Januzzi James L Jr, Sattar Naveed, Vaduganathan Muthiah, Magaret Craig A, Rhyne Rhonda F, Liu Yuxi, Masson Serge, Butler Javed, Hansen Michael K
Cardiology Division, Baim Institute for Clinical Research, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 0211, USA.
Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA.
Cardiovasc Diabetol. 2025 May 15;24(1):213. doi: 10.1186/s12933-025-02779-5.
Individuals with diabetic kidney disease (DKD) often suffer cardiac and kidney events. We sought to develop an accurate means by which to stratify risk in DKD.
Clinical variables and biomarkers were evaluated for their ability to predict the adjudicated primary composite endpoint of CREDENCE (Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation) by 3 years. Using machine learning techniques, a parsimonious risk algorithm was developed.
The final model included age, body-mass index, systolic blood pressure, and concentrations of N-terminal pro-B type natriuretic peptide, high sensitivity cardiac troponin T, insulin-like growth factor binding protein-7 and growth differentiation factor-15. The model had an in-sample C-statistic of 0.80 (95% CI = 0.77-0.83; P < 0.001). Dividing results into low, medium and high risk categories, for each increase in level the hazard ratio increased by 3.43 (95% CI = 2.72-4.32; P < 0.001). Low risk scores had negative predictive value of 94%, while high risk scores had positive predictive value of 58%. Higher values were associated with shorter time to event (log rank P < 0.001). Rising values at 1 year predicted higher risk for subsequent DKD events. Canagliflozin treatment reduced score results by 1 year with consistent event reduction across risk levels. Accuracy of the risk model was validated in separate cohorts from CREDENCE and the generally lower risk Canagliflozin Cardiovascular Assessment Study.
We describe a validated risk algorithm that accurately predicts cardio-kidney outcomes across a broad range of baseline risk.
CREDENCE (Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation; NCT02065791) and CANVAS (Canagliflozin Cardiovascular Assessment Study; NCT01032629/NCT01989754).
糖尿病肾病（DKD）患者常发生心脏和肾脏事件。我们试图开发一种准确的方法来对DKD患者的风险进行分层。
评估临床变量和生物标志物在3年内预测CREDENCE（卡格列净与糖尿病肾病临床评估中的肾脏事件）判定的主要复合终点的能力。使用机器学习技术，开发了一种简约风险算法。
最终模型包括年龄、体重指数、收缩压以及N末端B型脑钠肽前体、高敏心肌肌钙蛋白T、胰岛素样生长因子结合蛋白7和生长分化因子15的浓度。该模型的样本内C统计量为0.80（95%CI = 0.77 - 0.83；P < 0.001）。将结果分为低、中、高风险类别，每升高一个级别，风险比增加3.43（95%CI = 2.72 - 4.32；P < 0.001）。低风险评分的阴性预测值为94%，而高风险评分的阳性预测值为58%。较高的值与较短的事件发生时间相关（对数秩检验P < 0.001）。1年时值的升高预测随后发生DKD事件的风险更高。卡格列净治疗1年后降低了评分结果，且在各风险水平上事件减少情况一致。风险模型的准确性在来自CREDENCE以及风险普遍较低的卡格列净心血管评估研究的独立队列中得到验证。
我们描述了一种经过验证的风险算法，该算法能够准确预测广泛基线风险范围内的心脏 - 肾脏结局。
CREDENCE（卡格列净与糖尿病肾病临床评估中的肾脏事件；NCT02065791）和CANVAS（卡格列净心血管评估研究；NCT01032629/NCT01989754）。
