A 4-Variable Model to Predict Cardio-Kidney Events and Mortality in Chronic Kidney Disease: The Chronic Renal Insufficiency Cohort (CRIC) Study.

IF 4.3 3区 医学 Q1 UROLOGY & NEPHROLOGY American Journal of Nephrology Pub Date : 2023-01-01 Epub Date: 2023-09-06 DOI:10.1159/000533223
Luís Mendonça, Miguel Bigotte Vieira, João Sérgio Neves, Paulo Castro Chaves, Joao Pedro Ferreira
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引用次数: 0

Abstract

Introduction: Current prognostic models for chronic kidney disease (CKD) are complex and were designed to predict a single outcome. We aimed to develop and validate a simple and parsimonious prognostic model to predict cardio-kidney events and mortality.

Methods: Patients from the CRIC Study (n = 3,718) were randomly divided into derivation (n = 2,478) and validation (n = 1,240) cohorts. Twenty-nine candidate variables were preselected. Multivariable Cox regression models were developed using stepwise selection for various cardio-kidney endpoints, namely, (i) the primary composite outcome of 50% decline in estimated glomerular filtration rate (eGFR) from baseline, end-stage renal disease, or cardiovascular (CV) mortality; (ii) hospitalization for heart failure (HHF) or CV mortality; (iii) 3-point major CV endpoints (3P-MACE); (iv) all-cause death.

Results: During a median follow-up of 9 years, the primary outcome occurred in 977 patients of the derivation cohort and 501 patients of the validation cohort. Log-transformed N-terminal pro-B-type natriuretic peptide (NT-proBNP), log-transformed high-sensitive cardiac troponin T (hs-cTnT), log-transformed albuminuria, and eGFR were the dominant predictors. The primary outcome risk score discriminated well (c-statistic = 0.83) with a proportion of events of 11.4% in the lowest tertile of risk and 91.5% in the highest tertile at 10 years. The risk model presented good discrimination for HHF or CV mortality, 3P-MACE, and all-cause death (c-statistics = 0.80, 0.75, and 0.75, respectively). The 4-variable risk model achieved similar c-statistics for all tested outcomes in the validation cohort. The discrimination of the 4-variable risk model was mostly superior to that of published models.

Conclusion: The combination of NT-proBNP, hs-cTnT, albuminuria, and eGFR in a single 4-variable model provides a unique individual prognostic assessment of multiple cardio-kidney outcomes in CKD.

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预测慢性肾脏疾病心肾事件和死亡率的4变量模型:慢性肾功能不全队列(CRIC)研究
目前慢性肾脏疾病(CKD)的预后模型是复杂的,并且设计用于预测单一结果。我们的目的是建立和验证一个简单和简洁的预测模型来预测心肾事件和死亡率。方法:来自CRIC研究的患者(n = 3718)随机分为衍生组(n = 2478)和验证组(n = 1240)。预选了29个候选变量。采用逐步选择方法建立了多变量Cox回归模型,用于各种心肾终点,即:(i)主要复合终点肾小球滤过率(eGFR)较基线下降50%,终末期肾病或心血管(CV)死亡率;(ii)因心力衰竭(HHF)或CV死亡而住院;(iii) 3点主要CV终点(3P-MACE);(四)全因死亡。结果:在中位9年的随访期间,衍生队列的977例患者和验证队列的501例患者出现了主要结局。log-转化n端前b型利钠肽(NT-proBNP)、log-转化高敏心肌肌钙蛋白T (hs-cTnT)、log-转化蛋白尿和eGFR是主要预测因子。主要结局风险评分判别性较好(c-statistic = 0.83), 10年最低风险分位数的事件比例为11.4%,最高分位数的事件比例为91.5%。该风险模型对HHF或CV死亡率、3P-MACE和全因死亡具有良好的区分性(c-statistics分别= 0.80、0.75和0.75)。在验证队列中,4变量风险模型对所有测试结果实现了相似的c统计量。4变量风险模型的判别性大多优于已发表的模型。结论:NT-proBNP、hs-cTnT、蛋白尿和eGFR在单一4变量模型中的结合为CKD的多重心肾结局提供了独特的个体预后评估。
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来源期刊
American Journal of Nephrology
American Journal of Nephrology 医学-泌尿学与肾脏学
CiteScore
7.50
自引率
2.40%
发文量
74
审稿时长
4-8 weeks
期刊介绍: The ''American Journal of Nephrology'' is a peer-reviewed journal that focuses on timely topics in both basic science and clinical research. Papers are divided into several sections, including:
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