Cargando…
Kidney measures beyond traditional risk factors for cardiovascular prediction: A collaborative meta-analysis
BACKGROUND: The utility of estimated glomerular filtration rate (eGFR) and albuminuria for cardiovascular prediction is controversial. METHODS: We meta-analyzed individual-level data from 24 cohorts (with a median follow-up time longer than 4 years, varying from 4.2 to 19.0 years) in the Chronic Kid...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4594193/ https://www.ncbi.nlm.nih.gov/pubmed/26028594 http://dx.doi.org/10.1016/S2213-8587(15)00040-6 |
_version_ | 1782393421533544448 |
---|---|
author | Matsushita, Kunihiro Coresh, Josef Sang, Yingying Chalmers, John Fox, Caroline Guallar, Eliseo Jafar, Tazeen Jassal, Simerjot K. Landman, Gijs W.D. Muntner, Paul Roderick, Paul Sairenchi, Toshimi Schöttker, Ben Shankar, Anoop Shlipak, Michael Tonelli, Marcello Townend, Jonathan van Zuilen, Arjan Yamagishi, Kazumasa Yamashita, Kentaro Gansevoort, Ron Sarnak, Mark Warnock, David G. Woodward, Mark Ärnlöv, Johan |
author_facet | Matsushita, Kunihiro Coresh, Josef Sang, Yingying Chalmers, John Fox, Caroline Guallar, Eliseo Jafar, Tazeen Jassal, Simerjot K. Landman, Gijs W.D. Muntner, Paul Roderick, Paul Sairenchi, Toshimi Schöttker, Ben Shankar, Anoop Shlipak, Michael Tonelli, Marcello Townend, Jonathan van Zuilen, Arjan Yamagishi, Kazumasa Yamashita, Kentaro Gansevoort, Ron Sarnak, Mark Warnock, David G. Woodward, Mark Ärnlöv, Johan |
author_sort | Matsushita, Kunihiro |
collection | PubMed |
description | BACKGROUND: The utility of estimated glomerular filtration rate (eGFR) and albuminuria for cardiovascular prediction is controversial. METHODS: We meta-analyzed individual-level data from 24 cohorts (with a median follow-up time longer than 4 years, varying from 4.2 to 19.0 years) in the Chronic Kidney Disease Prognosis Consortium (637,315 participants without a history of cardiovascular disease) and assessed C-statistic difference and reclassification improvement for cardiovascular mortality and fatal and non-fatal cases of coronary heart disease, stroke, and heart failure in 5-year timeframe, contrasting prediction models consisting of traditional risk factors with and without creatinine-based eGFR and/or albuminuria (either albumin-to-creatinine ratio [ACR] or semi-quantitative dipstick proteinuria). FINDINGS: The addition of eGFR and ACR significantly improved the discrimination of cardiovascular outcomes beyond traditional risk factors in general populations, but the improvement was greater with ACR than with eGFR and more evident for cardiovascular mortality (c-statistic difference 0.0139 [95%CI 0.0105–0.0174] and 0.0065 [0.0042–0.0088], respectively) and heart failure (0.0196 [0.0108–0.0284] and 0.0109 [0.0059–0.0159]) than for coronary disease (0.0048 [0.0029–0.0067] and 0.0036 [0.0019–0.0054]) and stroke (0.0105 [0.0058–0.0151] and 0.0036 [0.0004–0.0069]). Dipstick proteinuria demonstrated smaller improvement than ACR. The discrimination improvement with kidney measures was especially evident in individuals with diabetes or hypertension but remained significant with ACR for cardiovascular mortality and heart failure in those without either of these conditions. In participants with chronic kidney disease (CKD), the combination of eGFR and ACR for risk discrimination outperformed most single traditional predictors; the c-statistic for cardiovascular mortality declined by 0.023 [0.016–0.030] vs. <0.007 when omitting eGFR and ACR vs. any single modifiable traditional predictors, respectively. INTERPRETATION: Creatinine-based eGFR and albuminuria should be taken into account for cardiovascular prediction, especially when they are already assessed for clinical purpose and/or cardiovascular mortality and heart failure are the outcomes of interest (e.g., the European guidelines on cardiovascular prevention). ACR may have particularly broad implications for cardiovascular prediction. In CKD populations, the simultaneous assessment of eGFR and ACR will facilitate improved cardiovascular risk classification, supporting current CKD guidelines. FUNDING: US National Kidney Foundation and NIDDK |
format | Online Article Text |
id | pubmed-4594193 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
record_format | MEDLINE/PubMed |
spelling | pubmed-45941932016-07-01 Kidney measures beyond traditional risk factors for cardiovascular prediction: A collaborative meta-analysis Matsushita, Kunihiro Coresh, Josef Sang, Yingying Chalmers, John Fox, Caroline Guallar, Eliseo Jafar, Tazeen Jassal, Simerjot K. Landman, Gijs W.D. Muntner, Paul Roderick, Paul Sairenchi, Toshimi Schöttker, Ben Shankar, Anoop Shlipak, Michael Tonelli, Marcello Townend, Jonathan van Zuilen, Arjan Yamagishi, Kazumasa Yamashita, Kentaro Gansevoort, Ron Sarnak, Mark Warnock, David G. Woodward, Mark Ärnlöv, Johan Lancet Diabetes Endocrinol Article BACKGROUND: The utility of estimated glomerular filtration rate (eGFR) and albuminuria for cardiovascular prediction is controversial. METHODS: We meta-analyzed individual-level data from 24 cohorts (with a median follow-up time longer than 4 years, varying from 4.2 to 19.0 years) in the Chronic Kidney Disease Prognosis Consortium (637,315 participants without a history of cardiovascular disease) and assessed C-statistic difference and reclassification improvement for cardiovascular mortality and fatal and non-fatal cases of coronary heart disease, stroke, and heart failure in 5-year timeframe, contrasting prediction models consisting of traditional risk factors with and without creatinine-based eGFR and/or albuminuria (either albumin-to-creatinine ratio [ACR] or semi-quantitative dipstick proteinuria). FINDINGS: The addition of eGFR and ACR significantly improved the discrimination of cardiovascular outcomes beyond traditional risk factors in general populations, but the improvement was greater with ACR than with eGFR and more evident for cardiovascular mortality (c-statistic difference 0.0139 [95%CI 0.0105–0.0174] and 0.0065 [0.0042–0.0088], respectively) and heart failure (0.0196 [0.0108–0.0284] and 0.0109 [0.0059–0.0159]) than for coronary disease (0.0048 [0.0029–0.0067] and 0.0036 [0.0019–0.0054]) and stroke (0.0105 [0.0058–0.0151] and 0.0036 [0.0004–0.0069]). Dipstick proteinuria demonstrated smaller improvement than ACR. The discrimination improvement with kidney measures was especially evident in individuals with diabetes or hypertension but remained significant with ACR for cardiovascular mortality and heart failure in those without either of these conditions. In participants with chronic kidney disease (CKD), the combination of eGFR and ACR for risk discrimination outperformed most single traditional predictors; the c-statistic for cardiovascular mortality declined by 0.023 [0.016–0.030] vs. <0.007 when omitting eGFR and ACR vs. any single modifiable traditional predictors, respectively. INTERPRETATION: Creatinine-based eGFR and albuminuria should be taken into account for cardiovascular prediction, especially when they are already assessed for clinical purpose and/or cardiovascular mortality and heart failure are the outcomes of interest (e.g., the European guidelines on cardiovascular prevention). ACR may have particularly broad implications for cardiovascular prediction. In CKD populations, the simultaneous assessment of eGFR and ACR will facilitate improved cardiovascular risk classification, supporting current CKD guidelines. FUNDING: US National Kidney Foundation and NIDDK 2015-05-28 2015-07 /pmc/articles/PMC4594193/ /pubmed/26028594 http://dx.doi.org/10.1016/S2213-8587(15)00040-6 Text en http://creativecommons.org/licenses/by-nc/4.0/ This manuscript version is made available under the CC BY-NC-ND 4.0 license. |
spellingShingle | Article Matsushita, Kunihiro Coresh, Josef Sang, Yingying Chalmers, John Fox, Caroline Guallar, Eliseo Jafar, Tazeen Jassal, Simerjot K. Landman, Gijs W.D. Muntner, Paul Roderick, Paul Sairenchi, Toshimi Schöttker, Ben Shankar, Anoop Shlipak, Michael Tonelli, Marcello Townend, Jonathan van Zuilen, Arjan Yamagishi, Kazumasa Yamashita, Kentaro Gansevoort, Ron Sarnak, Mark Warnock, David G. Woodward, Mark Ärnlöv, Johan Kidney measures beyond traditional risk factors for cardiovascular prediction: A collaborative meta-analysis |
title | Kidney measures beyond traditional risk factors for cardiovascular prediction: A collaborative meta-analysis |
title_full | Kidney measures beyond traditional risk factors for cardiovascular prediction: A collaborative meta-analysis |
title_fullStr | Kidney measures beyond traditional risk factors for cardiovascular prediction: A collaborative meta-analysis |
title_full_unstemmed | Kidney measures beyond traditional risk factors for cardiovascular prediction: A collaborative meta-analysis |
title_short | Kidney measures beyond traditional risk factors for cardiovascular prediction: A collaborative meta-analysis |
title_sort | kidney measures beyond traditional risk factors for cardiovascular prediction: a collaborative meta-analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4594193/ https://www.ncbi.nlm.nih.gov/pubmed/26028594 http://dx.doi.org/10.1016/S2213-8587(15)00040-6 |
work_keys_str_mv | AT matsushitakunihiro kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT coreshjosef kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT sangyingying kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT chalmersjohn kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT foxcaroline kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT guallareliseo kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT jafartazeen kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT jassalsimerjotk kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT landmangijswd kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT muntnerpaul kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT roderickpaul kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT sairenchitoshimi kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT schottkerben kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT shankaranoop kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT shlipakmichael kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT tonellimarcello kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT townendjonathan kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT vanzuilenarjan kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT yamagishikazumasa kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT yamashitakentaro kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT gansevoortron kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT sarnakmark kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT warnockdavidg kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT woodwardmark kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT arnlovjohan kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis AT kidneymeasuresbeyondtraditionalriskfactorsforcardiovascularpredictionacollaborativemetaanalysis |