Cost-Effectiveness of Sleep Apnea Diagnosis and Treatment in Hospitalized Persons With Moderate to Severe Traumatic Brain Injury.

OBJECTIVE: To assess the cost-effectiveness of alternative approaches to diagnose and treat obstructive sleep apnea (OSA) in patients with traumatic brain injury (TBI) during inpatient rehabilitation. SETTING: Data collected during the Comparison of Sleep Apnea Assessment Strategies to Maximize TBI Rehabilitation Participation and Outcome (C-SAS) clinical trial (NCT03033901) on an inpatient rehabilitation TBI cohort were used in this study. STUDY DESIGN: Decision tree analysis was used to determine the cost-effectiveness of approaches to diagnosing and treating sleep apnea. Costs were determined using 2021 Centers for Medicare and Medicaid Services reimbursement codes. Effectiveness was defined in terms of the appropriateness of treatment. Costs averted were extracted from the literature. A sensitivity analysis was performed to account for uncertainty. Analyses were performed for all severity levels of OSA and a subgroup of those with moderate to severe OSA. Six inpatient approaches using various phases of screening, testing, and treatment that conform to usual care or guideline-endorsed interventions were evaluated: (1) usual care; (2) portable diagnostic testing followed by laboratory-quality testing; (3) screening with the snoring, tiredness, observed apnea, high BP, BMI, age, neck circumference, and male gender (STOP-Bang) questionnaire; (4) Multivariable Apnea Prediction Index (MAPI) followed by portable diagnostic testing and laboratory-quality testing; (5) laboratory-quality testing for all; and (6) treatment for all patients. MAIN MEASURES: Cost, Effectiveness, and Incremental Cost-Effectiveness Ratio (ICER). RESULTS: Phased approaches utilizing screening and diagnostic tools were more effective in diagnosing and allocating treatment for OSA than all alternatives in patients with mild to severe and moderate to severe OSA. Usual care was more costly and less effective than all other approaches for mild to severe and moderate to severe OSA. CONCLUSIONS: Diagnosing and treating OSA in patients with TBI is a cost-effective strategy when compared with usual care.

Polysomnographic characteristics of excessive daytime sleepiness phenotypes in obstructive sleep apnea: results from the international sleep apnea global interdisciplinary consortium.

STUDY OBJECTIVES: Excessive daytime sleepiness (EDS) is a major symptom of obstructive sleep apnea (OSA). Traditional polysomnographic (PSG) measures only partially explain EDS in OSA. This study analyzed traditional and novel PSG characteristics of two different measures of EDS among patients with OSA. METHODS: Sleepiness was assessed using the Epworth Sleepiness Scale (>10 points defined as "risk of dozing") and a measure of general sleepiness (feeling sleepy ≥ 3 times/week defined as "feeling sleepy"). Four sleepiness phenotypes were identified: "non-sleepy," "risk of dozing only," "feeling sleepy only," and "both at risk of dozing and feeling sleepy." RESULTS: Altogether, 2083 patients with OSA (69% male) with an apnea-hypopnea index (AHI) ≥ 5 events/hour were studied; 46% were "non-sleepy," 26% at "risk of dozing only," 7% were "feeling sleepy only," and 21% reported both. The two phenotypes at "risk of dozing" had higher AHI, more severe hypoxemia (as measured by oxygen desaturation index, minimum and average oxygen saturation [SpO2], time spent < 90% SpO2, and hypoxic impacts) and they spent less time awake, had shorter sleep latency, and higher heart rate response to arousals than "non-sleepy" and "feeling sleepy only" phenotypes. While statistically significant, effect sizes were small. Sleep stages, frequency of arousals, wake after sleep onset and limb movement did not differ between sleepiness phenotypes after adjusting for confounders. CONCLUSIONS: In a large international group of patients with OSA, PSG characteristics were weakly associated with EDS. The physiological measures differed among individuals characterized as "risk of dozing" or "non-sleepy," while "feeling sleepy only" did not differ from "non-sleepy" individuals.

Five-year Transitions of Symptom Subtypes in Untreated Obstructive Sleep Apnea.

Objectives: It is unknown if symptom subtypes of obstructive sleep apnea (OSA) transition over time and what clinical factors may predict transitions. Methods: Data from 2,643 participants of the Sleep Heart Health Study with complete baseline and 5-year follow-up visits were analyzed. Latent Class Analysis on 14 symptoms at baseline and follow up determined symptom subtypes. Individuals without OSA (AHI<5) were incorporated as a known class at each time point. Multinomial logistic regression assessed the effect of age, sex, body mass index (BMI) and AHI on specific class transitions. Results: The sample consisted of 1,408 women (53.8%) and mean (SD) age 62.4 (10.5) years. We identified four OSA symptom subtypes at both baseline and follow-up visits: minimally symptomatic, disturbed sleep, moderately sleepy and excessively sleepy . Nearly half (44.2%) of the sample transitioned to a different subtype from baseline to follow-up visits; transitions to moderately sleepy were the most common (77% of all transitions). A five-year older age was associated with a 6% increase in odds to transit from excessively sleepy to moderately sleepy [OR (95% CI) = 1.06 (1.02, 1.12)]. Women had 2.35 times higher odds (95% CI: 1.27, 3.27) to transition from moderately sleepy to minimal symptoms . A 5-unit increase in BMI was associated with 2.29 greater odds (95% CI: 1.19, 4.38) to transition from minimal symptoms to excessively sleepy . Interpretation: While over half of the sample did not transition their subtype over 5 years, among those who did, the likelihood of transitioning between subtypes was significantly associated with a higher baseline age, higher baseline BMI and with women, but was not predicted by AHI. Clinical Trials: Sleep Heart Health Study (SHHS) Data Coordinating Center, (SHHS) https://clinicaltrials.gov/ct2/show/NCT00005275 , NCT00005275. Statement of significance: There is very little research assessing symptom progression and its contributions to clinical heterogeneity in OSA. In a large sample with untreated OSA, we grouped common OSA symptoms into subtypes and assessed if age, sex, or BMI predicted transitions between the subtypes over 5 years. Approximately half the sample transitioned to a different symptom subtype and improvements in symptom subtype presentation were common. Women and older individuals were more likely to transition to less severe subtypes, while increased BMI predicted transition to more severe subtype. Determining whether common symptoms like disturbed sleep or excessive daytime sleepiness occur early in the course of the disease or as a result of untreated OSA over an extended period can improve clinical decisions concerning diagnosis and treatment.

Facial and Intraoral Photographic Traits Related to Sleep Apnea in a Clinical Sample with Genetic Ancestry Analysis.

Rationale: Craniofacial and pharyngeal morphology influences risk for obstructive sleep apnea (OSA). Quantitative photography provides phenotypic information about these anatomical factors and is feasible in large samples. However, whether associations between morphology and OSA severity differ among populations is unknown. Objectives: The aim of this study was to examine this question in a large sample encompassing people from different ancestral backgrounds. Methods: Participants in SAGIC (Sleep Apnea Global Interdisciplinary Consortium) with genotyping data were included (N = 2,393). Associations between photography-based measures and OSA severity were assessed using linear regression, controlling for age, sex, body mass index, and genetic ancestry. Subgroups (on the basis of 1000 Genomes reference populations) were identified: European (EUR), East Asian, American, South Asian, and African (AFR). Interaction tests were used to assess if genetically determined ancestry group modified these relationships. Results: Cluster analysis of genetic ancestry proportions identified four ancestrally defined groups: East Asia (48.3%), EUR (33.6%), admixed (11.7%; 46% EUR, 27% Americas, and 22% AFR), and AFR (6.4%). Multiple anatomical traits were associated with more severe OSA independent of ancestry, including larger cervicomental angle (standardized ß [95% confidence interval (CI)] = 0.11 [0.06-0.16]; P < 0.001), mandibular width (standardized ß [95% CI] = 0.15 [0.10-0.20]; P < 0.001), and tongue thickness (standardized ß [95% CI] = 0.06 [0.02-0.10]; P = 0.001) and smaller airway width (standardized ß [95% CI] = -0.08 [-0.15 to -0.002]; P = 0.043). Other traits, including maxillary and mandibular depth angles and lower face height, demonstrated different associations with OSA severity on the basis of ancestrally defined subgroups. Conclusions: We confirm that multiple facial and intraoral photographic measurements are associated with OSA severity independent of ancestral background, whereas others differ in their associations among the ancestrally defined subgroups.

Scoring sleep with artificial intelligence enables quantification of sleep stage ambiguity: hypnodensity based on multiple expert scorers and auto-scoring.

STUDY OBJECTIVES: To quantify the amount of sleep stage ambiguity across expert scorers and to validate a new auto-scoring platform against sleep staging performed by multiple scorers. METHODS: We applied a new auto-scoring system to three datasets containing 95 PSGs scored by 6-12 scorers, to compare sleep stage probabilities (hypnodensity; i.e. the probability of each sleep stage being assigned to a given epoch) as the primary output, as well as a single sleep stage per epoch assigned by hierarchical majority rule. RESULTS: The percentage of epochs with 100% agreement across scorers was 46 ± 9%, 38 ± 10% and 32 ± 9% for the datasets with 6, 9, and 12 scorers, respectively. The mean intra-class correlation coefficient between sleep stage probabilities from auto- and manual-scoring was 0.91, representing excellent reliability. Within each dataset, agreement between auto-scoring and consensus manual-scoring was significantly higher than agreement between manual-scoring and consensus manual-scoring (0.78 vs. 0.69; 0.74 vs. 0.67; and 0.75 vs. 0.67; all p < 0.01). CONCLUSIONS: Analysis of scoring performed by multiple scorers reveals that sleep stage ambiguity is the rule rather than the exception. Probabilities of the sleep stages determined by artificial intelligence auto-scoring provide an excellent estimate of this ambiguity. Compared to consensus manual-scoring, sleep staging derived from auto-scoring is for each individual PSG noninferior to manual-scoring meaning that auto-scoring output is ready for interpretation without the need for manual adjustment.

A multifactorial intervention to increase adherence to oral appliance therapy with a titratable mandibular advancement device for obstructive sleep apnea: a randomized controlled trial.

PURPOSE: Obstructive sleep apnea (OSA) is a common chronic condition, associated with several conditions that account for leading causes of mortality. Adherence to treatment of a chronic condition is, along with treatment efficacy, a major determinant of treatment outcome. The aim of this study was to test whether or not a multifactorial intervention in addition to standard care increases adherence rates in patients using a titratable oral appliance for OSA. METHODS: All subjects were 18 years old or older, had a diagnosis of OSA, and were treated with an oral appliance with an embedded sensor to measure appliance wear time objectively. The control group received routine care, while the experimental subjects received an additional multifactorial intervention. Comparison of adherence was at 30 days (Phase I) and 90 days (Phase II) after appliance delivery. RESULTS: Data are reported for 82 subjects in Phase I (control 43; experimental 39) and 66 subjects in Phase II (control 36; experimental 30). There were no significant differences for age, sex, body mass index, and apnea-hypopnea index (p > 0.05) between groups. In both Phase I and Phase II, the mean number of nights the appliance was worn 4 or more hours and the mean time the appliance was worn nightly were significantly greater in the experimental than in the control group (p < 0.05). CONCLUSIONS: Interventions were well received by subjects and can be carried out by auxiliary personnel. The experimental interventions resulted in clinically important and statistically significant improvements in patient adherence to treatment.

Continuous positive airway pressure and adverse cardiovascular events in obstructive sleep apnea: are participants of randomized trials representative of sleep clinic patients?

STUDY OBJECTIVES: Randomized controlled trials (RCTs) have shown no reduction in adverse cardiovascular (CV) events in patients randomized to continuous positive airway pressure (CPAP) therapy for obstructive sleep apnea (OSA). This study examined whether randomized study populations were representative of OSA patients attending a sleep clinic. METHODS: Sleep clinic patients were 3,965 consecutive adults diagnosed with OSA by in-laboratory polysomnography from 2006 to 2010 at a tertiary hospital sleep clinic. Characteristics of these patients were compared with participants of five recent RCTs examining the effect of CPAP on adverse CV events in OSA. The percentage of patients with severe (apnea-hypopnea index, [AHI] ≥ 30 events/h) or any OSA (AHI ≥ 5 events/h) who met the eligibility criteria of each RCT was determined, and those criteria that excluded the most patients identified. RESULTS: Compared to RCT participants, sleep clinic OSA patients were younger, sleepier, more likely to be female and less likely to have established CV disease. The percentage of patients with severe or any OSA who met the RCT eligibility criteria ranged from 1.2% to 20.9% and 0.8% to 21.9%, respectively. The eligibility criteria that excluded most patients were preexisting CV disease, symptoms of excessive sleepiness, nocturnal hypoxemia and co-morbidities. CONCLUSIONS: A minority of sleep clinic patients diagnosed with OSA meet the eligibility criteria of RCTs of CPAP on adverse CV events in OSA. OSA populations in these RCTs differ considerably from typical sleep clinic OSA patients. This suggests that the findings of such OSA treatment-related RCTs are not generalizable to sleep clinic OSA patients.Randomized Intervention with Continuous Positive Airway Pressure in CAD and OSA (RICCADSA) trial, https://clinicaltrials.gov/ct2/show/NCT00519597, ClinicalTrials.gov number, NCT00519597.Usefulness of Nasal Continuous Positive Airway Pressure (CPAP) Treatment in Patients with a First Ever Stroke and Sleep Apnea Syndrome, https://clinicaltrials.gov/ct2/show/NCT00202501, ClinicalTrials.gov number, NCT00202501.Effect of Continuous Positive Airway Pressure (CPAP) on Hypertension and Cardiovascular Morbidity-Mortality in Patients with Sleep Apnea and no Daytime Sleepiness, https://clinicaltrials.gov/ct2/show/NCT00127348, ClinicalTrials.gov number, NCT00127348.Continuous Positive Airway Pressure (CPAP) in Patients with Acute Coronary Syndrome and Obstructive Sleep Apnea (OSA) (ISAACC), https://clinicaltrials.gov/ct2/show/NCT01335087, ClinicalTrials.gov number, NCT01335087.

Diagnostic Performance of Machine Learning-Derived OSA Prediction Tools in Large Clinical and Community-Based Samples.

BACKGROUND: Prediction tools without patient-reported symptoms could facilitate widespread identification of OSA. RESEARCH QUESTION: What is the diagnostic performance of OSA prediction tools derived from machine learning using readily available data without patient responses to questionnaires? Also, how do they compare with STOP-BANG, an OSA prediction tool, in clinical and community-based samples? STUDY DESIGN AND METHODS: Logistic regression and machine learning techniques, including artificial neural network (ANN), random forests (RF), and kernel support vector machine, were used to determine the ability of age, sex, BMI, and race to predict OSA status. A retrospective cohort of 17,448 subjects from sleep clinics within the international Sleep Apnea Global Interdisciplinary Consortium (SAGIC) were randomly split into training (n = 10,469) and validation (n = 6,979) sets. Model comparisons were performed by using the area under the receiver-operating curve (AUC). Trained models were compared with the STOP-BANG questionnaire in two prospective testing datasets: an independent clinic-based sample from SAGIC (n = 1,613) and a community-based sample from the Sleep Heart Health Study (n = 5,599). RESULTS: The AUCs (95% CI) of the machine learning models were significantly higher than logistic regression (0.61 [0.60-0.62]) in both the training and validation datasets (ANN, 0.68 [0.66-0.69]; RF, 0.68 [0.67-0.70]; and kernel support vector machine, 0.66 [0.65-0.67]). In the SAGIC testing sample, the ANN (0.70 [0.68-0.72]) and RF (0.70 [0.68-0.73]) models had AUCs similar to those of the STOP-BANG (0.71 [0.68-0.72]). In the Sleep Heart Health Study testing sample, the ANN (0.72 [0.71-0.74]) had AUCs similar to those of STOP-BANG (0.72 [0.70-0.73]). INTERPRETATION: OSA prediction tools using machine learning without patient-reported symptoms provide better diagnostic performance than logistic regression. In clinical and community-based samples, the symptomless ANN tool has diagnostic performance similar to that of a widely used prediction tool that includes patient symptoms. Machine learning-derived algorithms may have utility for widespread identification of OSA.

Heart rate variability during wakefulness as a marker of obstructive sleep apnea severity.

STUDY OBJECTIVES: Patients with obstructive sleep apnea (OSA) exhibit heterogeneous heart rate variability (HRV) during wakefulness and sleep. We investigated the influence of OSA severity on HRV parameters during wakefulness in a large international clinical sample. METHODS: 1247 subjects (426 without OSA and 821 patients with OSA) were enrolled from the Sleep Apnea Global Interdisciplinary Consortium. HRV parameters were calculated during a 5-minute wakefulness period with spontaneous breathing prior to the sleep study, using time-domain, frequency-domain and nonlinear methods. Differences in HRV were evaluated among groups using analysis of covariance, controlling for relevant covariates. RESULTS: Patients with OSA showed significantly lower time-domain variations and less complexity of heartbeats compared to individuals without OSA. Those with severe OSA had remarkably reduced HRV compared to all other groups. Compared to non-OSA patients, those with severe OSA had lower HRV based on SDNN (adjusted mean: 37.4 vs. 46.2 ms; p < 0.0001), RMSSD (21.5 vs. 27.9 ms; p < 0.0001), ShanEn (1.83 vs. 2.01; p < 0.0001), and Forbword (36.7 vs. 33.0; p = 0.0001). While no differences were found in frequency-domain measures overall, among obese patients there was a shift to sympathetic dominance in severe OSA, with a higher LF/HF ratio compared to obese non-OSA patients (4.2 vs. 2.7; p = 0.009). CONCLUSIONS: Time-domain and nonlinear HRV measures during wakefulness are associated with OSA severity, with severe patients having remarkably reduced and less complex HRV. Frequency-domain measures show a shift to sympathetic dominance only in obese OSA patients. Thus, HRV during wakefulness could provide additional information about cardiovascular physiology in OSA patients. CLINICAL TRIAL INFORMATION: A Prospective Observational Cohort to Study the Genetics of Obstructive Sleep Apnea and Associated Co-Morbidities (German Clinical Trials Register - DKRS, DRKS00003966) https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00003966.

Randomized clinical trials of cardiovascular disease in obstructive sleep apnea: understanding and overcoming bias.

Three recent randomized control trials (RCTs) found that treatment of obstructive sleep apnea (OSA) with continuous positive airway pressure (CPAP) did not reduce rates of future cardiovascular events. This article discusses the biases in these RCTs that may explain their negative results, and how to overcome these biases in future studies. First, sample selection bias affected each RCT. The subjects recruited were not patients typically presenting for treatment of OSA. In particular, subjects with excessive sleepiness were excluded due to ethical concerns. As recent data indicate that the excessively sleepy OSA subtype has increased cardiovascular risk, subjects most likely to benefit from treatment were excluded. Second, RCTs had low adherence to therapy. Reported adherence is lower than found clinically, suggesting it is in part related to selection bias. Each RCT showed a CPAP benefit consistent with epidemiological studies when restricting to adherent patients, but was underpowered. Future studies need to include sleepy individuals and maximize adherence. Since it is unethical and impractical to randomize very sleepy subjects to no therapy, alternative designs are required. Observational designs using propensity scores, which are accepted by FDA for studies of medical devices, provide an opportunity. The design needs to ensure covariate balance, including measures assessing healthy user and healthy adherer biases, between regular users of CPAP and non-users. Sensitivity analyses can evaluate the robustness of results to unmeasured confounding, thereby improving confidence in conclusions. Thus, these designs can robustly assess the cardiovascular benefit of CPAP in real-world patients, overcoming biases in RCTs.

Is snoring during pregnancy a predictor of later life obstructive sleep apnoea? A case-control study.

BACKGROUND: Obstructive sleep apnoea (OSA) appears common in pregnancy. Complications of pregnancy such as gestational diabetes and hypertension predispose women to cardiometabolic disease in later life. It is unknown if snoring during pregnancy is a risk marker for later-life OSA. METHODS: We analysed data from N = 897 women in the Sleep Apnea Global Interdisciplinary Consortium (SAGIC), which recruited patients attending sleep clinics at 11 sites. There were 577 cases with current OSA and 320 controls. Cases were further categorised into mild, moderate, and severe OSA based on apnoea-hypopnoea index. Retrospective self-report of snoring during pregnancy was the exposure of interest and was reported by 2.9% of cases and 3.4% of controls. RESULTS: Multinomial regression demonstrated that snoring during a previous pregnancy was not significantly associated with mild (OR 0.34, 95% CI 0.09-1.25, p = 0.10), moderate (OR 0.69, 95% CI 0.21-2.19, p = 0.52), or severe OSA (OR 1.86, 95% CI 0.77-4.48, p = 0.17) compared to no snoring during pregnancy. Results were unchanged after adjustment for age, body mass index, and ethnicity. 79% of women reported current snoring but all who snored during pregnancy reported current snoring. CONCLUSIONS: Women who snore during pregnancy continue snoring in later-life but do not appear more likely to develop OSA. These findings are limited by self-reported data, recall bias, and small numbers of women who reported snoring during pregnancy. A prospective study with objective measurement of sleep and snoring during pregnancy is needed to examine the links between sleep disorders in pregnancy with health in later life.