Heart Valve DiseasesNCT03724123
Machine Learning-Based Risk Profile Classification of Patients Undergoing Elective Heart Valve Surgery
An observational study of Heart Valve Diseases and Surgery--Complications, run by Kepler University Hospital.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Heart Valve Diseases, Surgery--Complications
- How many people
- 2,229 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2008to 31 Dec 2014
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients who underwent heart valve surgery of any kind between 2008-01-01 and 2014-12-31 were included.
Who may be able to join
- Patients who underwent heart valve surgery of any kind between 2008-01-01 and 2014-12-31 were included.
Who cannot join
- re-operation of the same patient
- patients that needed anterograde cerebral perfusion due to aortic arch surgery
- patients with grown-up congenital heart disease
About this study
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.
Read the 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.
- Conditions
- Heart Valve Diseases, Surgery--Complications
- Keywords
- random forests, heart valve surgery, support vector machines, artificial neural networks
- Medical subject headings
- Heart Valve Diseases
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the curve for different prediction models
Three different predictions models will be used.
Measured: Patients will included from 01.01.2008 - 31.12.2014
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Oct 2018
- Study start1 Jan 2008
- Primary completion31 Dec 2014
- Study completion31 Dec 2014
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Heart Valve DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 2,229 participants (actual)
Sponsor and investigators
- Lead sponsor
- Kepler University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Institute of Bioinformatics, JKU Linz
- Responsible party
- Principal investigator, Jens Meier, Kepler University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03724123
- Sponsor's protocol ID
- K-82-15
- Status last verified
- Oct 2018
- First submitted
- 25 Oct 2018
- Last updated
- 30 Oct 2018
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 34050368
Study at a glance
From the registry record, last updated by the sponsor on 30 Oct 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning-Based Risk Profile Classification of Patients Undergoing Elective Heart Valve Surgery
- ClinicalTrials.gov ID
- NCT03724123
- Acronym / sponsor ID
- K-82-15
- Conditions
- Heart Valve Diseases, Surgery--Complications
- Keywords
- random forests, heart valve surgery, support vector machines, artificial neural networks
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 2,229 participants (actual)
- Lead sponsor
- Kepler University HospitalUniversity, hospital or other
- Collaborators
- Institute of Bioinformatics, JKU Linz
- Study start
- 1 Jan 2008 (actual)
- Primary completion
- 31 Dec 2014 (actual)
- Study completion
- 31 Dec 2014 (actual)
- First posted
- 30 Oct 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who underwent heart valve surgery of any kind between 2008-01-01 and 2014-12-31 were included.
Key inclusion criteria (1 listed)
- Patients who underwent heart valve surgery of any kind between 2008-01-01 and 2014-12-31 were included.
Key exclusion criteria (3 listed)
- re-operation of the same patient
- patients that needed anterograde cerebral perfusion due to aortic arch surgery
- patients with grown-up congenital heart disease
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Oct 2018.
- Derived from this study: PubMed 34050368Bodenhofer 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Valve Diseases and Surgery--Complications, run by Kepler University Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 30 Oct 2018. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under the curve for different prediction models (time frame: Patients will included from 01.01.2008 - 31.12.2014). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03724123, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Oct 2018; AzTrials last refreshed its copy on 29 Oct 2026.