Stefan D Anker1, Iain A Gillespie2, Kai-Uwe Eckardt3, Florian Kronenberg4, Sharon Richards5, Tilman B Drueke6, Peter Stenvinkel7, Ronald L Pisoni8, Bruce M Robinson8, Daniele Marcelli9, Marc Froissart10, Jürgen Floege11. 1. Innovative Clinical Trials, Department of Cardiology & Pneumology, University Medical Center Göttingen (UMG), Göttingen, Germany. 2. Center for Observational Research (CfOR), Amgen Ltd., Uxbridge, United Kingdom. 3. Nephrology and Hypertension, University of Erlangen-Nuremberg, Germany. 4. Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria. 5. Global Biostatistics, Amgen Ltd., Uxbridge, United Kingdom. 6. Inserm U 1018, Paul Brousse Hospital, Paris Sud University, Villejuif, France. 7. Department of Renal Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden. 8. Arbor Research Collaborative for Health, Ann Arbor, MI, United States. 9. EMEALA Medical Board, Fresenius Medical Care, Bad Homburg, Germany and Danube University, Krems, Austria. 10. International Development Nephrology, Amgen Europe GmbH, Zug, Switzerland; 11. Nephrology, RWTH University of Aachen, Aachen, Germany.
Abstract
BACKGROUND: A simple clinical tool to predict cardiovascular disease risk does not exist for haemodialysis patients. The long-term coronary risk Framingham Heart Study Risk score (FRS), although used in this population, may be inadequate. Therefore, we developed separate risk-scores for cardiovascular mortality (CVM) and cardiovascular morbidity & mortality (CVMM) in a Fresenius Medical Care-based haemodialysis patient cohort (AROii). METHODS: Applying a modified FRS approach, we derived and internally validated two-year risk-scores in incident European adult patients randomly assigned to a development (N=4831) or a validation (N=4796) dataset. External validation was conducted in the third Dialysis Outcomes and Practice Patterns Study (DOPPS III) cohort. Additional discrimination comparing to the FRS was performed. RESULTS: The overall two-year CVM and CVMM event rates were 5.0 and 22.6 per 100 person-years respectively. Common risk predictors included increasing age, cardiovascular disease history, primary diabetic nephropathy, low blood pressure, and inflammation. The CVM score was more predictive in AROii (c-statistic 0.72) and in DOPPS III (c-statistic 0.73-0.74) than the CVMM score (c-statistic 0.66-0.67 & 0.63 respectively). The FRS was not predictive of either CVM (c-statistic 0.54) or CVMM (c-statistic 0.56) in AROii. CONCLUSIONS: We describe novel, easy-to-apply and interpret CV risk-scores for haemodialysis patients. Our improved cardiovascular prediction performance over traditional (FRS) scores reflected its tailored development and validation in haemodialysis populations, and the integration of non-classical cardiovascular risk factors. The lower expected versus observed CVM and CVMM risk suggests the existence of novel cardiovascular risk factors in this patient population not measured in this study.
RCT Entities:
BACKGROUND: A simple clinical tool to predict cardiovascular disease risk does not exist for haemodialysis patients. The long-term coronary risk Framingham Heart Study Risk score (FRS), although used in this population, may be inadequate. Therefore, we developed separate risk-scores for cardiovascular mortality (CVM) and cardiovascular morbidity & mortality (CVMM) in a Fresenius Medical Care-based haemodialysis patient cohort (AROii). METHODS: Applying a modified FRS approach, we derived and internally validated two-year risk-scores in incident European adult patients randomly assigned to a development (N=4831) or a validation (N=4796) dataset. External validation was conducted in the third Dialysis Outcomes and Practice Patterns Study (DOPPS III) cohort. Additional discrimination comparing to the FRS was performed. RESULTS: The overall two-year CVM and CVMM event rates were 5.0 and 22.6 per 100 person-years respectively. Common risk predictors included increasing age, cardiovascular disease history, primary diabetic nephropathy, low blood pressure, and inflammation. The CVM score was more predictive in AROii (c-statistic 0.72) and in DOPPS III (c-statistic 0.73-0.74) than the CVMM score (c-statistic 0.66-0.67 & 0.63 respectively). The FRS was not predictive of either CVM (c-statistic 0.54) or CVMM (c-statistic 0.56) in AROii. CONCLUSIONS: We describe novel, easy-to-apply and interpret CV risk-scores for haemodialysis patients. Our improved cardiovascular prediction performance over traditional (FRS) scores reflected its tailored development and validation in haemodialysis populations, and the integration of non-classical cardiovascular risk factors. The lower expected versus observed CVM and CVMM risk suggests the existence of novel cardiovascular risk factors in this patient population not measured in this study.
Authors: Nada Dimkovic; Georg Schlieper; Aleksandar Jankovic; Zivka Djuric; Marcus Ketteler; Tatjana Damjanovic; Petar Djuric; Jelena Marinkovic; Zoran Radojcic; Natasa Markovic; Jürgen Floege Journal: Int Urol Nephrol Date: 2018-02-13 Impact factor: 2.370
Authors: Stanislas Werfel; Georg Lorenz; Bernhard Haller; Roman Günthner; Julia Matschkal; Matthias C Braunisch; Carolin Schaller; Peter Gundel; Stephan Kemmner; Salim S Hayek; Christian Nusshag; Jochen Reiser; Philipp Moog; Uwe Heemann; Christoph Schmaderer Journal: Sci Rep Date: 2021-04-29 Impact factor: 4.379
Authors: Tobias Feldreich; Christoph Nowak; Tove Fall; Axel C Carlsson; Juan-Jesus Carrero; Jonas Ripsweden; Abdul Rashid Qureshi; Olof Heimbürger; Peter Barany; Peter Stenvinkel; Nicolas Vuilleumier; Philip A Kalra; Darren Green; Johan Ärnlöv Journal: J Nephrol Date: 2018-11-29 Impact factor: 3.902