03. Big Data approach to sleep in the Catalonian population

Sandra Bertran.
Lleida, Spain


Big data approach to sleep in the Catalonian population


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. 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. The 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.

Moreover, obstructive sleep apnea (OSA) has been associated with increased rates of morbidity and mortality due to its association with hypertension, cancer, and metabolic, cardiovascular and cerebrovascular diseases. The application of nocturnal continuous positive airway pressure (CPAP) effectively improves daytime symptoms and quality of life of OSA patients and moderately decreases arterial blood pressure in OSA patients with resistant hypertension. However, whether CPAP treatment reduces mortality at the population level remains unclear.

A total of 70,469 CPAP-treated patients and 184,112 controls matched (1:3) on 5-year age-group, gender and health region, attended by CatSalut during 2012-2013 were assessed to determine the relationship between CPAP treatment and mortality. Data on 2012-2013 comorbidities, and 2012-2015 mortality were recorded. The results suggest that the population of CPAP-treated patients in Catalonia had lower mortality rates than age-, gender- and region-matched controls, despite a higher prevalence of most comorbidities among CPAP-treated patients, that is, CPAP treatment is associated with reductions in mortality at the population level, although only in men. Therefore, further analyses should be planned within the frame of precision medicine6 to clarify which OSA patients could benefit the most from CPAP treatment and whether CPAP might be detrimental in some specific patient subgroups.

Sandra Bertran is graduated in mathematics by the Universitat Autonoma de Barcelona (UAB) and she holds a Master’s degree in Statistics and Operation research by Universitat Politecnica de Catalunya and Universitat de Barcelona. She is a statistician in the Group of Translational Research in Respiratory Medicine (TRRM) at the Biomedical Research Institute of Lleida (IRBLleida). She is currently working on the topics of sleep apnea, lung cancer and pulmonary diseases, in projects like ISAACC and Big Data, both of them targeting obstructive sleep apnea patients and focusing on the implications of CPAP treatment. In this sense, she has collaborated in several papers published in prestigious journals on the field (Am J Respir Crit Care Med, Chest and American Journal of Epidemiology). Her main statistical research interests are causal inference and Big Data based on the real world. In 2018 she has started a PhD on Causal Inference.

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