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

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.

Study Overview

• Status: Completed
• Conditions: Intensive Care Unit
• Intervention / Treatment: Other: Follow up Decision

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 Type: Observational
• Enrollment (Actual): 8043

Contacts and Locations

Study Locations

• Turkey
  • Istanbul, Turkey, 34303
    • Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital

Participation Criteria

Eligibility Criteria

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

• Sampling Method: Probability Sample

Study Population

Patients over the age of 18 of both genders who are consulted for anesthesia regarding intensive care needs will be included in the study.

Description

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 Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Anesthesiologists Decision; Intensive Care Unit Follow up need is decided by anesthesiologists.	Other: Follow up Decision; 0: No need to follow up in Intensive Care Unit 1: Need to follow up in Intensive Care Unit
Artificial Intelligence Decision; Intensive Care Unit Follow up need is decided by Artificial Intelligence	Other: Follow up Decision; 0: No need to follow up in Intensive Care Unit 1: Need to follow up in Intensive Care Unit

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Intensive Care Unit Need	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.	1 day

Collaborators and Investigators

• Sponsor: Kanuni Sultan Suleyman Training and Research Hospital
• Investigators: Principal Investigator: Engin ihsan Turan, Specialist, Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital

Study record dates

Study Major Dates

• Study Start (Actual): July 15, 2024
• Primary Completion (Actual): October 1, 2024
• Study Completion (Actual): October 2, 2024

Study Registration Dates

• First Submitted: July 3, 2024
• First Submitted That Met QC Criteria: July 3, 2024
• First Posted (Actual): July 10, 2024

Study Record Updates

• Last Update Posted (Actual): October 8, 2024
• Last Update Submitted That Met QC Criteria: October 7, 2024
• Last Verified: October 1, 2024

More Information

Terms related to this study

• Keywords: artificial intelligence
• Other Study ID Numbers: ICU-retro

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

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