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Journal of Otolaryngology - Head & Neck... Apr 2022Obstructive sleep apnea is a common clinical condition and has a significant impact on the health of patients if untreated. The current diagnostic gold standard for... (Review)
Review
BACKGROUND
Obstructive sleep apnea is a common clinical condition and has a significant impact on the health of patients if untreated. The current diagnostic gold standard for obstructive sleep apnea is polysomnography, which is labor intensive, requires specialists to utilize, expensive, and has accessibility challenges. There are also challenges with awareness and identification of obstructive sleep apnea in the primary care setting. Artificial intelligence systems offer the opportunity for a new diagnostic approach that addresses the limitations of polysomnography and ultimately benefits patients by streamlining the diagnostic expedition.
MAIN BODY
The purpose of this project is to elucidate the barriers that exist in the implementation of artificial intelligence systems into the diagnostic context of obstructive sleep apnea. It is essential to understand these challenges in order to proactively create solutions and establish an efficient adoption of this new technology. The literature regarding the evolution of the diagnosis of obstructive sleep apnea, the role of artificial intelligence in the diagnosis, and the barriers in artificial intelligence implementation was reviewed and analyzed.
CONCLUSION
The barriers identified were categorized into different themes including technology, data, regulation, human resources, education, and culture. Many of these challenges are ubiquitous across artificial intelligence implementation in any medical diagnostic setting. Future research directions include developing solutions to the barriers presented in this project.
Topics: Artificial Intelligence; Humans; Polysomnography; Sleep Apnea, Obstructive
PubMed: 35468865
DOI: 10.1186/s40463-022-00566-w -
Journal of Clinical Sleep Medicine :... Jan 2022We evaluated the interrater reliabilities of manual polysomnography sleep stage scoring. We included all studies that employed Rechtschaffen and Kales rules or American... (Meta-Analysis)
Meta-Analysis
STUDY OBJECTIVES
We evaluated the interrater reliabilities of manual polysomnography sleep stage scoring. We included all studies that employed Rechtschaffen and Kales rules or American Academy of Sleep Medicine standards. We sought the overall degree of agreement and those for each stage.
METHODS
The keywords were "Polysomnography (PSG)," "sleep staging," "Rechtschaffen and Kales (R&K)," "American Academy of Sleep Medicine (AASM)," "interrater (interscorer) reliability," and "Cohen's kappa." We searched PubMed, OVID Medline, EMBASE, the Cochrane library, KoreaMed, KISS, and the MedRIC. The exclusion criteria included automatic scoring and pediatric patients. We collected data on scorer histories, scoring rules, numbers of epochs scored, and the underlying diseases of the patients.
RESULTS
A total of 101 publications were retrieved; 11 satisfied the selection criteria. The Cohen's kappa for manual, overall sleep scoring was 0.76, indicating substantial agreement (95% confidence interval, 0.71-0.81; < .001). By sleep stage, the figures were 0.70, 0.24, 0.57, 0.57, and 0.69 for the W, N1, N2, N3, and R stages, respectively. The interrater reliabilities for stage N2 and N3 sleep were moderate, and that for stage N1 sleep was only fair.
CONCLUSIONS
We conducted a meta-analysis to generalize the variation in manual scoring of polysomnography and provide reference data for automatic sleep stage scoring systems. The reliability of manual scorers of polysomnography sleep stages was substantial. However, for certain stages, the results were poor; validity requires improvement.
CITATION
Lee YJ, Lee JY, Cho JH, Choi JH. Interrater reliability of sleep stage scoring: a meta-analysis. 2022;18(1):193-202.
Topics: Child; Humans; Observer Variation; Polysomnography; Reproducibility of Results; Sleep; Sleep Stages
PubMed: 34310277
DOI: 10.5664/jcsm.9538 -
Journal of Clinical Sleep Medicine :... Nov 2021
Topics: Humans; Polysomnography; Sleep Apnea, Obstructive
PubMed: 34666887
DOI: 10.5664/jcsm.9560 -
Journal of Clinical Sleep Medicine :... Sep 2022Supine-predominant obstructive sleep apnea (OSA) is highly prevalent. The proportion of time spent in the supine position may be overrepresented during polysomnography,...
STUDY OBJECTIVES
Supine-predominant obstructive sleep apnea (OSA) is highly prevalent. The proportion of time spent in the supine position may be overrepresented during polysomnography, which would impact on the apnea-hypopnea index (AHI) and have important clinical implications. We aimed to investigate the difference in body position during laboratory or home polysomnography compared to habitual sleep and estimate its effect on OSA severity. Secondary aims were to evaluate the consistency of habitual sleeping position and accuracy of self-reported sleeping position.
METHODS
Patients undergoing diagnostic laboratory or home polysomnography were recruited. Body position was recorded using a neck-worn device. Habitual sleeping position was the average time spent supine over 3 consecutive nights at home. Primary outcomes were the proportion of sleep time spent supine (% time supine) and AHI adjusted for habitual sleeping position.
RESULTS
Fifty-seven patients who underwent laboratory polysomnography and 56 who had home polysomnography were included. Compared to habitual sleep, % time supine was higher during laboratory polysomnography (mean difference 14.1% [95% confidence interval: 7.2-21.1]; = .0002) and home polysomnography (7.1% [95% confidence interval 0.9-13.3]; = .03). Among those with supine-predominant OSA, there was a trend toward lower adjusted AHI than polysomnography-derived AHI ( = .07), changing OSA severity in 31.6%. There was no significant between-night difference in % time supine during habitual sleep ( = .4). Self-reported % time supine was inaccurate (95% limits of agreement -49.2% to 53.9%).
CONCLUSIONS
More time was spent in the supine position during polysomnography compared to habitual sleep, which may overestimate OSA severity for almost one-third of patients with supine-predominant OSA.
CLINICAL TRIAL REGISTRATION
Registry: Australia and New Zealand Clinical Trials Registry (ANZCTR); Title: Sleeping position during sleep tests and at home; Identifier: ACTRN12618000628246; URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374873&isReview=true.
CITATION
Yo SW, Joosten SA, Wimaleswaran H, et al. Body position during laboratory and home polysomnography compared to habitual sleeping position at home. . 2022;18(9):2103-2111.
Topics: Humans; Polysomnography; Posture; Sleep; Sleep Apnea, Obstructive; Supine Position
PubMed: 35459447
DOI: 10.5664/jcsm.9990 -
Journal of Sleep Research Aug 2019Actigraphy is increasingly used in practice and research studies because of its relative low cost and decreased subject burden. How multiple nights of at-home actigraphy...
Actigraphy is increasingly used in practice and research studies because of its relative low cost and decreased subject burden. How multiple nights of at-home actigraphy compare to one independent night of in-laboratory polysomnography (PSG) has not been examined in people with insomnia. Using event markers (MARK) to set time in bed (TIB) compared to automatic program analysis (AUTO) has not been systematically evaluated. Subjects (n = 30) meeting DSM-5 criteria for insomnia and in-laboratory PSG sleep efficiency (SE) of <85% were studied. Subjects were free of psychiatric, sleep or circadian disorders, other chronic conditions and medications that effect sleep. Subjects had an in-laboratory PSG, then were sent home for 7 nights with Philips Actiwatch Spectrum Plus. Data were analysed using Philips Actiware version 6. Using the mean of seven nights, TIB, total sleep time (TST), SE, sleep-onset latency (SOL) and wake after sleep onset (WASO) were examined. Compared to PSG, AUTO showed longer TIB and TST and less WASO. MARK only differed from PSG with decreased WASO. Differences between the PSG night and the following night at home were found, with better sleep on the first night home. Actigraphy in people with insomnia over seven nights is a valid indicator of sleep compared to an independent in-laboratory PSG. Event markers increased the validity of actigraphy, showing no difference in TIB, TST, SE and SOL. AUTO was representative of SE and SOL. Increased SE and TST without increased TIB suggests possible compensatory sleep the first at night home after in-laboratory PSG.
Topics: Actigraphy; Female; Humans; Male; Middle Aged; Polysomnography; Sleep Initiation and Maintenance Disorders
PubMed: 30941838
DOI: 10.1111/jsr.12854 -
Journal of Clinical Sleep Medicine :... Apr 2022The objective of this meta-analysis was to analyze agreement in apnea-hypopnea index (AHI) determination between peripheral arterial tonometry (PAT) and polysomnography... (Meta-Analysis)
Meta-Analysis
STUDY OBJECTIVES
The objective of this meta-analysis was to analyze agreement in apnea-hypopnea index (AHI) determination between peripheral arterial tonometry (PAT) and polysomnography (PSG) studies.
METHODS
Mean AHI bias and standard deviation extracted from Bland-Altman plots reported in studies were pooled in a meta-analysis, which was then used to calculate percentage errors of limit agreement in AHI determination by PAT using PSG AHI as the reference. Individual participant data (where reported in studies) were used to compute Cohen's kappa to assess agreement between PSG and PAT on sleep apnea severity and for computing the sensitivity and specificity of PAT at different AHI thresholds using PSG AHI as the reference.
RESULTS
From 17 studies and 1,318 participants (all underwent simultaneous PSG and use of the WatchPAT device), a pooled mean AHI bias of 0.30 (standard error [SE], 0.74) and a WatchPAT AHI percentage error of 230% was calculated. The meta-analysis of Cohen's kappa for agreement between PSG and WatchPAT studies for classifying patients with no sleep apnea, mild, moderate, or severe sleep apnea severity was 0.45 (SE, 0.06), 0.29 (SE, 0.05), 0.25 (SE, 0.07), and 0.64 (SE, 0.05), respectively. At AHI thresholds of 5, 15 and 30 events/h, WatchPAT studies showed pooled sensitivities and specificities of 94.11% and 43.47%, 92.21% and 72.39%, and 74.11% and 87.10%, respectively. Likelihood ratios were not significant at any AHI threshold.
CONCLUSIONS
The results of this meta-analysis suggest clinically significant discordance between WatchPAT and PSG measurements of AHI, significant sleep apnea severity misclassification by PAT studies, and poor diagnostic test performance.
CITATION
Iftikhar IH, Finch CE, Shah AS, Augunstein CA, Ioachimescu OC. A meta-analysis of diagnostic test performance of peripheral arterial tonometry studies. 2022;18(4):1093-1102.
Topics: Diagnostic Tests, Routine; Humans; Manometry; Polysomnography; Sensitivity and Specificity; Sleep Apnea Syndromes; Sleep Apnea, Obstructive
PubMed: 34879903
DOI: 10.5664/jcsm.9808 -
Scientific Reports May 2022Consolidated memories can be returned to a labile state upon reactivation. The re-stabilization of reactivated memories, or reconsolidation, can allow for change in...
Consolidated memories can be returned to a labile state upon reactivation. The re-stabilization of reactivated memories, or reconsolidation, can allow for change in previously established memories. Given the role of sleep in the initial consolidation of memories, sleep may be important for reconsolidation as well. However, effects of sleep on reconsolidation and specific aspects of sleep that may contribute are unclear. Here, participants learned 30 picture-location pairs. After overnight sleep, initial consolidation was tested. Following either one day (Experiment 1) or one week (Experiment 2), participants were tested again to reactivate their memory and then learned 30 novel picture-location pairs. Control groups (Experiment 1) received no reactivation prior to new learning. Twelve hours later, after daytime wakefulness or overnight sleep, participants completed a final memory test. Sleep participants underwent polysomnography between reactivation and final tests. In Experiment 1, reactivation led to preservation of memory compared to no reactivation. Sleep was associated with less post-reactivation memory decline than waking, with memory preservation positively related to time spent in non-rapid-eye movement sleep. In Experiment 2, sleep was associated with greater post-reactivation memory improvement than waking, with improvement positively related to sigma activity. These results suggest sleep enhances reconsolidation-based strengthening of episodic memories.
Topics: Humans; Learning; Memory, Episodic; Polysomnography; Sleep; Wakefulness
PubMed: 35508568
DOI: 10.1038/s41598-022-11135-6 -
Journal of Medical Internet Research Dec 2023Sleep disturbances are core symptoms of psychiatric disorders. Although various sleep measures have been developed to assess sleep patterns and quality of sleep, the...
BACKGROUND
Sleep disturbances are core symptoms of psychiatric disorders. Although various sleep measures have been developed to assess sleep patterns and quality of sleep, the concordance of these measures in patients with psychiatric disorders remains relatively elusive.
OBJECTIVE
This study aims to examine the degree of agreement among 3 sleep recording methods and the consistency between subjective and objective sleep measures, with a specific focus on recently developed devices in a population of individuals with psychiatric disorders.
METHODS
We analyzed 62 participants for this cross-sectional study, all having data for polysomnography (PSG), Zmachine, Fitbit, and sleep logs. Participants completed questionnaires on their symptoms and estimated sleep duration the morning after the overnight sleep assessment. The interclass correlation coefficients (ICCs) were calculated to evaluate the consistency between sleep parameters obtained from each instrument. Additionally, Bland-Altman plots were used to visually show differences and limits of agreement for sleep parameters measured by PSG, Zmachine, Fitbit, and sleep logs.
RESULTS
The findings indicated a moderate agreement between PSG and Zmachine data for total sleep time (ICC=0.46; P<.001), wake after sleep onset (ICC=0.39; P=.002), and sleep efficiency (ICC=0.40; P=.006). In contrast, Fitbit demonstrated notable disagreement with PSG (total sleep time: ICC=0.08; wake after sleep onset: ICC=0.18; sleep efficiency: ICC=0.10) and exhibited particularly large discrepancies from the sleep logs (total sleep time: ICC=-0.01; wake after sleep onset: ICC=0.05; sleep efficiency: ICC=-0.02). Furthermore, subjective and objective concordance among PSG, Zmachine, and sleep logs appeared to be influenced by the severity of the depressive symptoms and obstructive sleep apnea, while these associations were not observed between the Fitbit and other sleep instruments.
CONCLUSIONS
Our study results suggest that Fitbit accuracy is reduced in the presence of comorbid clinical symptoms. Although user-friendly, Fitbit has limitations that should be considered when assessing sleep in patients with psychiatric disorders.
Topics: Humans; Polysomnography; Cross-Sectional Studies; Reproducibility of Results; Sleep; Sleep Wake Disorders; Electroencephalography; Actigraphy
PubMed: 38090797
DOI: 10.2196/51336 -
Sleep Nov 2020Obstructive sleep apnea (OSA) is characterized by recurrent partial to complete upper airway obstructions during sleep, leading to repetitive arousals and oxygen...
STUDY OBJECTIVES
Obstructive sleep apnea (OSA) is characterized by recurrent partial to complete upper airway obstructions during sleep, leading to repetitive arousals and oxygen desaturations. Although many OSA biomarkers have been reported individually, only a small subset have been validated through both cross-sectional and intervention studies. We sought to profile serum protein biomarkers in OSA in unbiased high throughput assay.
METHODS
A highly multiplexed aptamer array (SomaScan) was used to profile 1300 proteins in serum samples from 713 individuals in the Stanford Sleep Cohort, a patient-based registry. Outcome measures derived from overnight polysomnography included Obstructive Apnea Hypopnea Index (OAHI), Central Apnea Index (CAI), 2% Oxygen Desaturation index, mean and minimum oxygen saturation indices during sleep. Additionally, a separate intervention-based cohort of 16 individuals was used to assess proteomic profiles pre- and post-intervention with positive airway pressure.
RESULTS
OAHI was associated with 65 proteins, predominantly pathways of complement, coagulation, cytokine signaling, and hemostasis which were upregulated. CAI was associated with two proteins including Roundabout homolog 3 (ROBO3), a protein involved in bilateral synchronization of the pre-Bötzinger complex and cystatin F. Analysis of pre- and post intervention samples revealed IGFBP-3 protein to be increased while LEAP1 (Hepicidin) to be decreased with intervention. An OAHI machine learning classifier (OAHI >=15 vs OAHI<15) trained on SomaScan protein measures alone performed robustly, achieving 76% accuracy in a validation dataset.
CONCLUSIONS
Multiplex protein assays offer diagnostic potential and provide new insights into the biological basis of sleep disordered breathing.
Topics: Biomarkers; Cross-Sectional Studies; Humans; Polysomnography; Proteomics; Receptors, Cell Surface; Sleep Apnea Syndromes
PubMed: 32369590
DOI: 10.1093/sleep/zsaa086 -
Journal of Medical Internet Research Sep 2022American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without... (Review)
Review
BACKGROUND
American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard.
OBJECTIVE
We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA.
METHODS
We searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study.
RESULTS
Our search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression.
CONCLUSIONS
Although high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition.
TRIAL REGISTRATION
PROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339.
Topics: Adult; Bayes Theorem; Humans; Machine Learning; Neural Networks, Computer; Polysomnography; Sleep Apnea, Obstructive
PubMed: 36178720
DOI: 10.2196/39452