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dc.contributor.authorRafael-Palou, Xavier
dc.contributor.authorTurino, Cecilia
dc.contributor.authorSteblin, Alexander
dc.contributor.authorSánchez de la Torre, Manuel
dc.contributor.authorBarbé Illa, Ferran
dc.contributor.authorVargiu, Eloisa
dc.date.accessioned2018-10-05T08:20:28Z
dc.date.available2018-10-05T08:20:28Z
dc.date.issued2018
dc.identifier.issn1472-6947
dc.identifier.urihttp://hdl.handle.net/10459.1/64829
dc.description.abstractBackground: Patients 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. Methods: This 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. Results: Results 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. Conclusions: Best 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.ca_ES
dc.description.sponsorshipThis work is part of the myOSA project (RTC-2014-3138-1), funded by the Spanish Ministry of Economy and Competitiveness (Ministerio de Economía y Competitividad) under the framework “Retos-Colaboración”, State Scientific and Technical Research and Innovation Plan 2013-2016. The study was also partially funded by the European Community under “H2020-EU.3.1. – Societal Challenges – Health, demographic change and well-being” programme, project grant agreement number 689802 (CONNECARE).ca_ES
dc.language.isoengca_ES
dc.publisherBMCca_ES
dc.relationMINECO/PN2013-2016/RTC-2014-3138-1
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.1186/s12911-018-0657-zca_ES
dc.relation.ispartofBMC Medical Informatics and Decision Making, 2018, vol. 18, núm. 81, p. 1-14ca_ES
dc.rightscc-by (c) Xavier Rafael-Palou et al., 2018ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectObtrusive sleep apneaca_ES
dc.subjectContinuous positive airway pressureca_ES
dc.subjectPredictive methodsca_ES
dc.subjectMachine learningca_ES
dc.titleComparative analysis of predictive methods for early assessment of compliance with continuous positive airway pressure therapyca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.identifier.idgrec027410
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.1186/s12911-018-0657-z
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/689802/EU/CONNECARE


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cc-by (c) Xavier Rafael-Palou et al., 2018
Except where otherwise noted, this item's license is described as cc-by (c) Xavier Rafael-Palou et al., 2018