Matsushita Kunihiro, Jassal Simerjot K, Sang Yingying, Ballew Shoshana H, Grams Morgan E, Surapaneni Aditya, Arnlov Johan, Bansal Nisha, Bozic Milica, Brenner Hermann, Brunskill Nigel J, Chang Alex R, Chinnadurai Rajkumar, Cirillo Massimo, Correa Adolfo, Ebert Natalie, Eckardt Kai-Uwe, Gansevoort Ron T, Gutierrez Orlando, Hadaegh Farzad, He Jiang, Hwang Shih-Jen, Jafar Tazeen H, Kayama Takamasa, Kovesdy Csaba P, Landman Gijs W, Levey Andrew S, Lloyd-Jones Donald M, Major Rupert W, Miura Katsuyuki, Muntner Paul, Nadkarni Girish N, Naimark David Mj, Nowak Christoph, Ohkubo Takayoshi, Pena Michelle J, Polkinghorne Kevan R, Sabanayagam Charumathi, Sairenchi Toshimi, Schneider Markus P, Shalev Varda, Shlipak Michael, Solbu Marit D, Stempniewicz Nikita, Tollitt James, Valdivielso José M, van der Leeuw Joep, Wang Angela Yee-Moon, Wen Chi-Pang, Woodward Mark, Yamagishi Kazumasa, Yatsuya Hiroshi, Zhang Luxia, Schaeffner Elke, Coresh Josef
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Division of General Internal Medicine, University of California, San Diego and VA San Diego Healthcare, San Diego, California.
EClinicalMedicine. 2020 Oct 14;27:100552. doi: 10.1016/j.eclinm.2020.100552. eCollection 2020 Oct.
Chronic kidney disease (CKD) measures (estimated glomerular filtration rate [eGFR] and albuminuria) are frequently assessed in clinical practice and improve the prediction of incident cardiovascular disease (CVD), yet most major clinical guidelines do not have a standardized approach for incorporating these measures into CVD risk prediction. "CKD Patch" is a validated method to calibrate and improve the predicted risk from established equations according to CKD measures.
Utilizing data from 4,143,535 adults from 35 datasets, we developed several "CKD Patches" incorporating eGFR and albuminuria, to enhance prediction of risk of atherosclerotic CVD (ASCVD) by the Pooled Cohort Equation (PCE) and CVD mortality by Systematic COronary Risk Evaluation (SCORE). The risk enhancement by CKD Patch was determined by the deviation between individual CKD measures and the values expected from their traditional CVD risk factors and the hazard ratios for eGFR and albuminuria. We then validated this approach among 4,932,824 adults from 37 independent datasets, comparing the original PCE and SCORE equations (recalibrated in each dataset) to those with addition of CKD Patch.
We confirmed the prediction improvement with the CKD Patch for CVD mortality beyond SCORE and ASCVD beyond PCE in validation datasets (Δc-statistic 0.027 [95% CI 0.018-0.036] and 0.010 [0.007-0.013] and categorical net reclassification improvement 0.080 [0.032-0.127] and 0.056 [0.044-0.067], respectively). The median (IQI) of the ratio of predicted risk for CVD mortality with CKD Patch vs. the original prediction with SCORE was 2.64 (1.89-3.40) in very high-risk CKD (e.g., eGFR 30-44 ml/min/1.73m with albuminuria ≥30 mg/g), 1.86 (1.48-2.44) in high-risk CKD (e.g., eGFR 45-59 ml/min/1.73m with albuminuria 30-299 mg/g), and 1.37 (1.14-1.69) in moderate risk CKD (e.g., eGFR 60-89 ml/min/1.73m with albuminuria 30-299 mg/g), indicating considerable risk underestimation in CKD with SCORE. The corresponding estimates for ASCVD with PCE were 1.55 (1.37-1.81), 1.24 (1.10-1.54), and 1.21 (0.98-1.46).
The "CKD Patch" can be used to quantitatively enhance ASCVD and CVD mortality risk prediction equations recommended in major US and European guidelines according to CKD measures, when available.
US National Kidney Foundation and the NIDDK.
慢性肾脏病（CKD）指标（估算肾小球滤过率[eGFR]和蛋白尿）在临床实践中经常得到评估，且能改善对心血管疾病（CVD）发病风险的预测，但大多数主要临床指南在将这些指标纳入CVD风险预测方面没有标准化方法。“CKD补丁”是一种经过验证的方法，可根据CKD指标校准并改进既定方程的预测风险。
利用来自35个数据集的4143535名成年人的数据，我们开发了几个纳入eGFR和蛋白尿的“CKD补丁”，以通过汇总队列方程（PCE）增强对动脉粥样硬化性CVD（ASCVD）风险的预测，并通过系统性冠状动脉风险评估（SCORE）增强对CVD死亡率的预测。CKD补丁带来的风险增强通过个体CKD指标与其传统CVD风险因素预期值之间的偏差以及eGFR和蛋白尿的风险比来确定。然后我们在来自37个独立数据集的4932824名成年人中验证了这种方法，将原始的PCE和SCORE方程（在每个数据集中重新校准）与添加了CKD补丁的方程进行比较。
我们在验证数据集中证实，CKD补丁可改善SCORE对CVD死亡率的预测以及PCE对ASCVD的预测（c统计量变化分别为0.027[95%CI 0.018 - 0.036]和0.010[0.007 - 0.013]，分类净重新分类改善分别为0.080[0.032 - 0.127]和0.056[0.044 - 0.067]）。在极高风险CKD（如eGFR 30 - 44 ml/min/1.73m且蛋白尿≥30 mg/g）中，使用CKD补丁预测CVD死亡率与使用SCORE原始预测的风险比中位数（IQR）为2.64（1.89 - 3.40），在高风险CKD（如eGFR 45 - 59 ml/min/1.73m且蛋白尿30 - 299 mg/g）中为1.86（1.48 - 2.44），在中度风险CKD（如eGFR 60 - 89 ml/min/1.73m且蛋白尿30 - 299 mg/g）中为1.37（1.14 - 1.69），这表明SCORE对CKD的风险存在相当程度的低估。使用PCE对ASCVD的相应估计值分别为1.55（1.37 - 1.81）、1.24（1.10 - 1.54）和1.21（0.98 - 1.46）。
当有可用的CKD指标时，“CKD补丁”可用于根据CKD指标定量增强美国和欧洲主要指南中推荐的ASCVD和CVD死亡率风险预测方程。
美国国家肾脏基金会和美国国立糖尿病、消化和肾脏疾病研究所。
