Development and validation of a predictive model for diabetic kidney disease risk in patients with T2DM: a hospital data platform study - Takeaways - MDSpire

Development and validation of a predictive model for diabetic kidney disease risk in patients with T2DM: a hospital data platform study

  • By

  • ZhanLin Zhang

  • GuoXia Ma

  • MeiFang Ma

  • TianRong Guo

  • Yu Zhao

  • XiuSheng Cheng

  • Zhonglin Yan

  • ZhengMing Yin

  • Gangyi Wang

  • YongDong An

  • June 30, 2026

  • 0 min

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  • 1

    This study utilized LASSO regression to identify nine risk factors for diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM).

  • 2

    A nomogram prediction model was constructed to support early clinical screening of high-risk populations for DKD based on identified predictors.

  • 3

    The study included 23,152 patients with T2DM, with 21.68% diagnosed with DKD, demonstrating the prevalence of this complication.

  • 4

    The nomogram showed moderate discrimination with an AUC of 0.773 during internal validation, indicating its predictive capability.

  • 5

    Internal validation results suggest the model's potential as a decision-support tool, though multicenter external validation is needed before broader implementation.

Original Source(s)

Development and validation of a predictive model for diabetic kidney disease risk in patients with T2DM: a hospital data platform study

  • by ZhanLin Zhang, GuoXia Ma, MeiFang Ma, TianRong Guo, Yu Zhao, XiuSheng Cheng, Zhonglin Yan, ZhengMing Yin, Gangyi Wang, YongDong An

  • June 30, 2026

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