Sheng-Chia Chung1, Benjamin O'Brien2,3,4,5, Gregory Y H Lip6,7, Kara G Fields8, Jochen D Muehlschlegel8, Anshul Thakur9, David Clifton9, Gary S Collins10,11, Peter Watkinson11,12, Rui Providencia1,13. 1. Institute of Health Informatics Research, University College London, London, UK. 2. St. Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK. ben.obrien@charite.de. 3. Department of Cardiac Anesthesiology and Intensive Care Medicine, German Heart Center, Berlin, Germany. ben.obrien@charite.de. 4. Department of Cardiac Anesthesiology and Intensive Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany. ben.obrien@charite.de. 5. Outcomes Research Consortium, Cleveland, OH, USA. ben.obrien@charite.de. 6. Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, UK. 7. Department of Clinical Medicine, Aalborg University, Aalborg, Denmark. 8. Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 9. Department of Engineering Science, University of Oxford, Oxford, UK. 10. Centre for Statistics in Medicine, Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK. 11. NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK. 12. Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK. 13. St. Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK.
Abstract
OBJECTIVE: To develop a validated clinical prognostic model to determine the risk of atrial fibrillation after cardiac surgery as part of the PARADISE project (NIHR131227). METHODS: Prospective cohort study with linked electronic health records from a cohort of 5.6 million people in the United Kingdom Clinical Practice Research Datalink from 1998 to 2016. For model development, we considered a priori candidate predictors including demographics, medical history, medications, and clinical biomarkers. We evaluated associations between covariates and the AF incidence at the end of follow-up using logistic regression with the least absolute shrinkage and selection operator. The model was validated internally with the bootstrap method; subsequent performance was examined by discrimination quantified with the c-statistic and calibration assessed by calibration plots. The study follows TRIPOD guidelines. RESULTS: Between 1998 and 2016, 33,464 patients received cardiac surgery among the 5,601,803 eligible individuals. The final model included 13-predictors at baseline: age, year of index surgery, elevated CHA2DS2-VASc score, congestive heart failure, hypertension, acute coronary syndromes, mitral valve disease, ventricular tachycardia, valve surgery, receiving two combined procedures (e.g., valve replacement + coronary artery bypass grafting), or three combined procedures in the index procedure, statin use, and ethnicity other than white or black (statins and ethnicity were protective). This model had an optimism-corrected C-statistic of 0.68 both for the derivation and validation cohort. Calibration was good. CONCLUSIONS: We developed a model to identify a group of individuals at high risk of AF and adverse outcomes who could benefit from long-term arrhythmia monitoring, risk factor management, rhythm control and/or thromboprophylaxis.
OBJECTIVE: To develop a validated clinical prognostic model to determine the risk of atrial fibrillation after cardiac surgery as part of the PARADISE project (NIHR131227). METHODS: Prospective cohort study with linked electronic health records from a cohort of 5.6 million people in the United Kingdom Clinical Practice Research Datalink from 1998 to 2016. For model development, we considered a priori candidate predictors including demographics, medical history, medications, and clinical biomarkers. We evaluated associations between covariates and the AF incidence at the end of follow-up using logistic regression with the least absolute shrinkage and selection operator. The model was validated internally with the bootstrap method; subsequent performance was examined by discrimination quantified with the c-statistic and calibration assessed by calibration plots. The study follows TRIPOD guidelines. RESULTS: Between 1998 and 2016, 33,464 patients received cardiac surgery among the 5,601,803 eligible individuals. The final model included 13-predictors at baseline: age, year of index surgery, elevated CHA2DS2-VASc score, congestive heart failure, hypertension, acute coronary syndromes, mitral valve disease, ventricular tachycardia, valve surgery, receiving two combined procedures (e.g., valve replacement + coronary artery bypass grafting), or three combined procedures in the index procedure, statin use, and ethnicity other than white or black (statins and ethnicity were protective). This model had an optimism-corrected C-statistic of 0.68 both for the derivation and validation cohort. Calibration was good. CONCLUSIONS: We developed a model to identify a group of individuals at high risk of AF and adverse outcomes who could benefit from long-term arrhythmia monitoring, risk factor management, rhythm control and/or thromboprophylaxis.
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