Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP

Kunihiro Matsushita; Stephen Kaptoge; Steven H J Hageman; Yingying Sang; Shoshana H Ballew; Morgan E Grams; Aditya Surapaneni; Luanluan Sun; Johan Arnlov; Milica Bozic; Hermann Brenner; Nigel J Brunskill; Alex R Chang; Rajkumar Chinnadurai; Massimo Cirillo; Adolfo Correa; Natalie Ebert; Kai-Uwe Eckardt; Ron T Gansevoort; Orlando Gutierrez; Farzad Hadaegh; Jiang He; Shih-Jen Hwang; Tazeen H Jafar; Simerjot K Jassal; Takamasa Kayama; Csaba P Kovesdy; Gijs W Landman; Andrew S Levey; Donald M Lloyd-Jones; Rupert W Major; Katsuyuki Miura; Paul Muntner; Girish N Nadkarni; Christoph Nowak; Takayoshi Ohkubo; Michelle J Pena; Kevan R Polkinghorne; Toshimi Sairenchi; Elke Schaeffner; Markus P Schneider; Varda Shalev; Michael G Shlipak; Marit D Solbu; Nikita Stempniewicz; James Tollitt; José M Valdivielso; Joep van der Leeuw; Angela Yee-Moon Wang; Chi-Pang Wen; Mark Woodward; Kazumasa Yamagishi; Hiroshi Yatsuya; Luxia Zhang; Jannick A N Dorresteijn; Emanuele Di Angelantonio; Frank L J Visseren; Lisa Pennells; Josef Coresh

doi:10.1093/eurjpc/zwac176

Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP

Eur J Prev Cardiol. 2023 Jan 11;30(1):8-16. doi: 10.1093/eurjpc/zwac176.

Authors

Kunihiro Matsushita¹, Stephen Kaptoge², Steven H J Hageman³, Yingying Sang¹, Shoshana H Ballew¹, Morgan E Grams¹, Aditya Surapaneni¹, Luanluan Sun², Johan Arnlov⁴, Milica Bozic⁵, Hermann Brenner⁶, Nigel J Brunskill⁷, Alex R Chang⁸, Rajkumar Chinnadurai⁹, Massimo Cirillo¹⁰, Adolfo Correa¹¹, Natalie Ebert¹², Kai-Uwe Eckardt^{13

14}, Ron T Gansevoort¹⁵, Orlando Gutierrez¹⁶, Farzad Hadaegh¹⁷, Jiang He¹⁸, Shih-Jen Hwang¹⁹, Tazeen H Jafar^{20

21

22}, Simerjot K Jassal²³, Takamasa Kayama²⁴, Csaba P Kovesdy²⁵, Gijs W Landman²⁶, Andrew S Levey²⁷, Donald M Lloyd-Jones²⁸, Rupert W Major⁷, Katsuyuki Miura²⁹, Paul Muntner³⁰, Girish N Nadkarni³¹, Christoph Nowak⁴, Takayoshi Ohkubo³², Michelle J Pena³³, Kevan R Polkinghorne³⁴, Toshimi Sairenchi³⁵, Elke Schaeffner¹², Markus P Schneider¹³, Varda Shalev³⁶, Michael G Shlipak³⁷, Marit D Solbu³⁸, Nikita Stempniewicz³⁹, James Tollitt^{9

40}, José M Valdivielso⁵, Joep van der Leeuw³, Angela Yee-Moon Wang⁴¹, Chi-Pang Wen⁴², Mark Woodward^{1

43

44}, Kazumasa Yamagishi⁴⁵, Hiroshi Yatsuya⁴⁶, Luxia Zhang⁴⁷, Jannick A N Dorresteijn³, Emanuele Di Angelantonio^{2

48}, Frank L J Visseren³, Lisa Pennells², Josef Coresh¹

Affiliations

¹ Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
² Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
³ Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.
⁴ Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
⁵ Vascular & Renal Translational Research Group, IRBLleida, Spain and Spanish Research Network for Renal Diseases (RedInRen. ISCIII), Lleida, Spain.
⁶ Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ) and Network Aging Research, University of Heidelberg, Heidelberg, Germany.
⁷ John Walls Renal Unit, Department of Cardiovascular Sciences, Leicester General Hospital, University Hospitals of Leicester NHS Trust, University of Leicester, Leicester, United Kingdom.
⁸ Department of Nephrology and Kidney Health Research Institute, Geisinger Medical Center, Danville, Pennsylvania.
⁹ Department of Renal Medicine, Salford Care Organisation, Northern Care Alliance NHS Foundation Trust, Salford, UK.
¹⁰ Department of Public Health, University of Naples 'Federico II', Italy.
¹¹ University of Mississippi Medical Center, Jackson, MS, USA.
¹² Institute of Public Health, Charité-Universitätsmedizin Berlin, Berlin, Germany.
¹³ Department of Nephrology and Hypertension, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.
¹⁴ Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany.
¹⁵ Department of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
¹⁶ Departments of Epidemiology and Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
¹⁷ Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
¹⁸ Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
¹⁹ National Heart, Lung, and Blood Institute, Framingham, MA, USA.
²⁰ Program in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
²¹ Department of Medicine, Aga Khan University, Karachi, Pakistan.
²² Duke Global Health Institute, Duke University, Durham, NC, USA.
²³ Division of General Internal Medicine, University of California, San Diego and VA San Diego Healthcare, San Diego, CA.
²⁴ Global Center of Excellence, Yamagata University Faculty of Medicine, Yamagata, Japan.
²⁵ Medicine-Nephrology, Memphis Veterans Affairs Medical Center and University of Tennessee Health Science Center, Memphis, TN, USA.
²⁶ Gelre hospital location, Apeldoorn, The Netherlands.
²⁷ Division of Nephrology, Tufts Medical Center, Boston, MA, USA.
²⁸ Department of Preventive Medicine, Northwestern University, Chicago, IL, USA.
²⁹ NCD Epidemiology Research Center, Shiga University of Medical Science, Shiga, Japan.
³⁰ Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA.
³¹ Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³² Department of Hygiene and Public Health, Teikyo University School of Medicine, Tokyo, Japan.
³³ Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
³⁴ Monash University, Clayton, Australia.
³⁵ Medical Science of Nursing, Dokkyo Medical University School of Nursing, Mibu, Japan.
³⁶ Institute for Health and Research and Innovation, Maccabi Healthcare Services and Tel Aviv University, Tel Aviv, Israel.
³⁷ Kidney Health Research Collaborative, University of California, San Francisco, and San Francisco VA Healthcare System, San Francisco, CA, USA.
³⁸ Section of Nephrology, University Hospital of North Norway, Norway and UiT The Arctic University of Norway, Tromsø, Norway.
³⁹ AMGA (American Medical Group Association), Virginia and OptumLabs Visiting Fellow, Alexandria, VA, USA.
⁴⁰ Renal Department, University of Manchester, Oxford Road, Manchester, United Kingdom.
⁴¹ Department of Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong.
⁴² China Medical University Hospital, Taichung, Taiwan.
⁴³ George Institute for Global Health, Australia.
⁴⁴ George Institute for Global Health, Imperial College, London, United Kingdom.
⁴⁵ Department of Public Health Medicine, Faculty of Medicine, and Health Services Research and Development Center, University of Tsukuba, Japan.
⁴⁶ Department of Public Health, Fujita Health University School of Medicine, Japan and Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine, Aichi, Japan.
⁴⁷ Peking University First Hospital and Peking University, Beijing, China.
⁴⁸ Health Data Science Centre, Human Technopole, Milan, Italy.

Abstract

Aims: The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach.

Methods: In 3,054 840 participants from 34 datasets, we developed three Add-ons [eGFR only, eGFR + urinary albumin-to-creatinine ratio (ACR) (the primary Add-on), and eGFR + dipstick proteinuria] for SCORE2 and SCORE2-OP. We validated C-statistics and net reclassification improvement (NRI), accounting for competing risk of non-CVD death, in 5,997 719 participants from 34 different datasets.

Results: In the target population of SCORE2 and SCORE2-OP without diabetes, the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR) improved C-statistic by 0.006 (95%CI 0.004-0.008) and 0.016 (0.010-0.023), respectively, for SCORE2 and 0.012 (0.009-0.015) and 0.024 (0.014-0.035), respectively, for SCORE2-OP. Similar results were seen when we included individuals with diabetes and tested the CKD Add-on (eGFR + dipstick). In 57 485 European participants with CKD, SCORE2 or SCORE2-OP with a CKD Add-on showed a significant NRI [e.g. 0.100 (0.062-0.138) for SCORE2] compared to the qualitative approach in the ESC guideline.

Conclusion: Our Add-ons with CKD measures improved CVD risk prediction beyond SCORE2 and SCORE2-OP. This approach will help clinicians and patients with CKD refine risk prediction and further personalize preventive therapies for CVD.

Keywords: Cardiovascular disease; Chronic kidney disease; Meta-analysis; Risk prediction.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Aged
Aged, 80 and over
Albuminuria / diagnosis
Albuminuria / epidemiology
Cardiovascular Diseases* / diagnosis
Cardiovascular Diseases* / epidemiology
Cardiovascular Diseases* / prevention & control
Creatinine
Glomerular Filtration Rate
Heart Disease Risk Factors
Humans
Renal Insufficiency, Chronic* / diagnosis
Renal Insufficiency, Chronic* / epidemiology
Risk Factors

Substances

Creatinine

Abstract

Publication types

MeSH terms

Substances

Grants and funding