Early Prediction of ICU Hypotension Using Machine Learning (ICU-HypoAI)

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

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.

Study Overview

• Status: Recruiting
• Conditions: Hypotension
• Intervention / Treatment: Other: Routine ICU Data Collection

Detailed Description

Hypotension is a frequent hemodynamic event in critically ill patients and may occur before clear clinical deterioration is recognized. Earlier identification of patients at risk may support closer clinical attention and more timely evaluation. This study is designed as a prospective, observational machine learning study in adult intensive care unit patients.

Routinely available ICU data will be collected at five-minute intervals, including systolic, mean, and diastolic non-invasive blood pressure, heart rate, medication-dose entries, and fluid-balance records. These data will be used to construct time-dependent features reflecting recent values, short-term changes, and rolling trends. Hypotension will be defined at each five-minute time point as systolic blood pressure below 90 mmHg, mean arterial pressure below 65 mmHg, or diastolic blood pressure below 60 mmHg.

The primary prediction horizon will be 30 minutes. Separate secondary analyses will evaluate 5- and 15-minute prediction horizons. A gradient-boosted decision-tree model will be developed and internally validated using patient-level data partitioning to avoid assigning observations from the same patient to both training and validation sets. Model performance will be assessed using discrimination, classification performance, and calibration measures. Feature-importance analyses will be used to describe the variables contributing to model predictions.

The study is observational. No treatment, medication, device, alarm, or clinical decision will be assigned by the study protocol. The prediction model will be developed and evaluated using collected data and will not be used to guide real-time patient management during the study period.

• Study Type: Observational
• Enrollment (Estimated): 100

Contacts and Locations

• Study Contact: Name: Serkan Telli, MD; Phone Number: 905437156203; Email: serkan.telli@ksbu.edu.tr

Study Locations

• Turkey (Türkiye)
  • Kütahya
    • Kütahya, Kütahya, Turkey (Türkiye), 43100
      • Recruiting
      • Kutahya City Hospital
      • Contact: Serkan Telli, MD; Phone Number: +905437156203; Email: serkan.telli@ksbu.edu.tr

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Area Under the Receiver Operating Characteristic Curve for 5- and 15-Minute Hypotension Prediction	Discriminative performance of separate machine learning models for predicting hypotension at 5-minute and 15-minute prediction horizons.	From enrollment through the end of ICU monitoring, up to 4 months
Classification Performance of the Hypotension Prediction Model	Classification performance of the machine learning model will be assessed using sensitivity, specificity, positive predictive value, negative predictive value, and F1 score at predefined classification thresholds.	From enrollment through the end of ICU monitoring, up to 4 months

Collaborators and Investigators

• Sponsor: Kutahya Health Sciences University

Study record dates

Study Major Dates

• Study Start (Actual): March 15, 2026
• Primary Completion (Estimated): June 30, 2026
• Study Completion (Estimated): July 15, 2026

Study Registration Dates

• First Submitted: May 31, 2026
• First Submitted That Met QC Criteria: May 31, 2026
• First Posted (Actual): June 4, 2026

Study Record Updates

• Last Update Posted (Actual): June 4, 2026
• Last Update Submitted That Met QC Criteria: May 31, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Keywords: Intensive Care Unit; Artificial Intelligence; Prediction Model; Machine Learning; Hemodynamic Monitoring; Non-Invasive Blood Pressure
• Additional Relevant MeSH Terms: Vascular Diseases; Cardiovascular Diseases; Hypotension
• Other Study ID Numbers: 2026/03-15

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No