Validation of Smart Mask V1 System: A Clinical Study Comparing a System for Detecting Sleep Stages and Arousals to Manual Polysomnography Scoring
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 72
- 试验地点
- 6
- 主要终点
- Agreement Between Smart Mask V1 System and EnsoSleep in Detecting Arousals and Arousal Index
研究概览
简要总结
The objective of this clinical study is to evaluate the accuracy of the Smart Mask V1 System (herein 'Smart Mask') in measuring sleep stages-Stage N1/N2, Stage N3, Rapid Eye Movement (herein 'REM'), and WAKE-arousals, and the Arousal Index in adults diagnosed with sleep-disordered breathing, such as obstructive sleep apnea (herein 'OSA'). The Smart Mask operates in concert with a Wireless Access Module (herein 'WAM'), which is connected to a standard positive air pressure (herein 'PAP') device used in the treatment of OSA. Collectively the Smart Mask and WAM operate neural network classifier algorithms to determine sleep stages, arousals, and Arousal Index. These algorithms are coded into an embedded software system called the Sleep Staging and Arousal Module (herein 'SSAM') that operates directly on the WAM. The SSAM processes the following parameters, collected while the participant is asleep: 1) instantaneous values of pulse rate, determined from embedded optical sensors within the Smart Mask that measure photoplethysmogram waveforms (herein 'PPG'); and 2) full-resolution flow waveforms measured by sensors within the PAP device and retrieved by the WAM.
During the study, volunteer participants (preferably those with OSA) will undergo an overnight sleep study in sleep testing facility located at three separate clinical sites. The test device (comprising the SSAM operating on the WAM) will retrospectively determine sleep stages and arousals, after the participant's sleep session has concluded. To evaluate the accuracy of the test device, its values of sleep stages, arousals, and Arousal Index will be compared to those parameters determined by polysomnography (herein 'PSG', a recognized gold-standard reference) and EnsoSleep (a FDA-cleared predicate device, and specifically a software package that uses artificial intelligence (AI) to determine sleep stages and arousals). Each volunteer participant will wear an FDA-cleared wrist-worn pulse oximeter called the 'Checkme O2' which generates data (specifically PPG waveforms and values of SpO2 and pulse rate) for the EnsoSleep cloud-based software platform.
The main questions this study aims to answer are:
- Can the Smart Mask accurately identify different sleep stages compared to the EnsoSleep device?
- Can the Smart Mask accurately identify sleep arousals and calculate the Arousal Index compared to the EnsoSleep device?
Answers to these questions will be derived through comparative statistical analysis involving the test device, the gold-standard PSG reference, and the FDA-cleared predicate device, employing methodologies similar to those used in the validation of the EnsoSleep. The study will include two cohorts. The first cohort will include approximately 75 participants from a single clinical site and will be used for device training purposes. The second cohort will consist of approximately 72 different participants, and will be used to validate the test device. Participants in the second cohort will be distributed roughly evenly across two separate clinical sites.
详细描述
CLINICAL APPROACH: To validate the SSAM's performance in determining sleep stages (N1/N2, N3, REM, Wake), arousals, and Arousal Index, two separate clinical studies are proposed. Both studies will use the same success criteria: to demonstrate that SSAM's performance is not statistically different from, or is statistically better, compared to the performance of its predicate device. More specifically, the studies, which will take place simultaneously, will compare the SSAM-derived measurements to those obtained from both a gold-standard (manual PSG scoring) and its predicate device (EnsoSleep, which uses AI-powered automated scoring). The proposed clinical approach is essentially identical to that used for Smart Mask's predicate device, EnsoSleep, as described in its 510(k) Summary. It will use the same number of participants, statistical analyses, and success criteria, as summarized below.
The SSAM detects autonomic arousals by processing cardio-respiratory signals, whereas PSG allows the scoring of cortical arousals through the measurement of brain electrical activity and other physiological signals. Despite these differences, PSG is proposed as the gold-standard reference based on the following considerations:
- For determining sleep stages and arousals, SSAM's predicate device (EnsoSleep) was validated using PSG.
- For measuring autonomic arousals and, from these, an arousal index, there is no agreed-upon gold-standard reference for validating these measurements.
- Inter-observer error for PSG-determined cortical arousals is imperfect and suffers limited reliability, with recent studies reporting ICC values for the number of arousals and the arousal index between 0.52 and 0.802.
- Previous studies by Ross et al., which were conducted with a similar neural network-based classifier used by SSAM for arousal detection, indicates that agreement between autonomic arousals and cortical arousals are characterized by an ICC of 0.73, which is within the inter-observer error for PSG-determined cortical arousals.
Based on these considerations, the proposed validation study will be conducted using two clinical cohorts tested at three sleep testing facilities. The first cohort will be used for training the neural-network classifiers used by SSAM (for both sleep stages and arousals), and will feature about 75 participants tested at a first clinical site. The second cohort will be used to validation the neural-network classifiers used by SSAM, and will feature at least 72 participants tested at a second and third clinical site, both independent of the first clinical site. The sample size for the second, validation cohort has been powered using a statistical model, and is consistent with the sample size used in the EnsoSleep validation study. All participants used in both the training and validation cohorts will be representative of the SSAM's intended-use population (i.e. patients suffering sleep-disordered breathing, e.g. from central or obstructive sleep apnea).
The study will adhere to guidelines published by the American Academy of Sleep Medicine ('AASM'). All relevant documentations will be submitted for review and approval by a third-party Institutional Review Board ('IRB') and an IRB associated directly with one of the study sites. Study activities will not begin until the IRBs approve the clinical protocol and Informed Consent Form associated with the study.
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Single Group
- 主要目的
- Supportive Care
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Subjects must be at least 18 years of age at screening.
- •Subjects are individuals diagnosed with sleep-disordered breathing who have been prescribed PAP therapy or referred for a PSG.
- •Subjects must be willing and able to comply with the study requirements, which include using test, reference and predicate devices (if needed), completing training, interacting with study personnel and filling out questionnaires.
- •Subjects must be fluent in English.
- •Subject must be willing to undergo the screening and informed consent process prior to enrollment in the study.
- •Subjects must be deemed suitable candidates for this study based on the PI's evaluation of their condition and the features of the investigational device being tested.
排除标准
- •Subjects unable or unwilling to wear a PAP mask as required for the study.
- •Subjects currently employed by, previously employed by or in any way affiliated (consulting, etc.) with any manufacturer or provider of PAP equipment and/or services.
- •Subjects who are pregnant.
- •Subjects with or requiring an implantable device, such as an electronic defibrillator, pacemaker, or other device.
- •Subjects considered by the PI to be medically unsuitable for study participation.
研究组 & 干预措施
Phase I - Training
Participants in this arm will undergo a one-night, in-lab sleep study while wearing the Smart Mask and WatchPAT while receiving positive airway pressure ('PAP') therapy. Standard polysomnography ('PSG') data will be collected and stored. Three independent registered PSG technologists ('RPSGTs') will manually score the data to estimate sleep stages and arousals, following the guidelines in 'The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications', which is the definitive reference for the scoring of PSG and HSATs. PSG data will also be uploaded to EnsoSleep's cloud-based software system. Data from this phase will be used solely to train and refine the Smart Mask's neural network algorithms for detecting sleep stages and arousals. No algorithm validation will occur in this phase.
干预措施: SSAM (Device)
Phase I - Training
Participants in this arm will undergo a one-night, in-lab sleep study while wearing the Smart Mask and WatchPAT while receiving positive airway pressure ('PAP') therapy. Standard polysomnography ('PSG') data will be collected and stored. Three independent registered PSG technologists ('RPSGTs') will manually score the data to estimate sleep stages and arousals, following the guidelines in 'The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications', which is the definitive reference for the scoring of PSG and HSATs. PSG data will also be uploaded to EnsoSleep's cloud-based software system. Data from this phase will be used solely to train and refine the Smart Mask's neural network algorithms for detecting sleep stages and arousals. No algorithm validation will occur in this phase.
干预措施: Polysomnography (Diagnostic Test)
Phase I - Training
Participants in this arm will undergo a one-night, in-lab sleep study while wearing the Smart Mask and WatchPAT while receiving positive airway pressure ('PAP') therapy. Standard polysomnography ('PSG') data will be collected and stored. Three independent registered PSG technologists ('RPSGTs') will manually score the data to estimate sleep stages and arousals, following the guidelines in 'The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications', which is the definitive reference for the scoring of PSG and HSATs. PSG data will also be uploaded to EnsoSleep's cloud-based software system. Data from this phase will be used solely to train and refine the Smart Mask's neural network algorithms for detecting sleep stages and arousals. No algorithm validation will occur in this phase.
干预措施: EnsoSleep (Device)
Phase II - Validation
Participants in this arm will undergo a single overnight, in-lab sleep study while wearing the Smart Mask and WatchPAT device, while receiving PAP therapy. Standard PSG data will be collected and stored. Data from the Smart Mask will be processed using trained neural networks to detect sleep stages (N1/N2, N3, REM, Wake), arousals, and calculate the Arousal Index. These outputs will be compared to: 1) manual scoring of PSG data by three independent, blinded RPSGTs; and 2) results from the predicate device, EnsoSleep. Data from this phase will be used validate the Smart Mask's neural network algorithms for detecting sleep stages and arousals.
干预措施: SSAM (Device)
Phase II - Validation
Participants in this arm will undergo a single overnight, in-lab sleep study while wearing the Smart Mask and WatchPAT device, while receiving PAP therapy. Standard PSG data will be collected and stored. Data from the Smart Mask will be processed using trained neural networks to detect sleep stages (N1/N2, N3, REM, Wake), arousals, and calculate the Arousal Index. These outputs will be compared to: 1) manual scoring of PSG data by three independent, blinded RPSGTs; and 2) results from the predicate device, EnsoSleep. Data from this phase will be used validate the Smart Mask's neural network algorithms for detecting sleep stages and arousals.
干预措施: Polysomnography (Diagnostic Test)
Phase II - Validation
Participants in this arm will undergo a single overnight, in-lab sleep study while wearing the Smart Mask and WatchPAT device, while receiving PAP therapy. Standard PSG data will be collected and stored. Data from the Smart Mask will be processed using trained neural networks to detect sleep stages (N1/N2, N3, REM, Wake), arousals, and calculate the Arousal Index. These outputs will be compared to: 1) manual scoring of PSG data by three independent, blinded RPSGTs; and 2) results from the predicate device, EnsoSleep. Data from this phase will be used validate the Smart Mask's neural network algorithms for detecting sleep stages and arousals.
干预措施: EnsoSleep (Device)
结局指标
主要结局
Agreement Between Smart Mask V1 System and EnsoSleep in Detecting Arousals and Arousal Index
时间窗: One overnight sleep study session (6-8 hours per participant)
This outcome is also based on a non-inferiority analysis, and assesses the agreement between the test device's measurements arousals and arousal index (number of arousals per hour of sleep) and those from PSG and EnsoSleep, a software system that automatically scores PSG data to determine these parameters. The study will be conducted similarly to that described above, substituting arousals and arousal index for the various sleep stages. The outcome for this comparison will be determined similarly to that described above using NA, PA, OA, and Evals 1 and 2 involving test, predicate, and reference devices. Secondary outcomes will be determined using more quantitative approaches (e.g., Bland-Altman and Deming regression plots) to determine errors between the test device and reference devices. For this analysis, the outcome will evaluate if relative errors for the test device's measurements of arousals and arousal indices are within clinically accepted ranges.
Agreement Between Smart Mask V1 System and EnsoSleep in Detecting Arousals and Arousal Index
时间窗: One overnight sleep study session (6-8 hours per participant)
This outcome is also based on a non-inferiority analysis, and assesses the agreement between the test device's measurements arousals and arousal index (number of arousals per hour of sleep) and those from PSG and EnsoSleep, a software system that automatically scores PSG data to determine these parameters. The study will be conducted similarly to that described above, substituting arousals and arousal index for the various sleep stages. The outcome for this comparison will be determined similarly to that described above using NA, PA, OA, and Evals 1 and 2 involving test, predicate, and reference devices. Secondary outcomes will be determined using more quantitative approaches (e.g., Bland-Altman and Deming regression plots) to determine errors between the test device and reference devices. For this analysis, the outcome will evaluate if relative errors for the test device's measurements of arousals and arousal indices are within clinically accepted ranges.
次要结局
未报告次要终点