Supplementary Material for: A 4-variable model to predict cardio-kidney events and mortality in chronic kidney disease: The Chronic Renal Insufficiency Cohort (CRIC) Study

Introduction Current prognostic models for CKD are complex and were designed to predict one 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=3718) were randomly divided into derivation (n=2478) and validation (n=1240) cohorts. Twenty-nine candidate variables were pre-selected. Multivariable Cox regression models were developed using stepwise selection for various cardio-kidney endpoints, namely: i) the primary composite outcome of 50% decline in eGFR from baseline, end-stage renal disease or cardiovascular mortality; ii) hospitalization for heart failure (HHF) or cardiovascular 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 NT-proBNP, log-transformed 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 cardiovascular mortality, 3P-MACE and all-cause death (c-statistic=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.

引言 目前针对慢性肾脏病（Chronic Kidney Disease, CKD）的现有预后模型结构复杂，且仅能预测单一临床结局。本研究旨在开发并验证一款简洁且参数精简的预后模型，用于预测心肾事件与全因死亡。

方法 本研究纳入慢性肾功能不全队列研究（CRIC Study）的3718例慢性肾脏病患者，将其随机划分为推导队列（n=2478）与验证队列（n=1240）。预先选定29个候选变量，针对多项心肾终点采用逐步变量筛选法构建多变量Cox回归模型，具体终点包括：① 以估算肾小球滤过率（estimated Glomerular Filtration Rate, eGFR）较基线水平下降50%、终末期肾病或心血管死亡为组成的主要复合终点；② 心力衰竭住院（hospitalization for heart failure, HHF）或心血管死亡；③ 3项主要心血管不良事件（3-point Major Adverse Cardiovascular Events, 3P-MACE）；④ 全因死亡。

结果 中位随访时长为9年期间，推导队列中有977例患者发生主要结局事件，验证队列中有501例患者发生主要结局事件。经对数转换的N末端B型利钠肽原（N-terminal pro B-type natriuretic peptide, NT-proBNP）、经对数转换的高敏心肌肌钙蛋白T（high-sensitivity cardiac Troponin T, hs-cTnT）、经对数转换的蛋白尿以及eGFR为该模型的核心预测因子。该主要结局风险评分具有良好的区分能力（c统计量=0.83），10年随访时，风险最低三分位组的事件发生率为11.4%，风险最高三分位组的事件发生率高达91.5%。该风险模型对心力衰竭住院或心血管死亡、3P-MACE以及全因死亡均具有良好的区分能力（c统计量分别为0.80、0.75与0.75）。这款仅含4个变量的风险模型在验证队列中对所有测试结局的c统计量与推导队列相当，且其区分能力整体优于已发表的同类预后模型。

结论 将NT-proBNP、hs-cTnT、蛋白尿与eGFR整合为一款仅含4个变量的单一模型，可对慢性肾脏病患者的多项心肾结局进行个体化的综合预后评估。