Dataset Information

The Evaluation of Autonomic Arousals in Scoring Sleep Respiratory Disturbances with Polysomnography and Portable Monitor Devices: A Proof of Concept Study.

ABSTRACT: Background:Autonomic arousals can be considered as surrogates of electroencephalography (EEG) arousals when calculating respiratory disturbance index (RDI). The main objective of this proof of concept study was to evaluate the use of heart rate acceleration (HRa) arousals associated with sleep respiratory events in a population undergoing full polysomnography (type 1) and in another undergoing portable monitor study (type 3). Our hypothesis is that when compared to other commonly used indexes, RDI based on HRa will capture more events in both types of recording. Materials and Methods:A retrospective analysis was performed in two different populations of patients with suspected OSA: a) 72 patients undergoing one night of type 1 recording and b) 79 patients undergoing one night of type 3 recording. Variables for type 1 were 4% oxygen desaturation index (ODI), apnea/hypopnea index (AHI), RDI based on EEG arousals (RDIe), and RDI based on HRa with threshold of 5bpm (RDIa5). For type 3, variables were 4% ODI, AHI, and RDIa5 (it is not possible to calculate RDIe due to the absence of EEG). Calculated data were 1) Mean values for each sleep disturbance index in type 1 and 3 recordings; 2) Frequency of migration from lower to higher OSA severity categories using RDIa5 in comparison to AHI (thresholds: ?5/h mild, ?15/h moderate, ?30/h severe); and 3) Bland-Altman plots to assess agreement between AHI vs RDIe and RDIa5 in type 1 population, and AHI vs RDIa5 in type 3 populations. Results:More respiratory disturbance events were captured with RDIa5 index in both type 1 and type 3 recordings when compared to the other indexes. In type 1 recording, when using RDIa5 37% of patients classified as not having OSA with AHI were now identified as having OSA, and a total of 59% migrated to higher severity categories. In type 3 recording, similar results were obtained, as 37% of patients classified as not having OSA with AHI were now identified as having OSA using RDIa5, and a total of 55% patients migrated to higher severity categories. Mean differences for RDIa5 and AHI in type 1 and 3 populations were similar. Conclusion:The use of autonomic arousals such as HRa can help to detect more respiratory disturbance events when compared to other indexes, being a variable that may help to capture borderline mild cases. This becomes especially relevant in type 3 recordings. Future research is needed to determine its validity, optimization, and its clinical significance.

SUBMITTER: Mayer P

PROVIDER: S-EPMC7371436 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Evaluation of Autonomic Arousals in Scoring Sleep Respiratory Disturbances with Polysomnography and Portable Monitor Devices: A Proof of Concept Study.

Mayer Pierre P Herrero Babiloni Alberto A Beetz Gabrielle G Marshansky Serguei S Kaddaha Zeina Z Rompré Pierre H PH Jobin Vincent V Lavigne Gilles J GJ

Nature and science of sleep 20200716

<h4>Background</h4>Autonomic arousals can be considered as surrogates of electroencephalography (EEG) arousals when calculating respiratory disturbance index (RDI). The main objective of this proof of concept study was to evaluate the use of heart rate acceleration (HRa) arousals associated with sleep respiratory events in a population undergoing full polysomnography (type 1) and in another undergoing portable monitor study (type 3). Our hypothesis is that when compared to other commonly used in ...[more]

PMID: 32765141

Similar Datasets

Project description:IntroductionDifficulty swallowing (dysphagia) occurs frequently in patients with neurological disorders and can lead to aspiration, choking, and malnutrition. Dysphagia is typically diagnosed using costly, invasive imaging procedures or subjective, qualitative bedside examinations. Wearable sensors are a promising alternative to noninvasively and objectively measure physiological signals relevant to swallowing. An ongoing challenge with this approach is consolidating these complex signals into sensitive, clinically meaningful metrics of swallowing performance. To address this gap, we propose 2 novel, digital monitoring tools to evaluate swallows using wearable sensor data and machine learning.MethodsBiometric swallowing and respiration signals from wearable, mechano-acoustic sensors were compared between patients with poststroke dysphagia and nondysphagic controls while swallowing foods and liquids of different consistencies, in accordance with the Mann Assessment of Swallowing Ability (MASA). Two machine learning approaches were developed to (1) classify the severity of impairment for each swallow, with model confidence ratings for transparent clinical decision support, and (2) compute a similarity measure of each swallow to nondysphagic performance. Task-specific models were trained using swallow kinematics and respiratory features from 505 swallows (321 from patients and 184 from controls).ResultsThese models provide sensitive metrics to gauge impairment on a per-swallow basis. Both approaches demonstrate intrasubject swallow variability and patient-specific changes which were not captured by the MASA alone. Sensor measures encoding respiratory-swallow coordination were important features relating to dysphagia presence and severity. Puree swallows exhibited greater differences from controls than saliva swallows or liquid sips (p < 0.037).DiscussionDeveloping interpretable tools is critical to optimize the clinical utility of novel, sensor-based measurement techniques. The proof-of-concept models proposed here provide concrete, communicable evidence to track dysphagia recovery over time. With refined training schemes and real-world validation, these tools can be deployed to automatically measure and monitor swallowing in the clinic and community for patients across the impairment spectrum.

Project description:To evaluate whether objectively measured sleep characteristics are associated with mortality risk independent of inflammatory burden and comorbidity.The Osteoporotic Fractures in Men Sleep Study (conducted in 2003-2005) included community-dwelling older men (n = 2531; average [standard deviation {SD}] age = 76.3 (5.5) years). Sleep measures from in-home polysomnography and wrist actigraphy and assessments of serum inflammatory markers levels (C-reactive protein, interleukin-6, tumor necrosis factor ?, tumor necrosis factor ? soluble receptor II, and interferon-?) were obtained. Vital status was ascertained over an average (SD) follow-up of 7.4 (1.9 SD) years.Three of the seven main sleep measures examined were independently associated with greater inflammatory burden. Mortality risk associated with prolonged (?10% total sleep time) blood oxygen desaturation and short (<5 hours) sleep duration was attenuated to nonsignificance after adjusting for inflammatory burden or medical burden/lifestyle factors. Severe blood oxygen desaturation (adjusted hazard ratio [aHR] = 1.57, 95% confidence interval [CI] = 1.11-2.22), sleep fragmentation (aHR = 1.32, 95% CI = 1.12-1.57), and a lower percentage of sleep in rapid eye movement (aHR per SD = 0.90, 95% CI = 0.93-0.97) were independently associated with mortality.Short sleep duration and prolonged blood oxygen desaturation were independently associated with inflammatory burden, which attenuated associations between these sleep characteristics and mortality. Medical and life-style factors also substantially attenuated most sleep-mortality associations, suggesting complex relations between sleep, inflammation, and disease. Sleep fragmentation, severe blood oxygen desaturation, and the percentage of sleep time in rapid eye movement were independently related to mortality risk. Future studies with repeated measures of mediators/confounds will be necessary to achieve a mechanistic understanding of sleep-related mortality risk.

Dataset Information

The Evaluation of Autonomic Arousals in Scoring Sleep Respiratory Disturbances with Polysomnography and Portable Monitor Devices: A Proof of Concept Study.

Publications

The Evaluation of Autonomic Arousals in Scoring Sleep Respiratory Disturbances with Polysomnography and Portable Monitor Devices: A Proof of Concept Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets