Tangri Navdeep, Ferguson Thomas, Leon Silvia J, Anker Stefan D, Filippatos Gerasimos, Pitt Bertram, Rossing Peter, Ruilope Luis M, Farjat Alfredo E, Farag Youssef M K, Schloemer Patrick, Lawatscheck Robert, Rohwedder Katja, Bakris George L
Department of Internal Medicine, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada.
Seven Oaks Hospital Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada.
Clin Kidney J. 2024 Mar 6;17(4):sfae052. doi: 10.1093/ckj/sfae052. eCollection 2024 Apr.
Chronic kidney disease (CKD) affects >800 million individuals worldwide and is often underrecognized. Early detection, identification and treatment can delay disease progression. Klinrisk is a proprietary CKD progression risk prediction model based on common laboratory data to predict CKD progression. We aimed to externally validate the Klinrisk model for prediction of CKD progression in FIDELITY (a prespecified pooled analysis of two finerenone phase III trials in patients with CKD and type 2 diabetes). In addition, we sought to identify evidence of an interaction between treatment and risk.
The validation cohort included all participants in FIDELITY up to 4 years. The primary and secondary composite outcomes included a ≥40% decrease in estimated glomerular filtration rate (eGFR) or kidney failure, and a ≥57% decrease in eGFR or kidney failure. Prediction discrimination was calculated using area under the receiver operating characteristic curve (AUC). Calibration plots were calculated by decile comparing observed with predicted risk.
At time horizons of 2 and 4 years, 993 and 1795 patients experienced a primary outcome event, respectively. The model predicted the primary outcome accurately with an AUC of 0.81 for 2 years and 0.86 for 4 years. Calibration was appropriate at both 2 and 4 years, with Brier scores of 0.067 and 0.115, respectively. No evidence of interaction between treatment and risk was identified for the primary composite outcome ( = .31).
Our findings demonstrate the accuracy and utility of a laboratory-based prediction model for early identification of patients at the highest risk of CKD progression.
慢性肾脏病（CKD）影响着全球超过8亿人，且常常未被充分认识。早期检测、识别和治疗可延缓疾病进展。Klinrisk是一种基于常见实验室数据的专有的CKD进展风险预测模型，用于预测CKD进展。我们旨在对Klinrisk模型进行外部验证，以预测FIDELITY（对两项非奈利酮治疗CKD和2型糖尿病患者的III期试验进行的预先指定的汇总分析）中CKD的进展情况。此外，我们试图确定治疗与风险之间相互作用的证据。
验证队列包括FIDELITY中随访4年的所有参与者。主要和次要复合结局包括估计肾小球滤过率（eGFR）降低≥40%或肾衰竭，以及eGFR降低≥57%或肾衰竭。使用受试者工作特征曲线下面积（AUC）计算预测辨别力。通过十分位数比较观察到的风险与预测风险来计算校准图。
在2年和4年的时间范围内，分别有993例和1795例患者发生主要结局事件。该模型对主要结局的预测准确，2年时AUC为0.81，4年时为0.86。2年和4年时校准均合适，Brier评分分别为0.067和0.115。对于主要复合结局，未发现治疗与风险之间相互作用的证据（P = 0.31）。
我们的研究结果证明了一种基于实验室的预测模型在早期识别CKD进展风险最高患者方面的准确性和实用性。
