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

被引：26

作者：

Matsushita, Kunihiro ^{[1
]}

Kaptoge, Stephen ^{[2
]}

Hageman, Steven H. J. ^{[3
]}

Sang, Yingying ^{[1
]}

Ballew, Shoshana H. ^{[1
]}

Grams, Morgan E. ^{[1
]}

Surapaneni, Aditya ^{[1
]}

Sun, Luanluan ^{[2
]}

Arnlov, Johan ^{[4
]}

Bozic, Milica ^{[5
,6
]}

Brenner, Hermann ^{[7
,8
]}

Brunskill, Nigel J. ^{[9
]}

Chang, Alex R. ^{[10
,11
]}

Chinnadurai, Rajkumar ^{[12
]}

Cirillo, Massimo ^{[13
]}

Correa, Adolfo ^{[14
]}

Ebert, Natalie ^{[15
]}

Eckardt, Kai Uwe ^{[16
,17
]}

Gansevoort, Ron T. ^{[18
]}

Gutierrez, Orlando ^{[19
]}

Hadaegh, Farzad ^{[20
]}

He, Jiang ^{[21
]}

Hwang, Shih Jen ^{[22
]}

Jafar, Tazeen H. ^{[23
,24
,25
]}

Jassal, Simerjot K. ^{[26
,27
]}

Kayama, Takamasa ^{[28
]}

Kovesdy, Csaba P. ^{[29
,30
]}

Landman, Gijs W. ^{[31
]}

Levey, Andrew S. ^{[32
]}

Lloyd-Jones, Donald M. ^{[33
]}

Major, Rupert W. ^{[9
]}

Miura, Katsuyuki ^{[34
]}

Muntner, Paul ^{[19
]}

Nadkarni, Girish N. ^{[35
]}

Nowak, Christoph ^{[4
]}

Ohkubo, Takayoshi ^{[36
]}

Pena, Michelle J. ^{[37
]}

Polkinghorne, Kevan R. ^{[38
]}

Sairenchi, Toshimi ^{[39
]}

Schaeffner, Elke ^{[15
]}

Schneider, Markus P. ^{[16
]}

Shalev, Varda ^{[40
,41
]}

Shlipak, Michael G. ^{[42
,43
]}

Solbu, Marit D. ^{[44
,45
]}

Stempniewicz, Nikita ^{[46
,47
]}

Tollitt, James ^{[12
,48
]}

Valdivielso, Jose M. ^{[5
,6
]}

van der Leeuw, Joep ^{[3
]}

Wang, Angela Yee Moon ^{[49
]}

Wen, Chi Pang ^{[50
]}

机构：

[1] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Epidemiol, Baltimore, MD 21205 USA

[2] Univ Cambridge, Dept Publ Hlth & Primary Care, Cambridge, England

[3] Univ Med Ctr Utrecht, Dept Vasc Med, Utrecht, Netherlands

[4] Karolinska Inst, Dept Neurobiol Care Sci & Soc, Stockholm, Sweden

[5] IRBLleida, Vasc & Renal Translat Res Grp, Lleida, Spain

[6] Spanish Res Network Renal Dis RedInRen ISCIII, Lleida, Spain

[7] Heidelberg Univ, German Canc Res Ctr DKFZ, Div Clin Epidemiol & Aging Res, Heidelberg, Germany

[8] Heidelberg Univ, Network Aging Res, Heidelberg, Germany

[9] Univ Leicester, Univ Hosp Leicester NHS Trust, Leicester Gen Hosp, Dept Cardiovasc Sci,John Walls Renal Unit, Leicester, Leics, England

[10] Geisinger Med Ctr, Dept Nephrol, Danville, PA 17822 USA

[11] Geisinger Med Ctr, Kidney Hlth Res Inst Geisinger, Danville, PA 17822 USA

[12] Northern Care Alliance NHS Fdn Trust, Dept Renal Med, Salford Care Org, Salford, Lancs, England

[13] Univ Naples Federico II, Dept Publ Hlth, Naples, Italy

[14] Univ Mississippi Med Ctr, Jackson, MS USA

[15] Charite Univ Med Berlin, Inst Publ Hlth, Berlin, Germany

[16] Friedrich Alexander Univ Erlangen Nurnberg, Dept Nephrol & Hypertens, Erlangen, Germany

[17] Charite Univ Med Berlin, Dept Nephrol & Med Intens Care, Berlin, Germany

[18] Univ Groningen, Univ Med Ctr Groningen, Dept Nephrol, Groningen, Netherlands

[19] Univ Alabama Birmingham, Dept Epidemiol, Birmingham, AL USA

[20] Shahid Beheshti Univ Med Sci, Res Inst Endocrine Sci, Tehran, Iran

[21] Tulane Univ, Dept Epidemiol, Sch Publ Hlth & Trop Med, New Orleans, LA USA

[22] Natl Heart Lung & Blood Inst, Framingham, MA USA

[23] Duke Natl Univ Singapore Med Sch, Program Hlth Serv & Syst Res, Singapore, Singapore

[24] Aga Khan Univ, Dept Med, Karachi, Pakistan

[25] Duke Univ, Duke Global Hlth Inst, Durham, NC USA

[26] Univ Calif San Diego, Div Gen Internal Med, San Diego, CA 92103 USA

[27] VA San Diego Healthcare, San Diego, CA USA

[28] Yamagata Univ, Global Ctr Excellence, Fac Med, Yamagata, Japan

[29] Memphis Vet Affairs Med Ctr, Med Nephrol, Memphis, TN USA

[30] Univ Tennessee, Hlth Sci Ctr, Memphis, TN USA

[31] Gelre Hosp Locat, Apeldoorn, Netherlands

[32] Tufts Med Ctr, Div Nephrol, Boston, MA 02111 USA

[33] Northwestern Univ, Dept Prevent Med, Chicago, IL 60611 USA

[34] Shiga Univ Med Sci, NCD Epidemiol Res Ctr, Otsu, Shiga, Japan

[35] Icahn Sch Med Mt Sinai, Div Nephrol, Dept Med, New York, NY 10029 USA

[36] Teikyo Univ, Dept Hyg & Publ Hlth, Sch Med, Tokyo, Japan

[37] Univ Groningen, Univ Med Ctr Groningen, Dept Clin Pharm & Pharmacol, Groningen, Netherlands

[38] Monash Univ, Clayton, Vic, Australia

[39] Dokkyo Med Univ, Med Sci Nursing, Sch Nursing, Mibu, Tochigi, Japan

[40] Maccabi Healthcare Serv, Inst Hlth & Res & Innovat, Tel Aviv, Israel

[41] Tel Aviv Univ, Tel Aviv, Israel

[42] Univ Calif San Francisco, Kidney Hlth Res Collaborat, San Francisco, CA 94143 USA

[43] San Francisco VA Healthcare Syst, San Francisco, CA USA

[44] Univ Hosp North Norway, Sect Nephrol, Tromso, Norway

[45] UiT Arctic Univ Norway, Tromso, Norway

[46] AMGA Amer Med Grp Assoc, Alexandria, VA USA

[47] OptumLabs, Alexandria, VA USA

[48] Univ Manchester, Renal Dept, Oxford Rd, Manchester, Lancs, England

[49] Univ Hong Kong, Queen Mary Hosp, Dept Med, Hong Kong, Peoples R China

[50] China Med Univ Hosp, Taichung, Taiwan

来源：

EUROPEAN JOURNAL OF PREVENTIVE CARDIOLOGY | 2023年 / 30卷 / 01期

关键词：

Chronic kidney disease; Cardiovascular disease; Risk prediction; Meta-analysis; GLOMERULAR-FILTRATION-RATE; EPIDEMIOLOGY; ALBUMINURIA; MODEL;

D O I：

10.1093/eurjpc/zwac176

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

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.

引用

页码：8 / 16

页数：9

共 50 条

[31] SCORE2 Asia-Pacific: a comprehensive approach to prevention of cardiovascular disease
Hageman, Steven H. J.
Johar, Sofian
Visseren, Frank L. J.
EUROPEAN HEART JOURNAL, 2025,
[32] Flexible addition of risk modifiers on top of SCORE2 to improve long-term risk prediction in healthy individuals
Auer, Johann
Lamm, Gudrun
EUROPEAN JOURNAL OF PREVENTIVE CARDIOLOGY, 2023, 30 (15) : 1702 - 1704
[33] SCORE2 cardiovascular risk prediction models in an ethnic and socioeconomic diverse population in the Netherlands: an external validation study
Kist, Janet M.
Vos, Rimke C.
Mairuhu, Albert T. A.
Struijs, Jeroen N.
van Peet, Petra G.
Vos, Hedwig M. M.
van Os, Hendrikus J. A.
Beishuizen, Edith D.
Sijpkens, Yvo W. J.
Faiq, Mohammad A.
Numans, Mattijs E.
Groenwold, Rolf H. H.
ECLINICALMEDICINE, 2023, 57
[34] Estimation of 10-Year Cardiovascular Disease Risk in the Portuguese Population Using the Systematic Coronary Risk Evaluation 2 (SCORE2)
Santos, Maria
Sousa-uva, Mafalda
Namorado, Sonia
Goncalves, Teresa
Matias Dias, Carlos
Gaio, Vania
ACTA MEDICA PORTUGUESA, 2024, 37 (10) : 720 - 724
[35] Validation of SCORE2 on a sample from the Russian population and adaptation for the very high cardiovascular disease risk region
Svinin, Gleb E.
Kutsenko, Vladimir A.
Shalnova, Svetlana A.
Yarovaya, Elena B.
Imaeva, Asiia E.
Balanova, Yulia A.
Kapustina, Anna V.
Muromtseva, Galina A.
Drapkina, Oxana M.
PLOS ONE, 2024, 19 (04):
[36] Prediction of cardiovascular risk in patients type 2 diabetes using the SCORE2-Diabetes risk score
Cavender, M.
Sorensen, A.
Bonaca, M.
Cannon, C.
Clemmensen, K.
Buse, J.
Kvist, K.
EUROPEAN HEART JOURNAL, 2024, 45
[37] Body Mass Index and Waist Circumference as Predictors of Above-Average Increased Cardiovascular Risk Assessed by the SCORE2 and SCORE2-OP Calculators and the Proposition of New Optimal Cut-Off Values: Cross-Sectional Single-Center Study
Suwala, Szymon
Junik, Roman
JOURNAL OF CLINICAL MEDICINE, 2024, 13 (07)
[38] Prognostic change of adding coronary calcium score and genetic risk score to European SCORE2 in a moderate risk region
Temtem, M.
Palma Dos Reis, R.
Serrao, M.
Santos, M.
Sa, D.
Sousa, F.
Rodrigues, M.
Freitas, S.
Henriques, E.
Borges, S.
Guerra, G.
Ornelas, I.
Drumond, A.
Sousa, A. C.
Mendonca, M. I.
EUROPEAN HEART JOURNAL, 2023, 44
[39] Factors influencing Systemic Coronary Risk Estimation 2 (SCORE2)
Gryglewska-Wawrzak, Katarzyna
Banach, Maciej
Sakowicz, Agata
Sosnowska, Bozena
Adach, Weronika
Bielecka-Dabrowa, Agata
CARDIOLOGY JOURNAL, 2025,
[40] Replacing SCORE with SCORE2 in Portuguese primary care: News from the frontline of cardiovascular prevention
Ferreira, Antonio Miguel
REVISTA PORTUGUESA DE CARDIOLOGIA, 2024, 43 (08) : 457 - 458

← 1 2 3 4 5 →