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

October 26, 2018 updated by: Jens Meier, Kepler University Hospital
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.

Study Overview

Status

Completed

Conditions

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.

Study Type

Observational

Enrollment (Actual)

2229

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients who underwent heart valve surgery of any kind between 2008-01-01 and 2014-12-31 were included.

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
Patients will included from 01.01.2008 - 31.12.2014

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Kepler University Hospital

Collaborators

Institute of Bioinformatics, JKU Linz

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2008

Primary Completion (Actual)

December 31, 2014

Study Completion (Actual)

December 31, 2014

Study Registration Dates

First Submitted

October 25, 2018

First Submitted That Met QC Criteria

October 26, 2018

First Posted (Actual)

October 30, 2018

Study Record Updates

Last Update Posted (Actual)

October 30, 2018

Last Update Submitted That Met QC Criteria

October 26, 2018

Last Verified

October 1, 2018

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • K-82-15

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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