Cost AnalysisNCT07210866
An Artificial Intelligence Model for Intensive Care Length of Stay, Neurological Outcome and Costs Estimation After Cardiopulmonary Resuscitation: a Cohort Study.
An observational study of Cost Analysis and Length of ICU Stay, run by Bezmialem Vakif University.
Trial summary
The study aims to overview patients registered to Bezmialem Vakıf University Hospital Intensive Care Unit after successive cardiac arrest resuscitation from October 2010 to September 2025. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cost Analysis, Length of ICU Stay, Neurological Outcome
- What is being tested?
- no physical or medical interventions
- How many people
- 5,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Oct 2025to 1 Dec 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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: Patients above 18 years after successful cardiopulmonary resuscitation with ROSC registered at Bezmialem Vakıf University Hospital Intensive care unit from October 2010 to September 2025 will be included in the resesarch.
Who may be able to join
- age>18 years
- successive cardiopulmonary resuscitation
- at least 1 hour long admission to ICU after Return Of Spontaneous Circulation (ROSC)
Who cannot join
- age < 18 years
- >80% missing data in patient records
- patients with no ROSC
About this study
Official title: AN ARTIFICIAL INTELLIGENCE MODEL FOR INTENSIVE CARE LENGTH OF STAY, NEUROLOGICAL OUTCOME AND COSTS ESTIMATION AFTER CARDIOPULMONARY RESUSCITATION: A COHORT STUDY
The study aims to overview patients registered to Bezmialem Vakıf University Hospital Intensive Care Unit after successive cardiac arrest resuscitation from October 2010 to September 2025. The goal is to determine length of stay in reanimation, neurological clinical outcome and costs of these patients at discharge from the department. All these data is intended to be evaluated by artificial intelligence to evaluate a predictive model.
- Conditions
- Cost Analysis, Length of ICU Stay, Neurological Outcome
- Being tested
- no physical or medical interventions
- Keywords
- machine learning, cardiopulmonary rescucitation, lenght of stay, cost analysis, neurological outcome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
no physical or medical interventionsOther
Data from patients after successive rescucitaion will be evaluated by machine learning programs.
| Study group | Role | What they receive |
|---|---|---|
| Age >18 years patients after successful cardiopulmonary resuscitation observed in reanimation | Not described Receives: Other: no physical or medical interventions |
What is being measured
The primary outcome is the main question the study is designed to answer.
Machine Learning Python programme
The created database will be analyzed using a machine learning artificial intelligence algorithm with the Python programming language. After processing missing and incomplete data by artificial intelligence, the database will be divided into two parts: model training and model validation. Meaningful data will be selected through model training, and a prediction model will be built based on these data. To increase the interpretability of the prediction model and help users understand how and why certain predictions are made, the SHapley Additive exPlanations (SHAP) algorithm will be used. In machine learning, the SHAP technique is used to interpret the decision-making processes of complex machine learning models.
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Oct 2025
- Study start1 Oct 2025 (estimated)
- Primary completion1 Dec 2025 (estimated)
- Study completion1 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cost AnalysisStudies of the same condition, open and under way.
- More trials run by Bezmialem Vakif UniversityThe sponsor's other registered studies.
- 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
- Retrospective
- Enrolment
- 5,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Bezmialem Vakif University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Nıgar Kangarlı, Bezmialem Vakif University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07210866
- Sponsor's protocol ID
- Nkangarli002
- Status last verified
- Sep 2025
- First submitted
- 30 Sep 2025
- Last updated
- 7 Oct 2025
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 7 Oct 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AN ARTIFICIAL INTELLIGENCE MODEL FOR INTENSIVE CARE LENGTH OF STAY, NEUROLOGICAL OUTCOME AND COSTS ESTIMATION AFTER CARDIOPULMONARY RESUSCITATION: A COHORT STUDY
- ClinicalTrials.gov ID
- NCT07210866
- Acronym / sponsor ID
- Nkangarli002
- Conditions
- Cost Analysis, Length of ICU Stay, Neurological Outcome
- Keywords
- machine learning, cardiopulmonary rescucitation, lenght of stay, cost analysis, neurological outcome
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- Bezmialem Vakif UniversityUniversity, hospital or other
- Study start
- 1 Oct 2025 (estimated)
- Primary completion
- 1 Dec 2025 (estimated)
- Study completion
- 1 Dec 2025 (estimated)
- First posted
- 7 Oct 2025 (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.
The study aims to overview patients registered to Bezmialem Vakıf University Hospital Intensive Care Unit after successive cardiac arrest resuscitation from October 2010 to September 2025. The goal is to determine length of stay in reanimation, neurological clinical outcome and costs of these patients at discharge from the department. All these data is intended to be evaluated by artificial intelligence to evaluate a predictive model.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Age >18 years patients after successful cardiopulmonary resuscitation observed in reanimation | – | Other: no physical or medical interventions |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients above 18 years after successful cardiopulmonary resuscitation with ROSC registered at Bezmialem Vakıf University Hospital Intensive care unit from October 2010 to September 2025 will be included in the resesarch.
Key inclusion criteria (3 listed)
- age>18 years
- successive cardiopulmonary resuscitation
- at least 1 hour long admission to ICU after Return Of Spontaneous Circulation (ROSC)
Key exclusion criteria (3 listed)
- age < 18 years
- >80% missing data in patient records
- patients with no ROSC
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 |
|---|---|---|
| no physical or medical interventionsData from patients after successive rescucitaion will be evaluated by machine learning programs. | Other | Age >18 years patients after successful cardiopulmonary resuscitation observed in reanimation |
Primary outcome
Machine Learning Python programme
Time frame: 3 months
The created database will be analyzed using a machine learning artificial intelligence algorithm with the Python programming language. After processing missing and incomplete data by artificial intelligence, the database will be divided into two parts: model training and model validation. Meaningful data will be selected through model training, and a prediction model will be built based on these data. To increase the interpretability of the prediction model and help users understand how and why certain predictions are made, the SHapley Additive exPlanations (SHAP) algorithm will be used. In machine learning, the SHAP technique is used to interpret the decision-making processes of complex machine learning models.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Oct 2025.
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Cost Analysis and Length of ICU Stay, run by Bezmialem Vakif University.
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 3 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: opening soon, as of the sponsor's last update on 7 Oct 2025. Status can change between updates, so confirm with the study contact before making plans.
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?
This study is still under way. Results are not available in the source record yet.
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?
Machine Learning Python programme (time frame: 3 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 7 Oct 2025.