Impact of wait times for treatment on clinical outcomes in patients with obstructive sleep apnoea: protocol for a randomised controlled trial.

Background: Obstructive sleep apnoea (OSA) is a common chronic condition that is associated with significant morbidity and economic cost. Prolonged wait times are increasingly being recognised as a barrier to diagnosis and treatment of many chronic diseases; however, no study to date has prospectively evaluated the impact of wait times on health outcomes in OSA. Objective: The purpose of this study is to determine whether treatment outcomes for individuals with OSA differ between patients managed using an expedited versus standard pathway. Methods: A pragmatic randomised controlled trial design will be used with a target sample size of 200 adults. Participants with clinically significant uncomplicated OSA will be recruited through referrals to a large tertiary care sleep centre (Calgary, AB, Canada) and randomised to either early management (within 1 month) or usual care (∼6 months) with a 1:1 allocation using a concealed computer-generated randomisation sequence. The primary outcome will be adherence to positive airway pressure (PAP) therapy at 3 months after treatment initiation. Secondary outcomes will include change in sleepiness, quality of life, patient satisfaction, and patient engagement with therapy from baseline to 3 months after PAP initiation, measured using validated questionnaires and qualitative methods. Anticipated results: This study will determine whether expedited care for OSA leads to differences in PAP adherence and/or patient-reported outcomes. More broadly, the findings of this study may improve the understanding of how wait time reductions impact health outcomes for other chronic diseases.

Effects of Wait Times on Treatment Adherence and Clinical Outcomes in Patients With Severe Sleep-Disordered Breathing: A Secondary Analysis of a Noninferiority Randomized Clinical Trial.

Importance: Sleep-disordered breathing (SDB) is common and associated with substantial adverse health consequences. Long wait times for SDB care are commonly reported; however, it is unclear whether wait times for care are associated with clinical outcomes. Objective: To evaluate the association of wait times for care with clinical outcomes for patients with severe SDB. Design, Setting, and Participants: This study is a secondary analysis of a randomized clinical noninferiority trial comparing management by alternative care practitioners (ACPs) with traditional sleep physician-led care between October 2014 and May 2017. The study took place at Foothills Medical Centre Sleep Centre, a tertiary care multidisciplinary sleep clinic at the University of Calgary. Patients with severe SDB (defined as a respiratory event index ≥30 events per hour during home sleep apnea testing, mean nocturnal oxygen saturation ≤85%, or suspected sleep hypoventilation syndrome) were recruited for the study. Patients were excluded if they were suspected of having a concomitant sleep disorder other than SDB or had previously been treated with positive airway pressure (PAP) therapy for SDB. Data were analyzed from October 2017 to January 2020. Main Outcomes and Measures: Outcomes were assessed 3 months after treatment initiation with adherence to PAP therapy as the primary outcome. Secondary outcomes included Epworth Sleepiness Scale score, health-related quality of life, and patient satisfaction measured using the Visit-Specific Satisfaction Instrument-9. Multiple regression models were used to assess the associations between wait times and each of the outcomes. t tests were used to compare wait times for patients who were adherent to PAP therapy (≥4 hours per night for 70% of nights) with those for nonadherent patients. Results: One hundred fifty-six patients (112 [71.8%] men; mean [SD] age, 56 [12] years) were included in the analysis. The mean time from referral to initial visit was 88 days (95% CI, 79 to 96 days), and the mean time to treatment was 123 days (95% CI, 112 to 133 days). Shorter wait time to treatment initiation was associated with adherence to PAP therapy (odds ratio, 0.99; 95% CI, 0.98 to 0.99; P = .04), greater improvement in Epworth Sleepiness Scale score (mean coefficient, -9.37; 95% CI, -18.51 to -0.24; P = .04), and higher Visit-Specific Satisfaction Instrument-9 score (mean coefficient, -0.024; 95% CI, -0.047 to -0.0015; P = .04) at 3 months. Compared with nonadherent patients, those who were adherent to treatment waited a mean of 15 fewer days (95% CI, 12 to 19 days) for initial assessment (P = .07) and 30 fewer days (95% CI, 23 to 35 days) for treatment initiation (P = .008). Conclusions and Relevance: Earlier initiation of treatment for severe SDB was associated with better PAP adherence and greater improvements in daytime sleepiness and patient satisfaction. These findings suggest that system interventions to improve timely access may modify patient behavior and improve clinical outcomes. Trial Registration: ClinicalTrials.gov Identifier: NCT02191085.

A Randomized Controlled Trial of an Alternative Care Provider Clinic for Severe Sleep-disordered Breathing.

Rationale: Lack of timely access to diagnosis and treatment of sleep-disordered breathing (SDB) has sparked interest in using nonphysician providers. Previous studies of these alternative care providers (ACPs) excluded patients with more complicated forms of SDB and did not directly explore the impacts of a model incorporating ACPs on healthcare system performance, such as wait times.Objectives: To evaluate the use of ACPs in the management of patients with severe SDB from a clinical and system perspective.Methods: In this noninferiority study, patients with severe SDB (N = 156) were enrolled from October 2014 to July 2016 and randomized to either sleep physician management or management by ACP with same-day sleep physician review. Severe SDB was defined as one of 1) respiratory event index greater than 30/h, 2) mean nocturnal oxygen saturation less than 85%, and 3) arterial carbon dioxide greater than 45 mm Hg with respiratory event index greater than 15/h. The primary outcome was nightly positive airway pressure adherence at 3 months, using a noninferiority margin of 1 hour. Secondary outcomes included sleepiness, quality of life, patient satisfaction, wait times for diagnosis and treatment initiation, and demand for further testing and clinical assessment. Outcomes were evaluated using modified intention-to-treat and per-protocol analyses.Results: Care delivery using ACPs was indeterminate compared with sleep physician care with respect to treatment adherence, because the 95% confidence interval included the noninferiority margin of 1 hour (mean difference, -0.5 [-1.49 to 0.49] h). Patients in the ACP arm reported greater improvements in sleepiness and quality of life; wait times were shorter for initial assessment (28%) and treatment initiation (18%). There was no difference in demand for sleep testing or clinical follow-up. Per-protocol analysis revealed similar results.Conclusions: Management of severe SDB using ACPs was indeterminate compared with sleep physician care. The small decrease in adherence in the ACP arm was balanced by benefits in patient-reported outcomes and reduction in wait times. In systems with unacceptably long wait times for SDB diagnosis and treatment, a small decrease in treatment adherence, as was observed in this study, may be an acceptable trade-off to improve access to care for patients with severe SDB.Clinical trial registered with www.clinicaltrials.gov (NCT02191085).

Evaluation of an alternative care provider clinic for severe sleep-disordered breathing: a study protocol for a randomised controlled trial.

INTRODUCTION: Despite the high prevalence of sleep-disordered breathing (SDB) and the significant health consequences associated with untreated disease, access to diagnosis and treatment remains a challenge. Even patients with severe SDB (severe obstructive sleep apnoea or hypoventilation), who are at particularly high risk of adverse health effects, are subject to long delays. Previous research has demonstrated that, within a sleep clinic, management by alternative care providers (ACPs) is effective for patients with milder forms of SDB. The purpose of this study is to compare an ACP-led clinic (ACP Clinic) for patients with severe SDB to physician-led care, from the perspective of clinical outcomes, health system efficiency and cost. METHODS AND ANALYSIS: The study is a randomised, controlled, non-inferiority study in which patients who are referred with severe SDB are randomised to management by a sleep physician or by an ACP. ACPs will be supervised by sleep physicians for safety. The primary outcome is positive airway pressure (PAP) adherence after 3 months of therapy. Secondary outcomes include: long-term PAP adherence; clinical response to therapy; health-related quality of life; patient satisfaction; healthcare usage; wait times from referral to treatment initiation and cost-effectiveness. The economic analysis will be performed using the perspective of a publicly funded healthcare system. ETHICS AND DISSEMINATION: Ethics approval was obtained from the Conjoint Health Research Ethics Board (ID: REB13-1280) at the University of Calgary. Results from this study will be disseminated through presentations at scientific conferences and publication in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT02191085; Pre-results.

Using Operational Analysis to Improve Access to Pulmonary Function Testing.

Background. Timely pulmonary function testing is crucial to improving diagnosis and treatment of pulmonary diseases. Perceptions of poor access at an academic pulmonary function laboratory prompted analysis of system demand and capacity to identify factors contributing to poor access. Methods. Surveys and interviews identified stakeholder perspectives on operational processes and access challenges. Retrospective data on testing demand and resource capacity was analyzed to understand utilization of testing resources. Results. Qualitative analysis demonstrated that stakeholder groups had discrepant views on access and capacity in the laboratory. Mean daily resource utilization was 0.64 (SD 0.15), with monthly average utilization consistently less than 0.75. Reserved testing slots for subspecialty clinics were poorly utilized, leaving many testing slots unfilled. When subspecialty demand exceeded number of reserved slots, there was sufficient capacity in the pulmonary function schedule to accommodate added demand. Findings were shared with stakeholders and influenced scheduling process improvements. Conclusion. This study highlights the importance of operational data to identify causes of poor access, guide system decision-making, and determine effects of improvement initiatives in a variety of healthcare settings. Importantly, simple operational analysis can help to improve efficiency of health systems with little or no added financial investment.

Reporting, learning and the culture of safety.

Systems that provide healthcare workers with the opportunity ot report hazards, hazardous situations errors, close calls and adverse events make it possible for an organization that receives such reports tu use these opportunities to learn and /or hold people accountable for their actions. When organizational learning is the primary goal, reporting should be confidential, voluntary and easy to perform and should lead to risk mitigation strategies following appropriate analysis; conversely, when the goal is accountability, reporting is more likely to be made mandatory. reporting systems do not necessarily equate to safer patient care and have been criticized for capturing too many mundane events but only a small minority of important events. reporting has been inappropriately equated with patients safety activity and mistakenly used for "measuring" system safety. However, if properly designed and supported, a reporting system can be an important component of an organizational strategy ot foster a safety culture.

Building a safety and quality culture in healthcare: where it starts.

Healthcare in Canada underachieves stakeholders' expectations for safe, high-quality care. The authors maintain that a common understanding of, and vision for, what is required to achieve improved outcomes for patients is missing. Educating tomorrow's healthcare professionals is paramount to address this critical shortfall. However, healthcare educational institutions must themselves break out of a 20th-century paradigm of viewing healthcare safety and quality as functions of individual healthcare providers rather than as properties of the clinical micro- and meso-systems within which they function and are a part. Canadian healthcare systems are ailing; like treating a sick patient, interventions should be grounded on a solid understanding of anatomy (structure) and physiology (function). The Healthcare Encounter Safety and Quality Model (HESQM) highlights the structures underlying healthcare delivery and the key system functions required to achieve safe, high-quality care. The model has been used to frame the University of Calgary Faculty of Medicine's educational strategy for achieving safer, higher-quality care. The HESQM is based on leadership - leaders whose decisions and actions are guided by core safety and quality principles. Today's and especially tomorrow's healthcare leaders require a common understanding of how to achieve higher-performing healthcare systems; it is the responsibility of Canada's post-secondary institutions to deliver it.

What is the value and impact of quality and safety teams? A scoping review.

BACKGROUND: The purpose of this study was to conduct a scoping review of the literature about the establishment and impact of quality and safety team initiatives in acute care. METHODS: Studies were identified through electronic searches of Medline, Embase, CINAHL, PsycINFO, ABI Inform, Cochrane databases. Grey literature and bibliographies were also searched. Qualitative or quantitative studies that occurred in acute care, describing how quality and safety teams were established or implemented, the impact of teams, or the barriers and/or facilitators of teams were included. Two reviewers independently extracted data on study design, sample, interventions, and outcomes. Quality assessment of full text articles was done independently by two reviewers. Studies were categorized according to dimensions of quality. RESULTS: Of 6,674 articles identified, 99 were included in the study. The heterogeneity of studies and results reported precluded quantitative data analyses. Findings revealed limited information about attributes of successful and unsuccessful team initiatives, barriers and facilitators to team initiatives, unique or combined contribution of selected interventions, or how to effectively establish these teams. CONCLUSIONS: Not unlike systematic reviews of quality improvement collaboratives, this broad review revealed that while teams reported a number of positive results, there are many methodological issues. This study is unique in utilizing traditional quality assessment and more novel methods of quality assessment and reporting of results (SQUIRE) to appraise studies. Rigorous design, evaluation, and reporting of quality and safety team initiatives are required.

Excessive daytime sleepiness is associated with increased health care utilization among patients referred for assessment of OSA.

STUDY OBJECTIVES: Excessive daytime sleepiness is an important public health concern associated with increased morbidity and mortality. However, in the absence of sleep diagnostic testing, it is difficult to separate the independent effects of sleepiness from those of intrinsic sleep disorders such as obstructive sleep apnea (OSA). The objective of this study was to determine if excessive daytime sleepiness was independently associated with increased health care utilization among patients referred for assessment of OSA. DESIGN: Cross-sectional study. SETTING/PARTICIPANTS: 2149 adults referred for sleep diagnostic testing between July 2005 and August 2007. INTERVENTIONS: N/A. MEASUREMENTS: Subjective daytime sleepiness was defined as an Epworth Sleepiness Scale score ≥10. Health care use (outpatient physician visits, all-cause hospitalizations, and emergency department visits) was determined from Alberta Health and Wellness administrative databases for the 18-month period preceding their sleep study. Rates of health resource use were analyzed using negative binomial regression, with predictors of increased health care use determined using logistic regression. RESULTS: excessive daytime sleepiness was associated with an increased rate of outpatient physician visits after adjustment for demographic variables, sleep medication use, hypertension, diabetes, depression, and OSA severity (rate ratio [RR]: 1.09 (95% confidence interval [CI]: 1.01, 1.18, P = 0.02) compared to non-sleepy subjects. There was an interaction between severe OSA and sleepiness (RR: 1.22 [95% CI: 1.06, 1.41]), although OSA was not an independent predictor of health care use. Also, sleepy patients with treated depression had a lower likelihood of outpatient visits (RR: 0.95 [95% CI: 0.86, 1.05]). Finally, sleepiness was an independent predictor of increased health care use for outpatient physician visits (odds ratio [OR]: 1.25 [95% CI: 1.00, 1.57; P = 0.048]) and all-cause hospitalizations (OR: 3.94 [95% CI: 1.03, 15.04; P = 0.046]). CONCLUSIONS: Excessive daytime sleepiness is associated with increased health care utilization among patients referred for assessment of OSA. Further investigation is required to determine whether the findings are related to direct effects of sleepiness, or in part, to interactions with other comorbidity such as OSA.

Clinical usefulness of home oximetry compared with polysomnography for assessment of sleep apnea.

The practical purpose of diagnostic assessment in most cases of obstructive sleep apnea is to predict which patients have symptoms that will improve on treatment. We measured the accuracy with which clinicians make this prediction using polysomnography compared with oximeter-based home monitoring. Patients referred to a sleep center with suspicion of symptomatic obstructive sleep apnea were randomized to have polysomnography or home monitoring. Patients with comorbidity or physiologic consequences of sleep apnea were excluded. Sleep specialists estimated the likelihood of success of treatment as greater than 50% (predicted success) or less than 50% (predicted failure) on the basis of clinical data and test results. All patients were treated for 4 weeks with autoadjusting continuous positive airway pressure. Success was defined as an increase greater than 1.0 in Sleep Apnea Quality of Life Index. Correct prediction rates were compared. Two hundred eighty-eight patients were enrolled. Initial patient characteristics, compliance, and improvement in quality of life at 4 weeks were not different in the two groups. The correct prediction rate was 0.61 with polysomnography and 0.64 with home monitoring (not significant). We conclude that the ability of physicians to predict the outcome of continuous positive airway treatment in individual patients is not significantly better with polysomnography than with home oximeter-based monitoring.

Comparison of home oximetry monitoring with laboratory polysomnography in children.

STUDY OBJECTIVES: To measure the accuracy and reliability of a portable home oximetry monitor with an automated analysis for the diagnosis of obstructive sleep apnea (OSA) in children. DESIGN: Prospective cohort study. SETTING: Alberta Lung Association Sleep Center, Alberta Children's Hospital Sleep Clinic. STUDY SUBJECTS: Consecutive, otherwise healthy children, aged 4 to 18 years, presenting to the Pediatric Sleep Service at the Alberta Children's Hospital for assessment of possible OSA. INTERVENTIONS: All subjects underwent 2 nights of monitoring in the home with an oximetry-based portable monitor with an automatic internal scoring algorithm. A third night of monitoring was done simultaneously with computerized laboratory polysomnography according to American Thoracic Society guidelines. MEASUREMENTS AND RESULTS: Both test-retest reliability of the portable monitor-based desaturation index (DI) between 2 nights at home and between laboratory and home were high using the Bland and Altman analysis (mean agreement, 0.32 and 0.64; limits of agreement, - 8.00 to 8.64 and - 0.75 to 6.50, respectively). The polysomnographic apnea-hypopnea index (AHI) agreed poorly with the portable monitor DI (mean difference, 1.27; limits of agreement, - 12.02 to 15.02). The sensitivity and specificity of the monitor for the identification of moderate sleep apnea (polysomnography AHI > 5/h) were 67% and 60%, respectively. CONCLUSION: Portable monitoring based only on oximetry alone is not adequate for the identification of OSA in otherwise healthy children.

Measuring agreement between diagnostic devices.

There is growing interest in using portable monitoring for investigating patients with suspected sleep apnea. Research studies typically report portable monitoring results in comparison with the results of sleep laboratory-based polysomnography. A systematic review of this research has recently been completed by a joint working group of the American College of Chest Physicians, the American Thoracic Society, and the American Academy of Sleep Medicine. The methods for comparing the results of portable monitors and polysomnography include product-moment correlation, intraclass correlation, mean differences/limits of agreement, sensitivity, specificity, and likelihood ratios. Each approach has advantages and limitations, which are highlighted in this review.

Quality of life in sleep disorders.

Quality of life is a major outcome variable in choosing and evaluating treatment alternatives for sleep disorders. However, the number of well validated and sufficiently responsive quality of life measures for use with this population is limited. The SF-36, Nottingham Health Profile (NHP) and Sickness Impact Profile (SIP) are the most frequently used generic measures. The Functional Outcomes of Sleep Questionnaire (FOSQ) and Sleep Apnoea Quality of Life Index (SAQLI) are useful as condition/disease specific measures. However there are not yet specific measures in common use for other sleep disorders. Results across the sleep disorders that have been studied, primarily sleep apnea, narcolepsy, restless legs and insomnia, have consistently shown poorer quality of life than population norms prior to treatment, particularly in those dimensions related to sleep, energy and fatigue. Before treatment scorespes typically are of similar magnitude to those found among individuals with other chronic diseases such as hypertension and chronic obstructive pulmonary disease. With treatment quality of life scores may or may not improve to the level of population norms, suggesting that currently available treatments may not fully reverse the effects of the common sleep disorders.

State of home sleep studies.

Many different portable monitors have been used to assess patients with suspected sleep apnea. There is limited evidence for the use of type 2 monitors, especially in the unattended setting in which there may be high rates of data loss. Type 3 monitors have low likelihood ratios for negative tests and can be used to "rule out" sleep apnea. The ability of type 3 monitors to "rule in" sleep apnea is less convincing, but this may improve with the use of improved technology, such as nasal pressure transducers. Type 4 monitors usually use oximetry and can be used to "rule out" sleep apnea. Higher sampling rates and improved analysis algorithms can improve the specificity of these monitors; hence, likelihood ratios for a positive test result can be high enough with some monitors to "rule in" sleep apnea as well. Not all monitors record and analyze signals in the same way; it is not possible to generalize results from one monitor across all monitors of a particular type. Limited evidence is available for many portable monitors in the unattended setting, and further research is required in this area. Clinicians should identify how they plan to use a portable monitor: as a mechanism to exclude disease in asymptomatic snorers, to confirm disease in [figure: see text] patients with a high clinical probability of disease, or to risk stratify patients so that proper priority for polysomnography can be determined. This determination allows them to select a portable monitor with signals most appropriate to their needs. The quality of the validation studies for each portable monitor also should be evaluated carefully before implementation in clinical practice. The ability for a clinician to review raw data manually and consider artifact is a necessary feature. Measurement of oxygen saturation also is important to identify patients with previously unsuspected serious desaturation that would indicate the need for more urgent treatment. In centers in which polysomnography is not readily available, a clinical decision algorithm that incorporates a clinical prediction rule with the use of portable monitors can guide clinicians toward institution of therapy or further investigations. Intuitively, this approach could reduce waiting times for polysomnography and delays in diagnosis, but additional evidence for the validity and cost effectiveness of this approach is required.