HypotensionICU-HypoAINCT07627607
Early Prediction of ICU Hypotension Using Machine Learning
An observational study of Hypotension, run by Kutahya Health Sciences University at 1 site in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Hypotension
- What is being tested?
- Routine ICU Data Collection
- How many people
- 107 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 15 Mar 2026to 30 Jun 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
Read the 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.
- Conditions
- Hypotension
- Being tested
- Routine ICU Data Collection
- Keywords
- Intensive Care Unit, Machine Learning, Artificial Intelligence, Non-Invasive Blood Pressure, Hemodynamic Monitoring, Prediction Model
- Medical subject headings
- Hypotension
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Routine ICU Data CollectionOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Routine ICU Data Collection |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
Discriminative performance of separate machine learning models for predicting hypotension at 5-minute and 15-minute prediction horizons.
Measured: 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.
Measured: From enrollment through the end of ICU monitoring, up to 4 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Jun 2026
- Study start15 Mar 2026
- Primary completion30 Jun 2026
- Study completion30 Jun 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for HypotensionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 107 participants (actual)
Sponsor and investigators
- Lead sponsor
- Kutahya Health Sciences University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Serkan TELLİ, Kutahya Health Sciences University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07627607
- Sponsor's protocol ID
- 2026/03-15
- Acronym
- ICU-HypoAI
- Status last verified
- Jul 2026
- First submitted
- 31 May 2026
- Last updated
- 9 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Prospective Observational Machine Learning Study for the Early Prediction of Hypotension in Adult Intensive Care Unit Patients
- ClinicalTrials.gov ID
- NCT07627607
- Acronym / sponsor ID
- ICU-HypoAI / 2026/03-15
- Conditions
- Hypotension
- Keywords
- Intensive Care Unit, Machine Learning, Artificial Intelligence, Non-Invasive Blood Pressure, Hemodynamic Monitoring, Prediction Model
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 107 participants (actual)
- Lead sponsor
- Kutahya Health Sciences UniversityUniversity, hospital or other
- Study start
- 15 Mar 2026 (actual)
- Primary completion
- 30 Jun 2026 (actual)
- Study completion
- 30 Jun 2026 (actual)
- First posted
- 4 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Adult Intensive Care Unit Patients | – | Other: Routine ICU Data CollectionAdult 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (6 listed)
- 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
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Routine ICU Data CollectionRoutinely 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. | Other | Adult Intensive Care Unit Patients |
Primary outcome
Area Under the Receiver Operating Characteristic Curve for 30-Minute Hypotension Prediction
Time frame: From enrollment through the end of ICU monitoring, up to 4 months
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.
2 secondary outcomes
Area Under the Receiver Operating Characteristic Curve for 5- and 15-Minute Hypotension Prediction
Time frame: From enrollment through the end of ICU monitoring, up to 4 months
Classification Performance of the Hypotension Prediction Model
Time frame: From enrollment through the end of ICU monitoring, up to 4 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Jul 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hypotension, run by Kutahya Health Sciences University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 9 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Receiver Operating Characteristic Curve for 30-Minute Hypotension Prediction (time frame: From enrollment through the end of ICU monitoring, up to 4 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Jul 2026.