Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, München, Germany; Institute for Medical Information Processing, Biometry and Epidemiology—IBE, Faculty of Medicine, Ludwig-Maximilians-Universität in Munich, Munich, Germany.
Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany; Institute for Medical Information Processing, Biometry and Epidemiology—IBE, Faculty of Medicine, Ludwig-Maximilians-Universität in Munich, Munich, Germany.
Centro Studi Sanità Pubblica, Università Milano Bicocca, Milan, Italy.
Department of Public Health, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark; Center for Clinical Research and Prevention, Copenhagen University Hospital–Bispebjerg and Frederiksberg, Copenhagen, Denmark.
Center for Clinical Research and Prevention, Copenhagen University Hospital–Bispebjerg and Frederiksberg, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Finnish Institute for Health and Welfare, Helsinki, Finland.
Department of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Italy; Department of Medicine and Surgery, LUM University “Giuseppe Degennaro”, Casamassima, Italy.
Department of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Italy.
Centre for Public Health, Queen’s University of Belfast, Belfast, United Kingdom.
Department of Cardiovascular, Endocrine-metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy.
Department of Cardiovascular, Endocrine-metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy.
Department of Cardiovascular, Endocrine-metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy.
German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany; Department of General and Interventional Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.
Finnish Institute for Health and Welfare, Helsinki, Finland.
Finnish Institute for Health and Welfare, Helsinki, Finland.
Mathematical Science Research Centre, Queen’s University Belfast, Northern Ireland, Belfast, United Kingdom.
Department of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT TIROL, University for Health Sciences and Technology, Hall in Tirol, Austria.
Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy.
German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany; Department of General and Interventional Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.
Department of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT TIROL, University for Health Sciences and Technology, Hall in Tirol, Austria; Center for Health Decision Science, Depts. of Epidemiology and Health Policy & Management, Harvard Chan School of Public Health, MA, Boston, United States; Program on Cardiovascular Research, Institute for Technology Assessment, Dept. of Radiology, Massachusetts General Hospital, Harvard Medical School, MA, Boston, United States.
Introduction: Risk stratification scores such as the European Systematic COronary Risk Evaluation (SCORE) are used to guide individuals on cardiovascular disease (CVD) prevention. Adding high-sensitivity troponin I (hsTnI) to such risk scores has the potential to improve accuracy of CVD prediction. We investigated how applying hsTnI in addition to SCORE may impact management, outcome, and cost-effectiveness.
Methods: Characteristics of 72,190 apparently healthy individuals from the Biomarker for Cardiovascular Risk Assessment in Europe (BiomarCaRE) project were included into a discrete-event simulation comparing two strategies for assessing CVD risk. The standard strategy reflecting current practice employed SCORE (SCORE); the alternative strategy involved adding hsTnI information for further stratifying SCORE risk categories (S-SCORE). Individuals were followed over ten years from baseline examination to CVD event, death or end of follow-up. The model tracked the occurrence of events and calculated direct costs of screening, prevention, and treatment from a European health system perspective. Cost-effectiveness was expressed as incremental cost-effectiveness ratio (ICER) in € per quality-adjusted life year (QALYs) gained during 10 years of follow-up. Outputs were validated against observed rates, and results were tested in deterministic and probabilistic sensitivity analyses.
Results: S-SCORE yielded a change in management for 10.0% of individuals, and a reduction in CVD events (4.85% vs. 5.38%, p<0.001) and mortality (6.80% vs. 7.04%, p<0.001). S-SCORE led to 23 (95%CI: 20–26) additional event-free years and 7 (95%CI: 5–9) additional QALYs per 1,000 subjects screened, and resulted in a relative risk reduction for CVD of 9.9% (95%CI: 7.3–13.5%) with a number needed to screen to prevent one event of 183 (95%CI: 172 to 203). S-SCORE increased costs per subject by 187€ (95%CI: 177 € to 196 €), leading to an ICER of 27,440€/QALY gained. Sensitivity analysis was performed with eligibility for treatment being the most sensitive.
Conclusion: Adding a person’s hsTnI value to SCORE can impact clinical decision making and eventually improves QALYs and is cost-effective compared to CVD prevention strategies using SCORE alone. Stratifying SCORE risk classes for hsTnI would likely offer cost-effective alternatives, particularly when targeting higher risk groups.
Public Library of Science (PLoS), 2024. Vol. 19, no 7, article id e0307468