A Minimum Set of Physiological Parameters to Diagnose Obstructive Sleep Apnea Syndrome Using Non-Invasive Portable Monitors. A Systematic Review
Abstract
:1. Introduction
1.1. Review Questions
1.1.1. Main Review Question (MRQ)
1.1.2. Specific Review Questions (SRQ)
- Does the outcome (OSA detection) improve if the number of psychological parameters measured increases?
- What are the main requirements for the application of an in-home medical device to diagnose sleep apnea?
- Is there a set of minimum physiological signals that distinguish between detection of sleep and arousal?
- How does it affect the outcome (OSA detection) when PMs do not include oximetry measurement?
- What physiological signals are included in those PMs that meet the criteria of positive likelihood ratios (LR+) of ≥5 and sensitivities (Sen) of ≥0.825?
2. Materials and Methods
2.1. Eligibility Criteria
- ▪
- Study type: randomized controlled and clinical trials, research and review articles, and conference publications, along with clinical guidelines.
- ▪
- Population: studies with adult (>18) patients referred to sleep clinics with symptoms suggestive of OSA.
- ▪
- Grouping of studies: Differentiation between review publications and any other type of publication at the end of the search.
- ▪
- Outcome: set of physiological parameters of PM (preferably type III or type IV).
- ▪
- Exclusion criteria are shown as following:
- ▪
- The articles are not in English or German.
- ▪
- Published data are not available.
- ▪
- Studies are not related to monitoring or diagnosing OSA using a PM.
- ▪
- Studies of which the publication dates are older than 10 years when the systematic review is performed (2011–2021). Collop et al. conducted a review of studies covering PMs published prior to 2011 [10].
- ▪
- Studies where there are an underlying diseases and are not entirely focused on OSA. The accuracy of PMs for the detection of OSA may be affected, if there are comorbid medical conditions such as pulmonary disease, neuromuscular disease, or congestive heart failure.
- ▪
- PM (preferably type III or type IV) with a sensitivity of <0.825.
2.2. Search Strategy and Information Sources
2.3. Selection Process and Data Extraction
2.4. Assessment of the Risk of Bias
2.5. Synthesis Methods
3. Results
3.1. Study Selection
3.2. Study Characteristics and Individual Publications
3.3. Synthesis Results and Questions of Interest
3.3.1. MRQ: Is There a Minimum Set of Non-Invasive Parameters Measured to Diagnose, Detect or Monitor Sleep Apnea by In-Home PM?
3.3.2. SRQ-1: Does the Outcome (OSA Detection) Improve if the Number of Psychological Parameters Measured Increases?
3.3.3. SRQ-2: What Are the Main Requirements for the Application of an In-Home Medical Device to Diagnose Sleep Apnea?
3.3.4. SRQ-3: Is There a Set of Minimum Physiological Signals That Distinguish between the Detection of Sleep and Arousal?
3.3.5. SRQ-4: How Does It Affect the Outcome (OSA Detection) When PMs Do Not Include Oximetry Measurements?
3.3.6. SRQ-5: What Physiological Signals Were Included in Those PMs That Met the Criteria of LR+ of ≥5 and Sensitivities (Sen) of ≥0.825?
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Publication | SCOPER Cat | Sen | Spe | AHI | Pop | Type Device |
---|---|---|---|---|---|---|
Jané, R. et al., 2011 [15] | R5A1 | 83 | 100 | 15 | 35 | Research |
Driver et al., 2011 [16] | S3C4O1xP2E4R2 | 97 | 67 | 5 | 73 | Commercial |
80 | 97 | 15 | ||||
70 | 100 | 30 | ||||
Nigro et al., 2011 [17] | R2 | 89.3 | 60 | 5 | 96 | Commercial |
76.7 | 83 | 15 | ||||
88.5 | 95.3 | 30 | ||||
Cheliout-Heraut et al., 2011 [18] | S3O1xP2E2Rx | 83.6 | 81.8 | 5 | 90 | Commercial |
15 | ||||||
30 | ||||||
Oktay et al., 2011 [19] | R2 | 90 | 76.9 | 5 | 53 | Commercial |
79 | 88.2 | 15 | ||||
66.7 | 95.9 | 30 | ||||
Ferré et al., 2012 [20] | S2C4OxP2ExR2 | 91 | 77 | 5 | 68 | Commercial |
86 | 97 | 15 | ||||
61 | 96 | 30 | ||||
Weimin et al., 2013 [21] | S3C2O1x | 95.8 | 100 | 5 | 28 | Commercial |
93.7 | 91.7 | 15 | ||||
85.7 | 100 | 30 | ||||
Masa et al., 2013 [22] | R2 | 94 | 35 | 5 | 595 | Commercial |
80 | 83 | 15 | ||||
Pereira et al., 2013 [23] | O1xPxE1R2 | 87 | 67 | 5 | 128 | Commercial |
77 | 95 | 15 | ||||
50 | 93 | 30 | ||||
Kobayashi et al., 2013 [24] | O1xC4P2R5 | 100 | 66.7 | 5 | 60 | Commercial |
96.9 | 90.5 | 15 | ||||
Meissner et al., 2014 [25] | O1xR2E1 | 87.5 | 80 | 5 | 23 | Commercial |
Cairns et al., 2014 [26] | S3O1xP2E1R2Ax | 100 | 70 | 5 | 32 | Commercial |
92 | 85 | 15 | ||||
Fredheim et al., 2014 [27] | C4O1xR2 | 93 | 71 | 5 | 99 | Commercial |
94 | 94 | 15 | ||||
90 | 1.0 | 30 | ||||
Garg et al., 2014 [28] | S3C2O1x | 96 | 43 | 5 | 75 | Commercial |
92 | 77 | 15 | ||||
Rodriguez-Villegas et al., 2014 [29] | R5Ax | 89 | 100 | - | 30 | Research |
Levendowski et al., 2015 [30] | P2R5A1 | 85 | 90 | 5 | 24 | Commercial |
100 | 80.8 | 15 | ||||
de Vries et al., 2015 [31] | C4O1xR2 | 98.2 | 60.0 | 5 | 90 | Commercial |
92.9 | 91.9 | 15 | ||||
Zou et al., 2015 [32] | C4O1xR2 | 80.28 | 95.45 | 5 | 93 | Commercial |
87.04 | 84.62 | 15 | ||||
94.87 | 92.59 | 30 | ||||
Alshaer et al., 2015 [33] | R5A1 | 98.1 | 82.8 | 5 | 135 | Commercial |
77.4 | 97.3 | 15 | ||||
65.6 | 100 | 30 | ||||
Gutiérrez-Tobal et al., 2016 [34] | O1 | 90.6 | 80 | 5 | 320 | Commercial |
89.2 | 76.5 | 15 | ||||
63.9 | 89.1 | 30 | ||||
Alakuijala et al., 2016 [35] | R5A1 | 93.3 | 35.1 | 15 | 211 | Commercial |
Nagubadi et al., 2016 [36] | S3O1xE4R1 | 69 | 87 | 15 | 71 | Commercial |
87 | 66 | 30 | ||||
Ryan, C.M. et al., 2016 [37] | R5A1 | 90 | 84.6 | 15 | 23 | Commercial |
100 | 85.7 | 30 | ||||
Álvarez et al., 2016 [38] | O1 | 94.2 | 69.6 | 15 | 320 | Commercial |
Durán-Cantolla et al., 2017 [39] | S3C4O1P2E4R2A2 | 88.2 | 72.7 | 5 | 28 | Commercial |
70.0 | 94.4 | 15 | ||||
100 | 92.6 | 30 | ||||
Xu et al., 2017 [40] | S3O1xP2E1R2Ax | 95 | 69 | 5 | 80 | Commercial |
93 | 85 | 15 | ||||
63 | 93 | 30 | ||||
Barbieri et al., 2018 [41] | R5 | 83.3 | 60 | 30 | 21 | Commercial |
Gumb et al., 2018 [42] | O1 | 85.9 | 76.5 | 5 | 178 | Research |
Mosquera-López et al., 2018 [43] | P2R5 | 81.82 | 91.7 | 15 | 14 | Commercial |
Massie et al., 2018 [44] | S4C2O1xP2 | 98 | 80 | 5 | 101 | Commercial |
97 | 83 | 15 | ||||
90 | 97 | 30 | ||||
Weinreich et al., 2018 [45] | P2R5 | 97.9 | 41.7 | 5 | 57 | Commercial |
90.6 | 71.0 | 15 | ||||
Magnusdottir et al., 2018 [46] | C3 | 89 | 79 | 15 | 47 | Commercial |
Araújo et al., 2018 [47] | R2 | 81.8 | 61.5 | 5 | 35 | Research |
83.3 | 91.3 | 15 | ||||
Bonnesen et al., 2018 [48] | P2A1 | 100 | - | 5 | 23 | Commercial |
92.3 | - | 15 | ||||
Faßbender et al., 2018 [49] | O1xR2 | 100 | 44 | 5 | 48 | Research |
92 | 77 | 15 | ||||
Mosquera-López et al., 2019 [50] | P2R5 | 88.9 | 76.5 | 15 | 14 | Commercial |
Chang et al., 2019 [51] | S3C4O1xP2E1R2Ax | 95 | 78 | 5 | 90 | Commercial |
74 | 98 | 15 | ||||
58 | 98 | 30 | ||||
Hayano et al., 2020 [52] | C5 | 82 | 89 | 15 | 41 | Commercial |
Fitzpatrick et al., 2020 [53] | P2R5A1 | 85 | 48 | 5 | 233 | Commercial |
59 | 96 | 15 | ||||
Smith et al., 2020 [54] | O1xR2 | 82 | 92 | 15 | 100 | Research |
Mlynczak et al., 2020 [55] | S3P2A1 | 96 | 76 | 15 | 30 | Research |
Saha et al., 2020 [56] | S3P2R5A1 | 93.12 | 56.06 | 5 | 69 | Commercial |
91.42 | 89.29 | 15 | ||||
89.70 | 98.03 | 30 | ||||
Yamada et al., 2020 [57] | O1xP2R1,5A1 | 82.8 | 76 | 5 | 387 | Commercial |
75.8 | 80.4 | 30 | ||||
Dzieciolowska-Baran et al., 2020 [58] | C4O1xExRx | 91 | 95 | 15 | 68 | Research |
Ferrer-Lluis et al., 2020 [59] | S3P2 | 90 | - | 15 | 13 | Research |
Publication | Objective | Type of Publication |
---|---|---|
Hesselbacher et al., 2011 [11] | Discussing the technical aspects and options available for portable home testing devices to diagnose sleep apnea. | Review article |
Berry et al., 2012 [60] | Polysomnography (PSG), portable monitoring, and actigraphy when it comes to detecting OSA. | Book chapter |
Shayeb et al., 2014 [61] | Systematic review and meta-analysis of comparative studies of level 3 versus level 1 sleep tests in adults with suspected sleep-disordered breathing. | Review article |
Dawson et al., 2015 [62] | Comparison between the ability of the oxygen desaturation index (ODI) based on oximetry alone with a standalone pulse oximeter (SPO) and the respiratory disturbance index (RDI) to predict the AHI. | Research article |
Vat et al., 2015 [4] | Investigating the performance of four different hypopnea scoring criteria, using 3% or 4% oxygen desaturation levels, with or without PWA drops as surrogates for electroencephalogram (EEG) arousals, and determine the impact of the measured versus the reported sleep time on OSA diagnosis. | Research article |
Cooksey et al., 2016 [63] | Discussing society guidelines and recent research in the growing field of portable monitoring for OSA detection. | Review article |
Bianchi et al., 2017 [64] | Studying the feasibility of home sleep apnea tests (HSAT) kits as they are known to underestimate the severity of sleep apnea, in part due to lack of sleep staging to provide total sleep time. | Research article |
Jiang et al., 2018 [65] | Evaluating the combination modes of key physiological signals collected by portable sensor modules for OSA detection compared to PSG. | Research article |
Light et al., 2018 [66] | Validating a single-channel frontal EEG for scoring sleep versus wake against full EEG during PSG and then examining the utility of adding this single-channel EEG to standard HSAT to prevent false-negative results. | Research article |
Mendonça et al., 2018 [3] | Reviewing publications that show the performances of different devices for the ambulatory diagnosis of sleep apnea. | Review article |
Lachapelle et al., 2018 [67] | Testing the hypothesis that scoring hypopneas using heart rate accelerations as a surrogate marker for cortical arousal (autonomic hypopnea; AnH) improves the accuracy of HSAT for OSA diagnosis, using PSG as the diagnostic gold standard. | Research article |
Collop et al., 2020 [68] | HSAT overview. | Book chapter |
Kwon et al., 2021 [69] | Summarizing recent results in the development of novel portable and wearable sensors for sleep monitoring. | Review article |
Publication | SCOPER Cat | AHI-5-Sen | AHI-5-Spe | Pop | LR+ | LR− |
---|---|---|---|---|---|---|
Weimin et al., 2013 [21] | S3C2O1x | 95.80 | 100.00 | 28 | inf | 99.04 |
Levendowski et al., 2015 [30] | P2R5A1 | 85.00 | 90.00 | 24 | 8.5 | 99.05 |
Zou et al., 2015 [32] | C4O1xR2aAx | 80.28 | 95.45 | 93 | 17.6 | 99.15 |
Alshaer et al., 2015 [33] | R5A1 | 98.10 | 82.80 | 135 | 5.7 | 98.8 |
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Serrano Alarcón, Á.; Martínez Madrid, N.; Seepold, R. A Minimum Set of Physiological Parameters to Diagnose Obstructive Sleep Apnea Syndrome Using Non-Invasive Portable Monitors. A Systematic Review. Life 2021, 11, 1249. https://doi.org/10.3390/life11111249
Serrano Alarcón Á, Martínez Madrid N, Seepold R. A Minimum Set of Physiological Parameters to Diagnose Obstructive Sleep Apnea Syndrome Using Non-Invasive Portable Monitors. A Systematic Review. Life. 2021; 11(11):1249. https://doi.org/10.3390/life11111249
Chicago/Turabian StyleSerrano Alarcón, Ángel, Natividad Martínez Madrid, and Ralf Seepold. 2021. "A Minimum Set of Physiological Parameters to Diagnose Obstructive Sleep Apnea Syndrome Using Non-Invasive Portable Monitors. A Systematic Review" Life 11, no. 11: 1249. https://doi.org/10.3390/life11111249