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• W2890965714 abstract "Free AccessSnoringDigital Health and Sleep-Disordered Breathing: A Systematic Review and Meta-Analysis Talita Rosa, MD, MS, Kersti Bellardi, MS, Alonço Viana, MD, MS, Yifei Ma, MS, Robson Capasso, MD Talita Rosa, MD, MS Address correspondence to: Talita Rosa, 864 Ashbury St, Apt 06, San Francisco CA, 94117(415) 670-0665 E-mail Address: [email protected] Global Brain Health Institute, University of California, San Francisco (UCSF), San Francisco, California Search for more papers by this author , Kersti Bellardi, MS Department of Global Health, University of California, San Francisco (UCSF), San Francisco, California Search for more papers by this author , Alonço Viana, MD, MS Graduate Program of Neurology, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Brazil Search for more papers by this author , Yifei Ma, MS Department of Otolaryngology–Head and Neck Surgery, Stanford University, Stanford, California Search for more papers by this author , Robson Capasso, MD Department of Otolaryngology–Head and Neck Surgery, Division of Sleep Surgery, Stanford University, Stanford, California Search for more papers by this author Published Online:September 15, 2018https://doi.org/10.5664/jcsm.7346Cited by:12SectionsAbstractPDFSupplemental Material ShareShare onFacebookTwitterLinkedInRedditEmail ToolsAdd to favoritesDownload CitationsTrack Citations AboutABSTRACTStudy Objectives:Sleep disorders in most individuals remain undiagnosed and without treatment. The use of novel tools and mobile technology has the potential to increase access to diagnosis. The objective of this study was to perform a quantitative and qualitative analysis of the available literature evaluating the accuracy of smartphones and portable devices to screen for sleep-disordered breathing (SDB).Methods:A literature review was performed between February 18, 2017 and March 15, 2017. We included studies evaluating adults with SDB symptoms through the use mobile phones and/or portable devices, using standard polysomnography as a comparison. A qualitative evaluation of studies was performed with the QUADAS-2 rating. A bivariate random-effects meta-analysis was used to obtain the estimated sensitivity and specificity of screening SDB for four groups of devices: bed/mattress-based, contactless, contact with three or more sensors, and contact with fewer than three sensors. For each group, we also reported positive predictive values and negative predictive values for mild, moderate, and severe obstructive sleep apnea (OSA) screening.Results:Of the 22 included studies, 18 were pooled in the meta-analysis. Devices that were bed/mattress-based were found to have the best sensitivity overall (0.921, 95% confidence interval [CI] 0.870, 0.953). The sensitivity of contactless devices to detect mild OSA cases was the highest of all groups (0.976, 95% CI 0.899, 0.995), but provided a high false positive rate (0.487, 95% CI 0.137, 0.851). The remaining groups of devices showed low sensitivity and heterogeneous results.Conclusions:This study evidenced the limitations and potential use of portable devices in screening patients for SDB. Additional research should evaluate the accuracy of devices when used at home.Citation:Rosa T, Bellardi K, Viana A Jr, Ma Y, Capasso R. Digital health and sleep-disordered breathing: a systematic review and meta-analysis. J Clin Sleep Med. 2018;14(9):1605–1620.BRIEF SUMMARYCurrent Knowledge/Study Rationale: The current diagnostic resources available are not meeting the clinical demand for the evaluation of people suffering from sleep-disordered breathing. The use of novel tools and mobile technology has the potential to increase access to diagnostic tools, but the accuracy of such devices in diagnosing sleep-disordered breathing is unknown.Study Impact: The study is the first to assess the literature to show the potential use of novel tools and mobile technology in screening for sleep-disordered breathing in adults. The study evidences the need for further evaluation of such devices in the home environment.INTRODUCTIONSleep-related breathing disorders are highly prevalent and have increasingly received attention from the public, media, and the medical community in recent years.1,2 The prevalence of sleep-disordered breathing (SDB)—including snoring and obstructive sleep apnea (OSA)—is approximately 26% in the adult population worldwide.2 SDB is associated with an increased risk of cardiovascular, metabolic, and psychiatric diseases, and with the rising obesity epidemic, its prevalence and associated sequelae tend to increase.1,2Sleep disorders worldwide in most individuals remain un-diagnosed and without treatment.4 A supervised, laboratory-based polysomnography (PSG) is the gold-standard test for diagnosing SDB. The procedure provides a comprehensive measurement of various physiological parameters to detect and quantify sleep cycles as well as respiratory events. Although portable devices containing limited channels have seen an increased adoption by sleep specialists, health care resources for SDB evaluation and diagnosis have yet to meet the current clinical demand.3,4In resource-constrained environments, such as developing countries, access to specialists who manage sleep disorders is difficult because of the reduced number of trained medical staff, as well as economic and infrastructure constraints.5,6 In the United States, 25% of the available sleep medicine fellowship positions were unfilled in 2014, and the number of board-certified sleep specialists has been decreasing, further hampering access to specialized services.7 Therefore, innovative strategies to reduce barriers for sleep disorders screening and treatment are needed.1,3,8The use of novel tools and mobile technology has the potential to revolutionize the way that health services are delivered, increasing access to health care at a lower cost.8–10 Mobile technology is a fast-growing sector in both developing and developed countries. In 2014, there were 7.06 billion mobile connections worldwide, a number only slightly smaller than the total world population estimates for the same year.11,12 Developers have been actively working on innovations for screening, monitoring, and treating SDB, from questionnaires to more engineered utilization of mobile device sensors, such as motion/actigraphy measurements, audio, and video recording.13–16Initial studies have evaluated possible clinical applicability and usability of new technologies with promising results; however, no studies have attempted to systematically evaluate the available data. In this study, we sought to perform a quantitative and qualitative systematic review of the international literature in order to evaluate current knowledge on the use of smartphones, wearable electronic devices, and consumer devices for the evaluation of snoring and OSA.METHODSSearchWe performed a literature review of articles between February 18, 2017 and March 15, 2017 on the following databases: Em-base, PubMed, Cochrane Central Register of Controlled Trials, Web of Science, and CINHAL. We also conducted a “related article” search in PubMed and a gray literature search with keywords—restricting the search to relevant sites (.org, .edu, .gov) and PDF formats. The search was open to all available languages in these databases.The descriptors used were a combination of index terms (MeSH) and keywords: “mobile application”/exp OR “mobile application” OR “mobile apps”/exp OR “mobile apps” OR “gadgets” OR “mobile phone”/exp OR “mobile phone” OR “health tracker” OR “mhealth”/exp OR mhealth OR “wearable device”/exp OR “wearable device” OR “fitbit” OR “iphone”/exp OR “iphone” OR “android”/ exp OR android OR “cell phones” OR “cellular phones” OR “smartphones” OR “commecial accelerometer” OR “commercial actigraphy” OR “wrist-based” OR “handheld device” AND (“sleep disordered breathing”/ exp OR “sleep disordered breathing” OR “snoring”/exp OR snoring) AND [2007-2017/py]. The keyword combination used to search for gray literature in Google was: (“sleep apnea” OR snoring OR insomnia) AND (“mobile applications” OR “mobile apps” OR “cell phone” OR “mobile phone”). Additionally, we checked the reference lists of selected studies. Two reviewers independently screened titles and abstracts of retrieved citations according to the described inclusion and exclusion criteria. The reviewers then obtained the full text of the relevant studies, evaluated their eligibility, and recorded a list of studies excluded along with a brief explanation for their exclusion. Whenever there was a difference of opinion, a third author (sleep medicine specialist) reviewed the full text and determined the eligibility of the study. The selected articles were submitted to the procedures specified in a PRISMA flowchart (Figure 1).Figure 1: Flowchart of article selection (PRISMA).Quantity of articles in each of the following steps taken to select studies: identification of relevant articles, screening of studies through abstract review, and full-text articles assessment of eligibility.Download FigureThe search results were exported and merged into a database manager (Mendeley, version 1.17.11). A total of 315 titles were identified in the following databases: Embase (63), PubMed (93), Cochrane Central Register of Controlled Trials (14), Web of Science (32), CINHAL (97), and gray literature (16). The reviewers extracted the data from included studies in standardized forms based on the Cochrane Handbook of Systematic Reviews. If they were unable to extract the relevant data from the available reports, they attempted to contact the authors of the articles. The first reviewer added the data into an Excel sheet, while the second checked for data collection errors.Inclusion and Exclusion CriteriaInclusion CriteriaIncluded in this review were studies that reported on adults with SDB symptoms, such as nonrefreshing sleep or excessive sleepiness, decreased concentration or memory loss, snoring, irritability, reduced total sleep time, witnessed apneas, and gasping at night; these studies also had to measure interventions and physiological parameters through the use of internal or external sensors of mobile phones and/or portable devices with the aim of screening and/or diagnosing SDB. Acceptable technology included software applications that can be used in smartphones and other portable, handheld technologies, as well as consumer-level devices or wearable electronic devices that are either commercially available or in development. Because of the novelty of the topic under review, we included randomized and nonrandomized controlled trials, as well as observational studies (cross-sectional, case-control, and prospective cohort studies) that were performed in the past 10 years (2007–2017). Since the use of smartphone technology started its expansion after the iPhone launching in 2007, we think that any study prior to that year would be deemed irrelevant to current practices.Exclusion CriteriaExclusion criteria included studies that looked at interventions using only questionnaire-based software, interventions using one portion of the data obtained by a PSG as the index test (eg, pulse oximetry, electroencephalography), and interventions that looked at a retrospective dataset and not actual patients. Studies that evaluated a validated home sleep apnea test (HSAT) were also excluded from analysis, as validated home tests could already be in clinical use and were not the object of the current study.ComparisonWe included only studies that used in-laboratory PSG or a validated HSAT as a comparison. A qualified physician must have reviewed the PSG or HSAT.17OutcomesAccuracy of OSA Detection and SeverityThe standard diagnosis of OSA is accomplished by quantifying the number of detected respiratory obstructive events (respiratory disturbance index [RDI] or apnea-hypopnea index [AHI] ≥ 5 events/h), as a proportion of total sleep time, in the presence of symptoms.17 The OSA severity can be classified as mild (AHI 5 to < 15 events/h), moderate (AHI 15 to < 30 events/h), or severe (AHI ≥ 30 events/h). We assessed the accuracy of mobile technology and other novel tools in screening OSA at different severity stages when compared to the standard diagnosis. As measures of accuracy for OSA screening, we reported the sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV).Accuracy of Snoring DetectionPrimary or simple snoring can be identified in the PSG by audio recording or nasal pressure measurement in the presence of AHI < 5 events/h.18 We assessed the sensitivity, specificity, and accuracy of the mobile technology and other novel tools to screen patients with primary snoring when compared to the standard diagnosis.Qualitative EvaluationWe used the QUADAS-2 rating to perform a methodological evaluation of the selected studies to assess the risk of bias, and to evaluate the possible sources of heterogeneity.19Statistical AnalysisWe classified studies looking at OSA detection into two different groups according to the standard diagnostic test used: studies in which the index test was compared to PSG, and studies in which the index test was compared to HSAT. Because of the heterogeneity of devices being evaluated, we further classified studies into four additional categories: bed/mattress-based sensors, contactless devices, contact devices with fewer than three sensors, and contact devices with three or more sensors. Studies looking at snoring diagnosis were analyzed separately.When available, we extracted the true positive, true negative, false positive, and false negative results of each severity level evaluated by the index test. We used forest plots to present the sensitivity and specificity as well as their confidence intervals (CIs), which were estimated based on binomial distribution. For each group category of the index tests, summary sensitivity and specificity estimates and their CIs were estimated using bivariate random-effects meta-analysis with underlying joint normal distribution of logit false positive rate and true false negative rate. Additionally, we reported the median values and interquartile range (IQR) of PPV, NPV, positive likelihood ratio (LRp), and negative likelihood ratios (LRn). The pretest probability was based on the prevalence of OSA in the population of the included studies for each group category and severity level. Summary receiver operating characteristics (SROC) curve were presented showing overall results per AHI threshold.20,21 All the analyses were performed using “mada” package in R software (version 3.4.2, R foundation for Statistical Computing, Vienna, Austria).RESULTSWe selected 22 studies, as summarized in a PRISMA flow-chart (Figure 1). Of those, we included 18 articles in the meta-analysis. One article did not have enough power to be included in the analysis (Rofouei et al., n = 1),22 and another did not provide false positive, true positive, false negative, and true negative results (Nakano et al.).13 The studies using a HSAT as the standard test were only qualitatively evaluated due to the small number of articles found. Additionally, the thresholds used in the snoring detection studies were not comparable, and therefore not included in the meta-analysis.The detailed characteristics of all studies are described in Table 1. Among the studies using an in-laboratory PSG as the standard test (n = 20), 6 studied bed/mattress-based devices, 6 studied contactless devices, 5 studied contact devices with fewer than 3 sensors, and 3 studied contact devices with 3 or more sensors. Although in some cases the studies included participants who were suspected of OSA, central apneas, or primary snoring, the authors assessed the ability of the index test to screen sleep-related breathing disorders based on different RDI or AHI thresholds, but did not attempt to detect central apneas as an outcome. The sensitivity and false positive rate of OSA detection and severity classification in studies using laboratory PSG as a comparison are detailed in Table 2.Table 1 Summary of the selected articles.Table 1 Summary of the selected articles.Table 2 Sensitivity and false positive rate of OSA diagnosis and severity classification in studies using laboratory PSG as a comparison.Table 2 Sensitivity and false positive rate of OSA diagnosis and severity classification in studies using laboratory PSG as a comparison.We found one study with HSAT as standard comparison. The study compared the index test with both a PSG and HSAT. Two studies evaluated the accuracy of devices in detecting snoring (Table 1).Studies With Laboratory PSG as Standard ComparisonBed/Mattress-Based SensorsThe studies were performed in Japan, United States, Finland, and Australia. Overall, they evaluated very similar index tests. Three assessed the SD-101 sensor (Tsukahara et al., Agatsuma et al., and Takasaki et al.).23–25 All six studies, including Beattie et al., evaluated devices that used, at a minimum, several pressure sensors that measured respiratory and body movement to estimate AHI.26 Norman et al. evaluated a device that also measured acoustic features,27 and Tenhunem et al. additionally measured heart rate.28 Except for Takasaki et al., where the reference test was not described,25 all studies used the standard channel structure of in-laboratory PSG as a comparison. All studies used a similar recruitment strategy, having selected adults who underwent an evaluation at a sleep center. Demographic data of participants was similar across studies, with a mean age varying from 45.6 to 56 years, and mean body mass index (BMI) varying from 26.6 to 32.3 kg/m2.Among the studies included in the meta-analysis, only two received a QUADAS-2 evaluation with more than one domain presenting a high risk of bias or high risk of applicability issues: Norman et al. had consecutive and nonconsecutive recruitment of participants, the recruitment of controls was unclear, and different laboratory PSG tests were used as the standard test27; in the study by Tenhunem et al. the thresholds used in the index test were not prespecified, it was unclear if the interpretation of results was made without the knowledge of the reference test results, and the measurements of 32 participants were excluded from the study analysis because of technical errors28 (Table 3).Table 3 Qualitative evaluation of the selected articles using the QUADAS-2 criteria.Table 3 Qualitative evaluation of the selected articles using the QUADAS-2 criteria.Of the six studies, one (Tsukahara et al.23) was not included in the quantitative analysis because we were not able to obtain the true positive, true negative, false positive, and false negative values. A forest plot of the five remaining studies is shown in Figure 2. The studies evaluated, at a minimum, the OSA detection at an AHI or RDI threshold of 5 events/h, and OSA severity classification for the AHI or RDI thresholds of 15 and 30 events/h. There were a total 515 participants, of which 356 were males (69%), and 159 were females (31%). All participants were suspected of OSA diagnosis and recruited when attending a sleep center.Figure 2: Forest plot of index tests sensitivity at different AHI thresholds.Sensitivity and confidence intervals of all studies that provided false positive, false negative, true positive, and true negative in each group category. All AHI thresholds tested in the respective studies, including AHI thresholds not included in the meta-analysis are shown. (AHI > 10, 25, 40 events/h). AHI = apnea-hypopnea index.Download FigureBed/mattress-based devices were found to have the best sensitivity overall (0.921, 95% CI 0.870, 0.953) (Table 2). The bivariate random-effects meta-analysis of the bed/mattress-based devices showed that the sensitivity decreased and spec-ificity increased at higher AHI threshold values. Based on a pretest probability of 0.6 (IQR 0.42, 0.77), the overall median PPV and NPV was respectively 0.87 (IQR 0.83, 0.94), and 0.9 (IQR 0.82, 0.94) (Table 4). The highest median PPV was found in the severe threshold (0.92, IQR 0.83, 0.92), and the highest median NPV was found in moderate cases (0.92, IQR 0.92, 0.94). As shown by the SROC curves on Figure 3, the severe and moderate OSA detection presented with the lowest degree of heterogeneity. Overall, the variability in specificity is shown to be larger than the variability in sensitivity results across all thresholds values, with the exception of severe OSA diagnosis.Table 4 PPV and NPV of OSA diagnosis and severity classification in studies using laboratory PSG as a comparison.Table 4 PPV and NPV of OSA diagnosis and severity classification in studies using laboratory PSG as a comparison.Figure 3: SROC curves for bed/mattress-based sensors overall and at AHI cutoff values of 5, 15, and 30 events/h.Pooled data of the sensitivity and false positive results of index tests evaluating bed/mattress-based sensors. Not all studies are included in each AHI threshold shown, and the number of studies included depended on the information available by the authors. AHI = apnea-hypopnea index, SROC = summary receiver operating characteristic.Download FigureContactless Devices (Other Than Bed/Mattress-Based Sensors)The studies were performed in Spain, United States, Ireland, and Germany. All studies used a similar design (cross-sectional) and recruitment strategy (adults suspected of OSA referred to a sleep center). Four studies assessed devices that estimated AHI using data from participant's respiratory and body movement obtained either through the emitting of sound waves (Nandakumar et al.29), the emission of low-power radiofrequency energy (Zaffaroni et al.30 and Weinreich et al.31), or by using a piezoelectric sensor (Davidovich et al.32). Espinoza-Cuadros et al. used photograph images and speech recordings, estimating AHI through a standard vector machine (SVM) analysis,33 and Abad et al. used video recordings to analyze respiratory and body movement, estimating AHI through a SVM.34 With the exception of sex distribution, the demographic characteristics of participants were similar across studies. Espinoza-Cuadros et al. recruited only male participants.33 The mean age varied from 48.4 to 53.1 years, and the mean BMI varied from 30 to 34.3 kg/m2 (Table 5).Table 5 Demographic characteristics of participants.Table 5 Demographic characteristics of participants.Only one study (Espinoza-Cuadros et al.33) was a QUADAS-2 evaluation performed with more than one domain presenting a high risk of bias or applicability issues (Table 3). In this study, the threshold used to confirm the diagnosis of OSA in high-risk patients for both the index and standard tests was higher than what is currently recommended (AHI ≥ 10 events/h). However, upon request, the authors provided the data that enabled the analysis of OSA detection at AHI ≥ 5 events/h.All six studies were included in our quantitative analysis. All studies evaluated the accuracy for screening moderate OSA (AHI ≥ 15 events/h), but only five and four studies assessed OSA at the thresholds of 5 and 30 events/h, respectively. A sensitivity and specificity forest plot of all studies is shown in Figure 2 and Figure 4. A total of 594 participants were included in the analysis. One study (Abad et al.34) did not provide sex distribution data. Among those providing such data, 484 (88.9%) were male, and 60 (11.03%) were female participants.Figure 4: Forest plot of index tests specificity at different AHI thresholds.Specificity and confidence intervals of all studies that provided false positive, false negative, true positive, and true negative in each group category. All AHI thresholds tested in the respective studies, including AHI thresholds not included in the meta-analysis are shown (AHI > 10, 25, 40 events/h). AHI = apnea-hypopnea index.Download FigureThe sensitivity of bivariate meta-analysis of the contactless based devices can be seen in the Table 2. The overall sensitivity of contactless devices to detect OSA was 0.905 (95% CI 0.839, 0.946). The sensitivity to detect mild OSA cases was the highest of all groups (0.976, 95% CI 0.899, 0.995), but provided a high false positive rate (0.487, 95% CI 0.137, 0.851). Based on a pretest probability of 0.54 (IQR 0.41, 0.70), the median PPV and NPV was 0.89 (IQR 0.81, 0.93) and 0.89 (0.76, 0.94), respectively (Table 4). Both PPV and NPV were highest at moderate threshold levels. As shown in the SROC curves on Figure 5, the studies were fairly homogeneous, with the exception of a few outliers. For moderate and severe OSA, the variability of sensitivity is shown to be larger than the variability in specificity. For a cutoff value of 5 events/h, the sensitivity values are shown to present a very low degree of variability. The same is not true for specificity values, shown to be highly heterogeneous.Figure 5: SROC curves for contactless devices overall and at AHI cutoff values of 5, 15, and 30 events/h.Pooled data of the sensitivity and false positive results of index tests evaluating contactless devices. Not all studies are included in each AHI threshold shown, and the number of studies included depended on the information available by the authors. AHI = apnea-hypopnea index, SROC = summary receiver operating characteristic.Download FigureContact Devices With Three or More SensorsAlthough all studies were performed in a sleep center, only Benistant provided information on a specific location (The Netherlands).35 For all three studies, data were collected through a pulse oximeter and at least one accelerometer.22,35,36 Al-Mardini et al.36 was the only study using a built-in smart-phone accelerometer. Additionally, Al-Mardini et al. and Rofouei et al. used a microphone to capture sound,22,36 and Benistant used a nasal cannula pressure sensor.35Overall, the quality of studies evaluating contact devices with three or more sensors was low. None of the studies specified the in-laboratory PSG channel montage, and most did not provide study participants' demographic data. The study by Benistant was evaluated as having a low risk of bias and applicability problems in all domains of the QUADAS-2 assessment (Table 3) and the only one that showed the average age (40.3 ± 11.1 years) and BMI (28.7 ± 3.0 kg/m2) of participants.35 The study by Al-Mardini et al. was poorly rated as the standard and index tests were not done simultaneously, and it was unclear if the standard laboratory PSG was performed at the same laboratory for all participants. Additionally, the selected controls were healthy subjects with no symptoms of OSA.36Two studies were included in our quantitative analysis. The study by Rofouei et al. was a case study, and calculating true positive/negative and false positive/negative values was not possible.22 Among studies included in the quantitative analysis, there were 24 participants, of which 20 (83.3%) were male and 4 (16.7%) were female (Table 5). All studies assessed OSA screening at an AHI threshold of 5 events/h, except that of Al-Mardini et al., which only evaluated the classification of moderate and severe OSA.36 For that reason, the bivariate meta-analysis of severity classification was not possible.In general, the index test using devices with at least three sensors provided low sensitivity rates, with substantially large CIs. The sensitivity and false positive rate of devices using at least three sensors is shown in the Table 2. The overall sensitivity was 0.771 (95% CI 0.466, 0.929). As shown in Table 4, this group of devices have also shown the lowest overall PPV median value of all groups (0.83, IQR 0.50, 0.89). As presented by the SROC curves on Figure 6, the summary results of the index tests using devices with at least three sensors presented a high degree of heterogeneity, showing a high degree of variability specially in sensitivity results.Figure 6: SROC curves for contact devices with three or more sensors overall and at AHI cutoff 5 events/h and contact devices with fewer than three sensors overall and at AHI cutoff 15 events/h.Pooled results of the sensitivity and false positive results of the contact devices with three or more sensors and fewer than sensors are shown. Not all studies are included in each AHI threshold shown, and the number of studies included depended on the information available by the authors. AHI = apneahypopnea index, SROC = summary receiver operating characteristic.Download FigureContact Devices With Three or More SensorsThe studies were performed in Turkey, Japan, the United States, and in unspecified locations. Both Dinç et al.37 and Ozmen et al.38 evaluated the SleepStrip device containing air flow sensors as their index test, and presented with a low risk of bias in the QUADAS-2 evaluations (Table 3). Levendowski et al.39 and Selvaraj et al.40 evaluated the use of a neckworn device and a chest device, respectively, and presented with mostly unclear risk of bias for four of the seven domains being evaluated. Levendowski et al. included participants with previous diagnosis of OSA performing split-night testing, and it was unclear if the index test results were interpreted without the knowledge of the researchers.39 Selvaraj et al. used a broad exclusion criteria, including the exclusion of severe behavioral and neurological problems and did not provide the number of participants excluded from the study.40 Nakano et al. used snoring sounds recordings to estimate AHI in a group of symptomatic patients with suspicion of OSA attending a sleep center.13 The study by Nakano et al. showed a high risk of bias for patient selection in the QUADAS-2 evaluation (Table 3), relating no clear exclusion criteria. Additionally, it was unclear if the results of the standard test were analyzed without the knowledge of the index results. With the excep" @default.
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