Optimizing Patient Outcomes Requires More Than Single-Night Testing for Sleep Apnea

This article previously ran in the Q2 2023 issue of A₂Zzz.

Obstructive sleep apnea (OSA) is estimated to be the most prevalent sleep disorder, with approximately 34% of middle-aged men and 17% of middle-aged women in the general population expected to meet the diagnostic criteria for it.^1,2 Prevalence in patients with cardiovascular and cardiometabolic disease is higher, estimated as high as 40%-80%. In recent years there has been an increasing awareness of OSA but despite this, the disease remains frequently underdiagnosed or diagnosis is delayed, even in patients with moderate to severe symptoms.^3,4

OSA is characterized by recurrent complete (apneas) and partial (hypopneas) obstruction of the upper airway, causing intermittent hypoxemia, autonomic fluctuation, arousals and sleep fragmentation, and is associated with consequences that negatively affect health and quality of life of undiagnosed patients. The most common consequences of untreated OSA are hypertension,^5,6 cardiovascular and cardiometabolic disease^7,8 and stroke,^9,10 all included in the group of the costliest diseases in the U.S. Additionally, OSA increases allcause mortality,^11,12 and patients with undiagnosed OSA have higher rates of health care utilization and increased hospital readmissions, adding substantially to medical costs annually.^13-15 It is therefore of high importance to implement comprehensive, precision care in both diagnosis and treatment of OSA to improve patient outcomes.

Sleep is dynamic with sleep duration, the proportion of OSA severity and non-rapid eye movement (non-REM) and rapid eye movement (REM)-sleep changing from night to night.¹⁶ Polysomnography (PSG) is still the reference standard, recording sleep and breathing to calculate the apnea hypopnea index (AHI), defined as the average number of apnea and hypopnea events per hour during sleep, to diagnose and classify OSA severity. The limitations of PSG are well understood with high cost, complexity, lack of access and the requirement for a trained specialist to interpret results. It would be unreasonable to expect patients or payers to do a multi-night PSG for the purpose of OSA diagnosis or treatment tracking. Therefore, use of various, limited channel home sleep apnea testing (HSAT) devices has been increasing. These devices are simpler to use, patients can sleep in their natural sleep environment when tested for OSA and the output is simpler to analyze, making the process less costly and cumbersome.

Recent studies report a relevant night-to-night variability of apnea events that may lead to misdiagnosis of patients suspected of OSA in both adults and children when sleep is evaluated for only one night.^17-19 A study by Punjabi et al., utilizing type-3 HSAT to calculate AHI reports a substantial within-patient variability and that “approximately 20% of patients with mild and moderate sleep apnea on the first night were misdiagnosed either as not having sleep apnea or as having mild disease.”²⁰ A similar study by Tschopp et al. utilizing peripheral arterial tonometry (PAT) concluded that 24% of patients were misclassified when using devices for one night compared to AHI-average calculated from three-nights.²¹ A meta-analysis by Roeder et al. reported “on average 41% (95% CI 27% to 57%) of all participants showed changes of respiratory events >10/hour from night-to-night” and “49% of participants changed OSA severity class at least once in sequential sleep studies.”¹⁸

These results indicate that 1) misclassification and underdiagnosis of mild and moderate OSA are relatively common when based on a single-night study, and 2) single-night studies are imperfect, with multi-night testing possibly offering improvements in the diagnostic process. For this purpose, sleep-testing devices with a lower-cost structure offer more flexibility for multi-night testing for diagnosis and less burden for patients to use repeatedly if applicable for use for treatment tracking. Implementing multi-night testing should not only improve diagnostic accuracy, but additionally our understanding of the dynamics and variations of sleep and OSA severity over time. This will also offer the option to track responses to therapy that can be utilized as an adjunct to manage therapy for improvements in outcomes. While some single-patient, multi-use disposable devices improve the diagnosis by capturing intra-night variability of AHI, it is no less important to track treatment efficacy over time. Single-patient, non-disposable devices recently introduced to the market offer ease of use and cost-effectiveness.

The most novel of the disposable devices, Sunrise, is capable of recording three nights of sleep. The system utilizes mandibular jaw movements, pulse-rate and oxygen-saturation (SpO2) that is recorded during sleep with artificial intelligence (AI) powered algorithms to aid in evaluation of OSA.²² Sunrise can be utilized in patients 18 years and older with suspicion of OSA (FDA K222262).

Multiple photoplethysmogram (PPG) sensor devices are also on the market that use the plethysmography-signal (PLETH) to measure pulsatile volume changes in the arteries of peripheral vasculature of the fngers.²³ These devices combine recordings of the PLETH signal with SpO2 and actigraphy to provide estimates of sleep and to calculate an AHI. The devices that are marketed in the group of using the PPG include WatchPAT,^24,25 which uses a pressurized probe with a PPG sensor and offers a multi-patient reusable device with single-use disposable sensor and a single-patient disposable, one-night sleep-testing option. WatchPAT is intended for use with patients 12 years and older (for OSA) or 17 years and older (for central sleep apnea [CSA]) suspected to have sleep-related breathing disorders (FDA K183559).

Another PPG sensor device is NightOwl®,^26,27 which is a reusable, single-patient, disposable device that offers the option of recording up to 10 nights of sleep testing. NightOwl is intended to aid in the evaluation of OSA in adult patients suspected of sleep apnea (FDA K213463).

A third option is the SleepImage System®,^28,29 that the FDA cleared as a software as a medical device (SaMD) and utilizes a simple device for data collection. Both devices offered currently can be utilized for diagnostic purposes, a ring-device that can be utilized for multiple patients for testing (Figure 1) and a patient-centric finger-tip device. Patients who are diagnosed with OSA/CSA can then continue to utilize the patient-centric device as instructed by their health care provider to track treatment efficacy (Figure 2). The SleepImage System is intended to aid in the evaluation of sleep disorders as well as diagnosis and management of sleep-disordered breathing (SDB) in children, adolescents and adults (FDA K182618). The Sleep Quality Index (SQI), sleep fragmentation, sleep duration and sleep stage calculations are based on cardiopulmonary coupling (CPC)^30,31 analysis of data derived from the PLETH-signal collected with the PPG sensor, including SpO2 information to calculate AHI.

Historically in OSA diagnosis and management, focus has been on defining and quantifying disease severity by calculating the AHI. OSA is characterized by repeated paused breathing, which causes transient hypoxemia and sleep fragmentation that may affect sleep quality at different severity levels like the hypoxemia. Alternative metrics have been proposed with the aim to better predict clinical consequences of OSA, including sleepiness, cardiovascular and cardiometabolic health and effects on quality of life.³² These metrics include methods to better quantify the transient hypoxemia, apnea hypopnea event duration and hypoxic burden calculating the area under the oxyhemoglobin saturation curve.^33,34 Additionally they quantify sleep fragmentation by estimating arousal intensity,³⁵ the odds ratio product (ORP) quantifying sleep depth from analysis of electroencephalogram signal (EEG)³⁶ and the SQI calculated from automated CPC analysis of heart rate variability (HRV) and breathing.^37-40

Insomnia is common in the general population, with estimated 30%-50% of adults experiencing short-term insomnia and 10% chronic insomnia,⁴¹ with prevalence of insomnia in patients with OSA higher or 46%-60%.⁴² Patients suffering from both disorders, comorbid insomnia and sleep apnea (COMISA), are at greater cardiovascular risk^43,44 and higher impairments to daytime functioning and quality of life compared to those with either OSA or insomnia alone.⁴⁵ Therapy for both OSA and insomnia is more complex in patients with COMISA and often less effective.^46,47 Therefore, it is important to document both the hypoxemia as well as insomnia symptoms and sleep quality to optimize therapy outcomes. Diagnosis of insomnia is based on subjective self reported symptoms, and the Insomnia Severity Index (ISI) has been suggested to screen for insomnia in patients with OSA.^48,49 However, sleep quality is a multidimensional construct with both subjective and objective components, which are not always concordant.

Complementary methods as mentioned in this article are available to assess sleep quality, as are objective methods like the SleepImage System SQI, which measures sleep quality presented on a scale of 0-100 with higher intra-individual variability of patterns of sleep stability in patients with insomnia.⁵⁰

Having a defined unit of metric in medicine helps differentiate disease states from normal and categorize the severity of illness. It’s important to both define the state of disease and track therapy efficacy over time to improve outcomes. The SQI has demonstrated a direct relationship with health outcomes in clinical studies; improving SQI in adults has positive effects on hypertension and stroke,^51,52 adiponectin levels³⁹ glucose disposal⁵³ and depressive symptoms.⁵⁴ Healthy-weight children with high sleep quality have better cardiometabolic health than healthy-weight children with low sleep quality.³⁹ Children with high SQI also have better quality of life, attention and executive function, and are more likely to have spontaneous remission of OSA than children with low SQI.³⁸ Both AHI and SQI need to be treated successfully to improve cardiometabolic health, behavior and quality of life.⁴⁰

Additional utility for longitudinal sleep tracking may add value in continuous positive airway pressure (CPAP) management. CPAP-machine-detected respiratory events remains the approach to tracking CPAP efficacy. Although clinically convenient, studies have shown the difference in manual events and machine detected events to be clinically significant, especially during periods of unstable breathing.⁵⁵ The ability to identify such residual respiratory events could warrant further sleep evaluation and possibly improve outcomes. Using home sleep testing tools such as those noted in this article may unlock the potential for precise home CPAP optimization.

References

1. Peppard PE et al. Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol. 2013;177(9):1006-1014. https://doi.org/10.1093/aje/kws342
2. Senaratna CV et al. Prevalence of obstructive sleep apnea in the general population: A systematic review. Sleep Med Rev. 2017;34:70-81. https://doi.org/10.1016/j.smrv.2016.07.002
3. Costa LE et al. Potential underdiagnosis of obstructive sleep apnoea in the cardiology outpatient setting. Heart. 2015;101(16):1288-1292. https://doi.org/10.1136/heartjnl-2014-307276
4. Zhang H et al. Factors influencing patient delay in individuals with obstructive sleep apnoea: A study based on an integrated model. Ann Med. 2022;54(1):2828-2840. https://doi.org/10.1080/07853890.2022.2132417
5. Hou H et al. Association of obstructive sleep apnea with hypertension: A systematic review and meta-analysis. J Glob Health. 2018;8(1), 010405. https://doi.org/10.7189/jogh.08.010405
6. Young T et al. Population-based study of sleep-disordered breathing as a risk factor for hypertension. Arch Intern Med. 1997;157(15):1746-1752. https://www.ncbi.nlm.nih.gov/pubmed/9250236
7. Peker Y et al. An independent association between obstructive sleep apnoea and coronary artery disease. Eur Respir J. 1999;14(1):179-184. https://doi.org/10.1034/j.1399-3003.1999.14a30.x
8. Yeghiazarians Y et al. Obstructive Sleep Apnea and Cardiovascular Disease: A Scientifc Statement From the American Heart Association. Circulation.2021;144(3):e56-e67. https://doi.org/10.1161/CIR.0000000000000988
9. Barone DA & Krieger AC. Stroke and obstructive sleep apnea: A review. Curr Atheroscler Rep. 2013;15(7):334. https://doi.org/10.1007/s11883-013-0334-8
10. Li M, Hou WS, Zhang XW, & Tang ZY. Obstructive sleep apnea and risk of stroke: A meta-analysis of prospective studies. Int J Cardiol. 2014;172(2):466-469. https://doi.org/10.1016/j.ijcard.2013.12.230
11. Mashaqi S & Gozal D. The impact of obstructive sleep apnea and PAP therapy on all-cause and cardiovascular mortality based on age and gender - A literature review. Respir Investig. 2020;58(1):7-20. https://doi.org/10.1016/j.resinv.2019.08.002
12. Young T et al. Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin sleep cohort. Sleep. 2008;31(8):1071-1078. https://www.ncbi.nlm.nih.gov/pubmed/18714778
13. Huyett P & Bhattacharyya N. Incremental health care utilization and expenditures for sleep disorders in the United States. J Clin Sleep Med. 2021;17(10):1981-1986. https://doi.org/10.5664/jcsm.9392
14. Knauert M, Naik S, Gillespie MB, & Kryger M. Clinical consequences and economic costs of untreated obstructive sleep apnea syndrome. World J Otorhinolaryngol Head Neck Surg. 2015;1(1):17-27. https://doi.org/10.1016/j.wjorl.2015.08.001
15. Wickwire EM et al. CPAP adherence is associated with reduced inpatient utilization among older adult Medicare beneficiaries with pre-existing cardiovascular disease. J Clin Sleep Med. 2022;18(1):39-45. https://doi.org/10.5664/jcsm.9478
16. 16. Chouraki A, Tournant J, Arnal P, Pepin JL, & Bailly S. Objective multi-night sleep monitoring at home: Variability of sleep parameters between nights and implications for the reliability of sleep assessment in clinical trials. Sleep. 2022. https://doi.org/10.1093/sleep/zsac319
17. Qin H et al. Night-to-night variability in respiratory sleep parameters to diagnose obstructive sleep apnea in children: AA Quarter Two 2023 2Zzz 16 systematic review and meta-analysis. Int J Pediatr Otorhinolaryngol. 2022;162:111285. https://doi.org/10.1016/j.ijporl.2022.111285
18. Roeder M et al. Night-to-night variability of respiratory events in obstructive sleep apnoea: a systematic review and meta-analysis. Thorax. 2020;75(12):1095-1102. https://doi.org/10.1136/thoraxjnl-2020-214544
19. Thomas RJ, Chen S, Eden UT, & Prerau MJ. Quantifying statistical uncertainty in metrics of sleep disordered breathing. Sleep Med. 2020;65:161-169. https://doi.org/10.1016/j.sleep.2019.06.003
20. Punjabi NM, Patil S, Crainiceanu C, & Aurora RN. Variability and Misclassifcation of Sleep Apnea Severity Based on Multi-Night Testing. Chest. 2020;158(1):365-373. https://doi.org/10.1016/j.chest.2020.01.039
21. 21. Tschopp S, Wimmer W, Caversaccio M, Borner U, & Tschopp K. Night-to-night variability in obstructive sleep apnea using peripheral arterial tonometry: A case for multiple night testing. J Clin Sleep Med. 2021;17(9):1751-1758. https://doi.org/10.5664/jcsm.9300
22. Pepin JL et al. Assessment of Mandibular Movement Monitoring With Machine Learning Analysis for the Diagnosis of Obstructive Sleep Apnea. JAMA Netw Open. 2020;3(1):e1919657. https://doi.org/10.1001/jamanetworkopen.2019.19657
23. Schnall RP, Sheffy JK, & Penzel T. Peripheral arterial tonometry-PAT technology. Sleep Med Rev. 2022;61, 101566. https://doi.org/10.1016/j.smrv.2021.101566
24. Choi JH, Lee B, Lee JY, & Kim HJ. Validating the Watch-PAT for Diagnosing Obstructive Sleep Apnea in Adolescents. J Clin Sleep Med. 2018;14(10):1741-1747. https://doi.org/10.5664/jcsm.7386
25. Ioachimescu OC et al. Performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea in a large sleep clinic cohort. J Clin Sleep Med. 2020;16(10):1663-1674. https://doi.org/10.5664/jcsm.8620
26. Lyne CJ, Hamilton GS, Turton ARE, Stupar D, & Mansfeld DR. Validation of a single-use and reusable home sleep apnea test based on peripheral arterial tonometry compared to laboratory polysomnography for the diagnosis of obstructive sleep apnea. J Clin Sleep Med. 2023. https://doi.org/10.5664/jcsm.10568
27. Massie F et al. An Evaluation of the NightOwl Home Sleep Apnea Testing System. J Clin Sleep Med. 2018;14(10):1791-1796. https://doi.org/10.5664/jcsm.7398
28. Al Ashry HS, Hilmisson H, Ni Y, Thomas RJ, & Investigators A. Automated Apnea-Hypopnea Index from Oximetry and Spectral Analysis of Cardiopulmonary Coupling. Ann Am Thorac Soc. 2021;18(5):876-883. https://doi.org/10.1513/AnnalsATS.202005-510OC
29. Hilmisson H, Berman S, & Magnusdottir S. Sleep apnea diagnosis in children using software-generated apnea-hypopnea index (AHI) derived from data recorded with a single photoplethysmogram sensor (PPG): Results from the Childhood Adenotonsillectomy Study (CHAT) based on cardiopulmonary coupling analysis. Sleep Breath. 2020. https://doi.org/10.1007/s11325-020-02049-6
30. Thomas RJ, Mietus JE, Peng CK, & Goldberger AL. An electrocardiogram-based technique to assess cardiopulmonary coupling during sleep. Sleep. 2005;28(9):1151-1161. https://doi.org/10.1093/sleep/28.9.1151
31. Thomas RJ et al. Relationship between delta power and the electrocardiogram-derived cardiopulmonary spectrogram: possible implications for assessing the effectiveness of sleep. Sleep Med. 2014;15(1):125-131. https://doi.org/10.1016/j.sleep.2013.10.002
32. Malhotra A et al. Metrics of sleep apnea severity: beyond the apnea-hypopnea index. Sleep. 2021;44(7). https://doi.org/10.1093/sleep/zsab030
33. Butler MP, Emch JT, & Rueschman M. Apnea-Hypopnea Event Duration Predicts Mortality in Men and Women in the Sleep Heart Health Study. Am J Respir Crit Care Med. 2019;199(7):903-912. https://doi.org/10.1164/rccm.201804-0758OC
34. Kulkas A et al. Novel parameters for evaluating severity of sleep disordered breathing and for supporting diagnosis of sleep apnea-hypopnea syndrome. J Med Eng Technol. 2013;37(2):135- 143. https://doi.org/10.3109/03091902.2012.754509
35. Amatoury J et al. New insights into the timing and potential mechanisms of respiratory-induced cortical arousals in obstructive sleep apnea. Sleep. 2018;41(11). https://doi.org/10.1093/sleep/zsy160
36. 36. Younes M et al. Odds ratio product of sleep EEG as a continuous measure of sleep state. Sleep. 2015;38(4):641-654. https://doi.org/10.5665/sleep.4588
37. Hilmisson H, Lange N, & Magnusdottir S. Objective sleep quality and metabolic risk in healthy weight children results from the randomized Childhood Adenotonsillectomy Trial (CHAT). Sleep Breath. 2019;23(4):1197- 1208. https://doi.org/10.1007/s11325-019-01802-w
38. Magnusdottir S, Hilmisson H, & Thomas RJ. Cardiopulmonary coupling-derived sleep quality is associated with improvements in blood pressure in patients with obstructive sleep apnea at high-cardiovascular risk. J Hypertens. 2020;38(11):2287-2294. https://doi.org/10.1097/HJH.0000000000002553
39. Magnusdottir S, Thomas RJ, & Hilmisson H. Can improvements in sleep quality positively affect serum adiponectin-levels in patients with obstructive sleep apnea? Sleep Med. 2021;84:324-333. https://doi.org/10.1016/j.sleep.2021.05.032
40. Magnusdottir S, Witmans M, & Hilmisson H. Sleep quality, sleep apnea, and metabolic health in children treated with adenotonsillectomy. Sleep Breath. 2022. https://doi.org/10.1007/s11325-022-02747-3
41. Schutte-Rodin S, Broch L, Buysse D, Dorsey C, & Sateia M. Clinical guideline for the evaluation and management of chronic insomnia in adults. J Clin Sleep Med. 2008;4(5):487-504. https://www.ncbi.nlm.nih.gov/pubmed/18853708
42. Sweetman A, Lack L, & Bastien C. Co-Morbid Insomnia and Sleep Apnea (COMISA): Prevalence, Consequences, Methodological Considerations, and Recent Randomized Controlled Trials. Brain Sci. 2019;9(12). https://doi.org/10.3390/brainsci9120371
43. Hein M, Lanquart JP, Mungo A, & Loas G. Cardiovascular risk associated with co-morbid insomnia and sleep apnoea (COMISA) in type 2 diabetics. Sleep Sci. 2022;15(Spec 1):184-194. https://doi.org/10.5935/1984-0063.20220018
44. Lechat B et al. The association of co-morbid insomnia and sleep apnea with prevalentA Quarter Two 2023 2Zzz 17 cardiovascular disease and incident cardiovascular events. J Sleep Res. 2022;31(5):e13563. https://doi.org/10.1111/jsr.13563
45. Sweetman AM et al. Developing a successful treatment for co-morbid insomnia and sleep apnoea. Sleep Med Rev. 2017;33:28-38. https://doi.org/10.1016/j.smrv.2016.04.004 46. Nguyen XL, Chaskalovic J, Rakotonanahary D, & Fleury B. Insomnia symptoms and CPAP compliance in OSAS patients: A descriptive study using Data Mining methods. Sleep Med. 2010;11(8):777-784. https://doi.org/10.1016/j.sleep.2010.04.008
46. Williams J, Roth A, Vatthauer K, & McCrae CS. Cognitive behavioral treatment of insomnia. Chest. 2013;143(2):554-565. https://doi.org/10.1378/chest.12-0731
47. Bastien CH, Vallieres A, & Morin CM. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med. 2001;2(4):297-307. https://doi.org/10.1016/s13899457(00)00065-4
48. Wallace DM & Wohlgemuth WK. Predictors of Insomnia Severity Index Profiles in United States Veterans With Obstructive Sleep Apnea. J Clin Sleep Med. 2019;15(12):1827-1837. https://doi.org/10.5664/jcsm.8094
49. Thomas RJ, Wood C, & Bianchi MT. Cardiopulmonary coupling spectrogram as an ambulatory clinical biomarker of sleep stability and quality in health, sleep apnea, and insomnia. Sleep. 2018;41(2). https://doi.org/10.1093/sleep/zsx196
50. Magnusdottir S, Hilmisson H, Raymann R, & Witmans M. Characteristics of Children Likely to Have Spontaneous Resolution of Obstructive Sleep Apnea: Results from the Childhood Adenotonsillectomy Trial (CHAT). Children (Basel). 2021;8(11). https://doi.org/10.3390/children8110980
51. Thomas RJ et al. Prevalent hypertension and stroke in the Sleep Heart Health Study: association with an ECG-derived spectrographic marker of cardiopulmonary coupling. Sleep. 2009;32(7):897- 904. https://www.ncbi.nlm.nih.gov/pubmed/19639752
52. Pogach MS, Punjabi NM, Thomas N, & Thomas RJ. Electrocardiogram-based sleep spectrogram measures of sleep stability and glucose disposal in sleep disordered breathing. Sleep. 2012;35(1):139-148. https://doi.org/10.5665/sleep.1604
53. Schramm PJ, Zobel I, Monch K, Schramm E, & Michalak J. Sleep quality changes in chronically depressed patients treated with Mindfulness-based Cognitive Therapy or the Cognitive Behavioral Analysis System of Psychotherapy: A pilot study. Sleep Med. 2016;17:57-63. https://doi.org/10.1016/j.sleep.2015.09.022
54. Ni YN & Thomas RJ. A longitudinal study of the accuracy of positive airway pressure therapy machine-detected apnea-hypopnea events. J Clin Sleep Med. 2022;18(4):1121- 1134. https://doi.org/10.5664/jcsm.9814