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

Not yet recruiting

• Conditions: Cost Analysis; Length of ICU Stay; Neurological Outcome

• Registration Number: NCT07210866
• Lead Sponsor: Bezmialem Vakif University

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.

Detailed Description

Not available

Study Timeline

• Status Verified: 2025-09
• Study First Submitted: 2025-09-30
• Study First Submitted QC: 2025-09-30
• Last Update Submitted: 2025-09-30
• Study Start: 2025-10-01
• Study First Posted: 2025-10-07
• Last Update Posted: 2025-10-07
• Primary Completion: 2025-12-01
• Study Completion: 2025-12-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 5000

Inclusion Criteria

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

Exclusion Criteria

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Machine Learning Python programme	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.