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Completed

NCT Number: NCT03724123

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

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.

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Key information

Conditions

Heart Valve Diseases Cardiovascular Diseases Heart Diseases Surgery--Complications

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

  1. Area under the curve for different prediction models

    Time frame: Patients will included from 01.01.2008 - 31.12.2014

    Three different predictions models will be used.

Sponsors and collaborators

Lead sponsor

Kepler University Hospital

Other

Collaborators

  • Institute of Bioinformatics, JKU Linz

Registry information

Important dates

Study start
2008
Primary completion
2014
Study completion
2014
First posted
Oct 30, 2018
Registry last updated
Oct 30, 2018

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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