Ignite Creation Date:
2024-05-06 @ 7:48 PM
Last Modification Date:
2024-10-26 @ 3:14 PM
Study NCT ID:
NCT06146829
Status:
COMPLETED
Last Update Posted:
2024-04-10
First Post:
2023-11-19
Brief Title:
Machine Learning Models for Prediction of Acute Kidney Injury After Noncardiac Surgery
Sponsor:
Rao Sun
Organization:
Tongji Hospital
Study Overview
Official Title:
Development of Interpretable Machine Learning Models for Prediction of Acute Kidney Injury After Noncardiac Surgery
Status:
COMPLETED
Status Verified Date:
2024-04
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:
Acute kidney injury AKI is a common surgical complication characterized by a rapid decline in renal function Patients with AKI are at an increased risk of developing chronic kidney disease and end-stage renal disease which has been associated with an increased risk of morbidity mortality and financial burdens Identifying high-risk patients for postoperative AKI early can facilitate the development of preventive and therapeutic management strategies and prediction models can be helpful in this regard
The goal of this retrospective study is to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms and to simplify the models by including only preoperative variables or only important predictors
The goal of this retrospective study is to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms and to simplify the models by including only preoperative variables or only important predictors
Detailed Description:
None
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