| Title: |
Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP |
| Authors: |
Matsushita, K; Kaptoge, S; Hageman, SHJ; Sang, Y; Ballew, SH; Grams, ME; Surapaneni, A; Sun, L; Arnlov, J; Bozic, M; Brenner, H; Brunskill, NJ; Chang, AR; Chinnadurai, R; Cirillo, M; Correa, A; Ebert, N; Eckardt, KU; Gansevoort, RT; Gutierrez, O; Hadaegh, F; He, J; Hwang, SJ; Jafar, TH; Jassal, SK; Kayama, T; Kovesdy, CP; Landman, GW; Levey, AS; Lloyd-Jones, DM; Major, RW; Miura, K; Muntner, P; Nadkarni, GN; Nowak, C; Ohkubo, T; Pena, MJ; Polkinghorne, KR; Sairenchi, T; Schaeffner, E; Schneider, MP; Shalev, V; Shlipak, MG; Solbu, MD; Stempniewicz, N; Tollitt, J; Valdivielso, JM; Van Der Leeuw, J; Wang, AYM; Wen, CP; Woodward, M; Yamagishi, K; Yatsuya, H; Zhang, L; Dorresteijn, JAN; Di Angelantonio, E; Visseren, FLJ; Pennells, L; Coresh, J |
| Source: |
urn:ISSN:2047-4873 ; urn:ISSN:2047-4881 ; European Journal of Preventive Cardiology, 30, 1, 8-16 |
| Publisher Information: |
Oxford University Press (OUP) |
| Publication Year: |
2023 |
| Collection: |
UNSW Sydney (The University of New South Wales): UNSWorks |
| Subject Terms: |
32 Biomedical and Clinical Sciences; 3202 Clinical Sciences; Kidney Disease; Prevention; Heart Disease; Cardiovascular; Aging; Renal and urogenital; 3 Good Health and Well Being; Humans; Aged; 80 and over; Cardiovascular Diseases; Risk Factors; Creatinine; Renal Insufficiency; Chronic; Albuminuria; Glomerular Filtration Rate; Heart Disease Risk Factors; Cardiovascular disease; Chronic kidney disease; Meta-analysis; Risk prediction; anzsrc-for: 32 Biomedical and Clinical Sciences; anzsrc-for: 3202 Clinical Sciences; anzsrc-for: 3201 Cardiovascular medicine and haematology |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
unknown |
| Relation: |
https://hdl.handle.net/1959.4/unsworks_82716; https://doi.org/10.1093/eurjpc/zwac176 |
| DOI: |
10.1093/eurjpc/zwac176 |
| Availability: |
https://hdl.handle.net/1959.4/unsworks_82716; https://doi.org/10.1093/eurjpc/zwac176 |
| Rights: |
metadata only access ; http://purl.org/coar/access_right/c_14cb ; CC-BY ; https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.A9C66EA7 |
| Database: |
BASE |