Early Prediction of ICU Hypotension Using Machine Learning

NCT07627607 | Recruiting

• 1 other identifier: 2026/03-15
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief 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

Hypotension

Keywords

Intensive Care Unit; Machine Learning; Artificial Intelligence; Non-Invasive Blood Pressure; Hemodynamic Monitoring; Prediction Model

Outcome Measures

Primary Outcomes (1)

• Area Under the Receiver Operating Characteristic Curve for 30-Minute Hypotension Prediction

  Discriminative performance of the machine learning model for predicting hypotension 30 minutes before its occurrence. Hypotension will be defined as systolic blood pressure below 90 mmHg, mean arterial pressure below 65 mmHg, or diastolic blood pressure below 60 mmHg at a five-minute observation point.

  From enrollment through the end of ICU monitoring, up to 4 months

Secondary Outcomes (2)

• Area Under the Receiver Operating Characteristic Curve for 5- and 15-Minute Hypotension Prediction

  From enrollment through the end of ICU monitoring, up to 4 months

• Classification Performance of the Hypotension Prediction Model

  From enrollment through the end of ICU monitoring, up to 4 months

Study Arms (1)

Adult Intensive Care Unit Patients

Adult patients admitted to the intensive care unit who are monitored during routine clinical care. Routinely collected non-invasive blood pressure, heart rate, medication-dose, and fluid-balance data will be used for machine learning model development and internal validation. No treatment or clinical intervention will be assigned by the study protocol.

Other: Routine ICU Data Collection

Interventions

Routine ICU Data Collection OTHER

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.

Adult Intensive Care Unit Patients

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population will consist of consecutive adult patients monitored in the adult intensive care unit of Kutahya Health Sciences University during the study period. Routine clinical monitoring data, including non-invasive blood pressure, heart rate, medication-dose records, and fluid-balance data, will be used for machine learning model development and internal validation. No treatment or intervention will be assigned by the study protocol.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Kutahya Health Sciences University: lead

Study Sites (1)

Kutahya City Hospital

Kütahya, Kütahya, 43100, Turkey (Türkiye)

RECRUITING

MeSH Terms

Conditions

Hypotension

Condition Hierarchy (Ancestors)

Vascular Diseases; Cardiovascular Diseases

Central Study Contacts

Serkan Telli, MD

CONTACT

905437156203; serkan.telli@ksbu.edu.tr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor of Anesthesiology and Reanimation

Study Record Dates

• First Submitted: May 31, 2026
• First Posted: June 4, 2026
• Study Start: March 15, 2026
• Primary Completion (Estimated): June 30, 2026
• Study Completion (Estimated): July 15, 2026
• Last Updated: June 8, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)