Machine Learning Model to Predict Outcome in Acute Hypoxemic Respiratory Failure
NCT06333002
Acute Hypoxemic Respiratory Failure, Respiration Disorders
Ciudad Real, Spain
View Trial DetailsNCT Number: NCT05203536
To verify the association between respiratory system mechanical properties (ΔP, ΔPL,dyn, Pmus, Pplat and CRS and CL,dyn) assessed during assisted modes of ventilation (as average over the first three days since enrollment) and ICU mortality.
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Notify Me18 year and older
All sexes
Observational
Toronto General Hospital, Toronto, Canada
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Time frame: 90 days
Difference between mean values of driving pressure in survivors and non-survivors at ICU discharge.
Time frame: 90 days
Difference between mean values of respiratory system compliance in survivors and non-survivors at ICU discharge.
Time frame: 90 days
Difference between mean values of plateau pressure in survivors and non-survivors at ICU discharge.
Time frame: 90 days
Difference between mean values of pressure muscle index (PMI) in survivors and non-survivors at ICU discharge.
Time frame: 90 days
Difference between mean values of muscle pressure (Pmus) in survivors and non-survivors at ICU discharge.
University of Milano Bicocca
Other
ICEBERG STUDY. Respiratory Mechanics Assessment During Assisted Mechanical Ventilation
Acronym: ICEBERG
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