SLEEP

Fields, Barry G

doi: 10.1093/sleep/zsac192pmid: 35972951

Accepted manuscripts Accepted manuscripts are PDF versions of the author’s final manuscript, as accepted for publication by the journal but prior to copyediting or typesetting. They can be cited using the author(s), article title, journal title, year of online publication, and DOI. They will be replaced by the final typeset articles, which may therefore contain changes. The DOI will remain the same throughout. Article PDF first page preview Close This content is only available as a PDF. © The Author(s) 2022. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © The Author(s) 2022. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: [email protected]

Baril, Andrée-Ann; Beiser, Alexa S; DeCarli, Charles; Himali, Dibya; Sanchez, Erlan; Cavuoto, Marina; Redline, Susan; Gottlieb, Daniel J; Seshadri, Sudha; Pase, Matthew P; Himali, Jayandra J

doi: 10.1093/sleep/zsac185

Sonti, Shilpa; Grant, Struan F A

doi: 10.1093/sleep/zsac180pmid: 35908176

Sleep occurs universally and is a biological necessity for human functioning. The consequences of diminished sleep quality impact physical and physiological systems such as neurological, cardiovascular, and metabolic processes. In fact, people impacted by common complex diseases experience a wide range of sleep disturbances. It is challenging to uncover the underlying molecular mechanisms responsible for decreased sleep quality in many disease systems owing to the lack of suitable sleep biomarkers. However, the discovery of a genetic component to sleep patterns has opened a new opportunity to examine and understand the involvement of sleep in many disease states. It is now possible to use major genomic resources and technologies to uncover genetic contributions to many common diseases. Large scale prospective studies such as the genome wide association studies (GWAS) have successfully revealed many robust genetic signals associated with sleep-related traits. With the discovery of these genetic variants, a major objective of the community has been to investigate whether sleep-related traits are associated with disease pathogenesis and other health complications. Mendelian Randomization (MR) represents an analytical method that leverages genetic loci as proxy indicators to establish causal effect between sleep traits and disease outcomes. Given such variants are randomly inherited at birth, confounding bias is eliminated with MR analysis, thus demonstrating evidence of causal relationships that can be used for drug development and to prioritize clinical trials. In this review, we outline the results of MR analyses performed to date on sleep traits in relation to a multitude of common complex diseases.

Bensen-Boakes, Darah-Bree; Lovato, Nicole; Meaklim, Hailey; Bei, Bei; Scott, Hannah

doi: 10.1093/sleep/zsac187pmid: 35972329

People with insomnia reporting poorer sleep compared to estimates obtained from objective assessments is common across both research and clinical settings. Typically, individuals report less sleep and more wakefulness across a given sleep opportunity compared to that captured via objective methods (e.g. polysomnography) [1–3]. Many different terms have been used to describe this phenomenon since the 1970s [4], including but not limited to the following: sleep misperception [5], sleep-state misperception [6], sleep-state discrepancy [7], subjective-objective sleep discrepancy [3], sleep misestimation [8], and paradoxical- [9] and pseudo-insomnia [4]. The mechanisms underlying this phenomenon are not yet well understood [2] and require future research to inform developments in the diagnosis and treatment (or management) of the disorder. The aim of this letter is to facilitate such work by highlighting recent findings and proposing a new nomenclature to become standard practice for describing this phenomenon. Insomnia is regarded as a “subjective” disorder, for which individuals’ perceptions of sleep and daytime functioning form the basis of its assessment, diagnosis, and treatment. However, objective measurements of sleep may also hold an important role. Clinicians have anecdotally reported that discussing the discrepancy between perceived and objectively measured sleep can be therapeutic. Preliminary findings [10] support this anecdotal evidence, suggesting that personalized feedback about the discrepancy between self-report (sleep diary) and objectively measured (actigraphy) sleep reduced the discrepancy on subsequent nights in a group of 40 individuals with insomnia when compared to those who received no feedback [10]. However, there is limited knowledge about the mechanisms that underlie this phenomenon and the implications for the diagnosis and management of insomnia. Emerging evidence suggests that physiologically driven factors, at least to some degree, may underlie this phenomenon. For example, sleep macrostructure looks similar across individuals who demonstrate this phenomenon and those who do not, but sleep microstructure appears to differ [11]. These individuals show reduced electroencephalography power in the delta frequency band, increased power in theta/alpha, sigma, and beta frequency bands, as well as fewer and slower slow waves and more and faster sleep spindles [11]. Regional modulation of sleep [12] (i.e. “local sleep”) is also thought to influence individuals’ self-reported experience (thus, impacting this phenomenon), as local sleep intrusions may influence attentional lapses and perception of sleep and wakefulness [13]. This phenomenon is also associated with alterations in the salience network in individuals with insomnia [14], further demonstrating the possibility that this phenomenon may have physiologically driven foundations. Relatedly, individuals with insomnia may be more sensitive to sleep fragmentation and need longer continuous bouts of sleep (>30 minutes) to perceive that they had been asleep [15]. This may contribute to shorter perceived sleep duration than measured objectively. Notably, most studies have been conducted as a single night of polysomnography recording. This is problematic for interpretation as one night is unlikely to accurately represent typical sleep due to the high night-to-night variability [16] that is typical for insomnia. Additional known limitations of polysomnography (including the loss of information with 30-second scoring into categorical sleep stages and the considerable inter-scorer variability [17]) mean that objectively-obtained sleep estimates cannot be assumed to be “correct.” Therefore, suggesting that the individual is inaccurate in their reporting may thus be incorrect. When discussing this phenomenon with individuals with insomnia, a stance that implies their “inaccuracy” of perceived sleep, which individuals with insomnia may interpret as blame or invalidation of their sleep disturbance, is likely to be counter-productive, and at worse harmful, to the individual’s sleep. As we begin to understand the mechanisms underlying this phenomenon, it is perhaps appropriate to re-consider the accuracy of our terminology used to describe it. While terms such as “sleep misperception” or “sleep misestimation” have the benefit of being widely used and recognizable to many, their continued use is likely inappropriate and potentially counter-productive. These terms are used in conjunction with terminology such as “accurate” or “inaccurate,” in reference to the individual accurately or inaccurately reporting their sleep compared to polysomnography, placing blame on the individual. Some of the earliest mentions of this phenomenon in 1979 used the term “pseudo-insomnia” [4]. “Paradoxical insomnia” was adopted in the early 2000s to describe the paradoxical relationship (i.e. self-contradictory) between objective and subjective sleep reports [9]. Criticisms of “pseudo-insomnia” and “paradoxical insomnia” are that they both imply the insomnia is “fake” or “false.” In recent times, terms such as “Subjective-Objective Sleep Discrepancy (SOSD)” and “Discrepancy between Objectively measures and Self-report Sleep (DOSS)” have been adopted. Using the term “discrepancy” is advantageous, as it accurately describes the difference between the sleep measurement methods but does so in a neutral way. However, the term “subjective” still holds negative connotations in many medical fields as objective measures are often perceived as inherently more correct than subjective measures. Here, we proffer the term “sleep-wake state discrepancy” for use in future research and clinical practice. This term describes the phenomenon as a neutral discrepancy, thus reflects the state of the evidence about the potential underlying mechanisms of this phenomenon. Also, this term encompasses discrepancies of both sleep and wake, not just sleep. This term should be used in conjunction with directional terminology such as “longer” or “shorter” (e.g. “the individual self-reported shorter total sleep time than polysomnography measures recorded”), rather than ‘under-‘/“over-estimation” or “accurate”/“inaccurate” as these imply that the individual is incorrect in their perception. The preferred terminology may change over time, as more is understood about the underlying mechanisms. Nonetheless, the standardized use of respectful terminology is desirable to enable productive conversations in clinical care while also remaining true to the state of the evidence. This common phenomenon for individuals with insomnia potentially holds significant meaning in both clinical and research settings. While the underlying mechanisms of this phenomenon are still under investigation, it is imperative that we conduct research and clinical management in a considerate manner that does not place blame or assumed inaccuracy on the individual for this discrepancy. Overall, we hope to encourage people to acknowledge the phenomenon we suggest be labeled “sleep-wake state discrepancy” with regard for the individual’s best interest at the forefront of treatment and research. Acknowledgments The authors would like to acknowledge the assistance of their colleagues at Flinders University and Monash University. Disclosure Statement None declared. References 1. Trimmel K , et al. The (mis) perception of sleep: factors influencing the discrepancy between self-reported and objective sleep parameters . J Clin Sleep Med. 2021 ; 17 ( 5 ): 917 – 924 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Harvey AG , et al. (Mis) perception of sleep in insomnia: a puzzle and a resolution . Psychol Bull. 2012 ; 138 ( 1 ): 77 . Google Scholar Crossref Search ADS PubMed WorldCat 3. Kay DB , et al. Subjective–objective sleep discrepancy among older adults: associations with insomnia diagnosis and insomnia treatment . J Sleep Res. 2015 ; 24 ( 1 ): 32 – 39 . Google Scholar Crossref Search ADS PubMed WorldCat 4. Borkovec T , et al. Relaxation treatment of pseudoinsomnia and idiopathic insomnia: an electroencephalographic evaluation . J Appl Behav Anal. 1979 ; 12 ( 1 ): 37 – 54 . Google Scholar Crossref Search ADS PubMed WorldCat 5. Bastien C , et al. Insomnia and sleep misperception . Pathologie Biologie. 2014 ; 62 ( 5 ): 241 – 251 . Google Scholar Crossref Search ADS PubMed WorldCat 6. Salin-Pascual RJ , et al. Long-term study of the sleep of insomnia patients with sleep state misperception and other insomnia patients . Am J Psychiatry. 1992 ; 149 ( 7 ): 904 – 908 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 7. Kubala AG. The Effect of Short-term Exercise on Sleep and Daytime Impairment in Adults with Insomnia . University of Pittsburgh ; 2022 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 8. Lovato N , et al. Sleep misestimation among older adults suffering from insomnia with short and normal objective sleep duration and the effects of cognitive behavior therapy . Sleep. 2021 ; 44 ( 5 ). doi:10.1093/sleep/zsaa250 Google Scholar OpenURL Placeholder Text WorldCat 9. Perlis ML , et al. Etiology and pathophysiology of insomnia . In: Kryger MH, Roth T, Dement WC, eds. Principles and Practice of Sleep Medicine . Philadelphia (US): Saunders; 2005 ; 4 : 714 – 725 . Google Scholar OpenURL Placeholder Text WorldCat 10. Tang NK , et al. Correcting distorted perception of sleep in insomnia: a novel behavioural experiment? Behav Res Ther. 2004 ; 42 ( 1 ): 27 – 39 . Google Scholar Crossref Search ADS PubMed WorldCat 11. Andrillon T , et al. Revisiting the value of polysomnographic data in insomnia: more than meets the eye . Sleep Med. 2020 ; 66 : 184 – 200 . Google Scholar Crossref Search ADS PubMed WorldCat 12. Watanabe T , et al. Network-dependent modulation of brain activity during sleep . NeuroImage. 2014 ; 98 : 1 – 10 . Google Scholar Crossref Search ADS PubMed WorldCat 13. Andrillon T , et al. Does the mind wander when the brain takes a break? Local sleep in wakefulness, attentional lapses and mind-wandering . Front Neurosci. 2019 ; 13 : 949 . Google Scholar Crossref Search ADS PubMed WorldCat 14. Li Y , et al. Sleep discrepancy is associated with alterations in the salience network in patients with insomnia disorder: an EEG-fMRI study . NeuroImage: Clinical. 2022 ; 35 : 103111 . Google Scholar Crossref Search ADS PubMed WorldCat 15. Hermans LW , et al. Modeling sleep onset misperception in insomnia . Sleep. 2020 ; 43 ( 8 ). doi:10.1093/sleep/zsaa014 Google Scholar OpenURL Placeholder Text WorldCat 16. Suh S , et al. Clinical significance of night-to-night sleep variability in insomnia . Sleep Med. 2012 ; 13 ( 5 ): 469 – 475 . Google Scholar Crossref Search ADS PubMed WorldCat 17. Lechat B , et al. New and emerging approaches to better define sleep disruption and its consequences . Front Neurosci. 2021 ; 15 :1–17. doi:10.3389/fnins.2021.751730 Google Scholar OpenURL Placeholder Text WorldCat © The Author(s) 2022. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © The Author(s) 2022. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: [email protected]

Martinot, Jean-Benoit; Pépin, Jean-Louis; Malhotra, Atul; Le-Dong, Nhat-Nam

doi: 10.1093/sleep/zsac188pmid: 35997163

Dear Editor: Van Pee et al. [1] and the accompanying editorial [2] reported on a very promising methodological approach called near-boundary double-labeling (NBL) method classification for the evaluation of new sleep apnea diagnostic tools. Beyond the usual statistical methods (Bland-Altman, intraclass correlation coefficient, etc.), this complementary analysis led, at least in their peripheral arterial tonometry home sleep apnea test (HSAT) data set (derived from NightOwl technology), to a better agreement with polysomnography (PSG) scored by two independent investigators. This innovative approach allows to assign individuals with apnea hypopnea index (AHI) in predefined near-boundary zones to two different categories of AHI grades of severity. For example, one individual with an AHI close to the threshold of 15 events/hour was labeled with both mild and moderate grades of severity. This approach avoided reporting overly pessimistic results between two diagnostic methods for small AHI differences that are not clinically relevant. We have recently reported that the agreement between a mandibular movement (MM) HSAT and in-home PSG was similar to the agreement for the scoring of PSG between two expert centers (London and Grenoble) [3]. This finding demonstrated that unconscious biases associated with manual scoring are equivalent to some differences that can be found between simplified diagnostic techniques and ostensibly gold-standard PSG. In this context, in a clinical study aiming to validate a machine learning–based algorithm for mandibular movement signals (Sunrise, Namur, Belgium), we applied the innovative NBL approach. We postulated that the risk of AHI-based severity misclassification due to inter-human PSG rating could be reduced when considering borderline zones around the traditional fixed AHI thresholds. A sample including 289 consecutive participants presenting with obstructive sleep apnea (OSA) suspicion was evaluated with an in-laboratory PSG coupled with simultaneous MM recordings using Sunrise device. The PSG data were then manually scored by two experienced and blinded investigators. The double-scored AHI data sets were used to establish the intervals for NBL method classification rule, by using the same procedure described in the original Van Pee article [1]. The collected MM data were automatically analyzed by a machine learning algorithm developed by Sunrise, which has the capability to classify sleep/wake states, obstructive and central respiratory events, thus allowing for estimation of AHI values [4, 5]. Based on the conventional rules for severity grading, the participants could be categorized into non-OSA (n = 14; 4.8%), mild (n = 109; 37.7%), moderate (n = 113; 39.1%), and severe OSA (n = 53; 18.4%). Corresponding proportions of the seven categories in the NBL classification are presented in Table 1. Table 1. Definition and proportions of seven categories obtained by NBL classification rule. Class . Interval . n . % . Non-OSA 0–1.59 3 1.0 Non-OSA or mild 1.59–7.38 44 15.2 Mild 7.38–10.63 32 11.1 Mild or moderate 10.63–18.47 84 29.1 Moderate 18.47–25.96 55 19.0 Moderate or severe 25.96–36.10 36 12.5 Severe ≥ 36.10 35 12.1 Class . Interval . n . % . Non-OSA 0–1.59 3 1.0 Non-OSA or mild 1.59–7.38 44 15.2 Mild 7.38–10.63 32 11.1 Mild or moderate 10.63–18.47 84 29.1 Moderate 18.47–25.96 55 19.0 Moderate or severe 25.96–36.10 36 12.5 Severe ≥ 36.10 35 12.1 Open in new tab Table 1. Definition and proportions of seven categories obtained by NBL classification rule. Class . Interval . n . % . Non-OSA 0–1.59 3 1.0 Non-OSA or mild 1.59–7.38 44 15.2 Mild 7.38–10.63 32 11.1 Mild or moderate 10.63–18.47 84 29.1 Moderate 18.47–25.96 55 19.0 Moderate or severe 25.96–36.10 36 12.5 Severe ≥ 36.10 35 12.1 Class . Interval . n . % . Non-OSA 0–1.59 3 1.0 Non-OSA or mild 1.59–7.38 44 15.2 Mild 7.38–10.63 32 11.1 Mild or moderate 10.63–18.47 84 29.1 Moderate 18.47–25.96 55 19.0 Moderate or severe 25.96–36.10 36 12.5 Severe ≥ 36.10 35 12.1 Open in new tab As shown in Figure 1, applying the NBL method resulted in a significant improvement of agreement between the PSG readings and Sunrise automatic scoring. The four-way Cohen’s Kappa coefficient was improved from 0.80 to 0.86. The NBL rule had also a positive impact on the accuracy in OSA severity grading. The F1 score, indicating the harmonic mean of precision and sensitivity was improved from 0.93 to 0.96 for detecting moderate OSA, and from 0.87 to 0.88 for severe OSA. As Sunrise algorithmic scoring represents a constant approximation of the true clinical status without any human factor-induced variability, we can assume that the uncertainty in PSG scoring has been improved when assessing performances by the NBL method. Therefore, the application of the NBL approach could help to make better diagnostic and therapeutic decisions in patients who present PSG results within near-boundary intervals. We also suggest that applying NBL procedure in clinical research would allow for a better standardization, transparency, and reproducibility in PSG data analysis. Figure 1. Open in new tabDownload slide Distribution of PSG-AHI scores within four conventional severity levels and NBL-based categories. Disclosure Statement J.B.M. reports being a scientific advisor to Sunrise and being an investigator in pharmacy trials for Jazz Pharmaceuticals, Theranexus, and Desitin. J.L.P. reports being a scientific advisor to Sunrise; receiving grants and/or personal fees from ResMed, Philips, Fisher & Paykel, Sefam, AstraZeneca, AGIR à dom, Elevie, VitalAire, Boehringer Ingelheim, Jazz Pharmaceuticals, Night Balance and Itamar Medical; and receiving research support for clinical studies from Mutualia and Air Liquide Foundation. A.M. reports income related to medical education from Sunrise, Livanova, Equillium, Jazz Pharmaceuticals, and Corvus; ResMed provided a philanthropic donation to UCSD. N.N.L.D. is an employee of Sunrise. Non-financial disclosure: none. References 1. Van Pee B , et al. A multicentric validation study of a novel home sleep apnea test based on peripheral arterial tonometry . Sleep 2022 ; 45 ( 5 ). doi:10.1093/sleep/zsac028 Google Scholar OpenURL Placeholder Text WorldCat 2. Ioachimescu OC . On PAT, patterns and paths . Sleep 2022 ; 45 ( 5 ). doi:10.1093/sleep/zsac057 Google Scholar OpenURL Placeholder Text WorldCat 3. Kelly JL , et al. Diagnosis of sleep apnoea using a mandibular monitor and machine learning analysis: one-night agreement compared to in-home polysomnography . Front Neurosci. 2022 ; 33 : 3 . Google Scholar OpenURL Placeholder Text WorldCat 4. Pépin JL , et al. Assessment of mandibular movement monitoring with machine learning analysis for the diagnosis of obstructive sleep apnea . JAMA Network Open 2020 ; 3 ( 1 ): e1919657 – e1919657 . Google Scholar Crossref Search ADS PubMed WorldCat 5. Le-Dong NN , et al. Machine learning–based sleep staging in patients with sleep apnea using a single mandibular movement signal . Am J Respir Crit Care Med. 2021 ; 204 ( 10 ): 1227 – 1231 . Google Scholar Crossref Search ADS PubMed WorldCat © The Author(s) 2022. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/pages/standard-publication-reuse-rights) © The Author(s) 2022. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: [email protected]

Vo, Tina T; Pahlen, Shandell; Kremen, William S; McGue, Matt; Dahl Aslan, Anna; Nygaard, Marianne; Christensen, Kaare; Reynolds, Chandra A

doi: 10.1093/sleep/zsac140pmid: 35727734

While prior research has demonstrated a relationship between sleep and cognitive performance, how sleep relates to underlying genetic and environmental etiologies contributing to cognitive functioning, regardless of the level of cognitive function, is unclear. The present study assessed whether the importance of genetic and environmental contributions to cognition vary depending on an individual’s aging-related sleep characteristics. The large sample consisted of twins from six studies within the Interplay of Genes and Environment across Multiple Studies (IGEMS) consortium spanning mid- to late-life (Average age [Mage] = 57.6, range = 27–91 years, N = 7052, Female = 43.70%, 1525 complete monozygotic [MZ] pairs, 2001 complete dizygotic [DZ] pairs). Quantitative genetic twin models considered sleep duration as a primary moderator of genetic and environmental contributions to cognitive performance in four cognitive abilities (Semantic Fluency, Spatial-Visual Reasoning, Processing Speed, and Episodic Memory), while accounting for age moderation. Results suggested genetic and both shared and nonshared environmental contributions for Semantic Fluency and genetic and shared environmental contributions for Episodic Memory vary by sleep duration, while no significant moderation was observed for Spatial-Visual Reasoning or Processing Speed. Results for Semantic Fluency and Episodic Memory illustrated patterns of higher genetic influences on cognitive function at shorter sleep durations (i.e. 4 hours) and higher shared environmental contributions to cognitive function at longer sleep durations (i.e. 10 hours). Overall, these findings may align with associations of upregulation of neuroinflammatory processes and ineffective beta-amyloid clearance in short sleep contexts and common reporting of mental fatigue in long sleep contexts, both associated with poorer cognitive functioning.

Schokman, Aaron; Bin, Yu Sun; Naehrig, Diana; Cheung, Janet M Y; Kairaitis, Kristina; Glozier, Nick

doi: 10.1093/sleep/zsac156pmid: 35797589

Study ObjectivesTo systematically determine subjective and objective outcome measures used to measure the efficacy of narcolepsy interventions in randomized controlled trials (RCTs) in adults and children and assess psychometric properties of patient-reported outcome measures (PROMs) used.MethodsWe searched bibliographical databases and clinical trial registries for narcolepsy RCTs and extracted objective and subjective outcome measures. If PROMs were used, we searched for psychometric studies conducted in a narcolepsy population using bibliographical databases and appraised using Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines.ResultsIn total, 80 different outcome measures were used across 100 RCTs. Epworth Sleepiness Scale (ESS) (n = 49) and Maintenance of Wakefulness Test (n = 47) were the most frequently used outcome measures. We found 19 validation studies of 10 PROMs in narcolepsy populations. There was limited evidence for validity or responsiveness of the ESS; yet sufficient reliability (pooled ICC: 0.81–0.87). Narcolepsy Severity Scale (NSS) had sufficient reliability (pooled ICC: 0.71–0.92) and both adult and pediatric versions had sufficient discriminant validity (treated/untreated). Content validity was only evaluated in pediatric populations for ESS-CHAD and NSS-P and rated inconclusive. Quality of evidence of the psychometric studies for all scales ranged from very low to low.ConclusionsAlthough recognized by regulatory bodies and widely used as primary outcome measures in trials, there is surprisingly little evidence for the validity, reliability, and responsiveness of PROMs frequently used to assess treatment efficacy in narcolepsy. The field needs to establish patient-centered minimal clinically important differences for the PROMs used in these trials.

Johnson, Dayna A; Reiss, Benjamin; Cheng, Philip; Jackson, Chandra L

doi: 10.1093/sleep/zsac200pmid: 35999030

Accepted manuscripts Accepted manuscripts are PDF versions of the author’s final manuscript, as accepted for publication by the journal but prior to copyediting or typesetting. They can be cited using the author(s), article title, journal title, year of online publication, and DOI. They will be replaced by the final typeset articles, which may therefore contain changes. The DOI will remain the same throughout. Article PDF first page preview Close This content is only available as a PDF. © The Author(s) 2022. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © The Author(s) 2022. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: [email protected]

Kosky, Christopher; Madeira, Neill; Boulton, Kate; Hunter, Marie-Therese; Ling, Ivan; Reynor, Ayesha; Sturdy, Gavin; Walsh, Jennifer; Dhaliwal, Satvinder; Singh, Bhajan; Eastwood, Peter R; McArdle, Nigel

doi:

Sentner, Thom; Wang, Xiaowan; de Groot, Eline R; van Schaijk, Lieke; Tataranno, Maria Luisa; Vijlbrief, Daniel C; Benders, Manon J N L; Bartels, Richard; Dudink, Jeroen

doi: 10.1093/sleep/zsac143pmid: 35749799

Study ObjectivesSleep is an important driver of early brain development. However, sleep is often disturbed in preterm infants admitted to the neonatal intensive care unit (NICU). We aimed to develop an automated algorithm based on routinely measured vital parameters to classify sleep–wake states of preterm infants in real-time at the bedside.MethodsIn this study, sleep–wake state observations were obtained in 1-minute epochs using a behavioral scale developed in-house while vital signs were recorded simultaneously. Three types of vital parameter data, namely, heart rate, respiratory rate, and oxygen saturation, were collected at a low-frequency sampling rate of 0.4 Hz. A supervised machine learning workflow was used to train a classifier to predict sleep–wake states. Independent training (n = 37) and validation datasets were validation n = 9) datasets were used. Finally, a setup was designed for real-time implementation at the bedside.ResultsThe macro-averaged area-under-the-receiver-operator-characteristic (AUROC) of the automated sleep staging algorithm ranged between 0.69 and 0.82 for the training data, and 0.61 and 0.78 for the validation data. The algorithm provided the most accurate prediction for wake states (AUROC = 0.80). These findings were well validated on an independent sample (AUROC = 0.77).ConclusionsWith this study, to the best of our knowledge, a reliable, nonobtrusive, and real-time sleep staging algorithm was developed for the first time for preterm infants. Deploying this algorithm in the NICU environment may assist and adapt bedside clinical work based on infants’ sleep–wake states, potentially promoting the early brain development and well-being of preterm infants.