Early identification of heart failure deterioration through respiratory monitoring with adaptive servo-ventilation

The chronobiology of human heart failure: clinical implications and therapeutic opportunities

Circadian variation in cardiovascular and metabolic dynamics arises from interactions between intrinsic rhythms and extrinsic cues. By anticipating and accommodating adaptation to awakening and activity, their synthesis maintains homeostasis and maximizes efficiency, flexibility, and resilience. The dyssynchrony of cardiovascular load and energetic capacity arising from attenuation or loss of such rhythms is strongly associated with incident heart failure (HF). Once established, molecular, neurohormonal, and metabolic rhythms are frequently misaligned with each other and with extrinsic cycles, contributing to HF progression and adverse outcomes. Realignment of biological rhythms via lifestyle interventions, chronotherapy, and time-tailored autonomic modulation represents an appealing potential strategy for improving HF-related morbidity and mortality.

Nocturnal hypoxemic burden in patients with heart failure: Emerging prognostic role of its nonspecific component

BACKGROUND

Nocturnal hypoxemic burden has been shown to be a robust, independent predictor of all-cause mortality in patients with heart failure and reduced ejection fraction (HFrEF) and to occur in a severe form even in patients with low or negligible frequency of respiratory events (apneas/hypopneas). This suggests the existence of two components of hypoxemic burden: one unrelated to respiratory events and the other related. The aim of this study was to characterize these two components and to evaluate their prognostic value.

METHODS

Nocturnal hypoxemic burden was assessed in a cohort of 280 patients with HFrEF by measuring the percentage of sleep with an oxygen saturation (SpO2) <90% (T90), and the area of the SpO2 curve below 90% (Area90). Both indices were also recalculated within the sleep segments associated with respiratory events (event-related component: T90Eve, Area90Eve) and outside these segments (nonspecific component: T90Nspec, Area90Nspec). The outcome of the survival analysis (Cox regression) was all-cause mortality.

RESULTS

During a median follow-up of 60 months, 87 patients died. T90, Area90, and their components were significant in univariate analysis (P < .05 all). However, when these indices were adjusted for known risk factors, T90, T90Nspec, Area90, and Area90Nspec remained statistically significant (P = .018, hazard ratio (HR)=1.12, 95%CI=(1.02, 1.23); P = .007, HR=1.20, 95% CI = [1.05, 1.37]; P = .020, HR = 1.05, 95% CI = [1.01, 1.10]; P = .0006, HR = 1.15, 95% CI = [1.06, 1.25]), whereas T90Eve and Area90Eve did not (P = .27, P = .28). These results were internally validated using bootstrap resampling.

CONCLUSIONS

By demonstrating a significant independent association of nonspecific hypoxemic burden with all-cause mortality, this study suggests that this component of total nocturnal hypoxemic burden may play an important prognostic role in patients with HFrEF.

Sleep apnea in patients undergoing coronary artery bypass grafting: Impact on perioperative outcomes

Sleep-disordered breathing is common in patients with coronary artery disease undergoing coronary artery bypass grafting. Sleep-disordered breathing is associated with increased perioperative morbidity, arrhythmias (e.g. atrial fibrillation) and mortality. This study investigated the impact of sleep-disordered breathing on the postoperative course after coronary artery bypass grafting, including development of atrial fibrillation. This prospective single-centre cohort study included adults undergoing coronary artery bypass grafting. All were screened for sleep-disordered breathing (polygraphy) and atrial fibrillation (electrocardiogram) preoperatively; those with known sleep-disordered breathing or atrial fibrillation were excluded. Endpoints included new-onset atrial fibrillation, duration of mechanical ventilation, time in the intensive care unit, and postoperative infection. Regression analysis was performed to identify associations between sleep-disordered breathing and these outcomes. A total of 508 participants were included (80% male, median age 68 years). The prevalence of any (apnea-hypopnea index ≥ 5 per hr), moderate (apnea-hypopnea index = 15-30 per hr) and severe (apnea-hypopnea index > 30 per hr) sleep-disordered breathing was 52.9%, 9.3% and 10.2%, respectively. All-cause 30-day mortality was 0.98%. After adjustment for age and sex, severe sleep-disordered breathing was associated with longer respiratory ventilation support (crude odds ratio [95% confidence interval] 5.28 [2.18-12.77]; p < 0.001) and higher postoperative infection rates (crude odds ratio 3.32 [1.45-7.58]; p < 0.005), but not new-onset atrial fibrillation or mortality. New-onset atrial fibrillation was significantly associated with postoperative infection and prolonged hospital stay. The significant associations between sleep-disordered breathing and several adverse outcomes after coronary artery bypass grafting support the need for preoperative sleep-disordered breathing screening in individuals undergoing cardiac surgery.

Ventricular assist devices and sleep-disordered breathing-A mechanical heart stimulating a sleepy brain

Sleep-disordered breathing, including obstructive sleep apnea (OSA) and central sleep apnea (CSA), is common in severe heart failure (HF) patients. There is limited data on the effect of left ventricular assist devices (LVAD) on sleep apnea. We performed a retrospective review of 350 durable LVAD patients and found 5 with a history of pre- and post-LVAD sleep studies. All five patients had OSA, and three had concomitant CSA. We observed reduced apnea-hypopnea index following LVAD placement. This was due to a near abolishment of CSA in three mixed sleep apnea patients-as seen by a central apnea index improvement from an average of 25.9 ± 13.1 to 1.4 ± 2.5 events per hour (p = 0.063). LVAD placement was associated with an increase in thermodilution cardiac output from 2.7 ± 0.6 to 4.1 ± 1.1 L/min (p = 0.014). These findings support chemoreception physiology seen in patients with poor circulation and the effect of restoring this circulation with LVAD support.

Hypoxaemic burden in heart failure patients receiving adaptive servo-ventilation

AIMS

This study aimed to assess the effectiveness of adaptive servo-ventilation (ASV) for lowering hypoxaemic burden components in heart failure with reduced ejection fraction (HFrEF) patients.

METHODS AND RESULTS

Fifty-six stable HFrEF patients with left ventricular ejection fraction ≤ 40 were randomized to receive either ASV (n = 27; 25 males) or optimal medical management or optimal medical management alone (n = 29; 26 males). Patients underwent overnight polysomnography at baseline and a 12 week follow-up visit. We quantified hypoxaemic as time spent at <90% oxygen saturation (T90) decomposed into desaturation-related components (T90desaturation ) and non-specific drifts (T90non-specific ). In the ASV arm, T90 significantly shortened by nearly 60% from 50.1 ± 95.8 min at baseline to 20.5 ± 33.0 min at follow-up compared with 59.6 ± 88 and 65.4 ± 89.6 min in the control arm (P = 0.009). ASV reduced the apnoea-related component (T90desaturation ) from 37.7 ± 54.5 to 2.1 ± 7.3 min vs. 37.7 ± 54.5 and 40.4 ± 66.4 min in the control arm (P = 0.008). A significant non-specific T90 component of 19.6 ± 31.8 min persisted during ASV. In adjusted multivariable regression, T90desaturation was significantly associated with the ratio of the forced expiratory volume in the first second to the forced vital capacity of the lungs (β = 0.336, 95% confidence interval 0.080 to 0.593; P = 0.011) and T90non-specific with left ventricular ejection fraction (β = -0.345, 95% confidence interval -0.616 to -0.073; P = 0.014).

CONCLUSIONS

ASV effectively suppresses the sleep apnoea-related component of hypoxaemic burden in HFrEF patients. A significant hypoxaemic burden not directly attributable to sleep apnoea but related to the severity of heart failure remains and may adversely affect cardiovascular long-term outcomes.

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients' data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient's position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.

Recent Progress in Long-Term Sleep Monitoring Technology

Sleep is an essential physiological activity, accounting for about one-third of our lives, which significantly impacts our memory, mood, health, and children's growth. Especially after the COVID-19 epidemic, sleep health issues have attracted more attention. In recent years, with the development of wearable electronic devices, there have been more and more studies, products, or solutions related to sleep monitoring. Many mature technologies, such as polysomnography, have been applied to clinical practice. However, it is urgent to develop wearable or non-contacting electronic devices suitable for household continuous sleep monitoring. This paper first introduces the basic knowledge of sleep and the significance of sleep monitoring. Then, according to the types of physiological signals monitored, this paper describes the research progress of bioelectrical signals, biomechanical signals, and biochemical signals used for sleep monitoring. However, it is not ideal to monitor the sleep quality for the whole night based on only one signal. Therefore, this paper reviews the research on multi-signal monitoring and introduces systematic sleep monitoring schemes. Finally, a conclusion and discussion of sleep monitoring are presented to propose potential future directions and prospects for sleep monitoring.