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

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