Patient-Ventilator Asynchrony: Occurence and Clinical Impact in Usual Care
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: PVA classification software.
- Who it may be relevant to
- Registry conditions: Mechanical Ventilation, Patient-Ventilator Asynchrony. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Netherlands
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Unraveling the Clinical Impact of Patient-Ventilator Asynchrony in Usual Care
Overview
The goal of this observational study is to unravel the occurence, impact and relations of Patient-Ventilator Aynchrony (PVA) in mechanically ventilated patients. The main questions it aims to answer are: * How often does PVA occur? * What are relations between clinical characteristics and PVA occurence? * What are relations between PVA occurence and patient outcomes? All questions will be assessed using data collected during the whole course of mechanical ventilation. Mechanically ventilated patients' medical data will be re-used. PVAs will be automatically classified on ventilator waveform data, using validated Deep Breath software.
Detailed description
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.
Interventions
- Other PVA classification software
Patients will receive standard care, without an intervention. Data will be captured as part of standard care and analyzed for PVAs retrospectively, using dedicated offline software.
Primary outcome measures
- Asynchrony index over time (aggregated and per PVA type) [Time frame: 28 days]
Secondary outcome measures (12)
- Use of sedatives (cumulative dose and type) [Time frame: 28 days]
- Mechanical ventilation settings [Time frame: 28 days]
- Respiratory parameters [Time frame: 28 days]
- Hemodynamic parameters [Time frame: 28 days]
- Relevant medication [Time frame: 28 days]
- Use of assist devices [Time frame: 28 days]
- Gas exchange parameters [Time frame: 28 days]
- Blood inflammatory biomarkers [Time frame: 28 days]
- Sedation depth [Time frame: 28 days]
- Reported delirium [Time frame: 28 days]
- Illness severity score (SOFA-score) [Time frame: 28 days]
- ICU mortality [Time frame: 90 days]
Eligibility criteria
Inclusion criteria
- Age > 18 years old.
- Recordings available of ventilator waveforms synchronized with the patient's electronic health record during invasive mechanical ventilation.
- Duration of mechanical ventilation of at least 24 hours.
Exclusion criteria
- (Previous) registered objection of patient and/or relatives to re-use clinical data for research purposes
- No consent for re-use of data for research
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Netherlands · 3 centers
- Catharina Ziekenhuis Eindhoven (CZE) — Eindhoven
- Leiden University Medical Center (LUMC) — Leiden
- Erasmus Medical Center (EMC) — Rotterdam
Identifiers
NCT: NCT07624786 · MEC-2025-0353 · EMCLSH24018