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基于中国一项回顾性队列研究的2型糖尿病肾病进展预测列线图和风险表的开发与外部验证

Development and External Validation of a Nomogram and a Risk Table for Prediction of Type 2 Diabetic Kidney Disease Progression Based on a Retrospective Cohort Study in China.

作者信息

Gao Yue-Ming, Feng Song-Tao, Yang Yang, Li Zuo-Lin, Wen Yi, Wang Bin, Lv Lin-Li, Xing Guo-Lan, Liu Bi-Cheng

机构信息

Institute of Nephrology, Zhongda Hospital, Southeast University School of Medicine, Nanjing, 210009, People's Republic of China.

Institute of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, People's Republic of China.

出版信息

Diabetes Metab Syndr Obes. 2022 Mar 14;15:799-811. doi: 10.2147/DMSO.S352154. eCollection 2022.

Abstract

PURPOSE

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide. Risk assessment provides information about patient prognosis, contributing to the risk stratification of patients and the rational allocation of medical resources. We aimed to develop a model for individualized prediction of renal function decline in patients with type 2 DKD (T2DKD).

PATIENTS AND METHODS

In a retrospective observational study, we followed 307 T2DKD patients and evaluated the determinants of 1) risk of doubling in serum creatinine (Scr), 2) risk of eGFR<15 mL/min/1.73m using potential risk factors at baseline. A prediction model represented by a nomogram and a risk table was developed using Cox regression and externally validated in another cohort with 206 T2DKD patients. The discrimination and calibration of the prediction model were evaluated by the concordance index (C-index) and calibration curve, respectively.

RESULTS

Four predictors were selected to establish the final model: Scr, urinary albumin/creatinine ratio, plasma albumin, and insulin treatment. The nomogram achieved satisfactory prediction performance, with a C-index of 0.791 [95% confidence interval (CI) 0.762-0.820] in the derivation cohort and 0.793 (95% CI 0.746-0.840) in the external validation cohort. Then, all predictors were scored according to their weightings. A risk table with the highest score of 11.5 was developed. The C-index of the risk table was 0.764 (95% CI: 0.731-0.797), which was similar to the external validation cohort (0.763; 95% CI: 0.714-0.812). Additionally, the patients were divided into two groups based on the risk table, and significant differences in the probability of outcome events were observed between the high-risk (score >2) and low-risk (score ≤2) groups in the derivation and external validation cohorts ( < 0.001).

CONCLUSION

The nomogram and the risk table using readily available clinical parameters could be new tools for bedside prediction of renal function decline in T2DKD patients.

摘要

目的

糖尿病肾病(DKD)是全球终末期肾病的主要原因。风险评估可提供有关患者预后的信息,有助于患者的风险分层和医疗资源的合理分配。我们旨在开发一种模型,用于对2型糖尿病肾病(T2DKD)患者的肾功能下降进行个体化预测。

患者与方法

在一项回顾性观察研究中,我们对307例T2DKD患者进行了随访,并使用基线时的潜在风险因素评估了以下因素:1)血清肌酐(Scr)翻倍的风险;2)估算肾小球滤过率(eGFR)<15 mL/min/1.73m²的风险。使用Cox回归开发了由列线图和风险表表示的预测模型,并在另一组206例T2DKD患者中进行了外部验证。分别通过一致性指数(C-index)和校准曲线评估预测模型的辨别力和校准度。

结果

选择了四个预测因子来建立最终模型:Scr、尿白蛋白/肌酐比值、血浆白蛋白和胰岛素治疗。列线图实现了令人满意的预测性能,在推导队列中的C-index为0.791[95%置信区间(CI)0.762-0.820],在外部验证队列中的C-index为0.793(95%CI 0.746-0.840)。然后,根据其权重对所有预测因子进行评分。制定了一个最高评分为11.5的风险表。风险表的C-index为0.764(95%CI:0.731-0.797),与外部验证队列(0.763;95%CI:0.714-0.812)相似。此外,根据风险表将患者分为两组,在推导队列和外部验证队列中,高风险(评分>2)组和低风险(评分≤2)组之间观察到结局事件概率的显著差异(<0.001)。

结论

使用易于获得的临床参数的列线图和风险表可能是床边预测T2DKD患者肾功能下降的新工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b16/8933626/e9a970f1d210/DMSO-15-799-g0001.jpg

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