Early Prediction of ICU Hypotension Using Machine Learning

This prospective observational study aims to develop and internally validate a machine learning model for the early prediction of hypotension in adult intensive care unit patients. The model will use routinely collected non-invasive vital signs, heart rate, medication-dose records, and fluid-balance data recorded during standard ICU care. No intervention will be assigned by the study, and patient management will not be changed according to the model output. The primary aim is to predict hypotension 30 minutes before its occurrence; shorter 5- and 15-minute prediction horizons will also be evaluated.

Inclusion Criteria: * Age 18 years or older * Admission to the adult intensive care unit during the study period * Length of stay in the intensive care unit of at least 24 hours * Availability of routine intensive care unit monitoring data * Availability of non-invasive blood pressure and heart rate measurements recorded during ICU monitoring * Availability of medication-dose and/or fluid-balance records during ICU monitoring Exclusion Criteria: * Age younger than 18 years * Length of stay in the intensive care unit of less than 24 hours * Absence of usable blood pressure monitoring data * Records with irrecoverable timestamp inconsistencies * Insufficient monitoring duration for feature construction and future outcome labeling