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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

8043 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-07-15

Study Completion Date

2024-10-02

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Intensive Care Unit

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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Anesthesiologists Decision

Intensive Care Unit Follow up need is decided by anesthesiologists.

Follow up Decision

Intervention Type OTHER

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

Follow up Decision

Intervention Type OTHER

0: No need to follow up in Intensive Care Unit

1: Need to follow up in Intensive Care Unit

Interventions

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Follow up Decision

0: No need to follow up in Intensive Care Unit

1: Need to follow up in Intensive Care Unit

Intervention Type OTHER

Eligibility Criteria

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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