Title: Predicting AF after Cardiac Surgery - the PARADISE Score_x000D_ A Clinical Prediction Rule for Post-operative Atrial Fibrillation _x000D_ in Patients Undergoing Cardiac Surgery
Funder: Department of Health and Social Care

Abstract: We will develop two reliable prediction models to identify which patients develop an irregular heart rhythm called Atrial Fibrillation (AF) following heart surgery. One will predict the risk at assessment prior to surgery. As changes during surgery may alter the risk of AF we will also develop a model to be used just after surgery._x000D_ _x000D_ AF is a common heart problem causing an irregular heartbeat. It may make the heart beat more rapidly and reduce the heart’s ability to pump blood to the body. It allows blood clots to form in the heart, which can cause strokes if pumped to the brain. Avoiding AF is important. People who get AF stay longer in the Intensive Care Unit after surgery, are more likely to develop complications and have a higher risk of dying._x000D_ _x000D_ About 30-50% of people having heart surgery develop AF shortly after the operation. Different preventative treatments lessen AF, depending on peoples’ risk. Preventative treatments carry risks, so it is important to identify people most likely to benefit._x000D_ _x000D_ Currently there are no good tools to predict who will get AF after heart surgery. Previous tools are not used in clinical practice, partly because there are weaknesses in how the tools were developed. For example, some do not include modern data like ultrasound pictures of the heart that are now routine before surgery. A modern reliable prediction model is needed._x000D_ _x000D_ There are three parts to our proposal:_x000D_ First, we will produce a list of possible factors that alter the risk of getting AF after heart surgery. We will do this by:_x000D_ • doing a detailed review of what has been published,_x000D_ • looking for risks in a UK general practice database (CALIBER) including 90,000 people who had heart surgery,_x000D_ • asking experts_x000D_ • and by using a modern computer technique (machine learning) to look for previously unrecognised AF risk factors in a large United States (US) database (the 30,000 patient Partners database) and in the CALIBER database_x000D_ _x000D_ Using this list we will use standard statistical approaches and ‘machine learning’ within the Partners research database to develop prediction models to identify patients at increased risk of AFACS. Initially we will check model performance on another US research database._x000D_ Meanwhile we will work with two large UK heart centres that together do 6000 heart operations a year and up to two ongoing UK prospective trials to ensure the risk factor list and when a patient develops AF are documented precisely. We will then use this data to check our model works in current UK practice, in an optimal study environment and in NHS clinical practice._x000D_ _x000D_ Patient and public involvement is integral to our work. Representatives have been involved in this application, will take part in meetings considering the importance and identification of risk factors and are key to our publicity strategy. Two patient advocacy groups, StopAfib.org and the Arrhythmia Alliance / AF Association helped with this application and will help publicise our findings._x000D_ _x000D_ Along with the NIHR final report, we will describe our prediction models in medical journals and present them at conferences. We will use social media, podcasts and infographics to further publicise this work. Members of our team are key authors of the latest international guidelines for the prevention and treatment of AF after Cardiac Surgery and an Ambassador of the European Heart Rhythm Association. We will use our developed networks to publicise our findings.

Lay abstract: Research question_x000D_ To develop clinical prediction models for clinicians to reliably determine the pre- and immediately post-operative risk of a patient developing Atrial Fibrillation After Cardiac Surgery (AFACS)._x000D_ _x000D_ Background_x000D_ _x000D_ Atrial fibrillation after cardiac surgery (AFACS) is frequent, occurring in 30-50% of cases. Over 90% of those who develop AFACS do so by day 7 post-surgery. We reviewed available prediction models whilst producing international guidance. None include modern risk stratification information such as echocardiographic measurements or meet TRIPOD quality criteria. Prophylaxis decreases length of hospital stay and cost. However prophylaxis treatments are not risk-free. A prospectively validated score allowing targeted prophylaxis is the first step to improving outcomes, informing patients and resource planning._x000D_ _x000D_ Aims_x000D_ _x000D_ To develop validated clinical prediction models to determine the risk of a patient developing AFACS:_x000D_ _x000D_ • In the pre-operative assessment clinic or on admission for surgery_x000D_ • On arrival in the post-operative care unit_x000D_ _x000D_ Databases_x000D_ Two United States retrospective datasets (Partners and Brigham), two United Kingdom (UK) prospective datasets and data from UK prospective trials._x000D_ _x000D_ Objectives_x000D_ _x000D_ 1. Develop a long list of candidate pre-operative and intra/early post-operative risk factors_x000D_ 2. Enhance the Partners development database to include additional variables from 1._x000D_ 3. Develop and internally validate pre- and immediate post-operative models_x000D_ 4. Externally validate models retrospectively and prospectively _x000D_ 5. Compare performance to existing prediction models_x000D_ _x000D_ Methods_x000D_ _x000D_ 1. The long list of candidate risk factors will be developed from:_x000D_ • a systematic review of the literature_x000D_ • a large UK research platform (CALIBER)_x000D_ • expert (including public) opinion (Delphi process)_x000D_ • machine learning of (combinations of) input variables in the CALIBER and Partners databases_x000D_ _x000D_ We will then:_x000D_ _x000D_ 2. Enhance the Partners research database to include the additional variables identified in 1 by extracting the required additional features from the clinical data warehouses._x000D_ _x000D_ 3. Compare state of the art statistical methods and machine-learning (ML) approaches to develop pre-operative and immediately post-operative prediction models. We will learn from each method to maximise final model performance. We will internally validate the models._x000D_ _x000D_ 4. Assess external validity of the final models retrospectively using the Brigham dataset, to allow model adjustment if performance declines prior to prospective validation. We will also prospectively validate the final models within a prospective “real world” cohort from two major UK centres and using data from ongoing UK trials. _x000D_ _x000D_ 5. Compare model performance to published models identified in 1._x000D_ _x000D_ Delivery timelines_x000D_ 30 months - see project timeline_x000D_ _x000D_ Impact and dissemination_x000D_ _x000D_ We will:_x000D_ • collaborate with Arrhythmia and ICU patient groups to maximise patient/public awareness_x000D_ • publish our prediction model in relevant specialist medical journals and present it at conferences_x000D_ • use social media, podcasts and infographics to further publicise this work_x000D_ • use the networks we developed writing the latest international AFACS guidelines to publicise our findings, seeking endorsement by relevant professional societies _x000D_ _x000D_ The impact of our work will be substantial, providing an evidence base for rational AFACS prophylaxis throughout the UK and beyond.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CARDIOVASCULAR