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

Abstract

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.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0657-z) contains supplementary material, which is available to authorized users.

Are symptoms of obstructive sleep apnoea related to good continuous positive airway pressure compliance?

“…A study from Spain found that there were four predictors for CPAP compliance, including headache, psychological symptoms, hypertension, and quality of life [17]. In our study, only fatigue was an independent predictor for good CPAP compliance.…”

The Impact of National Health Insurance Coverage on Compliance With Positive Airway Pressure Therapy in Patients With Obstructive Sleep Apnea

“…According to previous studies on comorbidities and long-term compliance, hypertension is not an important predictor of long-term compliance; however, recently, Rafael-Palou et al [ 25 ] reported that hypertension was closely related to compliance at 1–3 months [ 26 , 27 ]. Our study confirmed these findings through between-group comparisons and multivariate analysis.…”

Management and Treatment of Patients With Obstructive Sleep Apnea Using an Intelligent Monitoring System Based on Machine Learning Aiming to Improve Continuous Positive Airway Pressure Treatment Compliance: Randomized Controlled Trial

“…Hospital lung specialists managing these patients and the CPAP provider (Oxigen salud) also had access to the MiSAOS website that provided relevant information and decision support according to the specific role and access rights of each professional user. Finally, the cloud-based MiSAOS platform connected all the devices for data exchange and hosted an intelligent monitoring system, based on machine learning, capable of predicting the expected compliance with the therapy by a given patient, thus providing adequate feedback and proposing personalized interventions to increase compliance [ 15 , 18 ]. Predictions of patient’s midterm compliance were based on patient’s characteristics, such as anthropometric data and clinical information, and early compliance data.…”