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Early Prediction of ICU Hypotension Using Machine Learning
Sponsor: Kutahya Health Sciences University
Summary
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.
Official title: A Prospective Observational Machine Learning Study for the Early Prediction of Hypotension in Adult Intensive Care Unit Patients
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
100
Start Date
2026-03-15
Completion Date
2026-07-15
Last Updated
2026-06-08
Healthy Volunteers
No
Conditions
Interventions
Routine ICU Data Collection
Routinely collected intensive care unit data, including non-invasive blood pressure, heart rate, medication-dose records, and fluid-balance data, will be recorded and analyzed for development and internal validation of a machine learning model. The study does not assign any treatment, medication, device, alarm, or clinical decision.
Locations (1)
Kutahya City Hospital
Kütahya, Kütahya, Turkey (Türkiye)