Unraveling the Clinical Impact of Patient-Ventilator Asynchrony in Usual Care

Many ventilated patients show excessive breathing efforts and abnormal, irregular breathing. This patient-ventilator asynchrony (PVA) is associated with serious discomfort, lung injury, sleep disruption and higher mortality. PVA exists in many forms and is reported in 10-90% of patients, but identifying and resolving it is challenging, even for expert clinicians. Hence, PVA prevalence and impact is likely highly underestimated, and the direct causal link with worse outcomes is inconclusive. PVAs should be better dettected, understood and resovled to optimize the individual patient's treatment.

In a previous study, the investigators validated an AI-based algorithm capable of reliable PVA detection (Deep Breath software). In this study, the investigators will apply this algorithm to the collected ventilator waveform data (offline processing), in order to reliably assess PVA occurrence, and its relation with clinical outcomes and patient characteristics in current clinical care. Data of minimally 110 patients collected over the whole course of mechanical ventilation will be assessed. Patients will be included in three ICUs to promote generalizability.

The primary outcome will be the asynchrony index (in total and per PVA type) over time. Secondary outcomes will include, but are not limited to: clinical characteristics (e.g. respiratory and hemodynamic parameters, sedation), (ICU) mortality, ventilator free days at day 28 and 90, duration of ventilation, weaning success and reintubation rate.