Scholarlink

Background <p>Overactive bladder (OAB) is a highly prevalent and burdensome complication of type 2 diabetes mellitus (T2DM), with a pathophysiology distinct from idiopathic OAB. Current management relies on symptom-based diagnosis, lacking tools for proactive risk stratification that reflect its multifactorial origins in neuropathy, microvascular injury, and inflammation.</p> Methods <p>In this retrospective cohort study, we developed and temporally validated a clinical prediction model using data from 1531 patients with T2DM. The model was derived via multivariable logistic regression with backward selection in a development cohort (n = 810, Jan 2018-Dec 2021) and tested in an independent validation cohort (n = 721, Jan 2022-Feb 2025). OAB was defined by the Overactive Bladder Symptom Score (OABSS). Model performance was assessed by discrimination (AUC), calibration, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) analysis elucidated variable contributions.</p> Results <p>Seven readily available clinical parameters were retained in the final model: diabetic peripheral neuropathy (DPN), HbA1c, urinary albumin-to-creatinine ratio (UACR) category, neutrophil-to-lymphocyte ratio (NLR), age, the Triglyceride-Glucose (TyG) Index, and post-void residual volume (PVR). The model demonstrated excellent discrimination (AUC 0.880 [0.844–0.917] in development; 0.858 [0.814–0.902] in validation) and was well-calibrated. Decision curve analysis confirmed clinical utility across a wide risk threshold range. SHAP analysis identified DPN as the strongest predictor, validating the model’s alignment with the underlying pathophysiological hierarchy.</p> Conclusion <p>We developed and validated a practical, pathophysiology-informed prediction model that accurately stratifies the risk of OAB in patients with T2DM. This tool integrates key domains of diabetic complications into a single assessment, enabling a shift towards proactive, personalized risk communication and targeted surveillance for high-risk individuals.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development and validation of a clinical prediction model for overactive bladder in type 2 diabetes: a pathophysiology-informed approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月23日

查看全文
  • Meiyuan Lv,
  • Guangxu Fu,
  • Dongfeng Yi,
  • Zhihua Zhang,
  • Liangbing Liu

摘要

Background

Overactive bladder (OAB) is a highly prevalent and burdensome complication of type 2 diabetes mellitus (T2DM), with a pathophysiology distinct from idiopathic OAB. Current management relies on symptom-based diagnosis, lacking tools for proactive risk stratification that reflect its multifactorial origins in neuropathy, microvascular injury, and inflammation.

Methods

In this retrospective cohort study, we developed and temporally validated a clinical prediction model using data from 1531 patients with T2DM. The model was derived via multivariable logistic regression with backward selection in a development cohort (n = 810, Jan 2018-Dec 2021) and tested in an independent validation cohort (n = 721, Jan 2022-Feb 2025). OAB was defined by the Overactive Bladder Symptom Score (OABSS). Model performance was assessed by discrimination (AUC), calibration, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) analysis elucidated variable contributions.

Results

Seven readily available clinical parameters were retained in the final model: diabetic peripheral neuropathy (DPN), HbA1c, urinary albumin-to-creatinine ratio (UACR) category, neutrophil-to-lymphocyte ratio (NLR), age, the Triglyceride-Glucose (TyG) Index, and post-void residual volume (PVR). The model demonstrated excellent discrimination (AUC 0.880 [0.844–0.917] in development; 0.858 [0.814–0.902] in validation) and was well-calibrated. Decision curve analysis confirmed clinical utility across a wide risk threshold range. SHAP analysis identified DPN as the strongest predictor, validating the model’s alignment with the underlying pathophysiological hierarchy.

Conclusion

We developed and validated a practical, pathophysiology-informed prediction model that accurately stratifies the risk of OAB in patients with T2DM. This tool integrates key domains of diabetic complications into a single assessment, enabling a shift towards proactive, personalized risk communication and targeted surveillance for high-risk individuals.