RecruitingNCT07627607

Early Prediction of ICU Hypotension Using Machine Learning

A Prospective Observational Machine Learning Study for the Early Prediction of Hypotension in Adult Intensive Care Unit Patients

Sponsor

Kutahya Health Sciences University

Enrollment

100 participants

Start Date

Mar 15, 2026

Study Type

OBSERVATIONAL

Conditions

Hypotension

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.

Eligibility

Min Age: 18 Years

Inclusion Criteria6

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 Criteria5

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

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Interventions

OTHERRoutine 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.