Model-driven survival prediction after congenital heart surgery

• Conditions: Q24.9; Congenital malformation of heart, unspecified

• Registration Number: DRKS00028551
• Lead Sponsor: Department of Congenital Heart Defects and Pediatric Cardiology, University Heart CenterFreiburg - Bad Krozingen

Brief Summary

Not available

Detailed Description

Not available

Study Timeline

• Study Completion: 2022-01-31
• Study Start: 2022-05-04
• Results First Posted: 2023-06-05
• Last Update Posted: 19 August 2024

Recruitment & Eligibility

• Status: Complete
• Sex: All
• Target Recruitment: 1765

Inclusion Criteria

Congenital heart surgery with cardiopulmonary bypass and postoperative stay in the intensive care unit for more than 24 hours.

Exclusion Criteria

No congenital heart surgery with cardiopulmonary bypass and postoperative stay in the intensive care unit for more than 24 hours.

Study & Design

• Study Type: observational
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
The primary endpoint is the identification of rapidly available perioperative risk indicators on the basis of which a machine learning model for individual postoperative survival assessment can be trained.

Secondary Outcome Measures

Name	Time	Method
Secondary endpoint is to test the generalizability of the model through a test data set.