Characterization of the CPAP-treated patient population in Catalonia

Turino, Cecilia; Bertran, Sandra; Gavaldà, Ricard; Teixidó, Ivan; Woehrle, Holger; Rué, Montserrat; Solsona, Francesc; Escarrabill, Joan; Colls, Cristina; García-Altés, Anna; Batlle, Jordi de; de-la-Torre, Manuel Sánchez; Barbé, Ferrán

doi:10.1371/journal.pone.0185191

Cited by 26 publications

(19 citation statements)

References 25 publications

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“…Our findings are of clinical relevance because they underline that asthmatic patients are under‐referred for sleep studies, despite the existing evidence of a high prevalence of OSA in asthma (Kong et al., ) and the positive effects of OSA treatment on asthma control (Kauppi et al., ; Wang et al., ). The prevalence of physician‐diagnosed asthma in our cohort was of the same order of magnitude as the prevalence rate of asthma recently found in CPAP‐treated patients (Turino et al., ). Certain phenotypes of asthma patients (i.e.…”

Section: Discussionsupporting

confidence: 87%

“…In the study by Greenberg‐Dotan and coworkers, the prevalence of asthma was 10.0% in OSA patients and 5.1% in controls, and significantly higher in women than in men (Greenberg‐Dotan et al., ). On the other hand, in a large cohort of OSA patients treated with CPAP, the asthmatic cluster accounted for only 5.8% of the cohort (Turino et al., ). Thus, data on the prevalence of asthma in OSA patients are variable, possibly reflecting differences in the studied samples and/or in the methods used to assess asthma and diagnose sleep apnea.…”

Section: Introductionmentioning

confidence: 99%

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Clinical presentation of patients with suspected obstructive sleep apnea and self‐reported physician‐diagnosed asthma in the ESADA cohort

Bonsignore

Pépin

Anttalainen

et al. 2018

Journal of Sleep Research

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Obstructive sleep apnea (OSA) and asthma are often associated and several studies suggest a bidirectional relationship between asthma and OSA. This study analyzed the characteristics of patients with suspected OSA from the European Sleep Apnea Database according to presence/absence of physician-diagnosed asthma. Cross-sectional data in 16,236 patients (29.1% female) referred for suspected OSA were analyzed according to occurrence of physician-diagnosed asthma for anthropometrics, OSA severity and sleepiness. Sleep structure was assessed in patients studied by polysomnography (i.e. 48% of the sample). The prevalence of physician-diagnosed asthma in the entire cohort was 4.8% (7.9% in women, 3.7% in men, p < 0.0001), and decreased from subjects without OSA to patients with mild-moderate and severe OSA (p = 0.02). Obesity was highly prevalent in asthmatic women, whereas BMI distribution was similar in men with and without physician-diagnosed asthma. Distribution of OSA severity was similar in patients with and without physician-diagnosed asthma, and unaffected by treatment for asthma or gastroesophageal reflux. Asthma was associated with poor sleep quality and sleepiness. Physician-diagnosed asthma was less common in a sleep clinic population than expected from the results of studies in the general population. Obesity appears as the major factor raising suspicion of OSA in asthmatic women, whereas complaints of poor sleep quality were the likely reason for referral in asthmatic men.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Clinical presentation of patients with suspected obstructive sleep apnea and self‐reported physician‐diagnosed asthma in the ESADA cohort

Bonsignore

Pépin

Anttalainen

et al. 2018

Journal of Sleep Research

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“…Afterwards, in order to determine patterns of pathology in CMC population, diagnoses related to each individual, coded through CCS, were used. Homogeneous groups of patients with similar diseases were identified in CMC population via K-means cluster analysis using the Jaccard similarity index 22 , as in similar studies 23 .…”

Section: Discussionmentioning

confidence: 99%

Socioeconomic inequalities in children under 15 with medical complexity: A population-based study

Carrilero

Ruiz-Muñoz

Dalmau-Bueno

et al. 2019

Preprint

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Background Health inequalities from the first stages of life onwards have consequences for children’s health status and for their future development. Children’s health is profoundly affected by the socioeconomic position (SEP) of their parents. The situation is even more disruptive in children with medically complex (CMC) conditions. The aim of this study is to describe the characteristics and related pathologies of CMC in Catalonia, and to assess the presence of socioeconomic inequalities.Methods Cross-sectional study and cluster analysis of the diseases in the CMC population under the age of 15 in Catalonia in 2016 using administrative data. Each pathology cluster obtained was described and its association with socioeconomic position (SEP) determined. The Adjusted Morbidity Groups (Catalan acronym GMA) classification system was used to identify the CMC. Main outcome measures: SEP, GMA score, sex, and age distribution were recorded in both populations (CMC and non-CMC) and in each of the clusters identified.Results 71% of the CMC population have at least one parent with no employment or an annual income of less than €18,000. Four comorbidity clusters were identified in CMC: respiratory (47%), neurodevelopmental (18%), hemato-oncological (12%), and perinatal disorders (23%). CMC experience higher levels of poverty and social disadvantage in all clusters. SES-cluster association results were: in the respiratory OR, 2.1 in boys and 2.1 in girls; in the neurodevelopmental disease, OR 2.0 in boys and 1.8 in girls; in the hemato-oncology OR, 1.7 in boys and 1.8 in girls; and in the perinatal OR, 1.7 in boys and 1.8 in girls.Conclusions There are socioeconomic health inequalities in childhood across a broad spectrum of health outcomes. Children with lower SEP are more likely to have CMC conditions. All clusters showed SEP inequalities and respiratory and neurodevelopment clusters are the most prevalent.

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“…However, these results are based on well‐managed cohorts and national or multinational registries mainly from academic environments with relatively small data sets (up to 18 000 OSA subjects) that do not necessarily reflect the wide range of real‐life situations and diverse clinical care pathways. A cluster analysis of Big Data acquired at a general population level showed that in an entire group of 72 217 CPAP‐treated patients certain OSA patient characteristics such as ‘neoplastic’ or ‘elderly with cardiac disease’ were associated with the highest mortality rates and healthcare resource use …”

Section: Opportunities Of Big Data In Understanding Sleep Breathing Dmentioning

confidence: 99%

Big Data in sleep apnoea: Opportunities and challenges

2019

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Sleep apnoea is now regarded as a highly prevalent systemic, multimorbid, chronic disease requiring a combination of long-term home-based treatments. Optimization of personalized treatment strategies requires accurate patient phenotyping. Data to describe the broad variety of phenotypes can come from electronic health records, health insurance claims, socio-economic administrative databases, environmental monitoring, social media, etc. Connected devices in and outside homes collect vast amount of data amassed in databases. All this contributes to 'Big Data' that, if used appropriately, has great potential for the benefit of health, well-being and therapeutics. Sleep apnoea is particularly well placed with regards to Big Data because the primary treatment is positive airway pressure (PAP). PAP devices, used every night over long periods by millions of patients across the world, generate an enormous amount of data. In this review, we discuss how different types of Big Data have, and could be, used to improve our understanding of sleep-disordered breathing, to identify undiagnosed sleep apnoea, to personalize treatment and to adapt health policies and better allocate resources. We discuss some of the challenges of Big Data including the need for appropriate data management, compilation and analysis techniques employing innovative statistical approaches alongside machine learning/artificial intelligence; closer collaboration between data scientists and physicians; and respect of the ethical and regulatory constraints of collecting and using Big Data. Lastly, we consider how Big Data can be used to overcome the limitations of randomized clinical trials and advance real-life evidence-based medicine for sleep apnoea.Big Data and sleep apnoea tory, Grenoble Alpes University) for the literature search and contribution to the pharmacovigilance and environmentalRespirology (2020) 25, 486-494

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Characterization of the CPAP-treated patient population in Catalonia

Cited by 26 publications

References 25 publications

Clinical presentation of patients with suspected obstructive sleep apnea and self‐reported physician‐diagnosed asthma in the ESADA cohort

Clinical presentation of patients with suspected obstructive sleep apnea and self‐reported physician‐diagnosed asthma in the ESADA cohort

Socioeconomic inequalities in children under 15 with medical complexity: A population-based study

Big Data in sleep apnoea: Opportunities and challenges

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