Day-night variation in heart rate variability changes induced by endotoxaemia in healthy volunteers

Association between postoperative delirium and heart rate variability in the intensive care unit and readmissions and mortality in elderly patients with cardiovascular surgery

This study aimed to investigate the relationship between heart rate variability (HRV), a parameter of the autonomic nervous system activity (ANSA), and postoperative delirium and postoperative events. This retrospective cohort study included elderly patients aged 65 years or older who were admitted to the intensive care unit (ICU) after cardiovascular surgery. ANSA was measured using HRV parameters for 1 h at daytime and 1 h at night-time before ICU discharge. The primary endpoint was the effect of HRV parameters and delirium on mortality and readmission rates within 1 year after discharge, and the secondary endpoint was the association between HRV parameters and delirium. Cox proportional hazards models were used to examine the association between HRV parameters and postoperative events by adjusting for delirium and pre and postoperative information. A total of 71 patients, 39 without delirium and 32 with delirium, met the inclusion criteria. The incidence of death and readmission within 1 year was significantly higher in the delirium group and in the group with higher daytime HF (high frequency power) and r-MSSD (square root of the squared mean of the difference of successive NN intervals), parameters of the parasympathetic nervous system activity (PNSA), than that in other groups. Furthermore, the delirium group had significantly higher HF and r-MSSD than the nondelirium group. Even after adjusting for confounding factors in the multivariate analysis, a trend of higher daytime HF and r-MSSD was observed, indicating a significant effect on the occurrence of combined events within 1 year of discharge. ICU delirium has been associated with higher daytime HF and r-MSSD, parameters of PNSA. ICU delirium was a prognostic factor, and increased daytime PNSA may worsen the prognosis of elderly patients after cardiovascular surgery.

Time-specific associations of wearable sensor-based cardiovascular and behavioral readouts with disease phenotypes in the outpatient setting of the Chronic Renal Insufficiency Cohort

Patients with chronic kidney disease are at risk of developing cardiovascular disease. To facilitate out-of-clinic evaluation, we piloted wearable device-based analysis of heart rate variability and behavioral readouts in patients with chronic kidney disease from the Chronic Renal Insufficiency Cohort and controls (n  =  49). Time-specific partitioning of heart rate variability readouts confirm higher parasympathetic nervous activity during the night (mean RR at night 14.4  ±  1.9 ms vs. 12.8  ±  2.1 ms during active hours; n  =  47, analysis of variance (ANOVA) q  =  0.001). The α2 long-term fluctuations in the detrended fluctuation analysis, a parameter predictive of cardiovascular mortality, significantly differentiated between diabetic and nondiabetic patients (prominent at night with 0.58  ±  0.2 vs. 0.45  ±  0.12, respectively, adj. p  =  0.004). Both diabetic and nondiabetic chronic kidney disease patients showed loss of rhythmic organization compared to controls, with diabetic chronic kidney disease patients exhibiting deconsolidation of peak phases between their activity and standard deviation of interbeat intervals rhythms (mean phase difference chronic kidney disease 8.3 h, chronic kidney disease/type 2 diabetes mellitus 4 h, controls 6.8 h). This work provides a roadmap toward deriving actionable clinical insights from the data collected by wearable devices outside of highly controlled clinical environments.

A Personalized Signature and Chronotherapy-Based Platform for Improving the Efficacy of Sepsis Treatment

Sepsis remains a major therapeutic challenge and is associated with a high rate of morbidity and mortality. It is a dynamic condition in which multiple parameters change over time, rendering it difficult to overcome the various injurious responses, which worsen the prognosis in these patients. The prognosis of sepsis is associated with a disbalance of compensatory responses to infectious triggers, part of which can be deleterious. Marked inter- and intra-patient variability characterizes the mechanisms that underlie sepsis progression and determine the response to therapy. In this paper, we review some of the data on the use of chronopharmacological approaches for the treatment of patients with sepsis and discuss the role of the autonomic nervous system in the mechanisms associated with immune response and chronotherapy in these patients. We describe the implementation of an individualized platform that is based on the personalized autonomic nervous system, immune, and chronobiology-derived parameters for generating a patient-tailored therapeutic regimen. The notion of overcoming the deleterious compensatory response in a highly dynamic system in sepsis is presented to ensure an improved response to current therapies.

Wharton's jelly-derived mesenchymal stem cells attenuate sepsis-induced organ injury partially via cholinergic anti-inflammatory pathway activation

Sepsis induces organ dysfunction due to overexpression of the inflammatory host response, resulting in cardiopulmonary and autonomic dysfunction, thus increasing the associated morbidity and mortality. Wharton's jelly-derived mesenchymal stem cells (WJ-MSCs) express genes and secrete factors with anti-inflammatory properties, neurological and immunological protection, as well as improve survival in experimental sepsis. The cholinergic anti-inflammatory pathway (CAP) is mediated by α7-nicotinic acetylcholine receptors (α7nAChRs), which play an important role in the control of systemic inflammation. We hypothesized that WJ-MSCs attenuate sepsis-induced organ injury in the presence of an activated CAP pathway. To confirm our hypothesis, we evaluated the effects of WJ-MSCs as a treatment for cardiopulmonary injury and on neuroimmunomodulation. Male Wistar rats were randomly divided into four groups: control (sham-operated); cecal ligation and puncture (CLP) alone; CLP+WJ-MSCs (1 × 10⁶ cells, at 6 h post-CLP); and CLP+methyllycaconitine (MLA)+WJ-MSCs (5 mg/kg body wt, at 5.5 h post-CLP, and 1 × 10⁶ cells, at 6 h post-CLP, respectively). All experiments, including the assessment of echocardiographic parameters and heart rate variability, were performed 24 h after CLP. WJ-MSC treatment attenuated diastolic dysfunction and restored baroreflex sensitivity. WJ-MSCs also increased cardiac sympathetic and cardiovagal activity. WJ-MSCs reduced leukocyte infiltration and proinflammatory cytokines, effects that were abolished by administration of a selective α7nAChR antagonist (MLA). In addition, WJ-MSC treatment also diminished apoptosis in the lungs and spleen. In cardiac and splenic tissue, WJ-MSCs downregulated α7nAChR expression, as well as reduced the phospho-STAT3-to-total STAT3 ratio in the spleen. WJ-MSCs appear to protect against sepsis-induced organ injury by reducing systemic inflammation, at least in part, via a mechanism that is dependent on an activated CAP.

Heart rate variability as predictor of mortality in sepsis: A prospective cohort study

Background

Sepsis is a serious medical condition with increasing prevalence and high mortality. The role of the autonomic nervous system in pathophysiology of sepsis has been increasingly researched. The objective of this study is to evaluate the Heart rate variability (HRV) as a predictor of mortality in septic patients.

Methods

This was a prospective cohort of patients diagnosed with sepsis. Patient recruitment was carried out at ICU in tertiary university hospital between March 2012 and February 2014. Clinical data and laboratory exams were collected at admission. Each patient underwent a 20-minute Holter and a 24-hour Holter on the first day of enrollment. The primary outcome was the 28-day all-cause mortality.

Results

A total of 63 patients were included. Patients were categorized into nonsurvivor group (n = 16) or survivor group (n = 47) depending on this endpoint. Survivors were younger (48.6 years vs. 63.0 years), had better renal function and lower values in severity scores (APACHE II and SOFA) compared to nonsurvivors. In the 20-minute Holter, SDNN, Total Power, VLF Power, LF Power and LF/HF of nonsurvivors were significantly lower than those of survivors (p = <0.001, p = 0.003, p = 0.002, p = 0.006, p = 0.009 respectively). ROC curve of SDNN was built, showing area under the curve of 0.772 (0.638–0.906) for mortality. The value of 17ms was chosen as best SDNN cutoff to discriminate survivors and nonsurvivors. In the Cox proportional regression, adjusted for SOFA score and for APACHE II, a SDNN ≤ 17ms was associated with a greater risk of death, with hazard ratios of 6.3 (1.4–28.0; p = 0.015) and 5.5 (1,2–24,8; p = 0.027), respectively. The addition of the dichotomized SDNN to the SOFA model reduced AIC and increased the concordance statistic and the R², indicating that predictive power of the SDNN + SOFA model is better than predictive power of SOFA only.

Conclusions

Several HRV parameters are reduced in nonsurviving septic patients. SDNN ≤17 is a risk factor for death in septic patients, even after adjusting for severity scores.

Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study

OBJECTIVES

Recent research indicates that Heart Rate Variability (HRV) is affected in Bipolar Disorders (BD) patients. To determine whether such alterations are a mere expression of the current mood state or rather contain longitudinal information on BD course, we examined the potential influence of states adjacent in time upon HRV features measured in a target mood state.

METHODS

Longitudinal evaluation of HRV was obtained in eight BD patients by using a wearable monitoring system developed within the PSYCHE project. We extracted time-domain, frequency-domain and non-linear HRV-features and trained a Support Vector Machine (SVM) to classify HRV-features according to mood state. To evaluate the influence of adjacent mood states, we trained SVM with different HRV-feature sets: 1) belonging to each mood state considered alone; 2) belonging to each mood state and normalized using information from the preceding mood state; 3) belonging to each mood state and normalized using information from the preceding and subsequent mood states; 4) belonging to each mood state and normalized using information from two randomly chosen states.

RESULTS

SVM classification accuracy within a target state was significantly greater when HRV-features from the previous and subsequent mood states were considered.

CONCLUSIONS

Although preliminary and in need of replications our results suggest for the first time that psychophysiological states in BD contain information related to the subsequent ones. Such characteristic may be used to improve clinical management and to develop algorithms to predict clinical course and mood switches in individual patients.