Intensive Care Unit (ICU) AdmissionICUNCT06726733
Assessing Intensive Care Unit (ICU) Indications: Human vs. ChatGPT-4o Predictions
An observational study of Intensive Care Unit (ICU) Admission and Emergency Department Patient, run by Bursa Yuksek Ihtisas Training and Research Hospital at 1 site in Turkey (Türkiye).
Trial summary
This retrospective study evaluates the accuracy of ICU admission indications by comparing clinical decisions with predictions from ChatGPT-4. Patient data, including demographics, vital signs, laboratory results, imaging findings, and clinical decisions, will be retrospectively collected and documented systematically… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Intensive Care Unit (ICU) Admission, Emergency Department Patient, Artificial Intelligence (AI)
- How many people
- 624 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Sep 2024to 28 Feb 2025
- 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
- Adult, Older adult
Study population: 500
Who may be able to join
- Patients aged 18 years and older who are consulted to the anesthesiology and reanimation clinic for ICU indications from the emergency department will be included in the study.
Who cannot join
- Patients consulted to the anesthesiology and reanimation clinic for ICU indications from inpatient services.
- Patients consulted to the anesthesiology and reanimation clinic from the emergency department for reasons other than ICU indications.
- Patients consulted to the anesthesiology and reanimation clinic for ICU indications from the emergency department but with insufficient recorded data (patients with data loss)
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Bursa Yuksek Ihtisas Training and Research Hospital
Bursa
About this study
Official title: Evaluation of the Accuracy of Intensive Care Unit (ICU) Admission Indications in Emergency Department Patients: A Comparison Between Clinical Decisions and ChatGPT-4o Prediction
This retrospective study evaluates the accuracy of ICU admission indications by comparing clinical decisions with predictions from ChatGPT-4. Patient data, including demographics, vital signs, laboratory results, imaging findings, and clinical decisions, will be retrospectively collected and documented systematically using Case Report Forms. The model will be trained using ICU admission guidelines and tasked to predict ICU needs based on collected patient data. This study aims to systematically assess the alignment between AI-based predictions and clinical decisions for ICU admissions.
Read the detailed description
This study has a retrospective design. The medical data of patients admitted to the emergency department and consulted to the anesthesiology and reanimation clinic for ICU indications will be collected retrospectively. Demographic information, vital signs, laboratory results, imaging findings, and clinical decisions of the patients will be recorded. These data will be systematically collected for each patient using an individual Case Report Form.
Inclusion Criteria for the Study:
Patients aged 18 years and older who were consulted to the anesthesiology and reanimation clinic for ICU indications from the emergency department will be included in the study.
Exclusion Criteria for the Study:
Patients consulted to the anesthesiology and reanimation clinic for ICU indications from inpatient services.
Patients consulted to the anesthesiology and reanimation clinic from the emergency department for reasons other than ICU indications.
Patients consulted to the anesthesiology and reanimation clinic for ICU indications from the emergency department but with insufficient recorded data (patients with data loss).
Model Training and Prediction Analysis:
ChatGPT-4 will be trained according to the guidelines in "Yoğun Bakım Hasta Kabul Kriterleri (Rehberleri)" by Çiftçi B, Erdoğan C, and Demiraran Y (5). The collected patient data will be presented to the ChatGPT-4 model to obtain predictions regarding whether the patients require ICU admission. The predictions made by ChatGPT will be compared with clinical decisions, and accuracy rate, false positive rate, and false negative rate will be analyzed.
Statistical Analysis Methods to Be Used in the Study:
Accuracy Rate: The rate at which ChatGPT correctly predicts ICU indications will be calculated.
False Positive Rate: The rate at which ChatGPT predicts ICU need for patients who do not require ICU admission will be evaluated.
False Negative Rate: The rate at which ChatGPT predicts no ICU need for patients who require ICU admission will be analyzed.
Kappa Statistics: The agreement between ChatGPT predictions and clinical decisions will be measured.
ROC Curve and AUC: The performance of ChatGPT will be evaluated using the ROC curve and AUC.
The Case Report Form used for each patient ensures detailed and systematic data collection of clinical information, aiming to meaningfully compare the alignment of ChatGPT's predictions with clinical decisions.
- Conditions
- Intensive Care Unit (ICU) Admission, Emergency Department Patient, Artificial Intelligence (AI), Clinical Decision-making
- Keywords
- AI, Intensive Care Unit, Clinical decision-making, Artificial Intelligenc
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy rate of ChatGPT-4 in predicting ICU indications
Measured: 3 month
False positive rate
Measured: 3 month
False negative rate
Measured: 3 month
Secondary outcomes (1)
Kappa statistic
Measured: 3 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Dec 2024
- Study start1 Sep 2024
- Primary completion30 Nov 2024
- Study completion28 Feb 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Intensive Care Unit (ICU) AdmissionStudies 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
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 624 participants (actual)
Sponsor and investigators
- Lead sponsor
- Bursa Yuksek Ihtisas Training and Research Hospital
- Sponsor type
- Other government
- Responsible party
- Principal investigator, Aycan KURTARANGİL DOĞAN, Bursa Yuksek Ihtisas Training and Research Hospital
- Investigators
- İlkay Ceylan, Principal investigator, ceylanilkay@yahoo.com
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06726733
- Sponsor's protocol ID
- BYIEAH-INKA
- Acronym
- ICU
- Status last verified
- Apr 2026
- First submitted
- 6 Dec 2024
- Last updated
- 14 Apr 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 14 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of the Accuracy of Intensive Care Unit (ICU) Admission Indications in Emergency Department Patients: A Comparison Between Clinical Decisions and ChatGPT-4o Prediction
- ClinicalTrials.gov ID
- NCT06726733
- Acronym / sponsor ID
- ICU / BYIEAH-INKA
- Conditions
- Intensive Care Unit (ICU) Admission, Emergency Department Patient, Artificial Intelligence (AI), Clinical Decision-making
- Keywords
- AI, Intensive Care Unit, Clinical decision-making, Artificial Intelligenc
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 624 participants (actual)
- Lead sponsor
- Bursa Yuksek Ihtisas Training and Research HospitalOther government
- Study start
- 1 Sep 2024 (actual)
- Primary completion
- 30 Nov 2024 (actual)
- Study completion
- 28 Feb 2025 (actual)
- First posted
- 10 Dec 2024 (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 retrospective study evaluates the accuracy of ICU admission indications by comparing clinical decisions with predictions from ChatGPT-4. Patient data, including demographics, vital signs, laboratory results, imaging findings, and clinical decisions, will be retrospectively collected and documented systematically using Case Report Forms. The model will be trained using ICU admission guidelines and tasked to predict ICU needs based on collected patient data. This study aims to systematically assess the alignment between AI-based predictions and clinical decisions for ICU admissions.
Detailed description
This study has a retrospective design. The medical data of patients admitted to the emergency department and consulted to the anesthesiology and reanimation clinic for ICU indications will be collected retrospectively. Demographic information, vital signs, laboratory results, imaging findings, and clinical decisions of the patients will be recorded. These data will be systematically collected for each patient using an individual Case Report Form.
Inclusion Criteria for the Study:
Patients aged 18 years and older who were consulted to the anesthesiology and reanimation clinic for ICU indications from the emergency department will be included in the study.
Exclusion Criteria for the Study:
Patients consulted to the anesthesiology and reanimation clinic for ICU indications from inpatient services.
Patients consulted to the anesthesiology and reanimation clinic from the emergency department for reasons other than ICU indications.
Patients consulted to the anesthesiology and reanimation clinic for ICU indications from the emergency department but with insufficient recorded data (patients with data loss).
Model Training and Prediction Analysis:
ChatGPT-4 will be trained according to the guidelines in "Yoğun Bakım Hasta Kabul Kriterleri (Rehberleri)" by Çiftçi B, Erdoğan C, and Demiraran Y (5). The collected patient data will be presented to the ChatGPT-4 model to obtain predictions regarding whether the patients require ICU admission. The predictions made by ChatGPT will be compared with clinical decisions, and accuracy rate, false positive rate, and false negative rate will be analyzed.
Statistical Analysis Methods to Be Used in the Study:
Accuracy Rate: The rate at which ChatGPT correctly predicts ICU indications will be calculated.
False Positive Rate: The rate at which ChatGPT predicts ICU need for patients who do not require ICU admission will be evaluated.
False Negative Rate: The rate at which ChatGPT predicts no ICU need for patients who require ICU admission will be analyzed.
Kappa Statistics: The agreement between ChatGPT predictions and clinical decisions will be measured.
ROC Curve and AUC: The performance of ChatGPT will be evaluated using the ROC curve and AUC.
The Case Report Form used for each patient ensures detailed and systematic data collection of clinical information, aiming to meaningfully compare the alignment of ChatGPT's predictions with clinical decisions.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- 500
Key inclusion criteria (1 listed)
- Patients aged 18 years and older who are consulted to the anesthesiology and reanimation clinic for ICU indications from the emergency department will be included in the study.
Key exclusion criteria (3 listed)
- Patients consulted to the anesthesiology and reanimation clinic for ICU indications from inpatient services.
- Patients consulted to the anesthesiology and reanimation clinic from the emergency department for reasons other than ICU indications.
- Patients consulted to the anesthesiology and reanimation clinic for ICU indications from the emergency department but with insufficient recorded data (patients with data loss)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Accuracy rate of ChatGPT-4 in predicting ICU indications
Time frame: 3 month
False positive rate
Time frame: 3 month
False negative rate
Time frame: 3 month
1 secondary outcome
Kappa statistic
Time frame: 3 month
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 14 Apr 2026.
- Individual participant dataSharing planned: No
Related trials for Intensive Care Unit (ICU) Admission
RecruitingObservational study
COMparative Performance of General Purpose Models
Neurosurgical Patients
Not yet recruitingObservational study
CAROTID DOPPLER AND CEREBRAL OXIMERY IN POST CARDIAC ARREST ICU PATIENTS
Cardiac Arrest (CA), Carotid Doppler
RecruitingObservational study
Comparison of Scores for Early Brain Damage Assessment at Intensive Care Unit Admission After Cardiac Arrest
Cardiac Arrest
Not yet recruitingObservational study
Platelet Transfusion Thresholds in Non-Bleeding Patients
Non Bleeding Patients With Thrombocytopenia
Not yet recruitingObservational study
Lactate/Albumin Ratio and Mortality in Respiratory Failure
Acute Respiratory Failure, Critical Illness
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.
İlkay Ceylan principal investigator
ceylanilkay@yahoo.com
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 Intensive Care Unit (ICU) Admission and Emergency Department Patient, run by Bursa Yuksek Ihtisas Training and Research Hospital 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 1 inclusion and 3 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 14 Apr 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?
Accuracy rate of ChatGPT-4 in predicting ICU indications (time frame: 3 month). The study lists 3 primary outcomes in all. 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 14 Apr 2026.