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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.

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.