Emerging Sleep Testing Methods

Transcription

1 Emerging Sleep Testing Methods Matt T. Bianchi MD PhD Director, Sleep Division MGH Neurology May 2015

2 Funding and Disclosures Funding: MGH Neurology Department Harvard Clinical Investigator Fellowship ( 09-11) CIMIT Young Clinician Award ( 11-13) Harvard Catalyst KL-2 ( 11-13) Milton Foundation ( 14-15) MGH-MIT Grand Challenge ( 14-15) Department of Defense ( 15-16) Disclosures: Patent pending (sleep shirt) Foramis (Advisory Board) Servier (travel funds)

3 Topics Why we need better diagnostics Limitations of current technology for practice Emerging solutions Clinically available devices Research-phase devices Consumer space

4 I m not sure what you think you measured, but that was not my sleep -patient, after in-lab PSG

5 Measuring Sleep: A Spectrum

6 PROs and CONs of current methods Method PROs CONs Diary Actigraphy Home Test Kits Lab Polysomnogram Easy & Free Home Setting Longitudinal Patterns User Friendly Objective Longitudinal Patterns Home environment less $ per night Data Rich Field Standard Effort dependent Recall Bias Misperception Movement only Not standard practice Over-estimates sleep Single night (usually) Reduced accuracy? Cost-effective Single night Unusual environment High cost

7 Do we need better home diagnostics? Current home sleep test (HST) kits have key limitations Technical failure (1-10%) Variability in ease-of-use lower sensitivity and specificity than in-lab PSG Position sensing absent or not validated Sleep EEG is only measured in 1 type ( ARES ) Formal Cost-Benefit analyses question the assumption of $ savings, largely due to false-negative risk of HST Chervin 1999, Reuven 2001, Deutsch 2006, Pietzsch 2011 American Academy of Sleep Medicine Clinical Guideline recommends against using kits for general screening No information beyond obstructive sleep apnea

8 AASM Technology Review (Collop 2011) 20 home sleep test (HST) kits reviewed Body position tracking: 11/20 (none were validated) Measuring sleep: EEG: ARES monitor 6 used actigraphy or other surrogate Central versus obstructive: not reported Bottom line: only use home kits in those without medical or sleep co-morbidities, and high (80%) chance of at least moderate OSA (AHI >15)

9 How do we get to a pre-test of 80%? Epworth Sleepiness Scale: Poor correlation with AHI: r = 0.1 (Eiseman 2012) Stop-BANG Scale: Better than ESS, but pre-test must be >50% for positive Stop-Bang to reach 80% (Bianchi 2009) Clinical impression of experienced physicians Sensitivity and Specificity <70% (Skomro 1999) Bottom line: insurance rules for pre-test are not validated, so any negative HST must be confirmed in lab, to avoid false negative risk.

10 Case example 1a: co-morbid insomnia In an 8 hour recording, the patient had objective sleep for only 4 hours, due to insomnia 32 respiratory events are recorded. The full-night index is 32/8 = 4 (within normal limits) The index should be 32/4 = 8 (mild OSA) Objective insomnia leads to under-estimation of OSA indices. Whether to treat mild OSA is still a clinical judgment but is it OK to give a sedative pill if OSA is present?

11 Case example 1b: co-morbid insomnia In an 8 hour recording, the patient reports sleep for only 4 hours. 8 hours of sleep actually occurs, ie misperception Using their report as the denominator, for 32 respiratory events, the index is 32/4 = 8 (mild OSA) The index should be 32/8 = 4 (normal) Using self-reported sleep may over-estimate OSA among insomniacs with misperception The solution is to have EEG-based sleep measures, the gold standard of which is in-lab PSG

12 Insomnia and OSA Occult OSA is found in 20-70% of chronic insomniacs (Lichtstein 1999; Krakow 2010; Wickwire 2010) Insomnia is more common in OSA than non-osa Misperception occurs in OSA patients (Castillo 2014) 40% under-estimated total sleep by >1 hour Of those, 25% under-estimated by >3 hours

13 Body position and OSA diagnostics 50% of OSA cases will be >2x worse while supine For a subset, OSA is mild/normal while non-supine Body position may vary night to night (Oksenberg 2013) Risk of OSA under-estimation: 0 20 None Mild Mod Sev <5 to >5: 16% <5 to >15: 9% <5 to >30: 3% % of cohort <15 to >15: 38% <15 to >30: 19% Eiseman, Westover, Ellenbogen, Bianchi, 2012 (JCSM)

14 Topics Why we need better diagnostics Limitations of current technology for practice Emerging solutions Clinically available devices Research-phase devices Consumer space

15 Treatment monitoring for CPAP users Insurance minimum: 4 hrs, 70% of nights For a patient who sleeps 8-hours, this amounts to only 35% of sleep time (40% for a 7hr sleeper) 8 hrs of sleep Night On CPAP Off CPAP

16 Case example: OSA on CPAP 65M with severe OSA Dx PSG: AHI = 50 On PAP, still sleepy so we get a data card Data shows AHI = 5.0 Mean use: 6 hrs per night 80% of nights used ===============GREAT compliance, looks like needs provigil right? Time On Mask (6hrs) Time Off Mask (2hrs) Effective AHI = 5*80% + 50*20% = = 14

17 Apnea burden among PAP users Data Card: Burden : AHI<5 AHI 5-15 AHI AHI > % of cohort Bianchi, Mojica 2014

18 Measuring OSA on vs off PAP in one night We only know about PAP efficacy (ie, on-pap data) We don t know what happens off-pap Emerging devices may inform this, so we can measure actual apnea burden, instead of estimating it X

19 EKG-based sleep monitor ( M1 )

20 EKG as a window into sleep Basis for using EKG for home monitoring: Expiration: rate declines Inspiration: rate increases Respiration is stage- and OSA- dependent EKG metrics track the respiration pattern Autonomic fluctuations with arousals So: EKG can track sleep (and OSA) Amplitude also tracks breathing (smaller on inspiration)

21 Normal cardiopulmonary coupling profile Freq (Hz) sleep HFC LFC Stable NREM sleep yields High Frequency Coupling peaks (HFC) Wake and REM yield Low Frequency peaks (LFC). Time (hours) (Thomas et al, 2005)

22 CPC example: OSA (split night) Pre-CPAP CPAP HFC LFC

23 CPC: obstructive versus central apnea Complex: Short and regular cycles, CPAP failure more likely Typical OSA: Variable cycles, CPAP success more likely (Thomas et al 2009)

24 CPC profile predicted CPAP titration failure in split-night PSGs better than central apnea index or any apnea severity metric Sleep, 2007

25 CPC: a new sleep quality metric? Risk of HTN, cardiovascular disease (Thomas, 2009) Sleep fragmentation in fibromyalgia (Thomas, 2010) Sleep fragmentation in heart failure (Yeh, 2008) Sleep fragmentation in major depression (Yang 2010) Heritability of sleep architecture in twins (Ibrahim, 2010) Relation to sleep EEG and delta power (Thomas, 2014) Sleep patterns in insomniacs (Bianchi, Thomas, in progress)

26 Topics Why we need better diagnostics Limitations of current technology for practice Emerging solutions Clinically available devices Research-phase device Consumer space

27 Smart-Textile Wearables

28 Validation: respiration movement AHI 100 Algorithm Index (hr -1 ) R 2 = PSG Apnea-Hypopnea Index (hr -1 ) Algorithm PSG gold standard +OSA none +OSA 30 1 none 3 11 Sens: Spec: 91% 92% Bianchi, Lipoma, Darling, Westover (2014)

29 Topics Why we need better diagnostics Limitations of current technology for practice Emerging solutions Clinically available devices Research-phase devices Consumer space

30 Emerging commercial sensing Movement based Bed / Mattress based EEG based

31 Movement sensing consumer space Jawbone Lark Fitbit Basis

32 Mattress-based sensing (consumer space) Aura (Withings) Heart Rate Respiration Movement Lack validation Position-dependence? Two-body problem? Beddit

33 Future Directions Algorithm developments Supervised Machine Learning & Prediction Unsupervised clustering for novel metrics Big Data analytics Cross-over between consumer technology and medical uses

34 Some final advice, when all else fails

35 Thank You!