Graeme L Hickey1, Ben Bridgewater2, Stuart W Grant3, John Deanfield4, John Parkinson4, Alan J Bryan5, Malcolm Dalrymple-Hay6, Neil Moat7, Iain Buchan8, Joel Dunning9. 1. University of Liverpool, Department of Biostatistics, Liverpool, L69 3GL, England2University College London, National Institute for Cardiovascular Outcomes Research (NICOR), London, EC1A 4NP, England. 2. University College London, National Institute for Cardiovascular Outcomes Research (NICOR), London, EC1A 4NP, England3Computer Science Corporation, Kings Cross, London, N1C 4AG, England. 3. University College London, National Institute for Cardiovascular Outcomes Research (NICOR), London, EC1A 4NP, England4Academic Surgery Unit, University of Manchester, Manchester Academic Health Science Centre, University Hospital of South Manchester, Manchester, M23 9LT, UK. 4. University College London, National Institute for Cardiovascular Outcomes Research (NICOR), London, EC1A 4NP, England. 5. Department of Cardiac Surgery, Bristol Heart Institute, Bristol Royal Infirmary, Bristol, BS2 8HW, England. 6. South West Cardiothoracic Centre, Derriford Hospital, Derriford, Plymouth, PL68DH, England. 7. Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, SW3 6NP, England. 8. University of Manchester, Manchester Academic Health Science Centre, Centre for Health Informatics, Vaughan House, Manchester, M13 9GB, England. 9. Department of Cardiothoracic Surgery, James Cook University Hospital, Middlesbrough, TS4 3BW, England.
Abstract
Importance: Postmarket evidence generation for medical devices is important yet limited for prosthetic aortic valve devices in the United Kingdom. Objective: To identify prosthetic aortic valve models that display unexpected patterns of mortality or reintervention using routinely collected national registry data and record linkage. Design, Setting, and Participants: This observational study used data from all National Health Service and private hospitals in England and Wales that submit data to the National Adult Cardiac Surgery Audit (NACSA). All patients undergoing first-time elective and urgent aortic valve replacement surgery (with or without coronary artery bypass grafting) with a biological (n = 15 series) or mechanical (n = 10 series) prosthetic valve from 5 primary suppliers, and satisfying prespecified data quality criteria (n = 43 782 biological; n = 11 084 mechanical) between 1998 and 2013 were included. Valves were classified into series of related models. Outcome tracking was performed using multifaceted record linkage. The median follow-up was 4.1 years (maximum, 15.3 years). Cox proportional hazards regression with random effects (frailty models) were used to model valve effects on the outcomes, with and without adjustment for preoperative and intraoperative covariates. Main Outcomes and Measures: Time to all-cause mortality or aortic valve reintervention (surgical or transcatheter). There were 13 104 deaths and 723 reinterventions during follow-up. Results: Of 79 345 isolated aortic valve replacement procedures with or without coronary artery bypass grafting, 54 866 were analyzed. Biological valve implantation rates increased from 59% in 1998 and 1999 to 86% in 2012 and 2013. Two series of valves associated with significantly increased hazard of death or reintervention were identified (first series: frailty, 1.18; 95% prediction interval [PI], 1.06-1.32 and second series: frailty, 1.19; 95% PI, 1.09-1.31). These results were robust to covariate adjustment and sensitivity analyses. There were 3 prosthetic valves with a significant reduction in hazard (valve 1: frailty, 0.88; 95% PI, 0.80-0.96; valve 2: frailty, 0.88; 95% PI, 0.80-0.96; and valve 3: frailty, 0.88; 95% PI, 0.78-0.98). Conclusions and Relevance: Meaningful evidence from the analysis of routinely collected registry data can inform postmarket surveillance of medical devices. Although the findings are associated with a number of caveats, 2 specific biological aortic valve series identified in this study may warrant further investigation.
Importance: Postmarket evidence generation for medical devices is important yet limited for prosthetic aortic valve devices in the United Kingdom. Objective: To identify prosthetic aortic valve models that display unexpected patterns of mortality or reintervention using routinely collected national registry data and record linkage. Design, Setting, and Participants: This observational study used data from all National Health Service and private hospitals in England and Wales that submit data to the National Adult Cardiac Surgery Audit (NACSA). All patients undergoing first-time elective and urgent aortic valve replacement surgery (with or without coronary artery bypass grafting) with a biological (n = 15 series) or mechanical (n = 10 series) prosthetic valve from 5 primary suppliers, and satisfying prespecified data quality criteria (n = 43 782 biological; n = 11 084 mechanical) between 1998 and 2013 were included. Valves were classified into series of related models. Outcome tracking was performed using multifaceted record linkage. The median follow-up was 4.1 years (maximum, 15.3 years). Cox proportional hazards regression with random effects (frailty models) were used to model valve effects on the outcomes, with and without adjustment for preoperative and intraoperative covariates. Main Outcomes and Measures: Time to all-cause mortality or aortic valve reintervention (surgical or transcatheter). There were 13 104 deaths and 723 reinterventions during follow-up. Results: Of 79 345 isolated aortic valve replacement procedures with or without coronary artery bypass grafting, 54 866 were analyzed. Biological valve implantation rates increased from 59% in 1998 and 1999 to 86% in 2012 and 2013. Two series of valves associated with significantly increased hazard of death or reintervention were identified (first series: frailty, 1.18; 95% prediction interval [PI], 1.06-1.32 and second series: frailty, 1.19; 95% PI, 1.09-1.31). These results were robust to covariate adjustment and sensitivity analyses. There were 3 prosthetic valves with a significant reduction in hazard (valve 1: frailty, 0.88; 95% PI, 0.80-0.96; valve 2: frailty, 0.88; 95% PI, 0.80-0.96; and valve 3: frailty, 0.88; 95% PI, 0.78-0.98). Conclusions and Relevance: Meaningful evidence from the analysis of routinely collected registry data can inform postmarket surveillance of medical devices. Although the findings are associated with a number of caveats, 2 specific biological aortic valve series identified in this study may warrant further investigation.
Authors: Suengwon Lee; Robert J Levy; Abigail J Christian; Stanley L Hazen; Nathan E Frick; Eric K Lai; Juan B Grau; Joseph E Bavaria; Giovanni Ferrari Journal: J Am Heart Assoc Date: 2017-05-08 Impact factor: 5.501
Authors: Sean Coffey; Ross Roberts-Thomson; Alex Brown; Jonathan Carapetis; Mao Chen; Maurice Enriquez-Sarano; Liesl Zühlke; Bernard D Prendergast Journal: Nat Rev Cardiol Date: 2021-06-25 Impact factor: 32.419