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Early Prediction of ICU Hypotension Using Machine Learning

NCT07627607 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 107

Last updated 2026-07-09

No results posted yet for this study

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.

Conditions

Interventions

OTHER

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.

Sponsors & Collaborators

  • Kutahya Health Sciences University

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-03-15
Primary Completion
2026-06-30
Completion
2026-06-30

Countries

  • Turkey (Türkiye)

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07627607 on ClinicalTrials.gov