cardiac autonomic modulation are still unknown. This study aimed to identify the risk of hypertension incidence related to low heart-rate variability profile in normotensive blood pressure subcategories: normal (<120/80 mmHg) and prehypertension (120/80-139/89 mmHg) in a 4-year follow-up. 7665 participants free of hypertension at baseline were examined. They were allocated into one of two groups (

Diabetes and subclinical hypothyroidism on heart rate variability.

BACKGROUND: We aimed to analyse if the effects of coexistent diabetes and subclinical hypothyroidism extend to the cardio autonomic nervous system, using heart rate variability baseline data from the Brazilian Longitudinal Study of Adult Health. MATERIALS AND METHODS: Heart rate variability analyses were performed by linear time and frequency domains in 5-minute time series collected in the supine position. The associations of diabetes and subclinical hypothyroidism with the lowest quartile group for heart rate and the highest quartile group for each heart rate variability parameter were analysed using additive and multiplicative terms in logistic models. For the first approach, the subsample was categorized into four groups: subjects without diabetes and normal thyroid function (controls); subjects without diabetes and subclinical hypothyroidism; patients with diabetes and normal thyroid function; and patients with diabetes and subclinical hypothyroidism. For the interaction alnalysis, diabetes and subclinical hypothyroidism diagnoses were included in separate, along with a multiplicative interaction term between them. RESULTS: Point odds ratio estimates for the 4th quartiles of heart rate, and 1st quartiles of all heart rate variability measurements were higher for subjects with combined diabetes and subclinical hypothyroidism than for diabetes only, independently of main sociodemographic and clinical variables (HR: 8.33 vs 2.63; SDNN: 2.59 vs 1.61; RMSSD: 2.37 vs 1.42; LF: 2.83 vs 1.71; HF: 3.06 vs 1.39), but not independently of HbA1c and TSH. Only the interaction term for the association with heart rate, adjusted for sociodemographic and clinical variables, had borderline statistical significance. CONCLUSION: Diabetes and subclinical hypothyroidism exert a potential joint impact on cardiac autonomic control, showed by additive effects between diabetes and subclinical hypothyroidism, as well as a significant interaction term for the association with heart rate.

Relationship between heart rate variability and carotid intima-media thickness in the Brazilian Longitudinal Study of Adult Health - ELSA-Brasil.

BACKGROUND: Both increased carotid intima-media thickness (cIMT) and low heart rate variability (HRV) have been associated with cardiovascular mortality and morbidity. Thus, the aim of this study was to investigate whether cardio autonomic alterations are accompanied or not by subclinical atherosclerosis in participants of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). METHODS: cIMT measures and 5-min HRV analyses were performed in apparently healthy adults. Heart rate variability was evaluated by linear time and frequency domain analyses. cIMT was defined as the average between the mean left and mean right cIMT values and was analysed as continuous and categorized variables (P≥75 or P<75). Multiple linear models using continuous variables and multivariate logistic regression with categorized cIMT and HRV quartiles were performed. RESULTS: Out of 7256 participants eligible for analyses, 23·4% presented cIMT ≥ 75th percentile. Heart rate variability variables were reduced in cIMT ≥ P75 in comparison with cIMT < P75: SDNN 33·0 versus 37·0 ms, P<0·001; RMSSD 22·0 versus 26·0 ms, P<0·001; LF 191·0 versus 260·0 ms2 , P<0·001; HF 164·0 versus 238·5 ms2 , P<0·001. In crude analysis, an increased odds ratio for cIMT ≥ P75 was verified within the lowest two quartiles of LF and HF, but significances did not remain after adjustments for anthropometric and clinical variables. CONCLUSIONS: Considering the entire sample, subjects with cIMT ≥ P75 presented lower HRV values, but no independent relationships were detected between cIMT and HRV after multivariate adjustment.

Decreased heart rate variability as a predictor for diabetes-A prospective study of the Brazilian longitudinal study of adult health.

AIM: To investigate whether heart rate variability (HRV) is a predictor for the incidence of diabetes in a 4-year follow-up. MATERIALS AND METHODS: The HRV of 9192 participants free of diabetes was analysed in time and frequency domains and stratified based on the reference values presented in the literature. The participants were then allocated to one of three groups, according to age-specific value distributions for each HRV domain: lower than the 25th percentile, between the 25th and 75th percentiles, and higher than the 75th percentile. The association between HRV and diabetes incidence at 4-year follow-up was analysed using Poisson regression models with robust estimator. RESULTS: Six hundred thirty-four participants (6.90%) developed diabetes within 4 years and five out of six HRV analysed indices showed increased relative risk of developing diabetes associated with low HRV: SDNN (RR = 1.29; 95% CI, 1.09-1.52; .003), pNN50 (RR = 1.33; 95% CI, 1.11-1.58; .001), RMSSD (RR = 1.29; 95% CI, 1.09-1.53; .004), LF (RR = 1.25; 95% CI, 1.05-1.48; .012), and HF (RR = 1.39; 95% CI, 1.16-1.63; .001). CONCLUSIONS: This study suggests that both overall variability and changes in parasympathetic modulation precede the incidence of diabetes. For four HRV indices below the 25th percentile, the risk for incident diabetes was 68% higher than for those participants who presented none. We concluded that HRV is an independent risk predictor of diabetes in a 4-year period.

Linear and nonlinear analyses of heart rate variability following orthostatism in subclinical hypothyroidism.

Subclinical hypothyroidism (SCHypo) is associated with autonomic disturbances that can interfere in physiological responses. This study was designed to evaluate linear and nonlinear variables of heart rate variability (HRV) following postural change, comparing subjects with SCHypo to euthyroid subjects.HRV analyses were performed in 5-minute time series collected in the supine and standing positions from a subsample of 855 participants of the ELSA-Brasil study. The cardiac autonomic nervous function was evaluated by linear time and frequency domain analyses (SDNN, RMSSD, LFms, HFms, and LF/HF ratio) as well as by nonlinear symbolic dynamics (0, 1, and 2 V).After exclusions, 509 (92.0%) euthyroid and 44 (8.0%) SCHypo participants were eligible for analyses. At the baseline supine rest measurement, the 0 V symbolic pattern was higher (27.7 vs 25.4, P = .02) and 2 V was lower (18.0 vs 22.9, P = .02) than in the euthyroid group. Comparing the variation between positions, the 0 V pattern showed a lower delta in SCHypo than in Euthyroid subjects (8.0 vs 10.8%, P = .04).SCHypo presented lower sympathetic and parasympathetic tonus at rest and a blunted sympathetic response to active postural change, marked by reduced variation in the 0 V of symbolic analysis (SA). Additionally, it is suggested that SA of HR dynamics is an alternative and, possibly, a more sensitive method for cardiac autonomic assessment following orthostatism in this population.

Differential Associations of Specific Selective Serotonin Reuptake Inhibitors With Resting-State Heart Rate and Heart Rate Variability: Implications for Health and Well-Being.

OBJECTIVE: Debate has focused on the effects of the selective serotonin reuptake inhibitor (SSRI) antidepressants on heart rate (HR) and HR variability (HRV), both of which are predictors of adverse cardiovascular events. Here, we examine the associations between specific SSRI antidepressants and resting state HR (and HRV) after accounting for a host of potential confounding factors using propensity score techniques. METHODS: Participants included 10,466 not taking antidepressants, 46 participants taking escitalopram, 86 taking citalopram, 66 taking fluoxetine, 103 taking paroxetine, and 139 taking sertraline. HR and HRV (root mean square of successive squared differences, high frequency) were extracted from 10-minute resting-state ECGs. Analyses including propensity score weighting and matching were conducted using R-statistics to control for potentially confounding variables. RESULTS: Major findings indicated that users of all SSRI medications-except fluoxetine-displayed lower HRV relative to nonusers. Users of paroxetine also displayed significantly lower HRV relative to users of citalopram (Cohen's d = 0.42), fluoxetine (Cohen's d = 0.54), and sertraline (Cohen's d = 0.35), but not escitalopram. Although associations were also observed for HR, these were less robust than those for HRV. CONCLUSIONS: Although paroxetine is associated with decreases in HRV relative to nonusers, as well as users of other SSRI medications, fluoxetine was the only medication not to display significant alterations in HR or HRV. These conclusions are limited by the cross-sectional design and nonrandomized nature of medication prescriptions. Findings highlight the importance of focusing on specific medications, rather than more heterogeneous groupings according to antidepressant action, and may have implications for health and well-being for the longer term.

Implementation of a Teleconsultation Service in the Primary Health Care in Brazil.

The implementation of a Teleconsultation service in the primary health care is a great challenge. This work presents the effort made in the Espirito Santo Telehealth Project to consolidate the teleconsultation service. The impact of each strategy made was evaluated in terms of the number of teleconsultations produced. The combination of periodic visits and the promotion of local workshops were responsible for a sustainable use of the service for the last six months.

Effects of depression, anxiety, comorbidity, and antidepressants on resting-state heart rate and its variability: an ELSA-Brasil cohort baseline study.

OBJECTIVE: Increases in resting-state heart rate and decreases in its variability are associated with substantial morbidity and mortality, yet contradictory findings have been reported for the effects of the mood and anxiety disorders and of antidepressants. The authors investigated heart rate and heart rate variability in a large cohort from Brazil, using propensity score weighting, a relatively novel method, to control for numerous potential confounders. METHOD: A total of 15,105 participants were recruited in the Brazilian Longitudinal Study of Adult Health. Mood and anxiety disorders were ascertained using the Portuguese version of the Clinical Interview Schedule-Revised. Heart rate and its variability were extracted from 10-minute resting-state electrocardiograms. Regressions weighted by propensity scores were carried out to compare participants with and without depressive or anxiety disorders, as well as users and non-users of antidepressants, on heart rate and heart rate variability. RESULTS: Use of antidepressants was associated with increases in heart rate and decreases in its variability. Effects were most pronounced for the tricyclic antidepressants (Cohen's d, 0.72-0.81), followed by serotonin and norepinephrine reuptake inhibitors (Cohen's d, 0.42-0.95) and other antidepressants (Cohen's d, 0.37-0.40), relative to participants not on antidepressants. Only participants with generalized anxiety disorder showed robust, though small, increases in heart rate and decreases in its variability after propensity score weighting. CONCLUSIONS: The findings may, in part, underpin epidemiological findings of increased risk for cardiovascular morbidity and mortality. Many factors that have an adverse impact on cardiac activity were controlled for in this study, highlighting the importance of cardiovascular risk reduction strategies. Further study is needed to examine whether, how, and when such effects contribute to morbidity and mortality.

Premature Ventricular beat classification using a dynamic Bayesian Network.

This paper investigates the viability of using the dynamic Bayesian Network framework as a tool to classify heart beats in long term ECG records. A Decision Support System composed by two layers is considered. The first layer performs the segmentation of each heartbeat available in the ECG record, whereas the second layer classifies the heartbeat as Premature Ventricular Contraction (PVC) or Other. The use of both static and dynamic Bayesian Networks is evaluated through using the records available in the MIT-BIH database, and the results show that the Dynamic one performs better, obtaining 95% of sensitivity and 98% of positive predictivity, showing that to consider the temporal relation among events is a good strategy to increase the certainty about present events.

Detection of premature ventricular beats in ECG records using bayesian networks involving the P-Wave and fusion of results.

This article proposes to use the Bayesian network (BN) framework to support medical decision in the problem of heart beat classification in long-term electrocardiogram (ECG) records. The motivation to use the BN approach is to take into account the uncertainty present in the clinical reasoning. The case study is the classification of Premature Ventricular Beats (PVC). Specifically speaking, it is discussed the use of the P-Wave as a network node, to check its capability to improve the performance of the PVC classification. In spite of concluding that the P wave is not definitive for the classification, such results have motivated the main proposal of this work: a fusion of the results obtained by training the implemented BN with two distinct datasets, which has indeed improved the system performance.

Incremental HMM training applied to ECG signal analysis.

This work discusses the implementation of incremental hidden Markov model (HMM) training methods for electrocardiogram (ECG) analysis. The HMMs are used to model the ECG signal as a sequence of connected elementary waveforms. Moreover, an adaptation process is implemented to adapt the HMMs to the ECG signal of a particular individual. The adaptation training strategy is based on incremental versions of the expectation-maximization, segmental k-means and Bayesian approaches. Performance of the training methods was assessed through experiments considering the QT and ST-T databases. The results obtained show that the incremental training improves beat segmentation and ischemia detection performance with the advantage of low computational effort.

ECG signal analysis through hidden Markov models.

This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms. The HMM framework has been visited because of its ability of beat detection, segmentation and classification, highly suitable to the electrocardiogram (ECG) problem. Our approach addresses a large panel of topics some of them never studied before in other HMM related works: waveforms modeling, multichannel beat segmentation and classification, and unsupervised adaptation to the patient's ECG. The performance was evaluated on the two-channel QT database in terms of waveform segmentation precision, beat detection and classification. Our waveform segmentation results compare favorably to other systems in the literature. We also obtained high beat detection performance with sensitivity of 99.79% and a positive predictivity of 99.96%, using a test set of 59 recordings. Moreover, premature ventricular contraction beats were detected using an original classification strategy. The results obtained validate our approach for real world application.