Cecilia Turino

Management of continuous positive airway pressure treatment compliance using telemonitoring in obstructive sleep apnoea

Continuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnoea (OSA), but treatment compliance is often unsatisfactory. This study investigated the efficacy and cost-effectiveness of telemonitoring for improving CPAP compliance. 100 newly diagnosed OSA patients requiring CPAP (apnoea–hypopnoea index >15 events·h−1) were randomised to standard management or a telemonitoring programme that collected daily information about compliance, air leaks and residual respiratory events, and initiated patient contact to resolve issues. Clinical/anthropometric variables, daytime sleepiness and quality of life were recorded at baseline and after 3 months. Patient satisfaction, additional visits/calls, side-effects and total costs were assessed. There were no significant differences between the standard and telemedicine groups in terms of CPAP compliance (4.9±2.2 versus 5.1±2.1 h·night−1), symptoms, clinical variables, quality of life and unwanted effects. Telemedicine was less expensive than standard management (EUR123.65 versus EUR170.97; p=0.022) and was cost-effective (incremental cost-effectiveness ratio EUR17 358.65 per quality-adjusted life-year gained). Overall patient satisfaction was high, but significantly more patients rated satisfaction as high/very high in the standard management versus telemedicine group (96% versus 74%; p=0.034). Telemonitoring did not improve CPAP treatment compliance and was associated with lower patient satisfaction. However, it was more cost-effective than traditional follow-up. Telemonitoring did not improve CPAP compliance, showed lower patient satisfaction, but proved to be cost-effective http://ow.ly/UvHX306AC0V

Effects of Ethnicity on the Prevalence of Obstructive Sleep Apnoea in Patients with Acute Coronary Syndrome: A Pooled Analysis of the ISAACC Trial and Sleep and Stent Study

BACKGROUND Obstructive sleep apnoea (OSA) is an emerging risk factor for acute coronary syndrome (ACS). We sought to determine the effects of ethnicity on the prevalence of OSA in patients presenting with ACS who participated in an overnight sleep study. METHODS A pooled analysis using patient-level data from the ISAACC Trial and Sleep and Stent Study was performed. Using the same portable diagnostic device, OSA was defined as an apnoea-hypopnoea index of ≥15 events per hour. RESULTS A total of 1961 patients were analysed, including Spanish (53.6%, n=1050), Chinese (25.5%, n=500), Indian (12.0%, n=235), Malay (6.1%, n=119), Brazilian (1.7%, n=34) and Burmese (1.2%, n=23) populations. Significant differences in body mass index (BMI) were found among the various ethnic groups, averaging from 25.3kg/m2 for Indians and 25.4kg/m2 for Chinese to 28.6kg/m2 for Spaniards. The prevalence of OSA was highest in the Spanish (63.1%), followed by the Chinese (50.2%), Malay (47.9%), Burmese (43.5%), Brazilian (41.2%), and Indian (36.1%) patients. The estimated odds ratio of BMI on OSA was highest in the Chinese population (1.17; 95% confidence interval: 1.10-1.24), but was not significant in the Spanish, Burmese or Brazilian populations. The area under the curve (AUC) for the Asian patients (ranging from 0.6365 to 0.6692) was higher than that for the Spanish patients (0.5161). CONCLUSION There was significant ethnic variation in the prevalence of OSA in patients with ACS. The magnitude of the effect of BMI on OSA was greater in the Chinese population than in the Spanish patients.

Characterization of the CPAP-treated patient population in Catalonia

There are different phenotypes of obstructive sleep apnoea (OSA), many of which have not been characterised. Identification of these different phenotypes is important in defining prognosis and guiding the therapeutic strategy. The aim of this study was to characterise the entire population of continuous positive airway pressure (CPAP)-treated patients in Catalonia and identify specific patient profiles using cluster analysis. A total of 72,217 CPAP-treated patients who contacted the Catalan Health System (CatSalut) during the years 2012 and 2013 were included. Six clusters were identified, classified as “Neoplastic patients” (Cluster 1, 10.4%), “Metabolic syndrome patients” (Cluster 2, 27.7%), “Asthmatic patients” (Cluster 3, 5.8%), “Musculoskeletal and joint disorder patients” (Cluster 4, 10.3%), “Patients with few comorbidities” (Cluster 5, 35.6%) and “Oldest and cardiac disease patients” (Cluster 6, 10.2%). Healthcare facility use and mortality were highest in patients from Cluster 1 and 6. Conversely, patients in Clusters 2 and 4 had low morbidity, mortality and healthcare resource use. Our findings highlight the heterogeneity of CPAP-treated patients, and suggest that OSA is associated with a different prognosis in the clusters identified. These results suggest the need for a comprehensive and individualised approach to CPAP treatment of OSA.

Predictors of obstructive sleep apnoea in patients admitted for acute coronary syndrome

Identifying undiagnosed obstructive sleep apnoea (OSA) patients in cardiovascular clinics could improve their management. Aiming to build an OSA predictive model, a broad analysis of clinical variables was performed in a cohort of acute coronary syndrome (ACS) patients. Sociodemographic, anthropometric, life-style and pharmacological variables were recorded. Clinical measures included blood pressure, electrocardiography, echocardiography, blood count, troponin levels and a metabolic panel. OSA was diagnosed using respiratory polygraphy. Logistic regression models and classification and regression trees were used to create predictive models. A total of 978 patients were included (298 subjects with apnoea–hypopnoea index (AHI) <15 events·h−1 and 680 with AHI ≥15 events·h−1). Age, BMI, Epworth sleepiness scale, peak troponin levels and use of calcium antagonists were the main determinants of AHI ≥15 events·h−1 (C statistic 0.71; sensitivity 94%; specificity 24%). Age, BMI, blood triglycerides, peak troponin levels and Killip class ≥II were determinants of AHI ≥30 events·h−1 (C statistic of 0.67; sensitivity 31%; specificity 86%). Although a set of variables associated with OSA was identified, no model could successfully predict OSA in patients admitted for ACS. Given the high prevalence of OSA, the authors propose respiratory polygraphy as a to-be-explored strategy to identify OSA in ACS patients. Given the high prevalence of OSA in patients suffering ACS, respiratory polygraphy should be routinely performed http://ow.ly/tmKE306wyDc

Cardiac Troponin Values in Patients With Acute Coronary Syndrome and Sleep Apnea: A Pilot Study

Background An analysis of cardiac injury markers in patients with OSA who sustain an episode of acute coronary syndrome (ACS) may contribute to a better understanding of the interactions and impact of OSA in subjects with ACS. We compared peak cardiac troponin I (cTnI) levels in patients with OSA and patients without OSA who were admitted for ACS. Methods Blood samples were collected every 6 hours from the time of admission until two consecutive assays showed a downward trend in the cTnI assay. The highest value obtained defined the peak cTnI value, which provides an estimate of infarct size. Results We included 89 patients with OSA and 38 patients without OSA with an apnea‐hypopnea index of a median of 32 (interquartile range [IQR], 20.8‐46.6/h and 4.8 [IQR, 1.6‐9.6]/h, respectively. The peak cTnI value was significantly higher in patients without OSA than in patients with OSA (median, 10.7 ng/mL [IQR, 1.78‐40.1 ng/mL] vs 3.79 ng/mL [IQR, 0.37‐24.3 ng/mL]; P = .04). The multivariable linear regression analysis of the relationship between peak cTnI value and patient group, age, sex, and type of ACS showed that the presence or absence of OSA significantly contributed to the peak cTnI level, which was 54% lower in patients with OSA than in those without OSA. Conclusions The results of this study suggest that OSA has a protective effect in the context of myocardial infarction and that patients with OSA may experience less severe myocardial injury. The possible role of OSA in cardioprotection should be explored in future studies.

Pulmonary Function and Sleep Breathing: Two New Targets for Type 2 Diabetes Care

Population-based studies showing the negative impact of type 2 diabetes (T2D) on lung function are overviewed. Among the well-recognized pathophysiological mechanisms, the metabolic pathways related to insulin resistance (IR), low-grade chronic inflammation, leptin resistance, microvascular damage, and autonomic neuropathy are emphasized. Histopathological changes are exposed, and findings reported from experimental models are clearly differentiated from those described in humans. The accelerated decline in pulmonary function that appears in patients with cystic fibrosis (CF) with related abnormalities of glucose tolerance and diabetes is considered as an example to further investigate the relationship between T2D and the lung. Furthermore, a possible causal link between antihyperglycemic therapies and pulmonary function is examined. T2D similarly affects breathing during sleep, becoming an independent risk factor for higher rates of sleep apnea, leading to nocturnal hypoxemia and daytime sleepiness. Therefore, the impact of T2D on sleep breathing and its influence on sleep architecture is analyzed. Finally, the effect of improving some pathophysiological mechanisms, primarily IR and inflammation, as well as the optimization of blood glucose control on sleep breathing is evaluated. In summary, the lung should be considered by those providing care for people with diabetes and raise the central issue of whether the normalization of glucose levels can improve pulmonary function and ameliorate sleep-disordered breathing. Therefore, patients with T2D should be considered a vulnerable group for pulmonary dysfunction. However, further research aimed at elucidating how to screen for the lung impairment in the population with diabetes in a cost-effective manner is needed.

Sleep Apnea and Cardiovascular Morbidity—a Perspective

Purpose of ReviewIn this paper, we analyzed the principal mechanisms linking OSA and cardiovascular diseases, the magnitude of this association, and the effect of treatment on cardiovascular risk reduction. Furthermore, we focused on big data and genomic approaches that may expand the innovative horizon aimed at personalized treatment of OSA.Recent FindingsPatients with obstructive sleep apnea (OSA) are more prone to suffer from cardiovascular diseases and their complications. Arousal from sleep, changes in intrathoracic pressures, and intermittent episodes of hypoxemia and re-oxygenation trigger intermediate mechanisms that are responsible for the cardiovascular consequences of OSA.SummaryThe impact of OSA on cardiovascular health, such as the effect of continuous positive airway pressure (CPAP) treatment on cardiovascular risk, may vary among different patient profiles. In spite of increasing interest towards personalized therapy for OSA, observational studies and randomized clinical trials have failed to conclusively determine which OSA phenotypes may benefit most from treatment, particularly in terms of reducing cardiovascular risk.

Comparative analysis of predictive methods for early assessment of compliance with continuous positive airway pressure therapy

BackgroundPatients suffering obstructive sleep apnea are mainly treated with continuous positive airway pressure (CPAP). Although it is a highly effective treatment, compliance with this therapy is problematic to achieve with serious consequences for the patients’ health. Unfortunately, there is a clear lack of clinical analytical tools to support the early prediction of compliant patients.MethodsThis work intends to take a further step in this direction by building compliance classifiers with CPAP therapy at three different moments of the patient follow-up, before the therapy starts (baseline) and at months 1 and 3 after the baseline.ResultsResults of the clinical trial shows that month 3 was the time-point with the most accurate classifier reaching an f1-score of 87% and 84% in cross-validation and test. At month 1, performances were almost as high as in month 3 with 82% and 84% of f1-score. At baseline, where no information of patients’ CPAP use was given yet, the best classifier achieved 73% and 76% of f1-score in cross-validation and test set respectively. Subsequent analyzes carried out with the best classifiers of each time point revealed baseline factors (i.e. headaches, psychological symptoms, arterial hypertension and EuroQol visual analog scale) closely related to the prediction of compliance independently of the time-point. In addition, among the variables taken only during the follow-up of the patients, Epworth and the average nighttime hours were the most important to predict compliance with CPAP.ConclusionsBest classifiers reported high performances after one month of treatment, being the third month when significant differences were achieved with respect to the baseline. Four baseline variables were reported relevant for the prediction of compliance with CPAP at each time-point. Two characteristics more were also highlighted for the prediction of compliance at months 1 and 3.Trial registrationClinicalTrials.gov Identifier, NCT03116958. Retrospectively registered on 17 April 2017.

Towards an Intelligent Monitoring System for Patients with Obstrusive Sleep Apnea

Due to the growing incidence of chronic diseases and aging populations, the pressure to control costs and the expectations of continuous improvements in the quality of service have increased the need to understand how healthcare is provided and to determine whether cost-effective improvements to care practices can be made. In the case of people suffering Obstructive Sleep Apnea, patients using self-administer nasal Continuous Positive Airway Pressure (CPAP) may receive information on the treatment only once they go to a visit with the lung specialist. In this paper, we propose an IoT-based Intelligent Monitoring System that relies on machine learning to achieve a threefold goal: (1) it is aimed at early detecting compliance in order to predict CPAP usage; (2) it monitors the actual adherence degree to the treatment to keep informed both the patient and the lung specialists; and (3) it sends recommendations to the patient to empower her/him and to better follow up. Received on 06 December 2017; accepted on 14 December 2017; published on 19 December 2017