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NCT Number: NCT07210866

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.

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Key information

Conditions

Cost Analysis Length of ICU Stay Neurological Outcome

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

no physical or medical interventions

Other

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

Primary outcomes

  1. 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.

Sponsors and collaborators

Lead sponsor

Bezmialem Vakif University

Other

Registry information

Official study title

AN ARTIFICIAL INTELLIGENCE MODEL FOR INTENSIVE CARE LENGTH OF STAY, NEUROLOGICAL OUTCOME AND COSTS ESTIMATION AFTER CARDIOPULMONARY RESUSCITATION: A COHORT STUDY

Important dates

Study start
2025
Primary completion
2025
Study completion
2025
First posted
Oct 7, 2025
Registry last updated
Oct 7, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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