Clinical trial NCT06494748: Evaluating the Efficacy of Artificial Intelligence Models in Predicting Intensive Care Unit Admission Needs
Official title: Evaluating the Efficacy of Artificial Intelligence Models in Predicting Intensive Care Unit Admission Needs
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Intensive Care Unit
Keywords: artificial intelligence
Lead sponsor: Kanuni Sultan Suleyman Training and Research Hospital
Sponsor class: OTHER
First posted: 2024-07-10
Enrollment: 8043
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Intensive Care Unit Need
timeFrame: 1 day
Secondary outcomes:
Other outcomes:
Locations:
city: Istanbul
country: Turkey
facility: Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital
Study officials:
affiliation: Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital
name: Engin ihsan Turan, Specialist
role: PRINCIPAL_INVESTIGATOR
References: