Home > Publications database > Including Measures of Chronic Kidney Disease to Improve Cardiovascular Risk Prediction by SCORE2 and SCORE2-OP. > print

001     181232
005     20240229145643.0
024 7 _ |a 10.1093/eurjpc/zwac176
|2 doi
024 7 _ |a pmid:35972749
|2 pmid
024 7 _ |a 2047-4873
|2 ISSN
024 7 _ |a 2047-4881
|2 ISSN
024 7 _ |a altmetric:134344889
|2 altmetric
037 _ _ |a DKFZ-2022-01878
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Matsushita, Kunihiro
|0 0000-0002-7179-718X
|b 0
245 _ _ |a Including Measures of Chronic Kidney Disease to Improve Cardiovascular Risk Prediction by SCORE2 and SCORE2-OP.
260 _ _ |a London [u.a.]
|c 2023
|b Sage Publ.
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1673519893_15371
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a 2023 Jan 11;30(1):8-16
520 _ _ |a The 2021 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, SCORE2 and SCORE2-OP, to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach.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.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.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.
536 _ _ |a 313 - Krebsrisikofaktoren und Prävention (POF4-313)
|0 G:(DE-HGF)POF4-313
|c POF4-313
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
650 _ 7 |a cardiovascular disease
|2 Other
650 _ 7 |a chronic kidney disease
|2 Other
650 _ 7 |a meta-analysis
|2 Other
650 _ 7 |a risk prediction
|2 Other
700 1 _ |a Kaptoge, Stephen
|0 0000-0002-1155-4872
|b 1
700 1 _ |a Hageman, Steven Hj
|0 0000-0003-2299-6745
|b 2
700 1 _ |a Sang, Yingying
|b 3
700 1 _ |a Ballew, Shoshana H
|0 0000-0002-7547-3764
|b 4
700 1 _ |a Grams, Morgan E
|0 0000-0002-4430-6023
|b 5
700 1 _ |a Surapaneni, Aditya
|b 6
700 1 _ |a Sun, Luanluan
|b 7
700 1 _ |a Arnlov, Johan
|b 8
700 1 _ |a Bozic, Milica
|b 9
700 1 _ |a Brenner, Hermann
|0 P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2
|b 10
|u dkfz
700 1 _ |a Brunskill, Nigel J
|b 11
700 1 _ |a Chang, Alex R
|b 12
700 1 _ |a Chinnadurai, Rajkumar
|0 0000-0003-3973-6595
|b 13
700 1 _ |a Cirillo, Massimo
|b 14
700 1 _ |a Correa, Adolfo
|b 15
700 1 _ |a Ebert, Natalie
|0 0000-0002-4463-3315
|b 16
700 1 _ |a Eckardt, Kai Uwe
|0 0000-0003-3823-0920
|b 17
700 1 _ |a Gansevoort, Ron T
|b 18
700 1 _ |a Gutierrez, Orlando
|b 19
700 1 _ |a Hadaegh, Farzad
|b 20
700 1 _ |a He, Jiang
|0 0000-0002-8286-9652
|b 21
700 1 _ |a Hwang, Shih Jen
|0 0000-0002-2129-5704
|b 22
700 1 _ |a Jafar, Tazeen H
|b 23
700 1 _ |a Jassal, Simerjot K
|b 24
700 1 _ |a Kayama, Takamasa
|b 25
700 1 _ |a Kovesdy, Csaba P
|b 26
700 1 _ |a Landman, Gijs W
|b 27
700 1 _ |a Levey, Andrew S
|b 28
700 1 _ |a Lloyd-Jones, Donald M
|b 29
700 1 _ |a Major, Rupert W
|b 30
700 1 _ |a Miura, Katsuyuki
|0 0000-0002-2646-9582
|b 31
700 1 _ |a Muntner, Paul
|b 32
700 1 _ |a Nadkarni, Girish N
|b 33
700 1 _ |a Nowak, Christoph
|0 0000-0001-8435-3978
|b 34
700 1 _ |a Ohkubo, Takayoshi
|b 35
700 1 _ |a Pena, Michelle J
|b 36
700 1 _ |a Polkinghorne, Kevan R
|b 37
700 1 _ |a Sairenchi, Toshimi
|b 38
700 1 _ |a Schaeffner, Elke
|b 39
700 1 _ |a Schneider, Markus P
|b 40
700 1 _ |a Shalev, Varda
|b 41
700 1 _ |a Shlipak, Michael G
|b 42
700 1 _ |a Solbu, Marit D
|0 0000-0002-4331-7548
|b 43
700 1 _ |a Stempniewicz, Nikita
|b 44
700 1 _ |a Tollitt, James
|0 0000-0002-9825-701X
|b 45
700 1 _ |a Valdivielso, José M
|b 46
700 1 _ |a van der Leeuw, Joep
|b 47
700 1 _ |a Wang, Angela Yee Moon
|b 48
700 1 _ |a Wen, Chi Pang
|b 49
700 1 _ |a Woodward, Mark
|b 50
700 1 _ |a Yamagishi, Kazumasa
|b 51
700 1 _ |a Yatsuya, Hiroshi
|b 52
700 1 _ |a Zhang, Luxia
|b 53
700 1 _ |a Dorresteijn, Jannick An
|0 0000-0002-0190-8526
|b 54
700 1 _ |a Di Angelantonio, Emanuele
|0 0000-0001-8776-6719
|b 55
700 1 _ |a Visseren, Frank Lj
|b 56
700 1 _ |a Pennells, Lisa
|0 0000-0002-8594-3061
|b 57
700 1 _ |a Coresh, Josef
|b 58
700 1 _ |a Consortium, Chronic Kidney Disease Prognosis
|b 59
|e Collaboration Author
773 _ _ |a 10.1093/eurjpc/zwac176
|g p. zwac176
|0 PERI:(DE-600)2646239-4
|n 1
|p 8-16
|t European journal of preventive cardiology
|v 30
|y 2023
|x 2047-4873
909 C O |p VDB
|o oai:inrepo02.dkfz.de:181232
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 10
|6 P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-313
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Krebsrisikofaktoren und Prävention
|x 0
914 1 _ |y 2022
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-02-04
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-02-04
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2023-08-24
|w ger
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b EUR J PREV CARDIOL : 2022
|d 2023-08-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-08-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-08-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-08-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-08-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2023-08-24
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b EUR J PREV CARDIOL : 2022
|d 2023-08-24
920 1 _ |0 I:(DE-He78)C070-20160331
|k C070
|l C070 Klinische Epidemiologie und Alternf.
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)C070-20160331
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21