Ignite Creation Date:
2024-07-17 @ 11:10 AM
Last Modification Date:
2024-10-26 @ 3:34 PM
Study NCT ID:
NCT06494748
Status:
COMPLETED
Last Update Posted:
2024-07-12
First Post:
2024-07-03
Brief Title:
Evaluating the Efficacy of Artificial Intelligence Models in Predicting Intensive Care Unit Admission Needs
Sponsor:
Kanuni Sultan Suleyman Training and Research Hospital
Organization:
Kanuni Sultan Suleyman Training and Research Hospital
Study Overview
Official Title:
Evaluating the Efficacy of Artificial Intelligence Models in Predicting Intensive Care Unit Admission Needs
Status:
COMPLETED
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 eg diabetes mellitus hypertension heart failure COPD cerebrovascular events and laboratory values eg 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
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 eg diabetes mellitus hypertension heart failure COPD cerebrovascular events and laboratory values eg 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None