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

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07210866

Enrollment

5000

Registered

2025-10-07

Start date

2025-10-01

Completion date

2025-12-01

Last updated

2025-10-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Cost Analysis, Length of ICU Stay, Neurological Outcome

Keywords

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

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.

Interventions

OTHERno physical or medical interventions

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

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

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

Design outcomes

Primary

Measure	Time frame	Description
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.

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026