During general anesthesia, intraoperative hypotension (IOH) is associated with increased morbidity and mortality. Mean arterial pressure (MAP) \< 65mmHg is the most common definition of hypotension. In order to reduce IOH, a complex method using machine learning called hypotensive prediction index (HPI) was shown to be superior to changes in MAP (ΔMAP) to predict hypotension (MAP between 65 and 75 excluded). Linear extrapolation of MAP (LepMAP) is also very simple and could be a better approach than ΔMAP. The main objective of the present study was to investigate whether LepMAP could predict IOH during anesthesia 1, 2 or 5 minutes before. Hypothesis : the area under the ROC curves (ROC Area Under Curves) at 1, 2 and 5 minutes of LepMAP would be superior to ΔMAP
Study Type
OBSERVATIONAL
Enrollment
80
The variable of the characteristic of the patients (i.e.: age, sex, hypertension, diabetes, atrial fibrillation, coronary arteries diseases, body mass index (BMI), surgery, medications) were retrieved from the anesthesia consultation file (Easily, Hospices Civiles de Lyon, France). We retrieved the mean arterial pressure from our local anesthesia software for each patient ( Diane®, Bow medical, Amiens France). We also performed an automatic extraction of data from our anesthesia software (Diane, Bow Medical, France) with a rate of 1 value / minute for some continuous arterial pressure. All data was extracted from our institutional database and collected by a physician who was not involved in the care of the study patients.
Department of Anesthesiology and Intensive Care, Louis Pradel University Hospital
Bron, France
AUC ROC of LepMAP
The primary outcome measure is the AUC ROC of LepMAP 1, 2 and 5 minute before hypotension to predict hypotension defined as a mean arterial pressure less tha 65 mmHg
Time frame: Only during perioperative period
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