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Machine Learning-Based Risk Profile Classification of Patients Undergoing Elective Heart Valve Surgery

Surgery--Complications Clinical Trial

Official title:
Machine Learning-Based Risk Profile Classification of Patients Undergoing Elective Heart Valve Surgery

Clinical Trial Summary

Machine learning methods potentially provide a highly accurate and detailed assessment of expected individual patient risk before elective cardiac surgery. Correct anticipation of this risk allows for improved counseling of patients and avoidance of possible complications. The investigators therefore investigate the benefit of modern machine learning methods in personalized risk prediction in patients undergoing elective heart valve surgery.

Clinical Trial Description

The investigators performe a monocentric retrospective study in patients who underwent elective heart valve surgery between January 1, 2008, and December 31, 2014 at our center. The investigators use random forests, artificial neural networks, and support vector machines to predict the 30-days mortality from a subset of demographic and preoperative parameters. Exclusion criteria were re-operation of the same patient, patients that needed anterograde cerebral perfusion due to aortic arch surgery, and patients with grown up congenital heart disease. ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT03724123
Study type Observational
Source Kepler University Hospital
Contact
Status Completed
Phase
Start date January 1, 2008
Completion date December 31, 2014
View Details
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