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| Name | Class |
|---|---|
| St. Bartholomew's Hospital | OTHER |
| University College, London | OTHER |
| Liverpool Heart and Chest Hospital NHS Foundation Trust | OTHER |
| Brigham and Women's Hospital |
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The PARADISE study aims to develop and validate prediction tools to identify patients at risk of Atrial Fibrillation (AF) after cardiac surgery.
Atrial Fibrillation (AF) is a common abnormal heart rhythm. AF causes the heart to beat irregularly and sometimes very rapidly. About 30-50% of patients develop AF after heart surgery. These patients stay longer on the Intensive Care Unit (ICU) after surgery, are more likely to develop complications and have a higher risk of dying. Avoiding AF is important.
Some drugs, including beta blockers and amiodarone may help prevent AF if given after surgery. However, these may also lead to complications (such as lung damage). It is therefore important to identify which patients are most likely to benefit from these treatments (i.e., where the benefits outweigh the risks). There are existing tools designed to predict the risk of suffering AF after heart surgery. However, they are unreliable and therefore not used in clinical practice. A modern, reliable risk prediction tool is needed.
The PARADISE study will develop and test new prediction tools to identify which patients are most at risk of developing AF after heart surgery. The investigators will focus our tools on those patients who most commonly develop AF, such as those who have had surgery to repair a valve or blood vessel in their heart.
To do this the investigators will:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Retrospective | Patients admitted to Mass Brigham Hospitals for cardiac surgery from 1st January 1998 to 31st December 2020 |
| |
| Prospective | Patients admitted to Barts Health, Liverpool Heart and Chest Hospital, or Oxford University Hospitals NHS Foundation Trust for cardiac surgery between 1st October 2021 to 31st July 2023 |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Not applicable as observational study | Other | Not applicable as observational study |
|
| Measure | Description | Time Frame |
|---|---|---|
| Model discrimination (c-statistic) to predict Atrial Fibrillation in external data set | Model discrimination (c-statistic) to predict Atrial Fibrillation in external data set | Within 7 days of cardiac surgery |
| Model calibration (intercept) to predict Atrial Fibrillation in external data set | Model calibration (intercept) to predict Atrial Fibrillation in external data set | Within 7 days of cardiac surgery |
| Model calibration (slope) to predict Atrial Fibrillation in external data set | Model calibration (slope) to predict Atrial Fibrillation in external data set | Within 7 days of cardiac surgery |
| Measure | Description | Time Frame |
|---|---|---|
| Additional model performance metrics to predict Atrial Fibrillation in external data set | Model positive and negative predictive values, sensitivity and specificity to predict Atrial Fibrillation in external data set | Within 7 days of cardiac surgery |
| Candidate risk factors for inclusion in new onset atrial fibrillation prognostic models |
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Inclusion Criteria:
Exclusion Criteria:
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Patients who have undergone cardiac surgery
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| Name | Affiliation | Role |
|---|---|---|
| Peter Watkinson, MD | University of Oxford | Principal Investigator |
| Benjamin O'Brien, MD | Deutsches Herzzentrum der Charité | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford | Oxford | Oxfordshire | OX3 9DU | United Kingdom |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39322590 | Derived | Bedford J, Fields KG, Collins GS, Lip GYH, Clifton DA, O'Brien B, Muehlschlegel JD, Watkinson PJ, Redfern OC. Atrial fibrillation after cardiac surgery: identifying candidate predictors through a Delphi process. BMJ Open. 2024 Sep 25;14(9):e086589. doi: 10.1136/bmjopen-2024-086589. |
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Individual participant data will not be made publicly available due to privacy and legal implications.
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot | Yes | No | No | Study Protocol | Jul 3, 2023 | May 10, 2024 | Prot_002.pdf |
| SAP | No | Yes | No | Statistical Analysis Plan | Feb 14, 2024 | Mar 8, 2024 | SAP_001.pdf |
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| ID | Term |
|---|---|
| D019370 | Observation |
| ID | Term |
|---|---|
| D008722 | Methods |
| D008919 | Investigative Techniques |
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| OTHER |
| Oxford University Hospitals NHS Trust | OTHER |
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Candidate risk factors for inclusion in new onset atrial fibrillation prognostic models, identified through Systematic literature review and analysis of the CALIBER database using statistical and machine learning methods. For pre-operative model, the investigators will include patient information available up to the time of surgery. For the post-operative model, the investigators will also include patient information available up to 12 hours after surgery. |
| Within 7 days of cardiac surgery |