Increases in Heart Rate Variability Signal Improved Outcomes in Rapid Response Team Consultations: A Cohort Study

Ultra Short Heart Rate Variability Predicts Clinical Outcomes in Patients with a Clinical Presentation Consistent with Myocarditis: A Derivation Cohort Analysis

Myocarditis prognosis varies substantially, hence identification of novel prognostic factors is crucial. The prognostic role of ultra-short heart-rate variability (HRV) in myocarditis remains unknown. In a retrospective study, adult patients admitted to a tertiary hospital due to clinically suspected myocarditis were included. Clinical, laboratory and HRV parameters were assessed as predictors of severe short term complications (heart failure (HF), dilated cardiomyopathy—DCM, ventricular arrhythmia—VA and death), utilizing logistic regression (LR). Accuracy was evaluated with receiver operating characteristic (ROC) curve area under the curve (AUC). HRV indices included standard deviation of normal beat intervals (SDNN) and root mean square of successive differences (RMSSD). 115 patients, aged 34 (±13) years old, were examined. Six patients (5%) developed severe HFrEF. RMSSD was included in a multivariate LR model (RMSSD < 10.72 ms adjusted odds ratio (AOR) 14.056, p-value 0.024). Model classification accuracy was very good, with an AUC of 86%. Eight patients (7%) developed DCM. RMSSD < 10.72 ms was included in a multivariate classification model (AOR 8.826, p-value 0.013); model classification AUC of 82%. HRV did not predict development of VA or death. SDNN and especially RMSSD may be prognostic indicators in myocarditis.

Impact of victory and defeat on the perceived stress and autonomic regulation of professional eSports athletes

Competitive sports involve physiological, technical and psychological skills, which influence directly on individuals’ performance. This study aims to investigate the levels of perceived stress and Heart Rate Variability (HRV) before and after matches with victory and defeat in professional eSports athletes. Our hypothesis was that the winners would have better autonomic and stress responses after match, thus corroborating the literature on neurocardiac connections. Fifty male eSport players were selected players from 10 different Brazilian teams. The experiment was carried out in 2 sessions. Firstly, after signing the informed consent form, 24 h before the game, anthropometric, physical activity levels and time of expertise data were recorded only for sample characterization and the players were familiarized with the perceived stress scale—10 (PSS-10) and the HRV measurements. Secondly, players performed the PSS-10 and HRV recording at rest by 10 min 60 and 30 min before the game (i.e., baseline time) and 10 min after the end of the game. Overall, concerning PSS-10 our findings show that VG had significant reduced scores in post-game time compared to baseline (BL) and pre-game times, while DG had significant increased scores in post-game time compared to BL and pre-game times. Regarding HRV, our results demonstrate that VG had significant increase in RR, SDNN, rMSSD, pNN50 and HF, and significant decrease in LF and LF/HF, while DG had a significant decrease in RR, SDNN, rMSSD and HF, and significant increase in LF and LF/HF. It was observed that VG had better HRV responses (greater parasympathetic activation) as well as lower levels of perceived stress, while DG had worst HRV responses (greater sympathetic activation) and higher levels of perceived stress.

Temporal Variability in the Sampling of Vital Sign Data Limits the Accuracy of Patient State Estimation

OBJECTIVES

Physiologic signals are typically measured continuously in the critical care unit, but only recorded at intermittent time intervals in the patient health record. Low frequency data collection may not accurately reflect the variability and complexity of these signals or the patient's clinical state. We aimed to characterize how increasing the temporal window size of observation from seconds to hours modifies the measured variability and complexity of basic vital signs.

DESIGN

Retrospective analysis of signal data acquired between April 1, 2013, and September 30, 2015.

SETTING

Critical care unit at The Hospital for Sick Children, Toronto.

PATIENTS

Seven hundred forty-seven patients less than or equal to 18 years old (63,814,869 data values), within seven diagnostic/surgical groups.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Measures of variability (SD and the absolute differences) and signal complexity (multiscale sample entropy and detrended fluctuation analysis [expressed as the scaling component α]) were calculated for systolic blood pressure, heart rate, and oxygen saturation. The variability of all vital signs increases as the window size increases from seconds to hours at the patient and diagnostic/surgical group level. Significant differences in the magnitude of variability for all time scales within and between groups was demonstrated (p < 0.0001). Variability correlated negatively with patient age for heart rate and oxygen saturation, but positively with systolic blood pressure. Changes in variability and complexity of heart rate and systolic blood pressure from time of admission to discharge were found.

CONCLUSIONS

In critically ill children, the temporal variability of physiologic signals supports higher frequency data capture, and this variability should be accounted for in models of patient state estimation.