This paper presents an algorithm for noninvasive estimation of alveolar pressure in mechanically ventilated patients who are spontaneously breathing. Continual monitoring of alveolar pressure is desirable to prevent ventilator-induced lung injury and to assess the intrinsic positive end-expiratory pressure (PEEPi), which is a parameter of clinical relevance in respiratory care and difficult to measure noninvasively. The algorithm is based on a physiological model of the respiratory system and, as such, it also provides insight into the respiratory mechanics of the patient under mechanical ventilation. In particular, the algorithm allows one to correctly estimate other clinical parameters of interest such as the patient's respiratory resistance and elastance, even in the presence of PEEPi.

In this paper we present a method for the estimation of leaks in non-invasive ventilation. Accurate estimation of leaks is a key component of a ventilator, since it determines the ventilator performance in terms of patient-ventilator synchrony and air volume delivery. In particular, in non-invasive ventilation, the patient flow is significantly different from the flow measured at the ventilator outlet. This is mostly due to the vent orifice along the tube that is used for exhalation, but also to the non-intentional leaks that occur elsewhere in the circuit (e.g., at the mask). Such leaks are traditionally quantified via a model with two parameters, but only one of them is continually updated - the other is fixed. The new algorithm allows for breath-by-breath update of both parameters. This was made possible by leveraging a model describing the patient respiratory mechanics.

Typical modern mechanical ventilators offer the clinician the possibility of automatically performing end-inspiratory occlusion maneuvers. The static conditions induced by such maneuvers are indeed favorable to estimating patient's respiratory mechanics in terms of total resistance (R) and elastance (E). These are parameters of wide clinical interest. However, in the presence of intrinsic PEEP, the standard formula used to compute E via the occlusion maneuver is known to be inaccurate. In this paper we propose an alternative method for the estimation of E via the occlusion maneuver that eliminates the bias by leveraging concepts derived from physiological modeling of the respiratory system dynamics. The proposed method is also capable of accounting for respiratory efforts triggering the breath, and hence can be applied in both passive and spontaneously breathing conditions.