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

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ClinicalTrials.gov Identifier: NCT03724123
Recruitment Status : Completed
First Posted : October 30, 2018
Last Update Posted : October 30, 2018
Sponsor:
Collaborator:
Institute of Bioinformatics, JKU Linz
Information provided by (Responsible Party):
Jens Meier, Kepler University Hospital

Study Description
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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.

Condition or disease
Heart Valve Diseases Surgery--Complications

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 Design
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Study Type : Observational
Actual Enrollment : 2229 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Machine Learning-Based Risk Profile Classification of Patients Undergoing Elective Heart Valve Surgery
Actual Study Start Date : January 1, 2008
Actual Primary Completion Date : December 31, 2014
Actual Study Completion Date : December 31, 2014
Groups and Cohorts
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Outcome Measures
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Primary Outcome Measures :
  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.


Eligibility Criteria
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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
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.
Criteria

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
Contacts and Locations
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Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information

No Contacts or Locations Provided
More Information
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Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information
Publications automatically indexed to this study by ClinicalTrials.gov Identifier (NCT Number):

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Responsible Party: Jens Meier, Prof. Dr., Kepler University Hospital
ClinicalTrials.gov Identifier: NCT03724123    
Other Study ID Numbers: K-82-15
First Posted: October 30, 2018    Key Record Dates
Last Update Posted: October 30, 2018
Last Verified: October 2018

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Jens Meier, Kepler University Hospital:
random forests
heart valve surgery
support vector machines
artificial neural networks
Additional relevant MeSH terms:
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Heart Valve Diseases
Heart Diseases
Cardiovascular Diseases