Rationale: Obesity is the most important risk factor for obstructive sleep apnea (OSA). However, the complex relationship between obesity and upper airway anatomy (craniofacial structure, soft tissues, and airway caliber) has not been robustly examined in patients with OSA. Objectives: To evaluate the relationship between obesity, on the basis of body mass index (BMI), and upper airway anatomic structures in adult patients with moderate or severe OSA. Methods: In this cross-sectional study, 583 patients with apnea-hypopnea index ≥15 events/h (mean age, 53.7 ± 10.4 yr; 81.0% men) were included from the Iceland Sleep Apnea Cohort. Airway sizes, soft tissue volumes, and craniofacial dimensions were quantified using three-dimensional magnetic resonance imaging. We examined how upper airway anatomy was associated with BMI using linear regression (continuous BMI) and analysis of covariance (BMI categories), adjusting for age, sex, and apnea-hypopnea index. Results: Most upper airway anatomy was significantly associated with BMI among patients with OSA. Higher BMI was associated with a different airway shape, including larger minimum anteroposterior distance at both the retropalatal and retroglossal regions and smaller minimum lateral distance in the retropalatal region. All pharyngeal soft tissues were larger with greater BMI, including the volumes of the tongue (and tongue fat), soft palate (and soft palate fat), lateral walls, fat pads, epiglottis, and pterygoids. Patients with lower BMIs had smaller craniofacial measures (e.g., distances between hyoid, retropogonion, and third cervical vertebrae, intramandibular volume, and nasooropharyngeal areas) and more retrognathia. BMI was only weakly associated with the proportion of mandibular space occupied by soft tissues (with no difference among BMI groups), suggesting comparable intraoral "crowdedness" among patients with OSA at different degrees of obesity, albeit for different reasons. Conclusions: The present results support associations between obesity and airway shape, soft tissue volumes, and craniofacial measures among patients with moderate to severe OSA. These relationships provide insights into anatomic traits leading to OSA in lean and obese patients and can inform more personalized treatment options.

Study Objectives Inter-scorer variability in sleep staging is largely due to equivocal epochs that contain features of more than one stage. We propose an approach that recognizes the existence of equivocal epochs and evaluates scorers accordingly.Methods Epoch-by-epoch staging was performed on 70 polysomnograms by six qualified technologists and by a digital system (Michele Sleep Scoring [MSS]). Probability that epochs assigned the same stage by only two of the six technologists (minority score) resulted from random occurrence of two errors was calculated and found to be <5%, thereby indicating that the stage assigned is an acceptable variant for the epoch. Acceptable stages were identified in each epoch as stages assigned by at least two technologists. Percent agreement between each technologist and the other five technologists, acting as judges, was determined. Agreement was considered to exist if the stage assigned by the tested scorer was one of the acceptable stages for the epoch. Stage assigned by MSS was likewise considered in agreement if included in the acceptable stages made by the technologists.Results Agreement of technologists tested against five qualified judges increased from 80.8% (range 70.5%-86.4% among technologists) when using the majority rule, to 96.1 (89.8%-98.5%) by the proposed approach. Agreement between unedited MSS and same judges was 90.0% and increased to 92.1% after brief editing.Conclusions Accounting for equivocal epochs provides a more accurate estimate of a scorer's (human or digital) competence in scoring sleep stages and reduces inter-scorer disagreements. The proposed approach can be implemented in sleep-scoring training and accreditation programs.

Abstract Introduction Scoring algorithms have the potential to increase polysomnography (PSG) scoring efficiency while also ensuring consistency and reproducibility. We sought to validate an updated sleep staging algorithm (Somnolyzer; Philips, Monroeville PA USA) against manual sleep staging, by analyzing a dataset we have previously used to report sleep staging variability across nine center-members of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC). Methods Fifteen PSGs collected at a single sleep clinic were scored independently by technologists at nine SAGIC centers located in six countries, and auto-scored with the algorithm. Each 30-second epoch was staged manually according to American Academy of Sleep Medicine criteria. We calculated the intraclass correlation coefficient (ICC) and performed a Bland-Altman analysis comparing the average manual- and auto-scored total sleep time (TST) and time in each sleep stage (N1, N2, N3, rapid eye movement [REM]). We hypothesized that the values from auto-scoring would show good agreement and reliability when compared to the average across manual scorers. Results The participants contributing to the original dataset had a mean (SD) age of 47 (12) years and 80% were male. Auto-scoring showed substantial (ICC=0.60-0.80) or almost perfect (ICC=0.80-1.00) reliability compared to manual-scoring average, with ICCs (95% confidence interval) of 0.976 (0.931, 0.992) for TST, 0.681 (0.291, 0.879) for time in N1, 0.685 (0.299, 0.881) for time in N2, 0.922 (0.791, 0.973) for time in N3, and 0.930 (0.811, 0.976) for time in REM. Similarly, Bland-Altman analyses showed good agreement between methods, with a mean difference (limits of agreement) of only 1.2 (-19.7, 22.0) minutes for TST, 13.0 (-18.2, 44.1) minutes for N1, -13.8 (-65.7, 38.1) minutes for N2, -0.33 (-26.1, 25.5) minutes for N3, and -1.2 (-25.9, 23.5) minutes for REM. Conclusion Results support high reliability and good agreement between the auto-scoring algorithm and average human scoring for measurements of sleep durations. Auto-scoring slightly overestimated N1 and underestimated N2, but results for TST, N3 and REM were nearly identical on average. Thus, the auto-scoring algorithm is acceptable for sleep staging when compared against human scorers. Support (if any) Philips.

In-laboratory and home sleep studies are important tools for diagnosing sleep disorders. However, a limited amount of measurements is used to inform disease severity and only specific measures, if any, are stored as structured fields into electronic health records (EHR). We propose a sleep study data extraction approach based on supervised machine learning to facilitate the development of specialized format-specific parsers for large-scale automated sleep data extraction. Using retrospective data from the Penn Medicine Sleep Center, we identified 64,100 sleep study reports stored in Microsoft Word documents of varying formats, recorded from 2001–2018. A random sample of 200 reports was selected for manual annotation of formats (e.g., layout) and type (e.g. baseline, split-night, home sleep apnea tests). Using text mining tools, we extracted 71 document property features (e.g., section dimensions, paragraph and table elements, regular expression matches). We identified 14 different formats and 7 study types. We used these manual annotations as multiclass outcomes in a random forest classifier to evaluate prediction of sleep study format and type using document property features. Out-of-bag (OOB) error rates and multiclass area under the receiver operating curve (mAUC) were estimated to evaluate training and testing performance of each model. We successfully predicted sleep study format and type using random forest classifiers. Training OOB error rate was 5.6% for study format and 8.1% for study type. When evaluating these models in independent testing data, the mAUC for classification of study format was 0.85 and for study type was 1.00. When applied to the large universe of diagnostic sleep study reports, we successfully extracted hundreds of discrete fields in 38,252 reports representing 33,696 unique patients. We accurately classified a sample of sleep study reports according to their format and type, using a random forest multiclass classification method. This informed the development and successful deployment of custom data extraction tools for sleep study reports. The ability to leverage these data can improve understanding of sleep disorders in the clinical setting and facilitate implementation of large-scale research studies within the EHR. American Heart Association (20CDA35310360).

Although mandibular advancement device (MAD) treatment of adults with obstructive sleep apnea (OSA) is generally less efficacious than positive airway pressure (PAP), the two treatments are associated, with similar clinical outcomes. As a sub‐analysis of a randomized trial comparing the effect of MAD versus PAP on blood pressure, this study compared objectively measured adherence to MAD versus PAP treatment in adults with OSA. Adults with OSA (age 54.1 ± 11.2 [standard deviation] years, 71.1% male, apnea–hypopnea index 31.6 ± 22.7 events/h) were randomized to MAD ( n = 89) or PAP ( n = 91) treatment for 3–6 months. Objective adherence was assessed with a thermal sensor embedded in the MAD and a pressure sensor in the PAP unit. In a per protocol analysis, no difference was observed in average daily hours of use over all days in participants on MAD ( n = 35, 4.4 ± 2.9 h) versus PAP ( n = 51, 4.7 ± 1.6 h, p = .597) treatment when days with missing adherence data were included as no use. MAD was used on a lower percentage of days (62.5 ± 36.4% versus 79.9 ± 19.8%, p = .047), but with greater average daily hours of use on days used (6.4 ± 1.9 h versus 5.7 ± 1.2 h, p = .013). Average daily hours of use in the first week were associated with long‐term adherence to MAD ( p < .0001) and PAP ( p = .0009) treatment. Similar results were obtained when excluding days with missing adherence data. In conclusion, no significant difference was observed in objectively measured average daily hours of MAD and PAP adherence in adults with OSA, despite differences in the patterns of use. MAD adherence in the first week predicted long‐term use.

Abstract Introduction Scoring algorithms have the potential to increase polysomnography (PSG) scoring efficiency while also ensuring consistency and reproducibility. We sought to validate an updated event detection algorithm (Somnolyzer; Philips, Monroeville PA USA) against manual scoring, by analyzing a dataset we have previously used to report scoring variability across nine center-members of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC). Methods Fifteen PSGs collected at a single sleep clinic were scored independently by technologists at nine SAGIC centers located in six countries, and auto-scored with the algorithm. Arousals, apneas, and hypopneas were identified according to the American Academy of Sleep Medicine recommended criteria. We calculated the intraclass correlation coefficient (ICC) and performed a Bland-Altman analysis comparing the average manual- and auto-scored apnea-hypopnea index (AHI), arousal index (ArI), apneas, obstructive apneas, central apneas, mixed apneas, and hypopneas. We hypothesized that the values from auto-scoring would show good agreement and reliability when compared to the average across manual scorers. Results Participants contributing to the original dataset had a mean (SD) age of 47 (12) years, AHI of 24.7 (18.2) events/hour, and 80% were male. The ICCs (95% confidence interval) between average manual- and auto-scoring were almost perfect (ICC=0.80–1.00) for AHI [0.989 (0.968, 0.996)], ArI [0.897 (0.729, 0.964)], hypopneas [0.992 (0.978, 0.997)], total apneas [0.973 (0.924, 0.991)], and obstructive apneas [0.919 (0.781, 0.972)], and moderately reliable (ICC=0.40–0.60] for central [0.537 (0.069, 0.815)] and mixed [0.502 (0.021, 0.798)] apneas. Similarly, Bland-Altman analyses supported good agreement for event detection between techniques, with a mean difference (limits of agreement) of only 1.45 (-3.22, 6.12) events/hour for AHI, total apneas 5.2 (-23.9, 34.3), obstructive apneas 1.8 (-45.9, 49.5), central apneas 1.8 (-9.7, 13.4), mixed apneas 1.6 (-14.8, 17.9), and hypopneas 4.3 (-12.4, 20.9). Conclusion Results support almost perfect reliability between auto-scoring and manual scoring of AHI, ArI, hypopneas, total apneas, and obstructive apneas, as well as moderate reliability for central and mixed apneas. There was good agreement between methods, with small mean differences; wider limits of agreement for specific type of apneas did not affect accuracy of the overall AHI. Thus, the auto-scoring algorithm appears reliable for event detection. Support (if any) Philips

It is unknown whether obesity modifies the effect of obstructive sleep apnea (OSA) and positive airway pressure (PAP) therapy on cardiac remodeling and NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels. We compared NT-proBNP and cardiac magnetic resonance imaging in adults without OSA (n=56) and nonobese (n=73; body mass index <30 kg/m 2 ) and obese (n=136; body mass index ≥30 kg/m 2 ) adults with OSA. We also investigated these traits in nonobese (n=45) and obese (n=78) participants with OSA adherent to 4 months of PAP treatment. At baseline, left ventricular mass to end-diastolic volume ratio, a measure of left ventricular concentricity, was greater in both nonobese and obese participants with OSA compared with those without OSA. Participants with OSA and obesity exhibited reduced phasic right atrial function. No significant differences in baseline NT-proBNP were observed across groups. The effect of PAP treatment on NT-proBNP and left atrial volume index was significantly modified by obesity. In nonobese participants, PAP therapy was associated with a decrease in NT-proBNP ( P <0.0001) without a change in left atrial volume index, whereas in obese participants, PAP was associated with an increase in left atrial volume index ( P =0.006) without a change in NT-proBNP. OSA was associated with left ventricular concentric remodeling independent of obesity and right atrial dysfunction in participants who were obese. PAP treatment was associated with reduced NT-proBNP in nonobese participants with OSA, but left atrial enlargement in obese participants with OSA, suggesting that PAP-induced reduction in BNP release (which is known to occur during obstructive apnea episodes) may lead to volume retention in obese participants with OSA. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01578031.

Rationale: Obesity is the primary risk factor for obstructive sleep apnea (OSA). Tongue fat is increased in obese persons with OSA, and may explain the relationship between obesity and OSA. Weight loss improves OSA, but the mechanism is unknown.Objectives: To determine the effect of weight loss on upper airway anatomy in subjects with obesity and OSA. We hypothesized that weight loss would decrease soft tissue volumes and tongue fat, and that these changes would correlate with reductions in apnea-hypopnea index (AHI).Methods: A total of 67 individuals with obesity and OSA (AHI ≥ 10 events/h) underwent a sleep study and upper airway and abdominal magnetic resonance imaging before and after a weight loss intervention (intensive lifestyle modification or bariatric surgery). Airway sizes and soft tissue, tongue fat, and abdominal fat volumes were quantified. Associations between weight loss and changes in these structures, and relationships to AHI changes, were examined.Measurements and Main Results: Weight loss was significantly associated with reductions in tongue fat and pterygoid and total lateral wall volumes. Reductions in tongue fat were strongly correlated with reductions in AHI (Pearson's rho = 0.62, P < 0.0001); results remained after controlling for weight loss (Pearson's rho = 0.36, P = 0.014). Reduction in tongue fat volume was the primary upper airway mediator of the relationship between weight loss and AHI improvement.Conclusions: Weight loss reduced volumes of several upper airway soft tissues in subjects with obesity and OSA. Improved AHI with weight loss was mediated by reductions in tongue fat. New treatments that reduce tongue fat should be considered for patients with OSA.

STUDY OBJECTIVES:Sleep spindles, a defining feature of stage N2 sleep, are maximal at central electrodes and are found in the frequency range of the electroencephalogram (EEG) (sigma 11-16 Hz) that is known to be heritable. However, relatively little is known about the heritability of spindles. Two recent studies investigating the heritability of spindles reported moderate heritability, but with conflicting results depending on scalp location and spindle type. The present study aimed to definitively assess the heritability of sleep spindle characteristics.METHODS:We utilized the polysomnography data of 58 monozygotic and 40 dizygotic same-sex twin pairs to identify heritable characteristics of spindles at C3/C4 in stage N2 sleep including density, duration, peak-to-peak amplitude, and oscillation frequency. We implemented and tested a variety of spindle detection algorithms and used two complementary methods of estimating trait heritability.RESULTS:We found robust evidence to support strong heritability of spindles regardless of detector method (h2 > 0.8). However not all spindle characteristics were equally heritable, and each spindle detection method produced a different pattern of results.CONCLUSIONS:The sleep spindle in stage N2 sleep is highly heritable, but the heritability differs for individual spindle characteristics and depends on the spindle detector used for analysis.

IntroductionThis study aimed to evaluate upper airway soft tissues and craniofacial structures differences between Chinese and Caucasian patients with obstructive sleep apnea (OSA).

STUDY OBJECTIVES:This study evaluated differences in upper airway, soft tissues and craniofacial structures between Asians from China and Europeans from Iceland with OSA using three-dimensional magnetic resonance imaging (MRI).METHODS:Airway sizes, soft tissue volumes, and craniofacial dimensions were compared between Icelandic (N = 108) and Chinese (N = 57) patients with oxygen desaturation index (ODI) ≥ 10 events/h matched for age, gender, and ODI. Mixed effects models adjusting for height or BMI and residual differences in age and ODI were utilized.RESULTS:In our matched sample, compared to Icelandic OSA patients, Chinese patients had smaller BMI (p < 0.0001) and neck circumference (p = 0.011). In covariate adjusted analyses, Chinese showed smaller retropalatal airway size (p ≤ 0.002), and smaller combined soft tissues, tongue, fat pads, and pterygoid (all p ≤ 0.0001), but male Chinese demonstrated a larger soft palate volume (p ≤ 0.001). For craniofacial dimensions, Chinese demonstrated bigger ANB angle (p ≤ 0.0196), differently shaped mandibles, including shorter corpus length (p < 0.0001) but longer ramus length (p < 0.0001), and a wider (p < 0.0001) and shallower (p ≤ 0.0001) maxilla.CONCLUSIONS:Compared to Icelandic patients of similar age, gender and ODI, Chinese patients had smaller retropalatal airway and combined soft tissue, but bigger soft palate volume (in males), and differently shaped mandible and maxilla with more bony restrictions. Results support an ethnic difference in upper airway anatomy related to OSA, which may inform targeted therapies.

Quality measures (QM) were developed by AASM in 2015 in order “to improve the quality of care for patients with sleep disorders” (AASM website) at sleep clinics and labs. Quality-of-life (QOL) measures are included as process and outcomes QMs for several sleep disorders. The Penn Sleep Center now collects the Epworth Sleepiness Scale (ESS), FOSQ-10 and PROMIS-10 Physical and Mental domains using an integrated EHR-based sleep questionnaire. EHR-based questionnaires are auto-linked to all new and return office visits and available for patients to complete from home or mobile using an EHR patient portal, or on waiting room tablets as part of the standard visit check-in process. EHR-based data were auto-exported, de-identified, and analyzed for benchmark QM. The aim was to determine the percentage of QOL data collected for all patients with sleep disorders at the Penn Sleep Center, and to evaluate QOL differences for new and returning patients. Of 2,844 patients seen over the 3 month study period, 700 (25%) were new and 2144 (75%) were returning patients. Nearly 80% of patients completed QOL metrics and >90% completed ESS. Each metric was better in all returning compared to all new patients, including the ESS (8.26 ± 5.26 vs. 9.03 ± 5.51, p=0.0016), FOSQ-10 (30.42 ± 7.56 vs. 29.16 ± 7.53, p=0.0003), and the PROMIS-10 Mental (13.03 ± 3.32 vs. 12.39 ± 3.36, p<0.0001) and Physical (13.87 ± 3.33 vs. 13.40 ± 3.25, p=0.0024) scores. Benchmark QOL data were successfully collected and exported using a recently developed EHR-based questionnaire in nearly 80% of patients. There were improved values for each metric in returning patients when compared to new patients during the same period. Obtaining these metrics systematically in a large majority of patients will allow for robust future analyses evaluating responses of QOL measures with therapy and comparisons between change of QOL for specific sleep disorders and therapies and for patient subgroups of interest. none.

Odds ratio product (ORP) is a continuous index of sleep depth derived from the relation of EEG powers in different frequency ranges to each other. It ranges from 0 (pattern only occurring during sleep) to 2.5 (pattern only occurring during wakefulness or in arousals). An ORP of 1.25 indicates a pattern with an equal probability of occurring during wakefulness or sleep. ORP has shown excellent correlation with arousability. Sleep deprivation triggers a homeostatic response in subsequent recovery sleep. We further characterized the properties of ORP by assessing its response on recovery sleep following prolonged sleep deprivation. 202 adults (mean age 28.1 ± 7.5 years; mean BMI 24.1 ± 4.3 kg/m2) with apnea-hypopnea and periodic limb movement indices <5 events/hr performed polysomnograms before and following a 36-hr sleep deprivation. ORP values from C3 and C4 signals were generated at 3-s intervals and averaged over NREM sleep (ORPNR). To assess the effects of sleep deprivation on ORP, we compared ORPNR in each participant on the baseline and recovery nights. Compared to baseline values, ORPNR decreased in recovery sleep in 189 subjects (94%) (mean±SD 0.57 ± 0.17 vs. 0.84 ± 0.20 (p =1E-54). An increase in delta and theta (collectively 0.33–6.3 Hz) powers, the conventional indicators of increased sleep pressure following sleep deprivation, occurred in 146 (72%) and 178 (88%), respectively. Beta power (16–35 Hz) decreased in 176 subjects (87%) during the recovery night. Subjects who had the highest ORP at baseline (lighter sleep) had the highest ORP during recovery, and vice versa (r=0.62; p=2E-22). The decrease in ORP on recovery sleep following sleep deprivation provides further strong evidence that ORP is a clinically useful measure of sleep depth. The apparent increase in sensitivity of ORP over conventional indices of sleep depth is likely related to the fact that ORP is a more inclusive index that is influenced not only by changes in slow frequencies but also by changes in beta power, which decreased following sleep deprivation. NIH 1P01-1HL094307.

Numerous studies have demonstrated that positive airway pressure (PAP) treatment of patients with obstructive sleep apnea (OSA) reduces blood pressure (BP), possibly by decreasing sympathetic activity. Obesity is a risk factor for OSA and hypertension and is characterized by increased sympathetic activity. In this study, we evaluated the impact of obesity on the BP response to PAP treatment in obese and non-obese OSA adults and the role of sympathetic activity in contributing to the BP response. We classified 188 subjects with OSA as obese (n=119) based on waist circumference (>107 cm in men and >96 cm in women) or non-obese (n=69). Participants underwent 24-hr ambulatory blood pressure monitoring (ABPM) and a 24-hr urinary catecholamine collection before and following 4 months of PAP treatment. Baseline apnea-hypopnea index was 37.3 ± 17.1[SD] and 34.2 ± 13.3 events/hr for obese and non-obese participants. There were no significant differences between groups in 24-hr mean arterial pressure (MAP, p=0.075) or 24-hr catecholamine excretion at baseline (p=0.769). Comparing changes from baseline between obese (n=65) and non-obese (n=35) OSA subjects adherent to PAP treatment, the two groups did not differ in the change in MAP (p=0.329), but the obese group had a greater reduction in nocturnal MAP (p=0.021), nocturnal diastolic BP (p =0.004), nocturnal-to-daytime ratios of MAP (p=0.043) and nocturnal diastolic BP (p=0.016). Mean 24-hr urinary norepinephrine decreased by 5.79 ng/mg creatinine (95% CI 8.04 to 3.54; p=0.0001) in obese OSA subjects, but no significant reduction occurred in non-obese OSA subjects. No significant relationship was observed between changes in log-transformed 24-hr urinary norepinephrine and changes in any ABPM measure. Compared to non-obese adults with OSA, obese adults with OSA had greater reductions in several nocturnal BP measures following 4 months of PAP treatment; however, these changes were not related to the large reduction in 24-hr urinary norepinephrine following PAP treatment in the obese group. Further studies are required to assess the physiologic basis of BP reduction induced by PAP in obese and non-obese OSA patients. NIH 1P01-1HL094307.

STUDY OBJECTIVES:Significant interindividual variability in sleepiness is observed in clinical populations with obstructive sleep apnea (OSA). This phenomenon is only partially explained by the apnea-hypopnea index (AHI). Understanding factors that lead to sleepiness is critical to effective management of patients with OSA. We examined demographic and other factors associated with sleepiness in OSA.METHODS:Prospective study of 283 patients with newly diagnosed OSA by polysomnography (AHI ≥ 5 per hour). Subjective sleepiness (Epworth Sleep Scale [ESS] ≥ 11) and objective sleepiness (psychomotor vigilance task [PVT] mean lapse ≥ 2) were assessed.RESULTS:Participants were classified into four groups (1: sleepy by ESS and PVT, 2: sleepy by PVT only, 3: sleepy by ESS only, and 4: nonsleepy reference group) and compared by generalized logit model. Shorter daily sleep duration by actigraphy and less morningness were associated with higher risk of sleepiness (Odds ratio [OR] = 0.52, 95% confidence interval [CI] 0.33-0.82 and OR = 0.89, CI 0.80-0.98, respectively). African-American race was associated with sleepiness (group 1, OR = 8.8, CI 2.8-27.3; group 2, OR = 16.6, CI 3.3-83.6; and group 3, OR = 3.3, CI 1.0-10.1). IL-6 level was higher in groups 1 and 3 (OR = 1.9, CI 1.0-3.4 and OR 2.0, CI 1.1-3.7, respectively).CONCLUSIONS:African-American race, short sleep duration, chronotype, and increased proinflammatory cytokine IL-6 level were associated with sleepiness in OSA. These findings will inform future investigations determining mechanisms of sleepiness in OSA.

CPAP and mask comfort is important to patient satisfaction and compliance. Yet the choice of the first mask varies across Sleep Centers and labs, DMEs, providers, patients, and coverage. We evaluated whether requesting masks changes after the initial CPAP fitting was associated with compliance or the type of medical insurance. We hypothesized that CPAP adherence would be higher in patients who changed their mask. Penn CPAP Program patients are all fitted by a lab technician, provider, or DME. We examined data on requests for replacement of the first mask among new CPAP prescriptions over a 6-month period (January - June 2017). We compared the rates of compliance in the first 90 days between those with and without mask replacements using chi-squared tests and whether these differences were dependent on specific insurers. Of 1065 new CPAP patients, 302 (28.4%) requested replacement of the first mask within the first 90 days. Rates of treatment compliance did not differ between those with (67.9%) or without (68.8%) CPAP mask replacements (p=0.7691). Patients with Medicaid and Medicare had a higher rate of mask replacements (31.1% and 32.3%, respectively) than patients with Commercial (25.9%) or other insurance (22.9%) on average, although the difference did not reach statistical significance (p=0.133). In this study of our patients with personal mask fittings, 28.4% requested a replacement of the first mask during the initial compliance period. At 90 days, their compliance was similar to those who did not change first masks. While changing a mask may take days/weeks out of the 90 day compliance window, compliance did not appear decreased by time taken to change to a second preferred mask. There was some evidence of increased mask replacement in Medicare or Medicaid patients. none.

The shift from volume-based to value-based payment models has increased the emphasis on quality measures (QM) when determining payment. Electronic health records (EHR) enable collection and use of large amounts of QM data, including patient reported outcomes (PRO). Benefits of EHR-based data include availability during individual patient visits and use for group-level data QM. However, efficient PRO collection into the EHR requires integration into the daily clinical workflow. We describe our experience in transitioning from paper-based to EHR-based collection of ESS for individual and group QM documentation. The ESS was administered with an EHR-based sleep questionnaire (SQ) to all new and returning patients at the Penn Sleep Center. Our goal was to collect ESS for >90% of all visits using the EHR. Originally, patients completed paper questionnaires in the waiting room and providers typed the ESS into EHR notes. Despite creation of an EHR provider digital data entry of ESS in 2010 and the use of PRO entry using the EHR-patient portal SQ in 2013, digital ESS collection remained inconsistent and <50%. In mid-April 2017, we introduced an additional ESS collection method using EHR-based waiting room tablets. Using auto-exported EHR-based ESS data, percent collection of EHR-based ESS were compared before (2014) and after (2017) tablet intervention. De-identified data were analyzed to compare percentages of ESS collection before and after the tablet intervention. EHR-based digital ESS data were collected for 1144 out of 2930 total visits (39.0%) from May-July 2014 (before tablets), and for 1776 out of the 1898 total visits (93.6%) from May-July 2017 (after tablets). Collection was significantly improved after tablets (p<0.0001). EHR-based ESS collection was improved from 2014 to 2017 with integration of tablets into the standard check-in workflow. Integration has enhanced digital EHR-based ESS collection to >90% of all clinic patients. ESS data auto-populate visit notes and save provider documentation time. Data also are available for EHR-based report exports to use for group PRO benchmarks and for AASM and CMS Outcomes QM. None.

STUDY OBJECTIVESThe American Academy of Sleep Medicine has published manuals for scoring polysomnograms that recommend time spent in non-rapid eye movement sleep stages (stage N1, N2, and N3 sleep) be reported. Given the well-established large interrater variability in scoring stage N1 and N3 sleep, we determined the range of time in stage N1 and N3 sleep scored by a large number of technologists when compared to reasonably estimated true values.METHODSPolysomnograms of 70 females were scored by 10 highly trained sleep technologists, two each from five different academic sleep laboratories. Range and confidence interval (CI = difference between the 5th and 95th percentiles) of the 10 times spent in stage N1 and N3 sleep assigned in each polysomnogram were determined. Average values of times spent in stage N1 and N3 sleep generated by the 10 technologists in each polysomnogram were considered representative of the true values for the individual polysomnogram. Accuracy of different technologists in estimating delta wave duration was determined by comparing their scores to digitally determined durations.RESULTSThe CI range of the ten N1 scores was 4 to 39 percent of total sleep time (% TST) in different polysomnograms (mean CI ± standard deviation = 11.1 ± 7.1 % TST). Corresponding range for N3 was 1 to 28 % TST (14.4 ± 6.1 % TST). For stage N1 and N3 sleep, very low or very high values were reported for virtually all polysomnograms by different technologists. Technologists varied widely in their assignment of stage N3 sleep, scoring that stage when the digitally determined time of delta waves ranged from 3 to 17 seconds.CONCLUSIONSManual scoring of non-rapid eye movement sleep stages is highly unreliable among highly trained, experienced technologists. Measures of sleep continuity and depth that are reliable and clinically relevant should be a focus of clinical research.

We previously analyzed CPAP use times in our urban sleep clinic before and after the 2009 CMS compliance rules for ongoing PAP coverage >90 days. We found no difference in use times despite changes in insurance rules, workflow and adherence interventions or ongoing mask, unit, tracking technology changes (2011 APSS abstract: CPAP Treatment Adherence in CPAP Clinics: Schutte-Rodin et al). Here, we examine whether type of insurance is associated with CPAP adherence rates. CPAP compliance rates were evaluated over a 30-month period (January 2015 to June 2017) among patients with Commercial insurance, Medicaid, Medicare or other insurance. Compliance was defined using the standard CMS rule PAP use times. We compared compliance rates among and between insurance carriers using chi-squared tests. De-identified data were analyzed for 4,285 patients with new PAPs set-up by a DME between January 1, 2015 and June 30, 2017. A total of 1776 (41.4%) of patients had Commercial insurance, 641 (15.0%) Medicaid, 1251 (29.2%) Medicare, and 617 (14.4%) had another or no insurance. The average PAP compliance among all patients was 68.6%. There were significant differences in compliance rates among insurance carriers (p<0.0001), with compliance rates among Commercial (73.9%) and Medicare (73.7%) patients significantly higher than compliance rates among patients with Medicaid (55.1%) or other or no insurance (57.4%). The identification of groups at increased risk of non-adherence to PAP is important in improving overall treatment adherence and efficacy, and, relatedly, in identifying risk-specific interventions to improve patient outcomes. Our data indicate that insurance type is an important predictor of CPAP adherence. Further studies are needed to understand the related factors mediating these associations. Additionally, given the increased focus on quality measures and provider pay for performance, and the expected negative effect of lower adherence, these factors should also be taken into account when evaluating performance metrics of individual providers, hospitals and DMEs. none.