Muscle Weakness and Walking Slowness for the Identification of Sarcopenia in the Older Adults from Northern Brazil: A Cross-Sectional Study.

Background: This study aimed to analyze the prevalence of sarcopenia in elderly people from Northern Brazil according to muscle weakness or walking slowness. Methods: The sample consisted of 312 elderly people (72.6 ± 7.8 years). For walking slowness, a gait speed ≤ 0.8 m/s was used as a cut-off value, and for muscle weakness the following handgrip strength criteria were used for men and women, respectively: CI: <27.0/16.0 kg; CII: <35.5/20.0 kg; CIII: grip strength corrected for body mass index (BMI) < 1.05/0.79; CIV: grip strength corrected for total fat mass: <1.66/0.65; CV: grip strength corrected for body mass: <0.45/0.34. Results: Walking speed was reduced in 27.0% of women and 15.2% of men (p < 0.05). According to grip strength criteria, 28.5% of women and 30.4% of men (CI), 58.0% of women and 75.0% of men (CII), 66.0% of women and 39.3% of men (CIII), 28.8% of women and 19.6% of men (CIV), and 56.5% of women and 50.0% of men (CV) were identified as having sarcopenia. Conclusions: Walking slowness is more prevalent in women and muscle weakness is more prevalent in men in Northern Brazil. Walking slowness proved to be more concordant with muscle weakness in both sexes when the CI for handgrip strength was adopted.

Sensitivity and specificity of different exercise oscillatory ventilation definitions to predict 2-year major adverse cardiovascular outcomes in chronic heart failure patients.

BACKGROUND: Exercise oscillatory ventilation (EOV) shows a four-fold greater risk of adverse events. This study aims to analyze the sensitivity and specificity of three EOV diagnostic definitions to predict adverse outcomes at a 2-year follow-up and to compare its EOV prevalence and relations with the patient's profile. METHODS: Cardiopulmonary exercise tests from 233 heart failure patients were analyzed. Two blinded reviewers used a semiautomated software to identify EOV cases pattern according to the definitions of Ben-Dov, Corrà, and Leite. Data were grouped in EOV-positive or EOV-negative according to each definition. Baseline characteristics, EOV prevalence, relative risk, sensitivity, and specificity to predict 2-years of major adverse cardiovascular outcomes were analyzed. RESULTS: The Corrà definition led to the best prediction of 2-year major cardiovascular adverse outcomes (HR 2.46 [1.16 to 5.25]; p = 0.019, AUC = 0.618; p = 0.007). EOV prevalence was 17.2%, 17.2%, and 9.4% applying Ben-Dov, Corrà, and Leite definition, respectively. The main clinical differences between EOV-positive and EOV-negative patients were: MECKI score and VE/VCO2 slope (all definitions), and BNP levels (Ben-Dov and Leite). BNP levels were correlated with amplitude (rho = 0.255; p = 0.033) and cycle length (rho = 0.388; p = 0.002). CONCLUSION: Corrà definition was the only one that exhibited the capacity to predict major adverse cardiovascular outcomes at a 2-year follow-up. Regardless of its definition, EOV was more often prevalent in patients with a greater MECKI score and VE/VCO2 slope values.

Can RR intervals editing and selection techniques interfere with the analysis of heart rate variability?

BACKGROUND: Oscillation between successive sinus beats or RR intervals, termed heart rate variability, is an important marker of autonomic function of the heart. However, its analysis may be influenced by the database recorded based on the occurrence of interference. OBJECTIVE: To evaluate if the techniques of identification and editing of artifacts, as well as the selection methods of RR intervals, can interfere with heart rate variability analysis. METHODS: The RR intervals of 56 subjects (30 aortic stenosis patients, 14 physically active individuals, 12 amateur athletes) were recorded for 10min using a heart rate monitor. Values with differences greater than 20%, higher than three standard deviations or outside of the normal curve (95% confidence interval) were considered artifacts. These points were corrected through data replacement, adjacent, linear and polynomial interpolation, or excluded. Then, the 256 highest stability points and the last 5min of recordings were chosen. The software programs, Kubios HRV and GraphPAD, were used to calculate and to analyze the indices of heart rate variability, respectively. RESULTS: Strong agreement was observed among the identification algorithms; there was no difference between the correction techniques (p=0.95); and the selection methods exhibited different sections (p<0.01) with a direct influence on approximated entropy (p<0.05). CONCLUSION: With short-term recordings, selection methods may interfere with the non-linear heart rate variability analysis. The confidence interval, the replacement by the average of previous data and the selection of 256 of the highest stability points of the signal seem to be the most adequate procedures to treat the data with prior to analysis.