Ignite Creation Date:
2026-03-26 @ 3:14 PM
Ignite Modification Date:
2026-03-30 @ 8:57 PM
Study NCT ID:
NCT07445061
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-03-03
First Post:
2026-02-24
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Machine Learning Prediction of Mortality After Prone Positioning in ARDS
Sponsor:
Shanghai Zhongshan Hospital
Organization:
Study Overview
Official Title:
A Machine Learning Model to Predict Mortality in Patients With Acute Respiratory Distress Syndrome After Prone Positioning
Status:
NOT_YET_RECRUITING
Status Verified Date:
2026-03
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 respiratory distress syndrome (ARDS) is a life-threatening condition with high mortality. Prone position ventilation (PPV) is an evidence-based therapy that improves oxygenation and survival in patients with moderate to severe ARDS; however, outcomes remain heterogeneous. Early identification of patients at high risk of mortality after PPV may improve clinical decision-making and individualized management.
This retrospective observational study aims to develop and validate a machine learning model to predict intensive care unit (ICU) mortality in ARDS patients receiving prone position ventilation. Clinical, laboratory, and treatment variables collected from ICU electronic medical records will be used to construct prediction models using multiple machine learning algorithms. The performance of these models will be evaluated and compared to identify the optimal model for mortality prediction.
This retrospective observational study aims to develop and validate a machine learning model to predict intensive care unit (ICU) mortality in ARDS patients receiving prone position ventilation. Clinical, laboratory, and treatment variables collected from ICU electronic medical records will be used to construct prediction models using multiple machine learning algorithms. The performance of these models will be evaluated and compared to identify the optimal model for mortality prediction.
Detailed Description:
None
Study Oversight
Has Oversight DMC:
False
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?: