Heart Valve Diseases, Surgery--Complications
Conditions
Keywords
random forests, heart valve surgery, support vector machines, artificial neural networks
Brief 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.
Detailed 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.
Interventions
None listed
Sponsors
Institute of Bioinformatics, JKU Linz
Kepler University Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
\* Patients who underwent heart valve surgery of any kind between 2008-01-01 and 2014-12-31 were included.
Exclusion criteria
* re-operation of the same patient * patients that needed anterograde cerebral perfusion due to aortic arch surgery * patients with grown-up congenital heart disease
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area under the curve for different prediction models | Patients will included from 01.01.2008 - 31.12.2014 | Three different predictions models will be used. |
Outcome results
None listed