Evaluating the Efficacy of Artificial Intelligence Models in Predicting Intensive Care Unit Admission Needs

Completed

• Conditions: Intensive Care Unit
• Interventions: Other: Follow up Decision

• Registration Number: NCT06494748
• Lead Sponsor: Kanuni Sultan Suleyman Training and Research Hospital

Brief Summary

This study aims to evaluate the efficacy of two artificial intelligence (AI) models in predicting the need for ICU admissions. By comparing the AI models' predictions with actual clinical decisions, we aim to determine their accuracy and potential utility in clinical decision support.

Detailed Description

Intensive care units (ICUs) are critical components of healthcare systems, providing life-saving care to patients with severe and life-threatening conditions. Timely and accurate prediction of ICU admission needs is essential for improving patient outcomes and optimizing hospital resource allocation. Delayed ICU admissions have been consistently associated with higher morbidity and mortality rates. With the advent of artificial intelligence (AI) in healthcare, there is an opportunity to enhance clinical decision-making by leveraging AI models to predict ICU needs accurately. AI models, such as ChatGPT and Gemini, can process vast amounts of complex data to identify patterns that might not be immediately evident to human clinicians, potentially improving the speed and accuracy of ICU admission decisions.

This is an observational retrospective study. Data were collected from electronic health records (EHRs) from a hospital retrospectively.

Data were extracted from EHRs and included:

Demographic data: Age, gender, and basic patient characteristics. Clinical parameters: Medication information, consultation details, ECG findings, imaging results, comorbid conditions (e.g., diabetes mellitus, hypertension, heart failure, COPD, cerebrovascular events), and laboratory values (e.g., hemoglobin, hematocrit, platelet count, PT, INR, procalcitonin, ALT, AST, bilirubin, sodium, potassium, chloride, glucose, creatinine, urea, albumin, thyroid function tests).

Prediction data: AI model predictions and actual ICU admission decisions.

Study Timeline

• Study First Submitted: 2024-07-03
• Study First Submitted QC: 2024-07-03
• Study First Posted: 2024-07-10
• Study Start: 2024-07-15
• Status Verified: 2024-10
• Primary Completion: 2024-10-01
• Study Completion: 2024-10-02
• Last Update Submitted: 2024-10-07
• Last Update Posted: 2024-10-08

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 8043

Inclusion Criteria

• Patients over the age of 18
• Patients consulted for anesthesia regarding intensive care needs
• Patients with sufficient data in the hospital's electronic health record system

Exclusion Criteria

• Patients with insufficient data in the hospital records

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Anesthesiologists Decision	Follow up Decision	Intensive Care Unit Follow up need is decided by anesthesiologists.
Artificial Intelligence Decision	Follow up Decision	Intensive Care Unit Follow up need is decided by Artificial Intelligence

Primary Outcome Measures

Name	Time	Method
Intensive Care Unit Need	1 day	The primary outcome measure of this study is the accuracy of the predictions made by the artificial intelligence (AI) models, ChatGPT and Gemini, regarding the need for ICU admissions. This will be evaluated by comparing the AI model predictions to the actual clinical decisions made regarding ICU admissions.

Trial Locations

Locations (1)

Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital; 🇹🇷 Istanbul, Turkey