Towards optimal Patient-Ventilator Interaction: The interacting signals

Background Patient-ventilator interaction (PVI) emerges from the continuous interaction between physiological signals generated by the patient and signals generated, measured, or displayed by the mechanical ventilator. Traditionally, PVI has been described predominantly through recognizable forms of patient-ventilator asynchrony. Although this approach remains clinically useful, the visible waveform abnormalities represent the final expression of interactions occurring between multiple neural, muscular, mechanical and ventilator signals. Methods This conceptual narrative review builds on contemporary PVI taxonomies by organizing bedside analysis around the relationships between selected patient and ventilator signals. Patient-derived physiological signals may reflect neural respiratory activity, respiratory muscle activation, and respiratory muscle pressure, whereas ventilator-derived signals include mechanical breath onset, assistance delivery, cycling-off, and ventilator-measured airway pressure, flow and volume. Rather than introducing new PVI categories, the framework uses four sequential relational questions: whether expected patient and ventilator signals correspond; whether their inspiratory onsets are appropriately aligned; whether their inspiratory offsets are appropriately aligned; and whether the magnitude of ventilator-delivered assistance is appropriate for the intended physiological target. Loss of expected correspondence produces abnormalities such as failed triggering and unintended (false) triggering. Abnormal temporal relationships between corresponding inspiratory onsets produce early or late triggering, whereas abnormal offset relationships produce early or late cycling. Appropriate timing, however, does not guarantee appropriate assistance: under-assistance and over-assistance represent abnormalities in the magnitude of ventilator contribution relative to patient respiratory demand and the intended unloading goal. The same signal-identification approach is applied across the expiratory phase, where active expiratory muscle contraction, expiratory-flow deformation, persistence of inspiratory activity after cycling-off, and expiratory muscle relaxation may alter waveform appearance and influence subsequent triggering. Directionality is treated as an interpretive modifier when temporal sequence alone is insufficient to establish the underlying mechanism. Reverse triggering illustrates this distinction, demonstrating that the interaction may proceed from the ventilator to the patient rather than from the patient to the ventilator. Conclusions A stepwise bedside algorithm is proposed to translate this signal-based framework into practical waveform interpretation. Understanding which signals are interacting, how they correspond, how their timing and magnitude relate, and how interactions propagate across respiratory phases provides a physiological path toward more precise interpretation of PVI and ultimately towards optimal patient-ventilator interaction.