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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Length of ICU Stay; Cost Analysis; Neurological Outcome
• Intervention / Treatment: Other: no physical or medical interventions

• Study Type: Observational
• Enrollment (Estimated): 5000

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

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 Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Age >18 years patients after successful cardiopulmonary resuscitation observed in reanimation	Other: no physical or medical interventions; Data from patients after successive rescucitaion will be evaluated by machine learning programs.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Bezmialem Vakif University

Study record dates

Study Major Dates

• Study Start (Estimated): October 1, 2025
• Primary Completion (Estimated): December 1, 2025
• Study Completion (Estimated): December 1, 2025

Study Registration Dates

• First Submitted: September 30, 2025
• First Submitted That Met QC Criteria: September 30, 2025
• First Posted (Estimated): October 7, 2025

Study Record Updates

• Last Update Posted (Estimated): October 7, 2025
• Last Update Submitted That Met QC Criteria: September 30, 2025
• Last Verified: September 1, 2025

More Information

Terms related to this study

• Keywords: machine learning; cost analysis; neurological outcome; lenght of stay; cardiopulmonary rescucitation
• Other Study ID Numbers: Nkangarli002

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No