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

NCT ID: NCT03724123

Last Updated: 2018-10-30

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

2229 participants

Study Classification

OBSERVATIONAL

Study Start Date

2008-01-01

Study Completion Date

2014-12-31

Brief Summary

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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

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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.

Conditions

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Heart Valve Diseases Surgery--Complications

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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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
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Institute of Bioinformatics, JKU Linz

UNKNOWN

Sponsor Role collaborator

Kepler University Hospital

OTHER

Sponsor Role lead

Responsible Party

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Jens Meier

Prof. Dr.

Responsibility Role PRINCIPAL_INVESTIGATOR

References

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Bodenhofer U, Haslinger-Eisterer B, Minichmayer A, Hermanutz G, Meier J. Machine learning-based risk profile classification of patients undergoing elective heart valve surgery. Eur J Cardiothorac Surg. 2021 Dec 1;60(6):1378-1385. doi: 10.1093/ejcts/ezab219.

Reference Type DERIVED
PMID: 34050368 (View on PubMed)

Other Identifiers

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K-82-15

Identifier Type: -

Identifier Source: org_study_id

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