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Urgent Need to Improve PAP Management: The Devil Is in Two (Fixable) Details

Published Online: by:15


Several high-profile, large prospective sleep apnea therapy trials have failed to meet expected outcomes: Apnea Positive Pressure Long-term Efficacy Study (APPLES) (cognition),1 the Treatment of Predominant Central Sleep Apnoea by Adaptive Servo Ventilation in Patients With Heart Failure (SERVE-HF) trial (heart failure),2 the Canadian Positive Airway Pressure Trial (CANPAP), the Sleep Apnea cardioVascular Endpoints (SAVE) study (general cardiovascular),3 and the Heart Bio-marker Evaluation in Apnea Treatment (HeartBEAT) (metabolic/hemodynamic).4 Each theoretically had the power to positively influence practice, but instead have cast doubt on the staple of our field: positive airway pressure. Struggling to navigate these findings, experts have invoked explanations ranging from inadequate use, too-short duration of therapy, overwhelming disease pathophysiology, treatment initiated too late in evolution of disease, and unknown pathophysiological constructs. Although these are important questions to advance our field, there are two arguably more fundamental details that must be addressed. First, the efficacy of continuous positive airway pressure (CPAP)—or adaptive ventilation in the case of the SERVE-HF study—remains unquestioned, either via titration data or via machine data download thresholds, despite emerging data suggesting otherwise. Second, off-positive airway pressure (PAP) sleep time is not measured or considered, yet it must be to understand overall PAP effectiveness. We propose that these two aspects must be addressed urgently, before we seek explanations beyond these fundamental aspects of PAP therapy to reconcile negative trial outcomes.


Modern PAP devices measure and store airflow and pressure data, but display only automated charts of residual event and compliance indices.5 This allows for tracking of presumed efficacy and adherence.6 However, vendor algorithms vary, and there are no specific guidelines or standards for capturing, measuring, or scoring the data.6 High-resolution flow data can also be reviewed directly, enabling visual/manual assessment of events. Several studies have examined the relationship between device-detected events or device-reported apnea-hypopnea index (AHI) based on flow measurements (AHIFLOW) and findings on polysomnography.5,712 The findings mostly demonstrate good correlation,811 with one study showing device overestimation.7 These studies did not (or could not) visualize flow directly. Event-by-event analysis in one study showed that the automatic detection had high specificity but only modest sensitivity (for a cutoff of AHI > 10 events/h, sensitivity was 0.58 and specificity was 0.94), with good agreement for apneas but less so for detecting hypopneas,5 an automatic detection limitation also seen in other studies.9,12 There is thus a concern that efficacy can be overestimated by machine downloads. High accuracy of device detection was recently reported,13 but the study has limited application, in our opinion, to general practice because: (1) hypopneas were scored with 4% desaturation, which makes little sense when contrasting with a treatment tracking device, as desaturations are readily minimized even by subtherapeutic CPAP; (2) patients with difficult or incomplete CPAP titrations (inability to normalize) were explicitly excluded,13 yet such individuals are exactly the population for whom we depend most urgently on reliable device performance. In recent analysis from an academic center cohort not limited to straightforward cases, manually/visually evaluating flow patterns, residual AHIFLOW > 5, 10, and 15/h was seen in 32.3%, 9.7%, and 1.8% versus 60.8%, 23%, and 7.8% of subjects based on automated versus manual scoring of waveform data.14 This suggests a substantial subset of patients may be incompletely treated despite reassuring machine values.

We have been using the SleepyHead freeware (based on the free and open-source software SleepyHead, developed and copyright by Mark Watkins 2011–2017; downloadable from to view data from ResMed devices, where the flow signals are not transmitted to AirView (ResMed, San Diego, CA). The Philips Respironics EncoreAnywhere (Philips Respironics, Murrysville, Pennsylvania, United States) system allows viewing flow waveforms. Using SleepyHead, a rich stream of data including inspiratory and expiratory time, flow, and estimated tidal volumes are also available for analysis. The software is a data viewer and does not generate new metrics. Presented in Figure 1 through Figure 7 are the types of errors in detection. Three categories of patients seem to demonstrate the greatest errors: (1) short-cycle periodic breathing; (2) ataxic breathing associated with opiates or neurological disorders such as Parkinson disease; and (3) adaptive ventilation that ignores pressure cycling. The algorithms are also “nonintelligent,” one event is detected while another similar-appearing event is not; algorithmic sophistication can surely provide a more biologically truthful summary output. During therapy, signals often evolve over longer periods and may fall out of the detection window.

Figure 1: High loop gain sleep apnea: short-cycle periodic breathing with obstructive features.

This 5-minute diagnostic polysomnogram snapshot shows cycle-lengths of less than 30 seconds, a self-similar appearance of respiratory events, and events which may be scored central or obstructive from event to event. Note snoring appearing during arousals that during the events, rather than during the respiratory event, a commonly seen feature when periodic breathing coexists with obstructive features. In purely obstructive disease, snoring occurs during the event and resolves during the arousal. This patient has exclusively non-rapid eye movement dominant apnea. The effects of continuous positive airway pressure are shown in Figure 2. M1 and M2 are mastoid left and right common references, F, C, and O standard frontal, central, and occipital sites. PTAF = pneumotachogram air flow, THERM = Thermistor, LAT/RAT = left and right anterior tibial electromyogram, EKG = electrocardiogram, SaO2 = fingertip oxygen saturation, Pleth = oximeter-derived plethysmography.

Figure 2: Residual undetected respiratory events during continuous positive airway pressure (CPAP) therapy in high loop gain sleep apnea.

The same subject as in Figure 1, after over 6 months of CPAP therapy. Residual sleepiness and fatigue persists. EncoreAnywhere (Philips Respironics) data, each horizontal line is 6 minutes. Note persistent short-cycle respiratory events almost entirely undetected by the algorithm, arrows identify two examples.

Figure 3: Opiate-associated sleep apnea.

Snapshot from EncoreAnywhere (Philips Respironics) in a patient who uses high dose opiates and is treated with continuous positive airway pressure (CPAP), acetazolamide and enhanced expiratory rebreathing space (dead space, EERS). Each horizontal line represents 6 minutes. Note: (1) Variability of expiratory duration, the typical opiate effect. (2) Variable respiratory rates including bradypnea. (3) False detection of obstructive events, with tags falling on all parts of the respiratory cycle-expiratory and inspiratory, suggesting that the detection algorithm is unable to accurately process ataxic breathing patterns. The machine-detected apnea-hypopnea index was 22, though the patient noted refreshing sleep. Long arrow identifies a short expiratory duration, short arrow identifies a long duration. H = hypopnea, OA = obstructive apnea.

Figure 4: Complex respiratory waveforms and software detection limitations.

Snapshot from EncoreAnywhere (Philips Respironics) in a 52-year-old man not using opiates but with uncertain cause of ataxic respiration. Brain and high spinal magnetic resonance imaging was normal. Note the substantially pathological respiratory patterns (apneas, ataxic rhythm, clusters of tachypnea) largely undetected by the software. Six minutes per horizontal line of flow. The two downward pointing black arrows are examples of points of abrupt shifts in rate and rhythm of respiration. CA = central apnea, H = hypopnea, OA = obstructive apnea.

Figure 5: Missed event during continuous positive airway pressure use.

Various samples, each from different patients, at different compressions to show events tagged and missed auto-detection via the algorithms. The missed events are tagged with star symbols. AHI = apnea-hypopnea index, CA = central apnea, H = hypopnea, OA = obstructive apnea.

Figure 6: Adaptive ventilation and pressure cycling.

(A) Effective adaptive ventilation with largely stable pressure and expiratory time over the course of the night. No events are detected. (B) Pressure cycling through much of the night. Note the variance in expiratory duration, and the relative paucity of events detected, only in the areas of greater pressure cycling. Pressure is in cm H2O, and flow rate in L/min. CA = clear airway event, presumably central apnea, Exp. Time = expiratory time (in seconds), Insp. Time = inspiratory time (in seconds), LL = large leak, OA = obstructive apnea, UA = unclassified apnea.

Figure 7: Pressure cycling at high resolution.

Two samples, from different patients, of pressure cycling in response to ongoing flow cycling, which appears to be subthreshold to detection algorithm (apnea-hypopnea index, zero). Arrows point to some examples of pressure surges, but note non-detection of flow events.


We do not measure sleep apnea events during off-PAP sleep, which for some patients can represent well over 50% of their total sleep duration. For over a decade we have known that event recurrence varies from PAP withdrawal studies, and the surgical literature has clearly indicated the need for measuring all of sleep to understand PAP effectiveness given the issues surrounding compliance.15

Objective apnea burden assessment requires use of a device to estimate event recurrence during off-PAP time, though predictions can be made easily under the “worst-case” assumption of resumed baseline severity.16 Options could include oximetry with analysis that goes beyond just desaturations (eg, arousal analysis form heart rate), the WatchPAT15 (Itamar Medical, Caesarea, Israel), or any respiration monitor that can be used for multiple nights and can be worn concurrently with PAP (standard HSAT devices cannot easily be used in this way). Electrocardiography-based cardiopulmonary coupling can estimate stable breathing during off-therapy and on-therapy periods. It is possible that several other wearable devices can provide warning signs of the quality of sleep when off therapy.


Current adaptive ventilators estimate residual disease using the tidal volume signal (typically requiring 50% reduction to tag events). This signal is the combination of patient + machine, thus, a relative contribution of 10% to 90% looks identical to 90% to 10%, but might represent different hemodynamics and arousal status. The tidal volume signal only reaches thresholds of event detection when this compensation fails. Thus, there can be severe ongoing periodic breathing, reflected by the pressure waveform (“pressure cycling”), whereas the AHI detection algorithm yields a minimum or even a zero value. With an analogy to auditory noise cancellation, the device is cancelling the variations in tidal volume but the driving “noise,” the high loop gain-based periodic breathing, continues unabated. Moreover, adaptive ventilation can cause substantial patient-ventilatory desynchrony,17 which remains unidentified. In the SERVE-HF trial, up to a device detected AHITIDAL 10 events/h of use was taken as a success, which likely overlooked a large degree of ongoing unrecognized periodic breathing, in addition to the basic scoring issues already mentioned.


Criteria for detection of abnormal breathing can be argued, but stable breathing is so distinct that an alternate approach would be to quantify this dimension. For CPAP and nonadaptive bi-level pressure, the flow signal would be sufficient to analyze, whereas for adaptive ventilation, for the aforementioned reasons, both flow and pressure signals would need integration. Inspiratory and expiratory time kinetics may also provide useful measures of respiratory stability.


Optimal therapy for sleep apnea requires the following: (1) Appropriate disease phenotyping, which can affect treatment choice and outcome. For example, current scoring is skewed toward “obstructive” designations despite the accumulating data describing discernable polysomnographic and signal-analytic high loop gain and sleep fragmentation/ arousal propensity phenotypes.18 Such phenotypes respond differently to PAP than pure obstructive disease. (2) Effective phenotype targeted therapy1921—current practice is to treat everyone with CPAP or autoPAP, assuming that it works with perfect efficacy. Adjunctive approaches (acetazolamide, oxygen, sedatives, hypocapnic minimization)2224 are rarely considered even when there is substantial non-obstructive disease (for example, allowing up to 50% of events being central, an already skewed scoring convention, to qualify as “OSA”). (3) Therapeutic devices need to generate accurate metrics of efficacy. The current devices have clear limitations here, but can be resolved in the short term with manual scoring (and in the long-term with better algorithms). (4) The true effectiveness of therapy needs to be dynamically assessed and tracked, which includes breathing assessments during on- and off-PAP sleep. (5) Downstream phenotypes require assessment and tracking, to establish the need for a second set of adjunctive therapies, targeting inflammation, dysglycemia, cognitive impairment, mood impairment, persistent nondipping of blood pressure, and endothelial dys-function, for example. We currently fail to optimize all of these dimensions.

Unless and until the issues of PAP efficacy and PAP effectiveness are addressed, we are fighting an uphill battle to demonstrate clinical effectiveness of this important treatment modality. Failure to do so will inevitably render clinical trials underpowered to reach their endpoints, and will result in errors of omission and commission in PAP management. For example, consider a patient who sleeps 8 hours per night, but uses PAP at only the minimum amount for coverage: 4 hours per night on 70% of nights. This amounts to 35% of total sleep duration, but insurance will cover PAP. By comparison, a similar 8-hour sleeper who uses PAP every other night, but for the entire night, will have their PAP confiscated because it does not meet the 4 h/70% rule, yet it represents 50% of total sleep duration—substantially more coverage than for the first patient. Clinical trials might consider the first patient a success and the second a failure, when in fact the opposite classification might be true. In clinic, the first patient might be prescribed a stimulant if sleepiness persisted “despite compliant PAP use,” without distinguishing whether the off-PAP resumption of OSA might be the explanation.

There is a perfect opportunity for industry and relevant associations such as the American Academy of Sleep Medicine, the American Thoracic Society, the American College of Chest Physicians, and other interested parties from the United States and elsewhere, to host a working group/conference that directly addresses the issue of estimation and tracking, formulating guidelines that are applicable to all manufacturers (just as there are recommendations for sleep recording montages). In the interim, the raw data must be viewed, regardless of the device, just as it is an expectation that the polysomno-gram be directly viewed in its entirety, prior to signing off on the interpretation of the results.


Institutions where work was performed: Beth Israel Deaconess Medical Center, Massachusetts General Hospital. Financial support: Beth Israel Deaconess Medical Center Chief Academic Officer's Innovation Grant. Dr. Thomas discloses: (1) Patent for a device to regulate CO2 in the positive airway pressure circuit, for treatment of central/complex apnea. (2) Patent and license for a ECG-based method to phenotype sleep quality and sleep apnea (to MyCardio, LLV, through Beth Israel Deaconess Medical Center). (3) Patent, past consultant – DeVilbiss-Drive, CPAP auto-titrating algorithm. (4) GLG Councils – general sleep medicine consulting. Dr. Bianchi discloses: Dr. Bianchi has received funding from the Department of Neurology, Massachusetts General Hospital, the Center for Integration of Medicine and Innovative Technology, the Milton Family Foundation, the MGH-MIT Grand Challenge, and the American Sleep Medicine Foundation. Dr. Bianchi has a patent pending on a home sleep monitoring device. Dr. Bianchi has research contracts with MC10 and Insomnisolv, a consulting agreement with McKesson and International Flavors and Fragrances, serves as a medical monitor for Pfizer, received payment for educational material from Oakstone Publishing, and has provided expert testimony in sleep medicine.


Thomas RJ, Bianchi MT. Urgent need to improve PAP management: the devil is in two (fixable) details. J Clin Sleep Med. 2017;13(5):657–664.


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