Home Sleep Testing Research Articles

Association between residential greenness and obstructive sleep apnoea among adults in Southern China

BackgroundNo previous study has explored the association of residential greenness with obstructive sleep apnoea (OSA) indexes. ObjectiveTo investigate the association of exposure to residential greenness with OSA indexes in adults in Guangdong Province, Southern China. MethodsFrom January 1, 2005 to December 31, 2015, a total of 3925 participants were recruited from the Sleep Center of Guangdong Provincial People's Hospital. Apnea-hypopnea index (AHI) and oxygen desaturation index (ODI) were measured by polysomnography or home sleep test (HST). Participants' daytime sleepiness scores were evaluated using The Epworth Sleeping Scale (ESS). The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were used to assess residential greenness levels. Generalized linear regression models were used to assess the associations of residential greenness with OSA indexes after adjusting for multiple covariates. ResultsThe mean (standard deviation) age of the participants was 63.3 (14.4) years. In adjusted models, an interquartile range (IQR) increase in 3-year average NDVI was significantly associated with 9.8 % (95 % confidence interval [95 % CI]: 17.5 %, 2.1 %); 14.5 % (95 % CI: 24.5 %, 4.4 %) and 6.9 % (95 % CI: 13.7 %, 0.0 %) decreases in AHI, ODI and ESS scores, respectively. Furthermore, an IQR increase in 3-year average EVI was significantly associated with 7.8 % (95 % CI: 13.7 %, 1.9 %); 10.8 % (95 % CI: 18.3 %, 3.2 %) and 7.2 % (95 % CI: 12.5 %, 2.0 %) declines in AHI, ODI and ESS scores, respectively. Significant associations were only observed among males, adults aged ≥65 years old, and in the warm season. ConclusionsOur study indicates that higher residential greenness was significantly associated with lower OSA indexes in adult population in South China, especially in males, in the elderly, and in the warm season.

Comparison of a home sleep test with in-laboratory polysomnography in the diagnosis of obstructive sleep apnea syndrome.

In-laboratory, polysomnography (PSG) is the gold standard for diagnosing obstructive sleep apnea syndrome (OSAS). However, the long waiting list and sleeping at a hospital make patients hesitate to undergo the examination, thereby delaying diagnosis. During coronavirus disease 2019 (COVID-19) pandemic, sleep labs are almost closed, and the delay is worsening. The home sleep test (HST) enables subjects to be tested at home, a familiar and comfortable environment, without a long waiting list. This study assessed the accuracy of a type III HST in diagnosing OSAS in the Taiwanese population and identified factors affecting the diagnostic accuracy. This retrospective study included 67 patients with clinically suspected OSAS. All patients were allocated to receive both PSG and the HST. The apnea-hypopnea index (AHI) measured through PSG was used as the standard. The sensitivity, specificity, and accuracy of the HST in diagnosing and evaluating the severity of OSAS were analyzed. Among the 67 patients, no significant difference was noted in the average AHI values obtained using PSG and the HST ( p = 0.103). The AHI obtained from HST was significantly correlated with that obtained from PSG, with the correlation coefficient being 0.779 ( p < 0.001). The sensitivity, specificity, and accuracy of the HST in diagnosing OSAS were 94.9%, 62.5%, and 91.0%, respectively, and 80.0%, 74.1%, and 77.6% in diagnosing moderate to severe OSAS. Furthermore, the difference in AHIs measured using the two tests were positively correlated with the severity of sleep apnea. The HST used in preliminary screening of patients with suspected OSAS achieved an accuracy of >90%. For patients with moderate to severe OSAS, the accuracy was below 80%. Therefore, for patients who receive an OSAS diagnosis through the HST, arrangement of PSG is recommended for determining the severity of the OSAS and giving proper treatment.

MT13 Measuring Real-World Impact of Subsidy Decision on Sleep Tests Utilisation for the Diagnosis of Obstructive Sleep Apnoea

Lab-based polysomnography (PSG), for diagnosing obstructive sleep apnoea (OSA), is a subsidised inpatient service in Singapore’s public healthcare institutions (PHIs). The alternative, home sleep test (HST), is cheaper and can be prescribed in the outpatient setting. Following health technology assessment performed by Agency for Care Effectiveness, HST was listed as a subsidised service in PHIs in May 2019. This study aims to assess the impact of the subsidy decision on sleep tests utilisation.

CPAP setup by mail: we're not there yet.

CPAP setup by mail: we're not there yet.

0779 Describing the OSA Patient Journey from Testing to PAP Treatment

Abstract Introduction Patients diagnosed with obstructive sleep apnea (OSA) and prescribed positive airway pressure (PAP) therapy for treatment may have differences in experience based on insurance provider, sex, age, or comorbidity status. This retrospective, real-world analysis investigated the factors that impact the patient’s OSA journey from initial sleep test to starting PAP therapy. Methods De-identified US administrative claims data for patients with OSA who had a claim for a sleep test were used for this analysis. Age, sex, and insurance coverage were characterized at the time of the first sleep test. Comorbidity status was evaluated in the year prior to the sleep test by assessing ICD-9/10 codes associated with healthcare encounters. This protocol was submitted to an Institutional Review Board and was determined to be exempt from oversight. Results In a population of 1,912,381 patients, 46.6% were female with mean age of 51.0 years. Insurance coverage was 70.6% commercial, 19.9% Medicaid, and 9.5% Medicare Advantage. Four comorbid cohorts were evaluated including those with hypertension (52.2%), type 2 diabetes (21.7%), COPD (9.7%), and atrial fibrillation (5.9%). Time from sleep test to receiving a PAP device was 7 days longer for females than males (median 49 days vs 42 days). Regardless of type of sleep test (polysomnography (PSG), home sleep test (HST)), those with commercial insurance received a device faster (median 41 days, Medicaid 61 days, Medicare Advantage 50 days). In comparison to those without the respective comorbidity, COPD was the only group with a noticeable increase in time from sleep test to device (median 54 days for those with COPD vs. 43 days for those without). No noticeable differences were observed across age categories. For those that had a titration, it occurred a median of 23 days after PSG or 34 days after HST. Consequently, the two fastest pathways were PSG with split night titration and HST without titration. Conclusion This retrospective study identified the length of time it takes in a real-world setting for patients to get a PAP device after a sleep test. Depending on demographic factors this time can vary from 1-2 months, delaying the start of OSA treatment. Support (If Any) ResMed

0764 Evaluation of cloud-connected home sensors of apnea-hypopnea index versus polysomnography and home sleep test during upper airway stimulation home titration

Abstract Introduction A growing number of cloud-connected sensors can longitudinally record apnea hypopnea index (AHI) at home. The Ectosense NightOwl is an FDA-approved peripheral arterial tomography device; the Withings Sleep Analyzer mat is an under-mattress sensor with AHI data for retrospective research use (CE marked, not FDA approved). We used these sensors to monitor patients with upper airway stimulation (UAS) therapy during therapy acclimatization and post-titration follow-up and compared sensor AHI to polysomnography (PSG) and home sleep test (HST) AHIs. Methods Enrolled patients underwent UAS implantation as part of regular clinical care and were required to have a smartphone. The Ectosense and Withings sensors were distributed at UAS device activation. Patients were asked to use NightOwl weekly, and Withings provided nightly AHI. Sleep studies (PSG &amp; HST) occurred at 3 and 6-mo post implant. Ectosense &amp; Withings AHI were averaged and correlated against the nearest 3- and 6-month sleep study AHIs. Results Patients were 67% male, aged 57 ± 10 years. Smartphone platforms were 59% Apple iOS, 24% Google Android, and 17% Unknown. As of October 2021, 45 patients have had near-simultaneous Withings and PSG/HST AHI, 35 have had near-simultaneous NightOwl and PSG/HST AHI. The Withings AHI was fairly linear against PSG/HST AHI, adjusted R2 = 0.47, p &amp;lt; 0.0001, (y=10 + 0.82x). Withings AHI &amp;lt; 15 (n=18) had a 90% positive predictive value for a PSG/HST AHI less than 15 (n=20). Ectosense data was less linear and more scattered, adjusted R2 = 0.15, p=0.01, (y=14 + 0.47x). Ectosense AHI &amp;lt; 15 (n=12) had an 80% positive predictive value of a PSG/HST AHI less than 15 (n=15). Conclusion Home-based sensors provide longitudinal SDB data in a home setting and may adequately classify patients with mild sleep apnea, and may have utility for home-based management of UAS patients in the post-implantation period. Support (If Any) Inspire Medical Systems

0778 Characterizing Women with Obstructive Sleep Apnea from Real World Data

Abstract Introduction Real-world evidence focused on women with obstructive sleep apnea (OSA) is lacking. This retrospective study aimed to characterize and evaluate the impact of age on female patients with OSA and their journey through OSA diagnosis and treatment. Methods De-identified US administrative claims data for patients with OSA who had a claim for a sleep test were used for this analysis. Age and insurance coverage were characterized at the time of the first sleep test. Comorbidity status was evaluated in the year prior to the sleep test by assessing ICD-9/10 codes associated with healthcare encounters. This protocol was submitted to an Institutional Review Board and was determined to be exempt from oversight. Results The study included 883,902 female OSA patients; mean age of 51.7 years; and 64.9% commercial, 24.7% Medicaid, and 10.3% Medicare Advantage insurance coverage. The most prevalent comorbidities were hypertension (54.9%), hyperlipidemia (46.5%), GERD (31.5%), type 2 diabetes (25.1%), depression (23.2%), and asthma (22.0%). When stratifying by age, the prevalence of all comorbidities increased with age except for affective disorders. Depression and anxiety decreased with age. In terms of the type of sleep test used to diagnose OSA, 58.8% had an in-lab polysomnography (PSG), 38.9% had a home sleep test (HST), and 2.3% had both a PSG and HST. About half (56.6%) of patients received a positive airway pressure (PAP) device in the year after being diagnosed. When stratifying the results by age, in-lab PSG testing was more prevalent in those over 65, while the percentage of those receiving a PAP device increased then slightly decreased with age (18-44y: 47.6%, 45-54y: 58.1%, 55-64y: 62.1%, 65-69y: 61.1%, &amp;gt;70y: 58.8%). Conclusion This retrospective study characterized the start of the women’s journey with OSA; describing the rates of sleep testing and PAP treatment from a sample of real-world data. These results begin to build an understanding of these patients and their journey to treatment, helping to raise awareness of undiagnosed OSA in women. Further research should be conducted to identify potential real-world impact of adherence to PAP on health outcomes. Support (If Any) ResMed

0786 Assessing the Adequacy of Auto Titrating Positive Airway Pressure Treatment in Patients with Severe Obstructive Sleep Apnea Diagnosed on Home Sleep Testing

Abstract Introduction Auto titrating positive airway pressure (Auto-PAP) therapy has been increasingly prescribed to treat obstructive sleep apnea (OSA) without reliance on in-laboratory PAP titration studies. Given the current pandemic and the limited use of in laboratory titrations, Auto-PAPs have been prescribed increasingly. The ability to achieve optimal control of obstructive events with Auto-PAP for patients with severe OSA (respiratory disturbance index (RDI) &amp;gt;30/hour of recording) on home sleep apnea testing (HSAT) has not been systematically investigated. Our study looks at the success of treatment on Auto-PAP in patients diagnosed with severe OSA on HSAT. Methods We retrospectively reviewed charts of patients who were diagnosed with severe OSA on HSAT and were prescribed an Auto-PAP at the Memorial Hermann – Texas Medical Center Sleep Disorders Center between September 2019 and May 2020. The usage data, residual AHI and adherence was assessed. Successful treatment was determined as a residual AHI (rAHI) &amp;lt;15. We excluded patients those who were non-adherent to PAP therapy. This study was conducted as a quality improvement initiative at our institution. Results We identified 24 patients diagnosed with severe OSA on HSAT and prescribed Auto-PAP. Nine patients were excluded due to non-adherence to Auto-PAP (37%). Of the remaining 15 patients, 80% were male, the average age was 53.7 years (range 36-69) and the average RDI on HSAT was 51.4 events/hour (SD 17.1). The average usage &amp;gt;4 hours was 76% in the adherent patients. The median pressure on Auto-PAP was 9 cm H2O (range 5.3-16.3). The average rAHI of 1.8 events/hour (range 0.3- 6.1). Usage data revealed only 1 patient with rAHI&amp;gt;5 and central apnea index of 3.9, who was then referred for an in-lab PAP titration study. Conclusion Sensors in the airway circuit of PAP devices measure airflow, vibration, and flattening of the airflow profile. Auto-adjusting PAP devices use this feedback to make online adjustments in pressure to maintain upper airway patency. Our study suggests that Auto-PAP is an effective initial therapy even for severe OSA, permitting both timely and adequate control of OSA especially when in laboratory titrations are limited. Close monitoring is however recommended to ensure adherence, and also to assess the potential emergence of central events. Support (If Any)

0377 Success Rates of Self-Applied Type 2 Studies in 191 Participants

Abstract Introduction Wait times for the gold standard Type 1 polysomnograms are lengthy, and the pandemic has only increased the backlog. The need for having home sleep testing has grown yet Type 3 home sleep apnea tests (HSAT) can be inaccurate despite their popularity. In this study, we assessed the reliability of a self-applied in-home Type 2 sleep test using the Cerebra Sleep System. Methods 213 sleep files were collected from 191 participants that had their Type 2 device shipped to their home with no prior training. Participants watched videos and read written instructions to self-apply the full Type 2 system. The lab-quality in-home PSG included a recording of EEG (Fp1,Fp2), EOG (E1, E2), EMG (chin), EMG (legs), respiratory channels (one effort or two RIP belts), nasal flow, and pulse oximetry. The study was considered successful if the head unit (EEG/EOG/EMG), leg EMG, nasal flow, and oximetry were present for recordings of 3 and 4 hours in duration. Results Based on a 4-hour success criterion, 82.6% of studies were considered successful. This percentage increased with 3 hours (85.4%). When studies did fail at the 4-hour criterion, 10.8% had failures of all signals, 5.4% had failures of 4/5 signals, 10.8% had failures of 3/5 signals, 16.2% had failures of 2/5 signals, and 56.8% had failures of 1/5 signals. The head unit failed on 15 records (7%), the right leg on 9 records (4.2%), the left leg on 6 records (2.8%), the cannula on 12 records (5.6%), and the oximeter on 7 records (3.3%). For 13 participants who had a failed study, the issues were resolved in 6 cases (46%) with a second night of recording. Conclusion We found that the Cerebra Sleep System Type 2 in-home test showed reliable performance with a high success rate. When studies did fail, in most cases, a single signal was the source of the problem. For patients suspected of having sleep disordered breathing or other sleep disorders, Type 2 testing is a reliable, convenient, and safe alternative to in-lab polysomnography and performs similar success rates to Type 3 HSATs. Support (If Any)

0755 Comparison of clinical pathways for upper airway stimulation management: in-laboratory titration polysomnography versus home-based efficacy sleep testing

Abstract Introduction Upper airway stimulation (UAS) therapy is an alternative treatment option for select CPAP-intolerant patients with obstructive sleep apnea. Current standard-of-care management uses in-laboratory polysomnography for titration of UAS stimulation amplitude (tPSG) after 3 months of patient self-titration at home. This home monitoring study was designed to evaluate whether tPSG or efficacy home sleep test (eHST) with tPSG by exception for eHST non-responders would have non-inferior apnea-hypopnea index (AHI) outcomes. Methods Enrolled patients underwent UAS implantation as part of regular clinical care and were randomized at the activation visit 1:1 between tPSG or eHST for the 3-month post-activation visit. If eHST results were suboptimal (AHI &amp;gt; 15 events/h or &amp;lt; 50% reduction from baseline AHI) patients underwent tPSG titration at 5 months. Both groups had 2-night eHSTs at 6 months post-activation. The primary endpoint was 6-month AHI equivalence between arms (defined as ± 15 events/h). Secondary endpoints were equivalence of Epworth Sleepiness Score (ESS; ± 2), oxygen desaturation index (ODI; ± 15 events/h), and nightly UAS device usage (± 0.5 h). Results The study randomized 60 patients from August 2020 through September 2021, who were primarily middle aged (57 ± 10 years), male (67%), Caucasian (98%), and overweight (BMI 29 ± 3 kg/m2), with severe OSA (AHI 35 ± 16). Eleven patients withdrew from the study early. As of December 2021, 41 and 36 patients have completed 3- and 6-month follow-up visits, respectively. Six-month visit AHI, ESS, ODI, and device usage data between arms is currently blinded and is expected to be complete by Q2 2021 prior to SLEEP 2022. Conclusion If the study demonstrates equivalent 6-month AHI, ESS, ODI, and usage outcomes, the use of eHST to ascertain therapy efficacy prior to tPSG could be a non-inferior alternative management option to tPSG. Support (If Any) Sponsored by Inspire Medical Systems, Inc.

Performance of STOP-BANG questionnaire as sleep apnoea screening tool and development of a novel pre-selection model in patients with atrial fibrillation - insights from the Virtual-SAFARI study

Abstract Funding Acknowledgements Type of funding sources: None. Background Sleep-disordered breathing (SDB) is highly prevalent in patients with atrial fibrillation (AF) and its screening is recommended in this population. The STOP-BANG questionnaire is widely used as SDB screening tool, but the subjective assessment of some of its parameters might limit its validity to detect SDB in AF patients. Purpose We assessed the performance of the STOP-BANG questionnaire to detect SDB with the use of a mobile health-based portable home sleep test given as reference in a cohort of AF patients. Moreover, we explored options to improve pre-selection for SDB screening in this cohort. Methods Consecutive AF patients who were referred for AF catheter ablation in two AF outpatient clinics and without previous history of SDB and/or SDB screening, were included prospectively. Patients characteristics together with STOP-BANG questionnaire were assessed at baseline visitation. STOP-BANG scores of 3-4 and 5-8 were interpreted as intermediate and high risk for SDB, respectively. All patients were digitally referred to a virtual SDB management pathway, independent of STOP-BANG score. Patients received a portable home sleep test to detect SDB, which patients used for one night at home after instructions. Sleep recordings were accessed by sleep physicians via a secured cloud and sleep reports were discussed with patients and referring physicians. Apnoea-hypopnoea-indexes (AHI) of &amp;gt;=15 and of &amp;gt;= 30 were interpreted as moderate-to-severe and severe SDB, respectively. Results of the STOP-BANG and the home sleep test were compared and performance of the STOP-BANG questionnaire was assessed. Uni- and multivariable logistic regression analyses based on patient characteristics were used to construct a nomogram to improve pre-selection for SDB screening in this cohort. Results A total of 156 patients (median age 65 years, 63.8% male) were included. According to results from the home sleep test, 53% of patients were diagnosed with moderate-to-severe SDB, including 16% with severe SDB. STOP-BANG questionnaire performed poorly with an area under the receiver operating characteristic curve (AUROC) of 0.661 and 0.684 in predicting moderate-to-severe and severe SDB, respectively. On multivariable logistic analysis, independent predictors for moderate-to-severe SDB were body-mass-index (BMI), age and apnoeas. A nomogram was built on this variables (Fig. 1), which performed good with an AUROC of 0.728 in predicting moderate-to-severe SDB. Conclusion In a cohort of AF patients scheduled for AF catheter ablation, SDB was highly prevalent. STOP-BANG questionnaire had poor value for the prediction of moderate-to-severe and severe SDB compared to a systematical SDB screening via home sleep test. Using STOP-BANG questionnaire as a pre-selection tool might lead to frequently undetected and untreated SDB. Whether the nomogram based on our analyses improves pre-selection for SDB screening in AF patients will be validated in a future cohort.

The impact of a structured polygraphy screening incorporated in a novel remote mobile health pathway on sleep apnoea prevalence in patients with atrial fibrillation

Abstract Funding Acknowledgements Type of funding sources: None. Background Untreated obstructive sleep apnoea (OSA) contributes to progression of atrial fibrillation (AF) and reduces the success rate of heart rhythm control strategies. OSA remains one of the most frequently underdiagnosed modifiable risk factors in AF patients due to a lack of standardized screening methods and low awareness. Purpose To assess the impact of implementation of a structured remote OSA screening and management pathway on the prevalence of OSA in AF patients scheduled for AF ablation procedures. Methods In October 2020, a novel remote OSA screening and management pathway (VIRTUAL-SAFARI) was introduced in two AF outpatient clinics in the Netherlands. Consecutive patients scheduled for AF ablation were offered OSA screening consisting of sending a portable home sleep test to patients’ homes to perform a remote sleep recording for one night, analysis by a sleep physician, discussion of results with the patient, and initiation of treatment (when applicable). The impact of this structural screening strategy was assessed by comparing the prevalence of concomitant OSA (defined as apnoea-hypopnoea index ≥5) for patients scheduled for AF ablation in the year before and after introduction of the OSA management pathway. Results A total of 733 patients was studied, 308 in the year before (Oct ’19 - Sep ’20) and 425 in the year after (Oct ’20 - Sep ’21) introduction of the VIRTUAL-SAFARI pathway. Median age was 65 [58-71], 64% was male and median body mass index (BMI) was 27 [25-30] kg/m2. Baseline characteristics were comparable for the groups before and after introduction of the pathway (Table 1). In the cohort before pathway introduction, OSA had been diagnosed in 26 patients (8%, Figure 1) and was treated with positive airway pressure in 10 cases (3%). In the cohort after pathway introduction, OSA had previously been diagnosed in 53 patients (12%). Eighty-eight percent of patients without previous OSA screening was referred via the remote pathway. Results of the sleep recordings were available for 213 (59%) at the time of this analysis. Previously unknown OSA was diagnosed in 184 patients (86% of available recordings), increasing the prevalence of confirmed OSA to 237 (55%). For 22% of patients, results of sleep recordings are pending. Absence of OSA was confirmed in 9%, and 13% of patients had not been screened (e.g. because of patient preference or logistical reasons). After pathway introduction, 82 patients (19%) were treated or received advice to start treatment with positive airway pressure. Treatment decisions are pending for 9% of the cohort. Conclusion After the implementation of structured remote OSA screening in a well characterized cohort of consecutive patients scheduled for AF ablation, the prevalence of diagnosed OSA increased from 8% to 55%. Whether appropriate risk factor management, including treatment of OSA identified by structured screening, will improve AF outcomes needs to be tested in future studies.

Bimaxillary Orthognathic Surgery Does Not Induce Obstructive Sleep Apnea in Skeletal Class III Patients

Since the relationship between mandibular setback surgery and obstructive sleep apnea (OSA) occurrence still remains controversial, the aim of this study was to assess the impact of bimaxillary orthognathic surgery on the probability of OSA development, using a home sleep test (HST) device. The authors implemented a double-blinded prospective cohort study. All healthy patients with skeletal class III deformity were included in this study. Subjects were candidates for bimaxillary orthognathic surgery. OSA monitoring was performed by the pulmonologist, week 1 preoperatively (T0), 1 and 6months postoperatively (T1, T2), with a specific brand of a HST device. The predictor variables were the amount of mandibular setback and maxillary advancement, separately. Changes in apnea-hypopnea index (AHI) and SpO2 1 and 6months after surgery relative to T0 were the outcome variables. OSA severity was measured using AHI, and classified as mild (5<AHI<15), moderate (15<AHI<30), and severe (AHI>30). Age, sex, and body mass index were the study covariates. The outcome assessor (pulmonologist), and the data analyzer were blind in this study. The significance level was set at 0.05, using the SPSS19. The sample was composed of 30 patients, (15 females, 15 males) with an average age of 25.73±5.26years and a mean body mass index of 19.90±3.6kg/m2. The mean amount of mandibular setback was 4.5±1.1 (ranged from 2-7mm), while the average maxillary advancement was 2.9±1.2mm (ranged 1-5mm). Mean AHI at T0, T1, and T2 was 1.8±1.0, 3.4±1.5, and 1.9±0.9 events per hour events, respectively. The AHI scores increased from T0 to T1 but again decreased until T2, which were statistically significant (P<.001). The mean amount of SpO2 at T0, T1, and T2 was 96.7±0.9, 94.0±1.3, 96.7±0.7%, respectively. Postoperative AHI in T1 and T2 had direct statistical significant relationships with the amount of mandibular setback (Rsp=.404, .574, respectively and P<.05). Postoperative AHI scores were lower in patients with <5mm mandibular setback in comparison to subjects who underwent ≥5mm setback (P<.05). Bimaxillary orthognathic surgery (concomitant maxillary advancement and mandibular setback) did not increase the incidence of OSA in young healthy non-obese class III patients, in the case of mandibular setback up to 7mm.

The effects of long-term continuous positive airway pressure on apnea-hypopnea index change following short-term that withdrawal in patients with obstructive sleep apnea.

The effect of long‐term continuous positive airway pressure (CPAP) treatment on apnea–hypopnea index (AHI) after CPAP withdrawal remains unclear, especially in obstructive sleep apnea (OSA) patients screened from the population. To examine that, 1241 civil servants who participated in the annual physical examination were screened for OSA between September and December 2017. Screened OSA firstly underwent 1‐week CPAP adherence assessment. Then, patients with good CPAP adherence would be freely provided CPAP to continued treatment. All OSA patients were followed for 2 years. At study end, all OSA patients underwent home sleep testing (HST) again within 1 week of CPAP withdrawal. The effect of 2‐year CPAP treatment on OSA severity was investigated by using linear regression and multinominal logistic regression. In total, 103 OSA patients were screened, including 41 cases (39.8%) in CPAP treatment group and 62 cases (60.2%) in non‐CPAP treatment group. At 2‐year follow‐up, compared with baseline, in CPAP treatment group, following CPAP withdrawal, a significant decrease in AHI was observed in patients with severe OSA (P = 0.014); in non‐CPAP treatment group, a significant increase in AHI was observed in patients with moderate OSA (P = 0.028). After adjustment for confounding factors, multivariate linear regression showed that △AHI was negatively associated with CPAP treatment (β = −4.930, 95% confidence interval [CI] [−9.361, −0.500], P = 0.030). Multinominal logistic regression showed that the AHI of patients not treated with CPAP tended to be unchanged or worsened with the AHI improvement group as a reference (OR [odds ration] [95% CI], 4.555 [1.307, 15.875], P = 0.017; 6.536 [1.171, 36.478], P = 0.032). In conclusion, active OSA screening and long‐term CPAP intervention may improve the severity of severe OSA patients following short‐term CPAP withdrawal in the general population.

Waist-hip ratio is an independent predictor of moderate-to-severe OSA in nonobese males: a cross-sectional study

BackgroundAdiposity is a well-established risk factor for obstructive sleep apnea (OSA), but whether a combination of preferable anthropometric measurements may improve the accuracy of detecting OSA is unknown. This study aimed to explore the accuracies of the waist-hip ratio (WHR) in conjunction with the body mass index (BMI) when identifying the severity of OSA.DesignA total of 2012 participants in the China-Japan Friendship Hospital from January 2018 to December 2019 underwent anthropometric measurements and an overnight home sleep test (HST). The 244 subjects who met the criteria for obstructive sleep apnea (apnea–hypopnea index (AHI) ≥ 5 events/hour) were divided into four groups: Group A (55 patients with WHR ≥ 0.9 and BMI ≥ 28 kg/m2); Group B (12 patients with WHR < 0.9 and BMI ≥ 28 kg/m2); Group C (69 patients with WHR ≥ 0.9 and BMI < 28 kg/m2); and group D (108 patients with WHR < 0.9 and BMI < 28 kg/m2).ResultsThe AHI, apnea index (AI), hypopnea index (HI), and oxygen desaturation index (ODI) were significantly different among the 4 groups (p < 0.05). The WHR was positively correlated with AHI (r = 0.22, p < 0.001), AI (r = 0.270, p = 0.004), and ODI (r = 0.286, p = 0.0022) and negatively correlated with lowest oxygen pulse saturation (LSpO2) (r = 0.246, p = 0.008) only in nonobese patients. Moreover, the WHR was found to be a screening marker for moderate-to-severe OSA in Group D (p < 0.05). When used to identify severe OSA in Group D, the WHR cut-off point of 0.873 yielded a sensitivity of 65% and specificity of 56% (p < 0.05).ConclusionIn nonobese male OSA patients, WHR is a moderate screening marker for moderate-to-severe OSA and an independent risk factor for OSA severity.

Toward sleep apnea detection with lightweight multi-scaled fusion network

Sleep apnea (SA) is a common sleep disorder that occurs during sleep, leading to the decrease of oxygen saturation in the blood. It would develop a variety of complications like diabetes, chronic kidney disease, depression, cardiovascular diseases, or even sudden death. Early SA detection can help physicians to take interventions for SA patients to prevent malignant events. This paper proposes a lightweight SA detection method of multi-scaled fusion network named SE-MSCNN based on single-lead ECG signals. The proposed SE-MSCNN mainly includes multi-scaled convolutional neural network (CNN) module and channel-wise attention module. In order to facilitate the SA detection performance, various scaled ECG information with different-length adjacent segments are extracted by three sub-neural networks. To overcome the problem of local concentration of feature fusion with concatenation, a channel-wise attention module with a squeeze-to-excitation block is employed to fuse the different scaled features adaptively. Furthermore, the ablation study and computational complexity analysis of the SE-MSCNN are conducted. Extensive experiment results show that the proposed SE-MSCNN has the performance superiority to the state-of-the-art methods for SA detection on the Apnea-ECG benchmark dataset. The SE-MSCNN with the merits of quick response and lightweight parameters can be potentially embedded into a wearable device to provide an SA detection service for individuals in home sleep test (HST). All codes and models are released at https://github.com/Bettycxh/Toward-Sleep-Apnea-Detection-with-Lightweight-Multi-scaled-Fusion-Network.

Our Continued Experience With Using Watchpat® (itamar Medical, Ltd.), Home Sleep Testing For The Diagnosis Of Sleep Apnea In Heart Failure And Orthotopic Heart Transplant Patients.

<h3>Introduction</h3> Untreated sleep disorders are well known to significantly increase risk of coronary artery disease (CAD), hypertension (HTN), obesity, diabetes (DM), anxiety and depression. Despite maximal medical therapy, approximately 50% of patients with heart failure (HF) have moderate-to-severe sleep apnea (SA). Moreover, obstructive sleep apnea (OSA) has also been described in cardiac transplant recipients (OHT) as a significant risk factor for late graft dysfunction, thus screening and diagnosis is critical^. Access to in-lab polysomnography (PSG) has been limited throughout the pandemic leading to more reliance on home sleep testing. WatchPAT® Home Sleep Testing (WPHST) has been validated to accurately detect both obstructive as well as central sleep apnea (CSA) in the general population yet there is limited data to date that support its use in advanced HF/OHT patients. <h3>Objective</h3> Herein we describe our prospective utilization of WPHST highlighting differences in sleep apnea patterns in our HF and OHT population. <h3>Methods</h3> This is a single center, prospective, observational analysis, beginning February 22, 2020 through April 7, 2021. 50 consecutive patients that screened positive on the STOP-BANG® sleep apnea screen questionnaire were consented and assigned to WPHST and analyzed for comorbidities, and SA patterns and severity. <h3>Results</h3> Of the 50 patients enrolled, 48 patients (n=38 HF and n=10 OHT) were included in the analysis (1 failed study in each arm). Baseline characteristics were equivalent; mean age of 60 years, BMI 28.6 kg/m2, and 75% were male. A high prevalence of co-morbid conditions included CAD 52%, DM 50% and HTN 98%, (fig 1). Central/Mixed Sleep apnea accounted for 10.4% of the total cohort. Apnea Hypopnea Index (AHI) was significantly greater in the HF patient cohort vs OHT (p=0.02). However, no statistical differences were noted in nocturnal 02 sat (p=0.49). REM% was lower in the OHT compared to the HF cohort (p=0.049). <h3>Conclusion</h3> : In this limited prospective study AHI was significantly higher in the HF cohort compared to OHT. More importantly, WPHST has contributed to greater access to sleep diagnostics for high-risk patient populations throughout the pandemic. Continued work on short- and long-term outcomes is warranted.

Use Of A Disposable At-Home System To Support Contactless Sleep Apnea Testing & Therapy Initiation In A HF Clinic During The Covid-19 Pandemic

Background: The COVID pandemic presented many challenges to providing care to our HF patients. One of our challenges was the closure of our sleep lab for sleep apnea testing and CPAP titration. We also encountered patient reluctance to perform sleep testing with re-usable home equipment. In attempt to address these issues, we employed the WatchPATTM ONE device (Itamar Medical) - a disposable Bluetooth-enabled technology that uses peripheral arterial tonometry - to assess patients for sleep-disordered breathing (SDB) without an in-person visit or a facemask. At-home CPAP initiation and auto-titration was performed in appropriate patients to create a fully contactless testing and treatment program.Methods: From March 2020 - March 2021 patients with suspected SDB during a virtual HF Clinic visit were referred for home sleep testing using the WatchPATTM ONE device. Suspicion for SDB breathing was based on meeting at least 1 of 5 screening criteria used in our HF Clinic and a STOP-BANG score > 3. After insurance approval, a device was mailed to the patient for overnight testing. Results were uploaded into the CloudPAT platform and read by our Center's sleep cardiologist. Patients without complex SDB, underwent remote device fitting via an online DME company followed by CPAP auto-titration. Time to therapy and compliance with CPAP was assessed using the cloud-based system.Cone Health SDB Screening CriteriaPoorly-controlled HTNRecurrent/refractory AFFrequent PVCsFatigue out of proportion to HF severityBMI > 35Results:Patients tested111Positive for SDB (AHI ≥ 5)81 (73%)Patients with significant central SA (cAHI > 5)20%Patients with successful remote CPAP initiation24 (30%)Mean time from virtual visit to therapy53dConclusions:• Sleep apnea is a common comorbidity in HF patients • The WatchPATTM ONE system was an effective way to continue to test our HF patients for SDB in a virtual setting with a disease prevalence similar to standard in-lab testing and had the ability to discern between obstructive and central sleep apnea • In our initial virtual sleep apnea program, nearly 1/3 of patients with SDB went onto CPAP treatment via a remote DME Turnkey service with a favorable time to treatment and a completely contactless workflow

A single-centre, observational, retrospective study to evaluate coexistence of cardiovascular comorbidities and obstructive sleep apnoea in South Indians using apnea link air home sleep testing device

Background: The aim of the study was to explore whether there is a relationship between Cardiovascular (CV) comorbidities and the prevalence and severity of Obstructive sleep apnea (OSA).Methods: Secondary data analysis of 146 patients with suspected sleep-disordered breathing was conducted who were presented to the department of medicine, KIMS Hospital, Hyderabad, from June 2021 to November 2021. Participants aged ≥18 years were included in the analysis. Demographic details, clinical history, comorbidities, medication history, were analyzed. AHI score, ODI (Oxygen desaturation index) score and average O2 saturation were recorded with the help of a home sleep testing device. Data were entered and analyzed with Epi info 7.Results: The overall prevalence of OSA was 78.8% in patients with suspected sleep-disordered breathing. Prevalence of mild, moderate, and severe OSA was 28.8%, 15.1%, and 34.9%, respectively. Proportions of diabetes and hypertension were significantly high among patients with obstructive sleep apnea. Correlation analysis revealed a weak positive linear relationship (r=0.14) between the number of risk factors and the AHI score.Conclusions: This study showed high prevalence of OSA among out patients presented with suspected sleep disordered breathing. Hypertension, diabetes and obesity were highly prevalent CV comorbidities among South Indian patients diagnosed with OSA.

RestEaze: An Emerging Technology to Characterize Leg Movements During Sleep

Abstract Several sleep disorders are characterized by periodic leg movements during sleep including Restless Leg Syndrome, and can indicate disrupted sleep in otherwise healthy individuals. Current technologies to measure periodic leg movements during sleep are limited. Polysomnography and some home sleep tests use surface electromyography to measure electrical activity from the anterior tibilias muscle. Actigraphy uses three-axis accelerometers to measure movement of the ankle. Electromyography misses periodic leg movements that involve other leg muscles and is obtrusive because of the wires needed to carry the signal. Actigraphy based devices require large amplitude movements of the ankle to detect leg movements (missing the significant number of more subtle leg movements) and can be worn in multiple configurations precluding precision measurement. These limitations have contributed to their lack of adoption as a standard of care for several sleep disorders. In this study, we develop the RestEaze sleep assessment tool as an ankle-worn wearable device that combines capacitive sensors and a six-axis inertial measurement unit to precisely measure periodic leg movements during sleep. This unique combination of sensors and the form-factor of the device addresses current limitations of periodic leg movements during sleep measurement techniques. Pilot data collected shows high correlation with polysomnography across a heterogeneous participant sample and high usability ratings. RestEaze shows promise in providing ecologically valid, longitudinal measures of leg movements that will be useful for clinicians, researchers, and patients to better understand sleep.