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International audience; Objective: In obstructive sleep apnea patients on continuous positive airway pressure (CPAP) treatment there is growing evidence for a significant impact of the type of mask on the residual apnea-hypopnea index (rAHI). Here, we propose a method for automatically classifying the impact of mask changes on rAHI.Methods: From a CPAP telemonitoring database of 3,581 patients, an interrupted time series design was applied to rAHI time series at a patient level to compare the observed rAHI after a mask-change with what would have occurred without the mask-change. rAHI time series before mask changes were modelled using different approaches. Mask changes were classified as: no effect, harmful, beneficial. The best model was chosen based on goodness-of-fit metrics and comparison with blinded classification by an experienced respiratory physician.Results: Bayesian structural time series with synthetic controls was the best approach in terms of agreement with the physician.s classification, with an accuracy of 0.79. Changes from nasal to facial mask were more often harmful than beneficial: 13.4% vs 7.6% (p-value < 0.05), with a clinically relevant increase in average rAHI greater than 8 events/hour in 4.6% of cases. Changes from facial to nasal mask were less often harmful: 6.0% vs 11.4% (p-value < 0.05).Conclusion: We propose an end-to-end method to automatically classify the impact of mask changes over fourteen days after a switchover.Significance: The proposed automated analysis of the impact of changes in health device settings or accessories presents a novel tool to include in remote monitoring platforms for raising alerts after harmful interventions.

Background/objective For obstructive sleep apnea (OSA) patients on continuous positive airway pressure (CPAP) treatment, the apnea-hypopnea index (AHI) is a key measure of treatment efficacy. However, the residual AHI is CPAP brand specific. Here, we studied changes in residual AHI in patients who used two different brands over their treatment history. Patients/methods Using our CPAP telemonitoring database of 3102 patients, we compared the residual AHI of 69 patients before and after change in their CPAP device. Results A paired Wilcoxon signed-rank test revealed a significant difference between brands in the reported AHI, which might be clinically misleading. Conclusions These findings suggest that physicians should be alerted to the differences between brands and learned societies should push for standardization of AHI reporting.

Background: Continuous positive airway pressure (CPAP), the reference treatment for obstructive sleep apnoea (OSA), is used by millions of individuals worldwide with remote telemonitoring providing daily information on CPAP usage and efficacy, a currently underused resource. Here, we aimed to implement state-of-the-art data science methods to describe heterogeneity and diversity of time-series of residual apnoea-hypopnoea indexes (rAHI) from CPAP telemonitoring. Methods: We analysed a CPAP telemonitoring database to model and cluster rAHI trajectories. Our primary objective was to use Hidden Markov models (HMMs) as a probabilistic model-based approach to extract features from rAHI time-series. Secondary goals were to identify clusters of rAHI trajectories and their relation to CPAP treatment outcomes, adherence and leaks. Findings: From telemonitoring records of 2,860 CPAP-treated patients (age: 66·31 ± 12·92 years, 69·9% male), HMM modelling revealed three states differing in variability within a given state and probability of shifting from one state to another. Six clusters of rAHI trajectories were identified ranging from well controlled CPAP-treated patients (Cluster 0: 669 (23%); mean rAHI 0·58 ± 0·59 events/hour) to the most unstable (Cluster 5: 470 (16%); mean rAHI 9·62 ± 5·62 events/hour). CPAP adherence was 30 minutes higher in cluster 0 compared to clusters 4 and 5 (p-value

Accurate indoor temperature forecasting can facilitate energy savings of the building without compromising the occupant comfort level, by providing more accurate control of the HVAC (heating, ventilating, and air conditioning) system. In order to make the best use of different input variables, a long short-term memory (LSTM) based sequence to sequence (seq2seq) model was proposed to make multi-step ahead forecasting. The out-of-sample forecasting capacity of the model was evaluated with regard to different forecast horizons by various evaluation metrics. A tailor-made metric was proposed to take account of the small daily-variation characteristic of indoor temperature. The model was benchmarked against Prophet and a seasonal naive model, showing that the current model is much more skillful and reliable in very short-term forecasting. A cross-series learning strategy was adopted to enable multi-zone indoor temperature forecasting with a more generalised model. Furthermore, the uncertainty in model parameters was quantified by prediction intervals created by Monte-Carlo dropout (MC-dropout) technique.