www.nature.com/npjpcrm PROTOCOL OPEN Coordinated program between primary care and sleep unit for the management of obstructive sleep apnea Mercè Mayos 1,2,3*, Patricia Peñacoba 1,3, Anna María Pedro Pijoan4, Carme Santiveri5, Xavier Flor3,6, Joan Juvanteny3,7, Gabriel Sampol3,8, Patricia Lloberes8, José Ignacio Aoiz 9, Joan Bayó9, Núria Grau10, Ana M. Fortuna1,3, Vicente Plaza1,3 and M. Antònia Llauger3,11 The purpose of this study is to develop and validate a work model in the primary health-care setting for identifying patients with obstructive sleep apnea–hypopnea syndrome (OSAHS) based on clinical variables and an ambulatory sleep monitoring study. After screening, patients with mild–moderate OSAHS could be managed by primary care physicians, whereas those identified with severe OSAHS would be referred to specialists from sleep units for starting specific treatment. The proposed model does not move the entire health-care process to a generally overburdened primary care level and favors the coordinated work and the necessary flexibility to adapt the model to challenges and perspectives of OSAHS. npj Primary Care Respiratory Medicine (2019)29:39 ; https://doi.org/10.1038/s41533-019-0151-9 1234567890():,; INTRODUCTION care taking a more active role in the diagnosis and even Obstructive sleep apnea–hypopnea syndrome (OSAHS) is a management of OSAHS.19,20 Different models of integral manage- common breathing disorder in the general population,1 with ment of patients with high clinical probability of OSAHS in the major clinical and socioeconomic consequences.2 OSAHS is primary care setting have been proposed, with non-inferior mid- characterized by repetitive episodes of upper airway collapse term results to those reported in sleep units.21–24 However, during sleep, resulting in cyclic decreases of arterial oxygen although these results open new perspectives in management of saturation and transient cortical arousals, leading to non- OSAHS, the feasibility of the implementation of these models in restorative sleep. Daytime sleepiness is a cardinal symptom of clinical practice has been questioned. Among other limitations, it OSAHS and if left untreated, it leads to cognitive dysfunction, has been argued that primary care physicians have to assume a decrements in health-related quality of life, impaired work central role in the care of patients with OSAHS, which simplifies performance, and increased risk for accidents in the workplace excessively the management of patients with an increasingly and traffic accidents.3,4 Also, repetitive episodes of hypoxia and complex disease.25 enhanced sympathetic activity trigger pathogenic pathways related to an increased risk for cardiovascular and metabolic morbidity5–8 and mortality.9,10 AIMS Nasal continuous positive airway pressure (CPAP) during sleep is The PASHOS project (PASHOS is the Spanish acronym of the leading therapy for OSAHS.11 CPAP is highly effective in Advanced Platform for Sleep Apnea Syndrome Assessment) is controlling obstructive respiratory events, improvement of symp- conceived with a different approach to the problem, that is, the toms and quality of life of patients affected with OSHAS,12 and implementation of a comprehensive inter-level coordinated significant reduction of blood pressure13,14 and insulin resis- program between primary care and specialist sleep centers for tance.15 Treatment with CPAP has been associated with a patients with OSAHS. Advantages of the program include screen- decrease of all-cause and cardiovascular mortality.9 ing and tentatively management of OSAHS in the primary care It has been estimated that the total cost of OSAHS would be setting and prioritization of primary care referrals to specialized higher than that of other chronic diseases such as asthma and care for starting CPAP treatment. The main objective is to develop chronic obstructive pulmonary disease (COPD) and similar to a simplified two-stage model for the management of patients with diabetes-related expenses.16 A high percentage of OSAHS-related OSAHS in the primary care setting based on a screening costs are derived from societal consequences, including decreased questionnaire followed by an ambulatory sleep monitoring study work productivity and the burden of road traffic accidents.17 at home, and based on that information, a decision to refer to a OSAHS is clearly underdiagnosed, which is largely attributed to sleep unit or to manage them in the same primary care setting. oversaturation of sleep units where management of the disease The secondary objectives are as follows: (1) to assess the validity of has been focused so far and where there are long lists of patients management decisions made by primary care physicians (indica- waiting for diagnosis and treatment.18 Currently, there is tion of CPAP versus no indication of CPAP), based on results of consensus that a disease with such relevant impact on the clinical evaluation and ambulatory sleep study as compared to individual’s health should involve all levels of care, with primary therapeutic decisions made by specialists at the sleep unit; and (2) 1 Sleep Unit, Department of Respiratory Diseases, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain. 2Centro de Investigación Biomédica en Red Enfermedades Respiratorias (CIBERES) (CB06/06), Instituto de Investigación Carlos III, Madrid, Spain. 3Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain. 4Àrea Bàsica de Salut (ABS) Gaudí, Institut Catalá de la Salut, Barcelona, Spain. 5Service of Pneumology, Hospital Dos de Maig, Barcelona, Spain. 6Centre d’Atenció Primària (CAP) Chafarinas, Barcelona, Spain. 7Centre d’Atenció Primària (CAP) Trinitat Vella, Barcelona, Spain. 8Service of Pneumology, Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain. 9Centre d’Atenció Primària(CAO) El Clot, Barcelona, Spain. 10Service of Pneumology, Hospital del Mar, Universitat Autònoma de Barcelona, Barcelona, Spain. 11Centre d’Atenció Primària (CAP) Els Encants, Barcelona, Spain. *email: MMayos@santpau.cat Published in partnership with Primary Care Respiratory Society UK M. Mayos et al. 2 to analyze the cost-effectiveness of the coordinated strategy Training protocol versus the strategy that includes assessment of OSAHS solely in Prior to the beginning of the study, the following training program was the sleep unit. carried out: Training of two reference professionals, one primary care physician, and DISCUSSION a nurse for each of the primary health-care centers participating in the study. This prospective and multicenter study protocol aims to validate a Theoretical training (4 h) including updated information of definition coordinating work model between primary care and the sleep unit and epidemiology of OSAHS, clinical presentation and management of for the management of OSAHS. The model includes training of specific questionnaires, clinical impact of the disease, diagnosis of reference health-care professionals from primary care and OSAHS, treatment and prevention, and aspects related to the care of validation of simple screening tools that allow the family physician patients in the framework of the local health-care system. Practical training for nursing (3 days, 6 h/day) including management of to select the patients who can be managed in the primary care home diagnostic equipment and interpretation of data, as well as basic setting (mild–moderate OSAHS) from those who have to be training in more complex sleep studies. referred to sleep units to start specific treatment. The study differs Training for primary care physicians (3 days, 6 h/day) including from other interesting recent clinical trials22–24 in that it may be indication and interpretation of the different diagnostic sleep studies applicable to the entire phenotypic spectrum of patients, because and clinical management of the patient with OSAHS in the primary care inclusion is not restricted to a certain “a priori” clinical probability setting. of OSAHS, which accounts for only 30% of the total OSAHS At the end of the training period, all doctors (primary care physicians population.26,27 On the other hand, the model does not move the and sleep specialists) analyzed 10 simulated cases of OSAHS with different entire health-care process to a generally overburdened primary levels of severity and registers with technical errors. The degree of agreement between each participating center (6 primary health-care care level and favors the coordinated work and the necessary centers, 4 sleep units) and the coordinating sleep unit that selected the flexibility to adapt the model to challenges and perspectives of simulation cases is shown in Table 2. The degree of agreement ranges from OSAHS.28,29 moderate (Cohen’s kappa coefficient [κ] 0.47) to perfect agreement (κ = The exclusion of patients with moderate-to-severe COPD 1.0). Concordance was higher in examples of extreme cases (OSAHS clearly 1234567890():,; deserves a comment. Patients with overlap syndrome (COPD/ present or clearly absent), whereas there was a greater variability in OSAHS) present differential clinical characteristics than OSAHS decision-making for cases with suspicion of mild-to-moderate OSAHS. patients without COPD. In these patients, screening question- Finally, therapeutic decisions simulation cases were reviewed in a naires (e.g., Epworth sleepiness scale, STOP-Bang sleep ques- subsequent joint meeting of all study participants. tionnaire) or other clinical parameters are not well predictors of OSAHS, and it has been suggested that specific tools are necessary Study variables and data collection to evaluate the risk of OSAHS in this population.30 Moreover, poor The following variables will be collected: anthropometric (weight, height, sleep quality in patients with moderate-to-severe COPD may affect body mass index) and sociodemographic (age, sex) data; history of the diagnostic reliability of the simplified ambulatory sleep study. cardiovascular diseases and cardiovascular risk factors; other comorbidities, They also may present nocturnal hypoxemia due to multiple including cerebrovascular, metabolic, neurologic, respiratory, and psychia- mechanisms, besides upper airway occlusion characteristic of tric diseases; and pharmacological treatment. Clinical history directed to OSAHS, such as ventilation/perfusion alterations or hypoventila- assessment of sleep breathing disorders, including a specific clinical tion. It is therefore a complex sleep respiratory disorder that questionnaire of suggestive symptoms of OSAHS, the Berlin question- naire,33 the STOP-Bang sleep apnea questionnaire,34 and daytime should be preferably studied with a full polysomnography.31 sleepiness using the Epworth sleepiness scale.35 Other studies include Limitations of the study include the upper age range of 75 years forced spirometry36 and conventional polysomnography/respiratory poly- as an inclusion criterion, so that it would be necessary to validate graphy37,38 recording the following data: apnea–hypopnea index (AHI), the model in older age groups. Polysomnography was not used as obstructive apnea index (episodes/h), hypopnea index (episodes/h), the standard diagnostic test for the diagnosis of OSA in adult central apnea index (episodes/h), mixed apnea index (episodes/h), mean patients. Although respiratory polygraphy can underestimate the peripheral oxygen saturation (SpO2), time spent at SaO2 < 90% (CT90), falls diagnosis of OSA, we adapt our model to common available in SpO2 ≥ 3% (oxygen desaturation index [ODI]-3%) and ≥ 4% (ODI-4%) per resources in routine clinical practice and to recommendations of hour of recording, and time spent in the supine position. guidelines suggesting the use of respiratory polygraphy only in The ambulatory monitoring sleep study will be carried out at the selected patients.32 patient’s home using a Bitmed Sleep&Go polygraph (Sibelmed, Barcelona, In summary, we believe that results of this project could Spain). The reference primary care nurse will be in charge of the analysis of sleep quality, discarding the periods of deficient signal but will not analyze represent a relevant change in the different levels of care for the all the events manually. The minimal valid recording time is defined as 5 h. management of patients with OSAHS, probably applicable to Polygraphy may be repeated if a poor signal acquisition is detected or the different health-care systems. patient reports poor sleep quality on the night of the study. The following variables will be recorded: mean SpO2, ODI-3%, ODI-4%, AHI (cut-off points 5–15 for mild OSAHS, >15–<30 for moderate OSAHS, and ≥30 for severe METHODS OSAHS), and percentage of time in supine position. Design and participants A file transfer protocol (FTP) has been developed for transmitting sleep This is a prospective multicenter study that includes the participation of recorded data from primary care centers to the corresponding reference sleep units from three tertiary care teaching hospitals in the urban area of sleep units. The FileZilla® software (version 3.3.0.1 - GNU General Public Barcelona (Spain), a sleep unit of a secondary care hospital (with capacity License, version 2, June 1991, Free Software Foundation Inc.) is the server to perform respiratory polygraphy studies and refer to a tertiary center if that supports FTP. The program has been installed in each participating polysomnography is needed), and six primary care teams of the reference center. A username and password was created for each research team, area of the four hospitals. The inclusion and exclusion criteria are shown in giving access to its folder only. Primary care teams can only upload files to Table 1. their folders, whereas physicians from the sleep units can only download This research has been already approved by the Clinical Research Ethics files from primary care centers of their reference area. The coordinating Committee of the 10 participating centers. Written informed consent will center has full access to folders of all participating centers in order to be obtained from all patients. The study has been registered at review and solve any possible incidents that may happen. All patients’ data ClinicalTrials.gov (identifier NCT02591979). will be anonymized. npj Primary Care Respiratory Medicine (2019) 39 Published in partnership with Primary Care Respiratory Society UK M. Mayos et al. 3 Table 1. Inclusion/exclusion criteria Inclusion criteria Exclusion criteria • Men and women • Cognitive impairment or psychosocial inability to perform the ambulatory sleep study • Age ≥18 and ≤75 years • Unstable or acute cardiovascular or cerebrovascular disease • Patients visited at the participating primary care centers for any reason will be • Chronic insomnia (<5 h of sleep/day) consecutively included according to a randomization schedule to reach the • Previous diagnosis of OSAHS necessary sample size • Relevant respiratory comorbidity that may interfere with arterial blood saturation measurements • Moderate-to-severe COPD (FEV1/FVC < 0.7%, FEV1 < 50% predicted) • History of neuromuscular disease • Patient’s refusal to participate in the study OSAHS obstructive sleep apnea hypopnea syndrome, COPD chronic obstructive pulmonary disease, FEV1 forced expiratory volume in one second, FVC forced vital capacity Table 2. Agreement between each participating center and the coordinating center Participating center Cohen’s kappa (κ) Asymptotic standard errora Approx. Tb Approx. Sig. Sleep unit #2 0.571 0.182 3.086 0.002 Sleep unit #3 1.0 0.0 5.331 0.000 Sleep unit #4 0.865 0.128 4.749 0.000 Primary care center #1 0.474 0.182 2.868 0.004 Primary care center #2 0.595 0.173 3.505 0.000 Primary care center #3 0.571 0.173 3.203 0.001 Primary care center #4 0.861 0.127 4.642 0.000 Primary care center #5 0.595 0.173 3.505 0.000 Primary care center #6 0.459 0.185 2.642 0.008 Coordinating center: sleep unit #1 a Not assuming the null hypothesis b Using the asymptomatic standard error assuming the null hypothesis Model design and validation protocols allow analyzing the diagnostic and therapeutic concordance between primary In all patients who fulfilled the inclusion criteria (Table 1), data on care and specialized care, using the same basic tools (clinical findings and anthropometric variables, clinical variables, and specific study question- ambulatory sleep monitoring) and with the final results of the gold standard naires (Berlin questionnaire, STOP-Bang sleep apnea questionnaire, and examination. The two stages of the model are shown in Figs 1 and 2. Epworth sleepiness scale) will be collected at the primary care setting. Patients with a high probability of OSAHS according to results of the Berlin Sample size calculation questionnaire and 1 out of 3 patients with a low probability of OSAHS will The sample size has been calculated considering a minimal prevalence of undergo the ambulatory monitoring sleep study and will be referred to the OSAHS of 25% in the population attended in the primary care setting. sleep unit for completion of OSAHS evaluation. The selection of 1 out of 3 Assuming a loss of 15% at follow-up, an alpha error of 5%, and a 90% patients with low probability of OSAHS is established in order to balance statistical power, a total sample of 198 patients would be required for 90% the study sample and tries to achieve a similar final proportion of patients sensitivity in the validation sample (99 patients in each arm with low/high with high and low probability of OSAHS, taking into account that the probability of OSAHS). However, the number of valid initial questionnaires expected OSAHS population is approximately 30%. should be much higher, given that all patients with a priori high probability On the basis of clinical data and results of ambulatory sleep study, the of OSAHS and 1:3 of those with low probability will be included. In this primary care physician will take a clinical decision according to the way, it is intended to adjust the performance of sleep studies and the following four diagnostic–therapeutic scenarios: (a) low suspicion of balance between OSAHS subjects/non-OSAHS subjects. Therefore, a OSAHS and no need of treatment; (b) mild/moderate suspicion of OSAHS minimum number of 396 valid questionnaires are planned. and conservative treatment; (c) suspicion of OSAHS and candidate for CPAP treatment; and (d) indeterminate in the presence of low quality of ambulatory sleep study recording or discrepancy between clinical data and Statistical analysis results of ambulatory sleep study. Categorical variables will be expressed as frequencies and percentages and All patients undergoing home sleep monitoring independently of the high quantitative variables as mean and standard deviation (SD), median and or low initial clinical probability of OSAHS will be referred to the sleep unit, interquartile range (25th–75th percentile), and 95% confidence interval where specialists, in a blinded fashion regarding primary care results and with (CI). Bivariate analysis will include chi-square (χ2) test or Fisher’s exact all documentation available, will take a further diagnostic–therapeutic probability test for categorical data and Student’s t test or Mann–Whitney decision according to the same four possible scenarios defined in primary U test for continuous data according to the conditions of application. The care. Finally, all patients referred to the sleep unit will undergo a complete degree of agreement between the participating centers regarding ten respiratory polygraphy or conventional polysomnography at the sleep unit to simulated cases has been analyzed with the Cohen’s kappa correlation establish a definite diagnosis and therapeutic indication. This approach will coefficient (Table 2). The clinical prediction model will be based on logistic Published in partnership with Primary Care Respiratory Society UK npj Primary Care Respiratory Medicine (2019) 39 M. Mayos et al. 4 Fig. 1 Steps involving validation of the model in primary care Fig. 2 Steps involving validation of the model in specialized care (sleep units) regression analysis. All variables statistically significant in the bivariate Received: 3 January 2019; Accepted: 2 October 2019; analysis and those considered to be clinically relevant will be included in the logistic regression model. 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