Patients with end‐stage chronic obstructive pulmonary disease (COPD) frequently develop chronic hypercapnic respiratory failure (CHRF), with disabling symptoms and poor survival. The use of long‐term nocturnal non‐invasive ventilation (NIV) to treat CHRF in COPD has long been subject of debate due to conflicting evidence. However, since the introduction of high‐intensity NIV (HI‐NIV) in COPD, physiological and clinical benefits have been shown. HI‐NIV refers to specific ventilator settings used for NIV aimed at achieving normocapnia or the lowest partial arterial carbon dioxide pressure (PaCO2) values as possible. This review will provide an overview of existing evidence of the efficacy of HI‐NIV stable COPD patients with CHRF. Secondly, we will discuss hypotheses underlying NIV benefit in stable hypercapnic COPD, providing insight into better patient selection and hopefully more individually titrated HI‐NIV. Finally, we will provide practical advice on how to initiate and follow‐up patients on HI‐NIV, with special emphasis on monitoring that should be available during the initiation and follow‐up of HI‐NIV, and will discuss more extended monitoring techniques that could improve HI‐NIV treatment in the future.

To optimize long-term nocturnal non-invasive ventilation in patients with chronic obstructive pulmonary disease, surface diaphragm electromyography (EMGdi) might be helpful to detect patient-ventilator asynchrony. However, visual analysis is labor-intensive and EMGdi is heavily corrupted by electrocardiographic (ECG) activity. Therefore, we developed an automatic method to detect inspiratory onset from EMGdi envelope using fixed sample entropy (fSE) and a dynamic threshold based on kernel density estimation (KDE). Moreover, we combined fSE with adaptive filtering techniques to reduce ECG interference and improve onset detection. The performance of EMGdi envelopes extracted by applying fSE and fSE with adaptive filtering was compared to the root mean square (RMS)-based envelope provided by the EMG acquisition device. Automatic onset detection accuracy, using these three envelopes, was evaluated through the root mean square error (RMSE) between the automatic and mean visual onsets (made by two observers). The fSE-based method provided lower RMSE, which was reduced from 298 ms to 264 ms when combined with adaptive filtering, compared to 301 ms provided by the RMS-based method. The RMSE was negatively correlated with the proposed EMGdi quality indices. Following further validation, fSE with KDE, combined with adaptive filtering when dealing with low quality EMGdi, indicates promise for detecting the neural onset of respiratory drive.

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