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Background: Demand for transcatheter aortic valve implantation (TAVI) has increased in the last decade, resulting in prolonged wait-times and undesirable health outcomes in many health systems. Risk-based prioritization and wait-times benchmarks can improve equitable access to patients., Methods: We used simulation models to follow-up a synthetic population of 50,000 individuals from referral to completion of TAVI. Based on their risk of adverse events, patients could be classified as "low-", "medium-" and "high-risk", and shorter wait-times were assigned for the higher risk groups. We assessed the impacts of the size and wait-times for each risk group on waitlist mortality, hospitalization and urgent TAVIs. All scenarios had the same resource constraints, allowing us to explore the trade-offs between faster access for prioritized patients and deferred access for non-prioritized groups., Results: Increasing the proportion of patients categorized as high-risk, and providing more rapid access to the higher-risk groups achieved the greatest reductions in mortality, hospitalizations and urgent TAVIs (relative reductions of up to 29%, 23% and 38%, respectively). However, this occurs at the expense of excessive wait-times in the non-prioritized low-risk group (up to 25 weeks). We propose wait-times of up to 3 weeks for high-risk patients and 7 weeks for medium-risk patients., Conclusions: Prioritizing higher-risk patients with faster access leads to better health outcomes, however this also results in unacceptably long wait-times for the non-prioritized groups in settings with limited capacity. Decision-makers must be aware of these implications when developing and implementing waitlist prioritization strategies., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Background: A lack of consensus exists across guidelines as to which risk model should be used for the primary prevention of cardiovascular disease (CVD). Our objective was to determine potential improvements in the number needed to treat (NNT) and number of events prevented (NEP) using different risk models in patients eligible for risk stratification., Methods: A retrospective observational cohort was assembled from primary care patients in Ontario, Canada between January 1st, 2010, to December 31st, 2014 and followed for up to 5 years. Risk estimation was undertaken in patients 40-75 years of age, without CVD, diabetes, or chronic kidney disease using the Framingham Risk Score (FRS), Pooled Cohort Equations (PCEs), a recalibrated FRS (R-FRS), Systematic Coronary Risk Evaluation 2 (SCORE2), and the low-risk region recalibrated SCORE2 (LR-SCORE2)., Results: The cohort consisted of 47,399 patients (59% women, mean age 54). The NNT with statins was lowest for SCORE2 at 40, followed by LR-SCORE2 at 41, R-FRS at 43, PCEs at 55, and FRS at 65. Models that selected for individuals with a lower NNT recommended statins to fewer, but higher risk patients. For instance, SCORE2 recommended statins to 7.9% of patients (5-year CVD incidence 5.92%). The FRS, however, recommended statins to 34.6% of patients (5-year CVD incidence 4.01%). Accordingly, the NEP was highest for the FRS at 406 and lowest for SCORE2 at 156., Conclusions: Newer models such as SCORE2 may improve statin allocation to higher risk groups with a lower NNT but prevent fewer events at the population level., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Aims: Systematic Coronary Risk Evaluation Model 2 (SCORE2) was recently developed to predict atherosclerotic cardiovascular disease (ASCVD) in Europe. Whether these models could be used outside of Europe is not known. The objective of this study was to test the validity of SCORE2 in a large Canadian cohort., Methods and Results: A primary care cohort of persons with routinely collected electronic medical record data from 1 January 2010 to 31 December 2014, in Ontario, Canada, was used for validation. The SCORE2 models for younger persons (YP) were applied to 57 409 individuals aged 40-69 while the models for older persons (OPs) were applied to 9885 individuals 70-89 years of age. Five-year ASCVD predictions from both the uncalibrated and low-risk region recalibrated SCORE2 models were evaluated. The C-statistic for SCORE2-YP was 0.74 in women and 0.69 in men. The uncalibrated SCORE2-YP overestimated risk by 17% in women and underestimated by 2% in men. In contrast, the low-risk region recalibrated model demonstrated worse calibration, overestimating risk by 100% in women and 36% in men. The C-statistic for SCORE2-OP was 0.64 and 0.62 in older women and men, respectively. The uncalibrated SCORE2-OP overestimated risk by more than 100% in both sexes. The low-risk region recalibrated model demonstrated improved calibration but still overestimated risk by 60% in women and 13% in men., Conclusion: The performance of SCORE2 to predict ASCVD risk in Canada varied by age group and depended on whether regional calibration was applied. This underscores the necessity for validation assessment of SCORE2 prior to implementation in new jurisdictions., Competing Interests: Conflict of interest: K.T. receives a Research Scholar Award from the Department of Family and Community Medicine at the University of Toronto. KT holds a Chair in Family and Community Medicine Research in Primary Care at UHN and receives a research Scholar Award from the Department of Family and Community Medicine at the University of Toronto. R.K. is an Associate Editor of JAMA. He receives support from the National Heart, Lung, and Blood Institute of the National Institutes of Health (under award K23HL153775) and the Doris Duke Charitable Foundation (under award 2022060022060). He also receives research support, through Yale, from Bristol-Myers Squibb and Novo Nordisk, unrelated to the current work. He is a co-inventor of U.S. Provisional Patent Applications 63/177 117, 63/428 569, 63/346 610, and 63/484 426. He is a co-founder of Evidence2Health, a precision health platform to improve evidence-based cardiovascular care. D.S.L. is the Ted Rogers Chair in Heart Function Outcomes, University Health Network, University of Toronto., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Aims: Chronic kidney disease (CKD) increases risk of cardiovascular disease (CVD). Less is known about how CVD associates with future risk of kidney failure with replacement therapy (KFRT)., Methods and Results: The study included 25 903 761 individuals from the CKD Prognosis Consortium with known baseline estimated glomerular filtration rate (eGFR) and evaluated the impact of prevalent and incident coronary heart disease (CHD), stroke, heart failure (HF), and atrial fibrillation (AF) events as time-varying exposures on KFRT outcomes. Mean age was 53 (standard deviation 17) years and mean eGFR was 89 mL/min/1.73 m2, 15% had diabetes and 8.4% had urinary albumin-to-creatinine ratio (ACR) available (median 13 mg/g); 9.5% had prevalent CHD, 3.2% prior stroke, 3.3% HF, and 4.4% prior AF. During follow-up, there were 269 142 CHD, 311 021 stroke, 712 556 HF, and 605 596 AF incident events and 101 044 (0.4%) patients experienced KFRT. Both prevalent and incident CVD were associated with subsequent KFRT with adjusted hazard ratios (HRs) of 3.1 [95% confidence interval (CI): 2.9-3.3], 2.0 (1.9-2.1), 4.5 (4.2-4.9), 2.8 (2.7-3.1) after incident CHD, stroke, HF and AF, respectively. HRs were highest in first 3 months post-CVD incidence declining to baseline after 3 years. Incident HF hospitalizations showed the strongest association with KFRT [HR 46 (95% CI: 43-50) within 3 months] after adjustment for other CVD subtype incidence., Conclusion: Incident CVD events strongly and independently associate with future KFRT risk, most notably after HF, then CHD, stroke, and AF. Optimal strategies for addressing the dramatic risk of KFRT following CVD events are needed., Competing Interests: Conflict of interest: P.B.M. reports grants and personal fees from Boehringer Ingelheim; personal fees and non-financial support from Napp, Astrazeneca; personal fees from GSK, Pharmacosmos, and Astellas, outside the submitted work. J.J.C. is a Statistical Editor for the European Heart Journal. K.M. reports grants from NIDDK, during the conduct of the study; grants and personal fees from Kyowa Kirin, personal fees from Akebia, Kowa, and Fukuda Denshi, outside the submitted work. M.E.G. reports grants from National Kidney Foundation and from Kidney Disease Improving Global Outcomes outside the submitted work. J.C. reports grants from National Institute of Health and National Kidney Foundation during the conduct of the study; consulting at Healthy.io and scientific advisor to SomaLogic outside the submitted work. J.Ch. reports grants from National Health and Medical Research Council of Australia and grants and personal fees from Servier International outside the submitted work. L.C. reports consulting from CSL Vifor, honorarium for giving a talk from Fresenius Medical Care, and grants from NIH-NIDDK outside the submitted work. A.R.C. reports personal fees from Novartis, Amgen, Reata; grants from Novo Nordisk, Bayer, outside the submitted work. D.d.Z. reports personal fees from Merck during the conduct of the study, Bayer, Boehringer Ingelheim, and Travere outside the submitted work. M.E. reports institutional grants from Astellas pharma, AstraZeneca, and Vifor Pharma; honoraria form Astellas pharma, AstraZeneca, Baxter healthcare, and Fresenius Medical Care; support for attending meetings from Baxter healthcare, participation on a DSMB or Advisory Board for Astellas pharma, AstraZeneca, and Vifor pharma, and a leadership or fiduciary role on the Steering Committee of the Swedish Renal Registry, outside the submitted work. O.M.G. reports personal fees from Akebia, Amgen, AstraZeneca, Reata, Ardelyx, and QED, outside the submitted work. H.J.L.H. reports grants and honoraria for steering committee to his institution from Abbvie; grants and honoraria for steering committee and payments for advisory boards to his institution from AstraZeneca; honoraria for steering committee and payments for advisory boards from Bayer; grants and honoraria for steering committee and payments for advisory boards to his institution from Boehringer Ingelheim; honoraria for steering committee to his institution from CSL Behring, Chinook, Dimerix, Gilead; grants and honoraria for steering committee to his institution from Janssen, honoraria for steering committee to his institution from Eli Lilly, Merck, Mitsubishi Tanabe; grants and honoraria for steering committee to his institution from Novo Nordisk; and honoraria for steering committee to his institution from Travere Pharmaceuticals outside the submitted work. W.G.H. reports SHARP was funded by Merck & Co., Inc., Whitehouse Station, NJ USA, during the conduct of the study; he received grants from Boehringer Ingelheim and Eli Lilly; grants and fellowship from MRC UK, and fellowship from Kidney Research UK outside the submitted work. R.W.M. reports grants from NIHR and Kidney Research UK during the conduct of the study. G.N.N. reports personal fees, non-financial support, and other support (Scientific Cofounder, have equity/stock options, royalties and consulting) from Renalytix; personal fees and non-financial support from Pensieve Health; non-financial support and other support (Scientific Cofounder, have equity/stock options) from Nexus I Connect, Sole proprietor of Data2Wisdom LLC; personal fees from Variant Bio, Qiming Capital, Cambridge Consulting, Daiichi Sankyo, and Menarini Health, outside the submitted work. M.R. reports grants from NIH during the conduct of the study. N.S. reports being a current employee of GSK and employed at AMGA at the time of this study. B.S. reports grants from AstraZeneca, GlaxoSmithKline, and Fresenius Medical Care outside the submitted work. D.C.W. reports personal fees from AstraZeneca during the conduct of the study; personal fees from Amgen, Astellas, Bayer, Boehringer Ingelheim, Gilead, GlaxoSmithKline, Janssen, Mundipharma, Merck Sharp and Dohme, Tricida, Vifor and Zydus; and personal fees from AstraZeneca outside the submitted work. J.v.d.B. reports being an employee and stakeholder of Binnovate Digital Health BV outside the submitted work. All other co-authors have nothing to disclose. Some of the data reported here have been supplied by the United States Renal Data System. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US Government., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)