An Artificial Intelligence Model for Intensive Care Length of Stay, Neurological Outcome and Costs Estimation After Cardiopulmonary Resuscitation: a Cohort Study.

NCT07210866 | Not Yet Recruiting

• 1 other identifier: Nkangarli002
• Study Type: observational
• Target: 5,000
• Locations: 0 countries
• Sites: N/A

Brief 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. 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

Keywords

machine learning; cardiopulmonary rescucitation; lenght of stay; cost analysis; neurological outcome

Outcome Measures

Primary Outcomes (1)

• 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.

  3 months

Study Arms (1)

Age >18 years patients after successful cardiopulmonary resuscitation observed in reanimation

Other: no physical or medical interventions

Interventions

no physical or medical interventions OTHER

Data from patients after successive rescucitaion will be evaluated by machine learning programs.

Age >18 years patients after successful cardiopulmonary resuscitation observed in reanimation

Eligibility Criteria

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

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.

You may qualify if:

• age\>18 years
• successive cardiopulmonary resuscitation
• at least 1 hour long admission to ICU after Return Of Spontaneous Circulation (ROSC)

You may not qualify if:

• age \< 18 years
• \>80% missing data in patient records
• patients with no ROSC

Sponsors & Collaborators

• Bezmialem Vakif University: lead

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Uzman Doctor

Study Record Dates

• First Submitted: September 30, 2025
• First Posted: October 7, 2025
• Study Start: October 1, 2025
• Primary Completion: December 1, 2025
• Study Completion: December 1, 2025
• Last Updated: October 7, 2025

Record last verified: 2025-09