Uncovering the Truth Behind CPAP Efficacy

Uncovering the Truth Behind CPAP Efficacy
In the realm of sleep medicine, positive airway pressure (PAP) therapy stands as the cornerstone treatment for obstructive sleep apnea (OSA), boasting high efficacy when utilized effectively. Yet, the accuracy of PAP machines in detecting respiratory events and calculating the apnea/hypopnea index (AHI) remains a subject of scrutiny, particularly in comparison to alternative diagnostic methods. This study delves into the accuracy of PAP devices for respiratory event detection, leveraging data collected from Wesper Home Sleep Apnea Testing (HSAT) to provide a comprehensive evaluation of PAP performance.

A cohort of five individuals, utilizing both PAP machines and Wesper HSAT, volunteered to share their data, enabling a detailed comparison of respiratory event detection between the two modalities. Notably, findings revealed a consistent pattern of underestimation by PAP machines, with 96% of tests showcasing lower reported AHIs compared to Wesper HSAT. Intriguingly, in a majority of cases, PAP devices inaccurately categorized patients as having a normal AHI, while Wesper HSAT indicated the persistence of sleep apnea, highlighting the potential for misdiagnosis and the importance of thorough assessment in sleep care.

The implications of these findings extend beyond diagnostic accuracy, shedding light on the potential consequences of relying solely on PAP readouts for treatment monitoring. The prevalence of underestimation by PAP machines raises concerns regarding the efficacy of long-term therapy management and underscores the need for supplementary evaluation methods. With Wesper HSAT offering enhanced accuracy and longitudinal data collection capabilities, sleep care providers are urged to consider alternative avenues for assessing PAP efficacy and ensuring optimal patient outcomes in the management of sleep apnea.




Sleep apnea is the second most common sleep disorder behind insomnia, and affects approximately 26% of men and 10% of women in the United States. The most prescribed therapy for sleep apnea is positive airway pressure (PAP) therapy, which reduces obstructive breathing events by delivering pressurized air into the upper airways though a hose and masks. PAP therapy is considered a first line treatment because it has high success when used appropriately.

Most modern PAP machines contain an internal computer that collects data about the patient such as how frequently they use their device, how long they use their device, if there are any leaks, and how often they remove their mask. The PAP machine also detects respiratory events and calculates the apnea/hypopnea index (AHI) to determine how well it’s improving the patient’s sleep apnea. 

PAP machines detect breathing events by analyzing measurements of airflow, vibration, and airflow profile flattening. Event detection is also used by autotirating (APAP) machines to regulate changes in delivered pressure [1]. PAP machines are not able to detect SPO2, and therefore cannot calculate desaturation events. 

Sleep physicians rely on the PAP data to assess if the device is successfully improving their patient’s sleep apnea. Health insurers and medicare typically use the data to determine if they should continue covering the therapy. More recently, patients have also gained access to their data though mobile apps and open source programs, like OSCAR.

To date, few clinical studies have evaluated the accuracy of PAP machines for detecting breathing events and calculating AHI. Recent studies comparing PAP device detection to polysomnography (PSG) showed some agreement, however PAP machines tended to overestimate the AHI at low AHI values and underestimate the AHI at higher AHI values [1]. A recent 2022 longitudinal study found substantial differences between manual and automated event estimates during PAP therapy, and events were commonly underestimated. Factors associated with inaccuracy included sex, air leaks, and the amount of unstable breathing during [2]. 

Nearly all studies evaluating event detection by PAP machines have compared the collected PAP data to a manually scored, concurrent PSG. The recent introduction of highly sensitive home sleep tests (HST) that are affordable, comfortable, and easy to use, have introduced a new way to assess PAP accuracy.

Wesper Lab is type 3 HST that is capable of long-term longitudinal testing. Wesper detects respiratory events though a combination of factors, including respiratory effort, airflow volume, and SPO2. Clinical validation showed a very high degree of accuracy for AHI detection, with 99% correlation to PSG, making Wesper as accurate as an in-lab sleep study. Due to its ease of use, Wesper patients take more tests on average compared to other HSTs. To date, the most tests a single Wesper user has taken is 369. Repeat testing allows for extensive comparison between Wesper data and PAP data, in users currently using a machine for sleep apnea therapy.

Using Wesper to Assess PAP Respiratory Event Detection Accuracy

To better understand the accuracy of PAP machines for detecting respiratory events, data was collected from PAP devices of Wesper users who volunteered to share their data. Five users participated in the study and a total of 29 Wesper tests and PAP reports were compared. Only tests with consistency between PAP use time and Wesper use time were included. Any tests that showed PAP-reported dysfunction, such as air leaks or excessive mask removed, were excluded from analysis. 



The analysis revealed that PAP machines almost always underestimated breathing events and AHI compared to Wesper, with 96% of tests showing that PAP reported a lower overall AHI. 62.5% of tests showed that PAP underestimated AHI by ≥5 points (Figure 1). The average point difference between PAP reported AHI and Wesper reported AHI was 5.5. The average reported PAP AHI was 1.8 and the average reported Wesper AHI was 7.2 (Figure 2).


Figure 1: The majority of tests showed at least a 5 point difference in PAP-reported AHI vs. Wesper-reported AHI


Figure 2: Average reported PAP AHI and Wesper reported AHI.


In many cases, PAP indicated an AHI within the normal range (AHI <5), while Wesper showed a mild (AHI: 5-14.9) to moderate (AHI: 15-29.9), demonstrating sleep apnea persistence (Figures 3-5). The percentage of tests where PAP showed a normal AHI and Wesper showed an abnormal AHI was 75%.


Figure 3: An example of a single individual’s PAP reported AHI and Wesper reported AHI.


Figure 4: A second example of a single individual’s PAP reported AHI and Wesper reported AHI. 

Figure 5: A third example of a single individual’s PAP reported AHI and Wesper reported AHI.


Our study showed that PAP machines are largely underestimating patient AHI. Concerning, the majority of tests showed that PAP devices were incorrectly scoring patients as having a normal AHI when their sleep apnea actually persisted.

Relying on PAP readouts alone may cause a false sense of security for sleep physicians and patients. Patients may also not report continuing sleep apnea symptoms if their machines report their breathing is normal.

Sleep care providers should consider using alternative or supplementary methods of evaluating long-term PAP efficacy, such as HSTs like Wesper, which are capable of more accurate testing and longitudinal data collection.

Author Note

This study is ongoing as we await for more Wesper users to volunteer their PAP data. This article will be updated periodically to reflect new findings. If you are a current Wesper user who also uses a PAP machine, or will be starting PAP in the near future, please reach out to sleep.expert@wesper.co if you are interested in participating.




  1. Berry RB, Kushida CA, Kryger MH, Soto-Calderon H, Staley B, Kuna ST. Respiratory event detection by a positive airway pressure device. Sleep. 2012 Mar 1;35(3):361-7. doi: 10.5665/sleep.1696. PMID: 22379242; PMCID: PMC3274337.

  2. 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 Apr 1;18(4):1121-1134. doi: 10.5664/jcsm.9814. PMID: 34886948; PMCID: PMC8974380.

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