Clinical Benefits & The Löwenstein Difference

The Löwenstein devices consistently demonstrate clinical efficacy (AHI<5/hour), with further key clinical features explored in this whitepaper.

CL I N I CAL BENE F I TS WH I T EPAPER

Löwenstein Sleep Therapy Products

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Clinical Benefits

What does successful sleep therapy treatment look like? There is no set formula for the successful treatment of sleep disordered breathing (i.e. obstructive sleep apnoea). The treatment process is a complex matter & intensely individual, when it comes to PAP therapy, a practical test & trial process fine-tuned & tailored to an individual’s experience & needs by a trained professional is the consistent way to optimise the treatment outcome. When asked what defines successful sleep apnoea treatment, the usual metric clinicians & specialists refer to is AHI (Apnoea Hypopnea Index) per hour. A score of less than 5 AHI/hr is the typical theoretical definition of clinical effectiveness in PAP therapy. In reality, therapy outcome revolves around more than AHI/hr. The following chart offers insight into how Santé & Löwenstein view therapy success.

Information/Motivation

Noticeable recuperative effect is prerequisite for better compliance

Prerequisite for an effective SDB therapy

COMPLIANCE

Affected by comorbidities, PAP, sleep hygiene, lifestyle

THERAPY SUCCESS

Mask & mouth are sealed

LOW LEAKAGE

RESTORATIVE SLEEP

Minimum requirement: elimination of respiratory events

Prerequisite for pressure build-up & event recognition

AHI

PAP effectiveness: mode, titration

The Löwenstein Difference The Löwenstein devices consistently demonstrate clinical efficacy (AHI<5/hour), with further key clinical features as follows: » Prisma RECOVER - Innovative feature that estimates deep sleep. » High accuracy of AHI detection - 96% accuracy level with PG as reference. » Proactive algorithm regulation - epoch-based response with structured treatment pattern. » Dynamic & Standard Algorithm - 2 settings to suit different patients & stage of treatment. » Stability of pressure adjustment - therapeutically effective with no unnecessary fluctuation.

» Efficiency of pressure - consistently low Pmax & Pmean. » Pressure relief SoftPAP - No compromise on clinical outcome. Features are explored in detail below & in subsequent appendices.

Prisma RECOVER The Löwenstein white paper edition “Sleep Quality in CPAP/APAP Therapy”* challenges the traditional perception of AHI as the only metric in APAP/CPAP Therapy. The paper outlines the importance of restorative or deep sleep (slow-wave-sleep) which shows a greater correlation to an improvement in daytime sleepiness compared to AHI. The innovative Prisma RECOVER algorithm (inbuilt within Prisma devices) analyses respiratory minute volume to provide an estimation of deep sleep. Deep sleep could be considered an important new metric of sleep quality in PAP therapy.

to treating sleep disordered breathing efficiently. The prisma devices have a forced oscillation amplitude of approx. 0.4 hpa (or cmH20) which is superimposed by the device blower.* The minor amplitude would suggest little chance of disruption to the patient’s sleep, with a precise event recognition achieved. “Study Hunter: Benchtest of AHI agreement in APAP (20A)”** was carried out with 4 devices: AirSense 10 (Resmed), Dreamstation Auto (Philips), S. Box (Sefam), Prisma20A (Löwenstein). It was concluded that Prisma20A together with Dreamstation Auto showed the highest accuracy level at 96% with PG as reference.

*Refer to Appendix 2 pages 1-8 **Refer to Appendix 3 Proactive Algorithm

*Refer to Appendix 1

Accuracy of AHI via FOT Modern algorithms frequently use forced oscillation technique (FOT) to reliably distinguish between obstructive & central sleep events, which is a prerequisite

Löwenstein devices measure each breath of the patient & airway obstruction occurring, this can then be observed in detailed graphs: respiratory flow graph & respiratory

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minute volume, & obstruction graph upon download & review of the detailed signal data. The presence of sleep disordered breathing is categorised within the detailed data into ‘epochs’ of 2-minute duration. There are 3 levels of epochs according to degree of severity of obstruction: eFL (epoch flow limitation), eMO (epoch moderate obstruction) & eSO (epoch severe obstruction). In response to 2-minute epoch’s, pressure adjustment is provided through a balanced, logical & consistent structure* which deviates the extent of pressure adjustment provided depending on where the current pressure is at on the Pmin to Pmax scale & severity of the obstruction. Since Pmin to Pmax is tailored to an individual’s needs, the pressure is regulated in a way which avoids applying uncomfortable or sub-optimum pressure levels. The option of ‘Standard’ & ‘Dynamic’ algorithm provides a whole further dimension to how proactive the treatment is (refer to the following sub- heading).

*Refer to Appendix 2 page 9 onwards

the same, although ‘Dynamic’ will yield a pressure increase immediately upon 6 breaths of snoring. The key difference is that ‘Dynamic’ will continue to treat epochs of flow limitations throughout the first three quartiles of pressure range. This setting suits patients with UARS (Upper Airway Respiratory Syndrome), & helps to further optimise the numerical results of users who have become more accustomed to APAP therapy.

Dynamic & Standard Algorithm Prisma devices offer 2 algorithm settings called ‘Standard’ & ‘Dynamic.’* ‘Standard’ algorithm goal is to maximise patient acceptance through lowest possible pressures. With ‘Standard,’ respiratory events such as repeated snoring within a 2-minute epoch & RERA’s are categorised & treated effectively & efficiently according to the treatment structure at the end of the 2-minute epoch. Obstructive Apnoea’s & Hypopnea’s are identified & treated immediately up to 2 times within a 2-minute epoch. However, epochs of flow limitations are only treated within the first quartile of pressure range. This setting suits new APAP users & patients not tolerant of fluctuating or high pressure. ‘Dynamic’ algorithm goal is designed to treat partial obstructions with greater sensitivity & more proactively before these turn into more severe obstructions. The treatment to all respiratory events is much

*Refer to Appendix 2 page 9 onwards

Stability of Pressure Adjustment The challenge of the APAP algorithm is to provide stable, therapeutically effective & lowest possible pressures. • Fast pressure decreases cause anew serious obstructions & arousals. • Fast or extreme pressure increase in response to obstructions can cause arousal, lead to high mean pressures or high-pressure variability (more fast decreases). • Inadequate increase to severe

obstructions results in lengthy periods of subtherapeutic pressures. The pressure regulation in Prisma20A (Auto) is therapeutically effective without unnecessary fluctuation*, whereby treatment is likely to be comfortable & well-balanced. Given the pleasant nature of the pressure regulation, it could be expected that the treatment dynamic offers benefits of both CPAP & APAP. Overall pressure graph appears gradual, gentle & consistent.** *Refer to Appendix 4 & 5, supported by Appendix 7 **Refer to Appendix 6, page 5, graph F (SOMNObalance e) & graph G (Prisma20A) Efficiency of Pressure The Löwenstein philosophy is to provide a pressure regulation that strives for the most efficient pressures possible. Results show low AHI/events (less than 5 AHI/hr) with little difference between the Pmean & Pmax pressure, with both figures relatively low.*

maintained no events, with measured mean pressures respectively 1.1 cmH2O & 0.8 cmH2O lower than the conventional required CPAP pressure. The only other pressure-relief feature tested which maintained no events measured mean pressure at just 0.2 cmH2O less than the conventional required CPAP pressure. By leaving no events & lowering mean pressure, it can be deduced that the timing of Löwenstein SoftPAP settings provide considerable pressure relief on exhalation without clinical impairment.

*Refer to Appendix 9, Page 3, Table 2

Conclusion Evidence suggests Löwenstein devices exhibit proactive, efficient & stable pressure adjustments when treating sleep disordered breathing. This gives insight into why many Löwenstein users experience benefits to their sleep & health, including: less arousals from pressure & leakage independent of AHI events, lower blood pressure, less thoracic demand, more natural breathing, greater sleep quality (i.e. more deep sleep), less hours of sleep, & greater PAP compliance (prerequisite to improving outcome of therapy). These results reportedly carry over to quality of life, i.e. feeling fresh, energetic & motivated. From a clinical standpoint, the accuracy of AHI, understanding of the structured algorithm response, option for 2 algorithm settings, extensive clinical data with epoch-based categorisation, & deep sleep indicator via respiratory minute volume provide extensive & varied information for professionals to optimise the treatment outcome for their patients. Lastly, the option of adding suitably timed pressure relief ‘SoftPaP’ settings during treatment adds another dimension to patient comfort without any evidence of clinical impact.

*Refer to Appendix 6, page 6, Device F (SOMNObalance e) & Device G (Prisma20A), & Appendix 8, page 3, APAP Device E (Somnobalance).

Pressure relief SoftPAP Löwenstein devices all come with options for SoftPAP expiration relief 1 (mild relief) & 2 (moderate relief). Additionally, Prisma20A/C has a softPAP 3 option which provides moderate relief as well as pressure support slightly above set pressure at the beginning of the inspiration phase, this provides effective comfort to the overall breathing process & feelings of shortness of breath. SoftPAP demonstrates minimal clinical impact to AHI index according to a respiratory bench model evaluation of pressure-relief features*. By manually titrating devices to the exact point of fixed pressure at which the respiratory model simulated AHI of 60/h was normalised to residual AHI of 0/h (no events), & then applying pressure relief features, the clinical impact of adding these features could be assessed. Out of 6 different pressure- relief features tested, 3 were able to maintain no events, whereas the other 3 resulted in residual AHI returning to 60/h. Löwenstein SoftPAP 2 & 3 were tested, both of which

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Appendix 1

Sleep Quality in CPAP/APAPTherapy links compliance, leakage, AHI and therapeutic success

Sleep Quality in CPAP/APAPTherapy links compliance, leakage,AHI and therapeutic success

Despite good compliance and unremarkable AHI in PAP therapy, patients may still suffer from non-restorative sleep with related symptoms. Therefore in the future, devices in the prisma series will offer another criterion for the assessment of therapy success: a deep sleep indicator.

The key to success with PAP therapy

Compliance : With good compliance, CPAP therapy can reduce symptoms, pre- vent secondary cardiovascular disease and prolong life. Diverse studies have pro- ven the effectiveness of CPAP therapy in connection with compliance or with compliance of more than four hours (Palm, Midgren, Theorell-Haglöw, Janson, & Lindberg, 2017), (Antic et al., 2011), (Billings M.E. et al., 2014), (Bouloukaki I. et al., 2017), (Kasai T., Narui K. et al., 2008), (Kingshott R.N. et al., 2000), (Peker Y. et al., 2016), (Abuzaid A.S. et al., 2017), (Weaver et al., 2007). AHI/Leakage: A second decisive factor is effectiveness. Only therapeutically effective CPAP – and not sub-therapeutic treatment – improves symptoms and se- condary diseases (Siccoli M.M. et al., 2008), (Mulgrew et al., 2010), (Habukawa M. et al., 2005), (Bakker et al., 2014). Leaks at mask and mouth, which are unpleasant for the patient, impair pressure stability, event recognition and APAP regulation of the PAP devices.

Information/ Motivation

COM- PLIANCE

Noticeable recuperative effect is prerequisite for better compliance

Prerequisite for an effective SDB therapy

Affected by comorbidities, PAP, sleep hygiene, lifestyle

Mask and mouth are sealed

THERAPY SUCCESS

LOW LEAKAGE

RESTORATIVE SLEEP

Minimum requirement: elimination of respiratory events

Prerequisite for pressure build-up and event recognition

AHI

PAP effectiveness: mode, titration

Figure 1: The PAP therapy chain of effects with patient‘s therapy success at the center.

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Appendix 1 Pgs. 3-4

Restorative Sleep : Even with good compliance and effectively reduced AHI, increased daytime sleepiness remains a problem for up to 40 percent of patients (Antic et al., 2011). AHI is a poor predictor of improvement in daytime function (Kingshott R.N. et al., 2000), (Weaver, Woodson, & Steward, 2005), (Kirkham, Heckbert, & Weaver, 2015). Causes other than a persistent respiratory disorder may be responsible for continued poor sleep. An increased risk of reduced compliance exists for patients with insomnia because the patients consider the respiratory mask and the therapy device to be particular- ly bothersome. Insomnia persists in up to 30 percent of patients treated with PAP (Björnsdóttir E. et al., 2013), (Philip et al., 2017). Compared to AHI, hypnogram-based parameters with and without PAP therapy show that the amount of deep sleep has a higher correlation with an improvement in symptoms (McArdle N. & Douglas N.J., 2001), (Walsh et al., 2008), (Kasai T. et al., 2008). Even the extent of a fall in blood pressure in CPAP treatment correlates more strongly with improvements in the sleepiness scale than with improvements in AHI/ODI resulting from therapy (Robinson G.V., Langford B.A., Smith D.M., & Stradling J.R., 2008).

When symptoms improve, the patient is motivated to adhere to therapy. When compliance improves, therapy succeeds and thus the chain of events continues in a self-reinforcing feedback loop. Restorative Sleep is the true goal of PAP therapy.

prisma RECOVER: Estimation of deep sleep from breathing pattern The innovative prisma RECOVER algorithm continuously analyzes the patient ’ s breathing pattern during PAP therapy. Respiration during deep sleep is more stable than in all other sleep or wake stages (see Figure 2).

Slow-Wave-Sleep

REM-Sleep

Figure 2: Stable breathing in deep sleep stage as compared to variable breathing in REM sleep; above: airflow [l/min]; below: relative minute volume [%]

Sleep Quality in CPAP/APAPTherapy

prisma RECOVER determines the current breathing variability from the fluctuations in respirato- ry minute volume, which are based on the deviation of rMV from 100 percent. When the variabi- lity is lower than the threshold optimized across several patients, stable breathing indicates deep sleep; the respective time period is then added to the estimated length of the deep sleep stage. It can thus be assessed whether the patient has slept soundly and long enough. This means of assessment requires no additional sensors or extra effort. Stable NREM sleep combined with a low AHI throughout the night can indicate sufficient undisturbed REM sleep. In summary, in devices of the prisma series for the first time therapy success can be evalua- ted with regard to sleep quality in prisma JOURNAL, prismaTS or in telemonitoring with prisma CLOUD.

DEEP SLEEP PERIOD - PSG: 144 min - Respiratory data: 162 min

Disturbed

Stable

Detected deep sleep phases

W R

N 1 N 2 SWS

Course of the night

Figure 3: Example of deep sleep recognition in a patient during the first night of APAP therapy

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Appendix 1 Pgs. 5-6

Internal validation data compared to PSG recording A retrospective comparison (re-simulation of respiratory signals with prisma RECOVER) with n=41 patients in APAP therapy yielded a correlation of r = 0.649, p < 0.0001.

250

200

150

100

200

100

150

Minutes of deep sleep manually scored from PSG

Figure 4: Comparison of length of deep sleep, determined from PSG and respiratory data

In an assessment of agreement among human scorers using the EEG, the intraclass correlation for time spent in deep sleep was 0.628 (R&K) and 0.698 (AASM) (Danker-Hopfe et al., 2009). The re- sults underscore the performance of prisma RECOVER. Because different measurement methods and even different scorers using identical measurement methods can deviate in the assessment of deep sleep duration, patients should always be asked about their subjectively perceived symptoms in a suspected case and as part of routine monitoring.

Limitations : - Periods of sleep without PAP use cannot be taken into account for deep sleep duration. They do not correspond per se to the therapy goal, and it must be assumed that such periods have a limited restorative effect for patients affected by Sleep-Disordered Breathing (SDB). - In the presence of increased undesired leaks, the airflow signals measured by the PAP device are likely to be faulty. Consequently, the time spent in deep sleep is underestimated. Before AHI and deep sleep are evaluated, problems with mask and mouth leaks should be resolved.

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Mean values for length of deep sleep periods The literature (Dorffner, Vitr, & Anderer, 2015) provides the following age-dependent mean values for deep sleep periods of healthy subjects with application of AASM 2012 rules.

Age

Women

Men

40 years 60 years 80 years

99 minutes 94 minutes 90 minutes

84 minutes 69 minutes 55 minutes

Possible causes of non-restorative sleep in PAP therapy

Sub-optimum PAP therapy : In isolated cases moderately high AHIs or leakage values can disturb sleep; in such cases other events such as snoring, RERAs (Respi- ratory Effort-Related Arousals) and flow limitations should be investigated. In the case of increased central AHI (e.g., TECSA or treatment-emergent Central Sleep Apnea), the AcSV mode (prismaCR) should be used. Impairment caused by PAP therapy itself: Patients may be bothered by dry mouth, mask problems or the therapy pressure (Kasai T. et al., 2008). That applies especially to patients with anatomic narrowing of the upper airways (Park P. et al., 2017). If necessary, a change of mask should be made or a humidifier – additionally with heated tube system – should be used (Palm et al., 2017). Comorbidities: Diverse disorders such as insomnia (Björnsdóttir E. et al., 2013), (Philip et al., 2017), PLM (Mwenge G.B., Rougui I., & Rodenstein D., 2017), dia- betes, allergies, anemia, depression (Fernandez-Mendoza et al., 2015) can impair restorative sleep. They must be treated separately from respiratory disorders in order to improve sleep. The devices in the prisma series are distinguished by their very low operating sound and proven comfort functions. Furthermore, the pressure reaction in APAP mode is therapeutically effective without unnecessarily high pressure increases, according to an independent bench test (Isetta et al., 2016).

External factors: Sleep hygiene, stress, noise, diet, alcohol consumption, not enough sleeping time can likewise impair sleep‘s restorative power. These factors can be identified in a conversation with the patient and action taken to improve them.

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Appendix 1 Pgs. 7-8

Conclusion Assessing and optimizing the time spent in deep sleep in combination with compliance, AHI and leakage can significantly improve therapeutic success under PAP therapy. Such results conform to the objective of sleep medicine, which is not only the elimination of respiratory events, but the improvement in the restorative effect of sleep.

References Abuzaid A.S., Al Ashry H.S., Elbadawi A., Ld H., Saad M., Elgendy I.Y., Lal C. (2017). Meta-Analysis of Cardi- ovascular Outcomes With Continuous Positive Airway Pressure Therapy in Patients With Obstructive Sleep Apnea. Am. J. Cardiol., 120(4), 693–699. https://doi.org/10.1016/j.amjcard.2017.05.042 Antic, N. A., Catcheside, P., Buchan, C., Hensley, M., Naughton, M. T., Rowland, S., McEvoy, R. D. (2011). The effect of CPAP in normalizing daytime sleepiness, quality of life, and neurocognitive function in patients with moderate to severe OSA. Sleep, 34(1), 111–119. Bakker, J. P., Edwards, B. A., Gautam, S. P., Montesi, S. B., Duran-Cantolla, J., Aizpuru, F., Malhotra, A. (2014). Blood pressure improvement with continuous positive airway pressure is independent of obstructive sleep apnea severity. Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine, 10(4), 365–369. https://doi.org/10.5664/jcsm.3604 Billings M.E., Rosen C.L., Auckley D., Benca R., Foldvary-Schaefer N., Iber C., Kapur V.K. (2014). Psychometric performance and responsiveness of the functional outcomes of sleep questionnaire and sleep apnea quality of life index in a randomized trial: The HomePAP study. Sleep, 37(12), 2017–2024. https://doi.org/10.5665/ sleep.4262 Björnsdóttir E., Janson C., Sigurdsson J.F., Gehrman P., Perlis M., Juliusson S., Benediktsdóttir B. (2013). Sym- ptoms of insomnia among patients with obstructive sleep apnea before and after two years of positive airway pressure treatment. Sleep, 36(12), 1901–1909. https://doi.org/10.5665/sleep.3226 Bouloukaki I., Mermigkis C., Tzanakis N., Giannadaki K., Mauroudi E., Moniaki V., Schiza S.E. (2017). The role of compliance with PAP use on blood pressure in patients with obstructive sleep apnea: Is longer use a key-factor? J. Hum. Hypertens., 31(2), 106–115. https://doi.org/10.1038/jhh.2016.47 Danker-Hopfe, H., Anderer, P., Zeitlhofer, J., Boeck, M., Dorn, H., Gruber, G., Dorffner, G. (2009). Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard. Journal of Sleep Research, 18(1), 74–84. https://doi.org/10.1111/j.1365-2869.2008.00700.x Dorffner, G., Vitr, M., & Anderer, P. (2015). The effects of aging on sleep architecture in healthy subjects. Advances in Experimental Medicine and Biology, 821, 93–100. https://doi.org/10.1007/978-3-319-08939-3_13 Fernandez-Mendoza, J., Vgontzas, A. N., Kritikou, I., Calhoun, S. L., Liao, D., & Bixler, E. O.:. (2015). Natural history of excessive daytime sleepiness: Role of obesity, weight loss, depression, and sleep propensity. Sleep, 38(3), 351–360. https://doi.org/10.5665/sleep.4488 Habukawa M., Uchimura N., Nose I., Kotorii N., Yamamoto K., Matsuyama S., Maeda H. (2005). Emotional states and quality of life in patients with obstructive sleep apnea. Sleep Biol. Rhythms, 3(3), 99–105. https://doi. org/10.1111/j.1479-8425.2005.00171.x Isetta, V., Montserrat, J. M., Santano, R., Wimms, A. J., Ramanan, D., Woehrle, H., Farré, R. (2016). Novel Approach to Simulate Sleep Apnea Patients for Evaluating Positive Pressure Therapy Devices. PloS One, 11(3), e0151530. https://doi.org/10.1371/journal.pone.0151530 Kasai T., Narui K., Dohi T., Yanagisawa N., Ishiwata S., Ohno M., Momomura S.-I. (2008). Prognosis of patients with heart failure and obstructive sleep apnea treated with continuous positive airway pressure. Chest, 133(3), 690–696. https://doi.org/10.1378/chest.07-1901 Kasai T., Takaya H., Dohi T., Yanagisawa N., Yaguchi K., Moriyama A., Narui K. (2008). Subjective sleepiness among patients with obstructive sleep apnea-hypopnea syndrome who were treated with a continuous positive airway pressure device. Sleep Biol. Rhythms, 6(3), 155–162. https://doi.org/10.1111/j.1479-8425.2008.00354.x Kingshott R.N., Vennelle M., Hoy C.J., Engleman H.M., Deary I.J., & Douglas N.J. (2000). Predictors of impro- vements in daytime function outcomes with CPAP therapy. Am. J. Respir. Crit. Care Med., 161(3 I), 866–871. Kirkham, E. M., Heckbert, S. R., & Weaver, E. M. (2015). Relationship between Clinical and Polysomnography Measures Corrected for CPAP Use. Journal of Clinical Sleep Medicine : JCSM : Official Publication of the Ame- rican Academy of Sleep Medicine, 11(11), 1305–1312. https://doi.org/10.5664/jcsm.5192

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McArdle N., & Douglas N.J. (2001). Effect of continuous positive airway pressure on sleep architecture in the sleep apnea-hypopnea syndrome: A randomized controlled trial. Am. J. Respir. Crit. Care Med., 164(8 I), 1459–1463. Mulgrew, A. T., Lawati, N. A., Ayas, N. T., Fox, N., Hamilton, P., Cortes, L., & Ryan, C. F. (2010). Residual sleep apnea on polysomnography after 3 months of CPAP therapy: clinical implications, predictors and patterns. Sleep Medicine, 11(2), 119–125. https://doi.org/10.1016/j.sleep.2009.05.017 Mwenge G.B., Rougui I., & Rodenstein D. (2017). Effect of changes in periodic limb movements under cpap on adherence and long term compliance in obstructive sleep apnea. Acta Clin. Belg. Int. J. Clin. Lab. Med., 1–8. https://doi.org/10.1080/17843286.2017.1405137 Palm, A., Midgren, B., Theorell-Haglöw, J., Janson, C., & Lindberg, E. (2017). Factors influencing compliance to continuous positive airway pressure treatment in obstructive sleep apnea and mortality associated with treatment failure. Sleep Medicine, 40, e250. Park P., Kim J., Song Y.J., Lim J.H., Cho S.W., Won T.-B., Kim H.J. (2017). Influencing factors on CPAP adherence and anatomic characteristics of upper airway in OSA subjects. Medicine, 96(51). https://doi.org/10.1097/MD.0000000000008818 Peker Y., Glantz H., Eulenburg C., Wegscheider K., Herlitz J., & Thunström E. (2016). Effect of positive air- way pressure on cardiovascular outcomes in coronary artery disease patients with nonsleepy obstructive sleep apnea: The RICCADSA randomized controlled trial. Am. J. Respir. Crit. Care Med., 194(5), 613–620. https://doi.org/10.1164/rccm.201601-0088OC Philip, P., Altena, E., Monteyrol, P. -J., Coste, O., Guichard, K., Bioulac, S., Micoulaud, F. J. -A. (2017). Insomnia severity and self-efficacy optimally predict adherence to CPAP in apneic patients. Sleep Medicine, 40, e259. Robinson G.V., Langford B.A., Smith D.M., & Stradling J.R. (2008). Predictors of blood pressure fall with continuous positive airway pressure (CPAP) treatment of obstructive sleep apnoea (OSA). Thorax, 63(10), 855–859. https://doi.org/10.1136/thx.2007.088096 Siccoli M.M., Pepperell J.C.T., Kohler M., Craig S.E., Davies R.J.O., & Stradling J.R. (2008). Effects of continuous positive airway pressure on quality of life in patients with moderate to severe obstructive sleep apnea: Data from a randomized controlled trial. Sleep, 31(11), 1551–1558. Walsh, J. K., Snyder, E., Hall, J., Randazzo, A. C., Griffin, K., Groeger, J., Schweitzer, P. K. (2008). Slow wave sleep enhancement with gaboxadol reduces daytime sleepiness during sleep restriction. Sleep, 31(5), 659–672. Weaver, E. M., Woodson, B. T., & Steward, D. L. (2005). Polysomnography indexes are discordant with qua- lity of life, symptoms, and reaction times in sleep apnea patients. Otolaryngology--Head and Neck Surgery : Official Journal of American Academy of Otolaryngology-Head and Neck Surgery, 132(2), 255–262. https://doi.org/10.1016/j.otohns.2004.11.001 Weaver, T. E., Maislin, G., Dinges, D. F., Bloxham, T., George, C. F. P., Greenberg, H., Pack, A. I. (2007). Re- lationship between hours of CPAP use and achieving normal levels of sleepiness and daily functioning. Sleep, 30(6), 711–719.

Löwenstein Medical Technology Kronsaalsweg 40, 22525 Hamburg, Germany

T: +49 40 54702-0, F: +49 40 54702-461

info@loewensteinmedical.de, www.loewensteinmedical.de

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Appendix 2

APAP Algorithm

ApneaDifferentiation 01

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APAPFunction

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ProactiveRegulationof theAlgorithm The best way to distinguish different types of apnea:

• The automatic regulation of APAP / (E)EPAP requires reliable apnea information. In cases of obstructive apnea, therapy pressure is raised in order to prevent further obstructions. • In fixed modes such as CPAP, S, ST, apnea information is important in determining whether the therapy is going smoothly or if setting or even the mode have to be changed • Two measuring processes are available: FOT: Forced Oscillation Technique (in all modes WITHOUT backup frequency) FBT: Forced Breath Technology (in all modes WITH backup frequency)

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Appendix 2 Pgs. 5-8

FOT- Functionality • With the help of Forced Oscillation Technique (FOT), our CPAP & APAP therapy devices reliably distinguish between obstructive & central apnea. • The sensitive FOT process uses airflow & pressure to measure resistance & obstruction in the upper airways to which the device then reacts.

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APAPFunctionality In the event of apnea, an oscillation signal is applied which has a frequency of about 4 Hz & an amplitude of 0.4 mbar. The flow response is the critical criterion in determining whether the apnea is central or obstructive.

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Exampleof oAwithFOT Oscillation frequency: 4 Hz (4 oscillations /s) Oscillation Amplitude: 0.4 hPa

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Exampleof cAwithFOT Oscillation frequency: 4 Hz (4 oscillations /s) Oscillation Amplitude: 0.4 hPa

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Appendix 2 Pgs. 9-12

TwoAPAPSettings 02 ProactiveRegulationof theAPAP • Challenge: To combine stable, therapeutically effectiveness & lowest possible pressure. • A pressure decrease that is too fast or too extreme causes anew serious obstructions & arousals. • A pressure increase that is too fast or too extreme in response to mild obstructions leads to high mean pressures or high pressure variability (another fast decrease) • An inadequate increase in the event of severe obstructions results in long periods with subtherapeutic pressures. NEWwith two APAP settings: The right therapy for every patient

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TwoAPAPSettings - Standardvs. Dynamic Choose your preferred setting:

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TwoAPAPSettings - Standardvs. Dymanic

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Appendix 2 Pgs. 13-16

TwoAPAPSettings You have a choice between two different settings for APAP management.

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Standard-APAP: AlgorithmDetails • An epoch = 2 minutes • Goal: Acclimation phase to a new pressure, no provocation of events or arousals • In general: pressure change at the end of the epoch • In event of obstructive hypopnea (oH) & obstructive apnea (oA), up to two times in the epoch • eSO events: oA, oH Snoring > eight breaths in the epoch • eMO events: RERA, Snoring > four breaths in the epoch

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Dynamic-APAP: AlgorithmDetails • An epoch = 2 minutes • Goal: Acclimation phase to a new pressure, no provocation of events or arousals • In general: pressure change at the end of the epoch • Response to snoring (after six breaths) , immediate response to obstructive hypopnea (oH) & obstructive apnea (oA), up to two times in the epoch • eSO events: oA, oH Snoring > six breaths in the epoch • eMO events: RERA, Snoring > three breaths in the epoch

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APAP: AlgorithmDetails • Pressure range (Pmax-Pmin) is divided into four quartiles • Dynamic depending on pressure range • Example Pmax 18 hPa, Pmin 10 hPa

Q1: 10-12 hPa Q2: 12-14 hPa Q3: 14-16 hPa Q4: 16-18 hPa

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APAP:WaitingTime toPressureDecrease • An event-free waiting time must occur before the first pressure decrease takes place. • The decrease also depends on the quartile & severity of the previous obstruction. • If several epochs with obstructions occur, the one that counts is the longest within the related waiting time, not the sum of all or something similar.

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APAP: PressureDecrease • If no events occur, the pressure is decreased again. • Example: Pmin 8 hPa, Pmax 16 hPa

Q1: 8h Pa - 10h Pa Q2: 10h Pa - 12h Pa Q3: 12h Pa - 14h Pa Q4 14h Pa - 16h Pa

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prisma -AlgorithminDetail

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prisma -AlgorithminDetail • Basic protection: Pressure reaction to severe obstructions (AHI-relevant!) strong & long enough to guarantee a low Rest Index. Identical in APAP Standard & Dynamic • Extra Protection: Milder obstructions delay the decrease

or lead to further increases, depending on the quartile: Normalization of respiratory curve, “safe distance” to pressure ranges with severe obstructions In APAP Dynamic stronger, additional increase in Standard

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Appendix 3

technology

Study Hunter: Benchtest of AHI agreement in APAP (20A) Benchtest Evaluation of AHI agreement between PAP algorithm and polygraph in predominant CSA patients Y. Rétory; S. Liu; S. Hardy; F. Cottin; G. Roisman, M. Petitjean; Poster presentation ERS 2019. Background: A reliable event detection is the prerequisite to treat sleep disordered breathing efficiently, the major parameter being the apnea-hypopnea-index (AHI). A correctly detected AHI is as well essential for an effective intervention based on the device software and especially based on telemonitoring. It is known that there are differences in detecting and scoring methods between the devices of different manufacturers that might significantly influence the patients’ therapy. In this benchtest by ALEHOS the AHI detected by different devices have been compared to the data measured by a polygraph and scored according to AASM to evaluate the reliability of apnea-hypopnea detection as well as to visualize the differences. Overall Message: Switching from one APAP-device to another without further PG control might have significant consequences on the patients’ therapy due to very different accuracy levels of AHI scoring. Main Results: Even though there was a high correlation between AHI detected by the different devices and detected by the PG overall, there are differences in AHI scoring between the tested devices. AHI generated by the device may not be an absolute indicator of treatment efficiency in patients with suspicious CSA, but prisma20A shows the highest accuracy level (together with Philips dreamstation) of 96% with PG as reference. Resmed and Sefam reached an accuracy level of 84% and 88% respectively. Overall apnea detection of PAP devices correlated better to the results by PG than hypopnea detection of PAP devices. Method: • 25 breathing sequences measured on 4 central sleep apnoea (CSA)patients • Simulated on an artificial lung • on 4 devices that have been compared: AirSense 10 (Resmed), Dreamstation Auto (Philips), S.Box (Sefam), prisma20A (Löwenstein)

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Appendix 4

technology

Study Hunter: APAP Effectiveness in prisma Examination of the Effectiveness of FOT-based auto-CPAP Therapy Device in the Treatment of Patients with Obstructive Sleep Apnea S.D. Herkenrath, M. Treml, N. Anduleit, K. Pietruska, M. Schwaibold, W.J. Randerath, Scientific Institute for Pneumology at University of Cologne. Poster (German, English, French), ERS 2018 Background: The Forced Oscillation Technique (FOT) is proven to be a sound method for apnea classification in APAP devices. This clinical study examines - The effectiveness of pressure regulation and - analyses associations between applied therapy pressure and mask leakage. Overall Message: The algorithm in prisma20A does not need to fear the comparison with competition! The algorithm is effective. The results apply to all APAP modes in prisma devices as well. Main results: - Obstructions were treated effectively. - Even at higher pressures there was no risk that APAP pressure is “overshooting”. - Even though most patients wore a nasal mask there was hardly any mouth leakage measured. Further Conclusion: - Our devices do not provoke or induce central events. - It was proven that APAP pressure was only moderately increased to treat events effectively (Löwenstein philosophy). Method:

- 34 OSAS patients, baseline diagnosis of AHI >15 - Open pressure thresholds from 4hPa – 20hPa - 1 diagnosis night, 1 therapy night - Standard APAP regulation Further Comment: Further publications are in progress. You will be informed.

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Appendix 5

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Study Hunter: APAP effectiveness in prisma Extended evaluation of the efficacy of a proactive forced oscillation technique-based auto-CPAP algorithm S. D. Herkenrath, M. Treml, N. Anduleit, K. Richter, A. Pietzke-Calcagnile, M. Schwaibold, R. Schäfer, R. Alshut, A. Grimm, L. Hagmeyer, W. J. Randerath, Sleep and Breathing, https://doi.org/10.1007/s11325-019-01901-8 Background: As published by Zhu et. al there are significant differences in the efficacy of APAP devices. 1 There are also limitations to APAP in favor of CPAP (e.g. blood pressure reduction), that is why a reliable pressure regulation is even more important. The Forced Oscillation Technique (FOT) is a proven method for reliable apnea classification to ensure adequate pressure regulation in APAP devices. In a prospective, interventional trial the APAP mode in prisma was examined regarding: - The effectiveness of pressure regulation in terms of evaluating upper airway obstructions, - Flow contour analyses during hypopneas. Overall Message: The APAP algorithm in prisma-devices show optimal suppression of respiratory events with adequate pressure regulation (therapy P50=7hPa). The majority of rare residual respiratory events was detected by prisma. Sleep quality increased independently from pressure level. Main Results: (1) Apneas were differentiated reliably, obstructions were treated effectively, the arousal index significantly decreased. (2) The APAP mode significantly increased REM and slow wave sleep; independently from therapy pressure level, thus improving sleep quality overall. Further Conclusion: • Five patients presented TECSA (treatment emergent central sleep apnoea) and were supposedly mainly suffering from central apneas coinciding with upper airway obstructions, they also showed significantly higher (mouth) leakage (associated with higher CO 2 washout) and pressure levels. These events were correctly detected by FOT, but pose challenges to adequate therapy. 2 3) It was proven that APAP pressure was only moderately increased to treat events effectively (Löwenstein philosophy in standard regulation). Method: • Inclusion: 46 patients with severe OSAS (AHI 36), mostly obese with routine PSG for suspected OSA • 43 patients with nasal mask. • prisma LAB in APAP standard mode, open pressure threshold from 5-20hPa, 15min softSTART. • 1 diagnosis night, 1 therapy night 1 Zhu K, Roisman G, Aouf S, Escourrou P (2015) All APAPs are not equivalent for the treatment of sleep disordered breathing: a bench evaluation of eleven commercially available devices. J Clin Sleep Med 11:725–734. https://doi.org/10.5664/jcsm.4844 2 Please refer to the therapy option of adjustable pressure limits (Pmax oA) in prisma- devices, also in detail described in the White Paper Edition by LMT “Central Respiratory Events during CPAP / EPAP Therapy ”

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Appendix 6

RESEARCH ARTICLE Novel Approach to Simulate Sleep Apnea Patients for Evaluating Positive Pressure Therapy Devices Valentina Isetta 1,2 , Josep M. Montserrat 2,3,4 , Raquel Santano 1 , Alison J. Wimms 5 , Dinesh Ramanan 5 , Holger Woehrle 5 , Daniel Navajas 1,2,6 , Ramon Farré 1,2,4 * 1 Unitat de Biofísica i Bioenginyeria, Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain, 2 CIBERES, Madrid, Spain, 3 Sleep Laboratory, Pneumology Department, Hospital Clinic, Barcelona, Spain, 4 Institut d'Investigacions Biom è diques August Pi i Sunyer, IDIBAPS, Barcelona, Spain, 5 ResMed Science Centre, Munich, Germany, 6 Institute for Bioengineering of Catalonia, IBEC, Barcelona, Spain

* rfarre@ub.edu

Abstract Bench testing is a useful method to characterize the response of different automatic positive airway pressure (APAP) devices under well-controlled conditions. However, previous mod- els did not consider the diversity of obstructive sleep apnea (OSA) patients ’ characteristics and phenotypes. The objective of this proof-of-concept study was to design a new bench test for realistically simulating an OSA patient ’ s night, and to implement a one-night exam- ple of a typical female phenotype for comparing responses to several currently-available APAP devices. We developed a novel approach aimed at replicating a typical night of sleep which includes different disturbed breathing events, disease severities, sleep/wake phases, body postures and respiratory artefacts. The simulated female OSA patient example that we implemented included periods of wake, light sleep and deep sleep with positional changes and was connected to ten different APAP devices. Flow and pressure readings were recorded; each device was tested twice. The new approach for simulating female OSA patients effectively combined a wide variety of disturbed breathing patterns to mimic the response of a predefined patient type. There were marked differences in response between devices; only three were able to overcome flow limitation to normalize breathing, and only five devices were associated with a residual apnea-hypopnea index of < 5/h. In conclusion, bench tests can be designed to simulate specific patient characteristics, and typical stages of sleep, body position, and wake. Each APAP device behaved differently when exposed to this controlled model of a female OSA patient, and should lead to further understanding of OSA treatment.

OPEN ACCESS

Citation: Isetta V, Montserrat JM, Santano R, Wimms AJ, Ramanan D, Woehrle H, et al. (2016) Novel Approach to Simulate Sleep Apnea Patients for Evaluating Positive Pressure Therapy Devices. PLoS ONE 11(3): e0151530. doi:10.1371/journal. pone.0151530

Editor: Gennady Cymbalyuk, Georgia State University, UNITED STATES

Received: September 23, 2015

Accepted: February 28, 2016

Published: March 15, 2016

Copyright: © 2016 Isetta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper. Funding: The work presented here has been partially supported by a research agreement (number 307990) between ResMed Science Centre and Universitat de Barcelona (PI: Ramon Farré). The work has been also partially funded by Ministerio de Economía y Competitividad (PI14/0004). There was no additional funding received for this study. ResMed Science Centre provided support in the form of salaries for authors AJW, DR, and HW, but did not have any additional role in the study design, data

Introduction Obstructive sleep apnea (OSA) is a prevalent breathing disorder and is considered a major pub- lic health issue, affecting 5 – 15% of the general population and increasing with both body mass index and age (up to at least 60 – 65 years) [1,2]. OSA is characterized by repetitive narrowing

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New OSA Patient Simulator for Testing PAP Devices

collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section. Competing Interests: The work presented here has been partially supported by a research agreement (number 307990) between ResMed Science Centre and Universitat de Barcelona (PI: Ramon Farré). AJ Wimms, D Ramanan, and H Woehrle are employed by ResMed Science Centre. There are no patents, products in development, or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.

and closure of the upper airway during sleep [3] that results in brain arousal, intermittent hyp- oxia, negative intrathoracic pressure swings, and increased sympathetic activity. OSA is associ- ated with a reduction in quality of life, daytime sleepiness, traffic accidents, neurocognitive impairment, metabolic, cardiovascular disease [4] and malignancies [5]. The treatment of choice for OSA is the application of continuous positive airway pressure (CPAP) to the patient ’ s nose or mouth through a mask during sleep at home. This pressure in the mask is transmitted to the pharyngeal area, splinting the collapsible upper airway walls thereby avoiding obstruction. Auto-adjusting positive airway pressure (APAP) devices, which are increasingly being used, are driven by algorithms that measure abnormal sleep breathing events, analyze the patient ’ s breathing pattern and eventually increase the delivered pressure in response to airway obstruction, or decrease pressure when breathing is stable to increase patient comfort [6 – 11]. In theory, APAP devices should be ideal for treating a range of patients with different characteristics, and for effectively treating OSA despite within-night and night-to-night varia- tions in the upper airway collapsibility experienced by each individual patient [12 – 16]. However, commercially available APAP devices contain undisclosed proprietary algorithms, and therefore the way that they measure and respond to specific breathing patterns varies [17]. In addition, some APAP manufacturers are introducing new algorithms based on specific patient characteris- tics. This move towards personalized medicine in the treatment of OSA means greater choice for patients and more variability in APAP algorithms. Therefore, understanding how each device responds to different OSA patterns requires comparative studies using well defined references. Bench testing is a useful method to characterize the response of different APAP algorithms under well-controlled conditions, thus avoiding the biological variability inherent in clinical trials. However, previously used bench test models have been based on subjecting the APAP device under test to a repetitive string of disturbed breathing patterns, without providing a suf- ficiently wide spectrum of events. These limitations mean that variety in patient characteristics and phenotypes, or the changes that occur during different sleep stages and body positions over the course of a night ’ s sleep, cannot be taken into consideration. This is particularly rele- vant given that different subpopulations of OSA patients (e.g. children, men, women, the elderly) exhibit specific traits in their sleep-related breathing disorders [18]. Therefore, the aims of this proof-of-concept study were: 1) to design a new complex and versatile bench test approach for realistically simulating respiratory events throughout the course of the night in an OSA patient, mimicking breathing disturbances across different phe- notypes, and 2) to implement a full night example of a female OSA phenotype and use this to compare the responses of several currently-available APAP devices. Materials and Methods The hardware of our new model was based on a previously described bench test [19]. This fully computer-driven model comprises a servo-controlled pump able to deliver a flow that repli- cates any breathing waveform stored in the computer. An obstruction valve allows the simula- tion of controlled obstructive events by imposing mechanical impedances previously recorded in patients with OSA. Two other valves can mimic leaks and mouth breathing, and a loud- speaker-in-box system can superimpose simulated snoring onto the breathing flow. The test bench is equipped with two sensors, one to measure pressure at the simulated patient entrance and one to measure the actual flow generated by the patient simulator. A calibrated leak based on a 4-mm internal diameter (ID) orifice [20] mimics the mask leak (exhalation port) in nasal masks. In previous studies, this system was fed by a collection of disturbed breathing events, such as obstructive and central apneas, hypopneas, flow limitation, mask leaks and mouth expi- ration [19,21].

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New OSA Patient Simulator for Testing PAP Devices

To design the new OSA simulator model we developed a novel approach aimed at realisti- cally replicating a typical night of sleep for a female patient. With this aim, we considerably expanded our library of disturbed breathing patterns anonymously extracted from polysomno- graphy recordings obtained from real OSA patients and we incorporated several new adjustable features into the simulator. Specifically, the new patient model can be set to react to the pres- sure delivered by the APAP device (PAP-responsive mode) or to reproduce a fixed scenario of disturbed breathing events (Steady mode), depending on the device characteristics being tested. Moreover, the severity of the simulated OSA profile is now fully modifiable by changing the frequency and duration of each breathing event. Various artefacts were introduced into the event spectrum, such as changes in tidal volume and breath rate, to replicate typical events dur- ing wake such as irregular breathing, swallowing, moving and talking. By combining these new features, we aimed to create a new OSA model concept model that can realistically replicate a whole night of sleep, including phases of wake, rapid eye movement (REM) and non-REM sleep, and change in body position, each one designed to mimic different characteristics in terms of upper airway collapsibility. For this study specifically, as an example of an entire night of sleep-disordered breathing (SDB), the bench test model was set to simulate the disturbed patterns of a female OSA patient with the following characteristics: long sleep latency (45 min), low positive airway pressures (PAPs) required to overcome obstructive events, high proportion of flow limitation events ver- sus apneas, higher apnea-hypopnea index (AHI) during REM sleep, and only minor positional effects on upper airway collapsibility. The features and structure of this female-specific OSA patient simulation are detailed in Table 1. The breathing pattern of the simulated patient depended on the PAP applied by the device under test, with a total duration of 4 hours and 15 minutes. APAP pressure values required to normalize breathing during each stage of the simu- lation are shown in Fig 1. The simulated night consisted of programming the different stages described in Table 1, starting with 45 minutes of simulated awake stage (sleep onset) followed by a succession of different sleep stages with the features detailed in Table 1 (e.g. breathing fre- quency, number and types of respiratory events) and a final awake short period. In this way we were able to model a patient exhibiting different sleep breathing characteristics throughout consecutive sleep stages. Ten different commercially available APAP devices were tested using the new bench test model and the female-specific simulation described above: AirSense 10 (A) and AirSense 10 AutoSet for Her (B) by ResMed; Dreamstar by Sefam (C); Icon by Fisher & Paykel (D); Resmart by BMC (E); Somnobalance (F) and Prisma 20A (G) by Weinmann; System One by Respironics (H); iCH (I) and XT-Auto by Apex (J). Each APAP device was connected with its own tube to the bench model. Default APAP settings were used (minimum pressure 4 cmH 2 O, maximum pressure 20 cmH 2 O). Each device was tested twice and the results averaged to obtain the final values. Results The new OSA patient simulator could effectively combine a great variety of SDB elements to mimic the response of the predefined patient type. The responses of the assessed APAP devices to the new female-specific bench test model are summarized in Table 2. There was considerable variation among devices, particularly with respect to the mean and maximum nasal pressures applied, and the ability to overcome obstructive events and flow limitation, The residual AHI was calculated as the number of residual obstructive events per hour and the residual flow limi- tation was measured as the portion of the test in minutes (excluding the initial 45-minute wake period) that the simulated patient remained on flow limitation.

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