Zhou Ting, Wang Yiyun, Shen Li, Li Xiaomei, Jiao Qiong, Li Ze, Jia Junjie, He Li, Zhang Qunzi, Wang Niansong, Fan Ying
Department of Nephrology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Department of Emergency, Shanghai United Family Hospital, Shanghai, China.
Kidney Dis (Basel). 2021 Sep 3;8(1):93-101. doi: 10.1159/000518222. eCollection 2022 Jan.
Clinical indicators or pathological features alone cannot reliably predict renal survival in patients with biopsy-confirmed diabetic nephropathy (DN). Therefore, this analysis sought to develop and validate a predictive model incorporating both clinical and pathological markers to predict renal outcomes in patients with biopsy-confirmed DN.
A predictive nomogram was developed based upon data pertaining to 194 patients with biopsy-confirmed DN. The prognostic relevance of individual clinicopathological variables was assessed through univariate and multivariate Cox regression analyses. A prognostic nomogram was then developed and validated based upon concordance (C)-index values and calibration curves. Internal validation was conducted through bootstrap resampling, while the clinical utility of this model was assessed via a decision curve analysis (DCA) approach.
Nephrotic-range 24-h proteinuria, late-stage CKD, glomerular classification III-IV, and IFTA score 2-3 were all identified as independent predictors of poor renal outcomes in DN patients and were incorporated into our final nomogram. Calibration curves revealed good agreement between predicted and actual 3- and 5-year renal survival in DN patients with the C-index value for this nomogram at 0.845 (95% CI: 0.826-0.864). DCA revealed that our nomogram was superior to models based solely upon clinical indicators.
A predictive nomogram incorporating clinical and pathological indicators was developed and validated for the prediction of renal survival outcomes in patients with biopsy-confirmed DN. This model will be of value to clinicians, as it can serve as an easy-to-use and reliable tool for physicians to guide patient management based on individualized risk.
仅靠临床指标或病理特征无法可靠地预测经活检确诊的糖尿病肾病（DN）患者的肾脏生存率。因此，本分析旨在开发并验证一个整合临床和病理标志物的预测模型，以预测经活检确诊的DN患者的肾脏结局。
基于194例经活检确诊的DN患者的数据开发了一个预测列线图。通过单因素和多因素Cox回归分析评估各个临床病理变量的预后相关性。然后根据一致性（C）指数值和校准曲线开发并验证了一个预后列线图。通过自助重采样进行内部验证，同时通过决策曲线分析（DCA）方法评估该模型的临床实用性。
肾病范围的24小时蛋白尿、晚期慢性肾脏病、肾小球分级III-IV级以及间质纤维化和肾小管萎缩（IFTA）评分2-3均被确定为DN患者肾脏不良结局的独立预测因素，并被纳入我们最终的列线图。校准曲线显示，DN患者预测的和实际的3年及5年肾脏生存率之间具有良好的一致性，该列线图的C指数值为0.845（95%CI：0.826-0.864）。DCA显示，我们的列线图优于仅基于临床指标的模型。
开发并验证了一个整合临床和病理指标的预测列线图，用于预测经活检确诊的DN患者的肾脏生存结局。该模型对临床医生具有重要价值，因为它可以作为一种易于使用且可靠的工具，帮助医生根据个体风险指导患者管理。
