Establishment and external validation of an early warning model of diabetic peripheral neuropathy based on random forest and logistic regression.

IF 2.8 3区 医学 Q3 ENDOCRINOLOGY & METABOLISM
Lujie Wang, Jiajie Li, Yixuan Lin, Huilun Yuan, Zhaohui Fang, Aihua Fei, Guoming Shen, Aijuan Jiang
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引用次数: 0

Abstract

Objective: The primary objective of this study was to investigate the risk factors for diabetic peripheral neuropathy (DPN) and to establish an early diagnostic prediction model for its onset, based on clinical data and biochemical indices.

Methods: Retrospective data were collected from 1,446 diabetic patients at the First Affiliated Hospital of Anhui University of Chinese Medicine and were split into training and internal validation sets in a 7:3 ratio. Additionally, 360 diabetic patients from the Second Affiliated Hospital were used as an external validation cohort. Feature selection was conducted within the training set, where univariate logistic regression identified variables with a p-value < 0.05, followed by backward elimination to construct the logistic regression model. Concurrently, the random forest algorithm was applied to the training set to identify the top 10 most important features, with hyperparameter optimization performed via grid search combined with cross-validation. Model performance was evaluated using ROC curves, decision curve analysis, and calibration curves. Model fit was assessed using the Hosmer-Lemeshow test, followed by Brier Score evaluation for the random forest model. Ten-fold cross-validation was employed for further validation, and SHAP analysis was conducted to enhance model interpretability.

Results: A nomogram model was developed using logistic regression with key features: limb numbness, limb pain, diabetic retinopathy, diabetic kidney disease, urinary protein, diastolic blood pressure, white blood cell count, HbA1c, and high-density lipoprotein cholesterol. The model achieved AUCs of 0.91, 0.88, and 0.88 for the training, validation, and test sets, respectively, with a mean AUC of 0.902 across 10-fold cross-validation. Hosmer-Lemeshow test results showed p-values of 0.595, 0.418, and 0.126 for the training, validation, and test sets, respectively. The random forest model demonstrated AUCs of 0.95, 0.88, and 0.88 for the training, validation, and test sets, respectively, with a mean AUC of 0.886 across 10-fold cross-validation. The Brier score indicates a good calibration level, with values of 0.104, 0.143, and 0.142 for the training, validation, and test sets, respectively.

Conclusion: The developed nomogram exhibits promise as an effective tool for the diagnosis of diabetic peripheral neuropathy in clinical settings.

基于随机森林和逻辑回归的糖尿病周围神经病变早期预警模型的建立和外部验证。
研究目的本研究的主要目的是调查糖尿病周围神经病变(DPN)的危险因素,并根据临床数据和生化指标建立DPN发病的早期诊断预测模型:方法:收集安徽中医药大学第一附属医院 1446 名糖尿病患者的回顾性数据,按 7:3 的比例分成训练集和内部验证集。此外,第二附属医院的 360 名糖尿病患者被用作外部验证队列。特征选择在训练集中进行,其中单变量逻辑回归确定了具有 p 值的变量 结果:利用逻辑回归建立了一个提名图模型,主要特征包括:肢体麻木、肢体疼痛、糖尿病视网膜病变、糖尿病肾病、尿蛋白、舒张压、白细胞计数、HbA1c 和高密度脂蛋白胆固醇。该模型在训练集、验证集和测试集上的 AUC 分别为 0.91、0.88 和 0.88,在 10 倍交叉验证中的平均 AUC 为 0.902。Hosmer-Lemeshow 检验结果显示,训练集、验证集和测试集的 p 值分别为 0.595、0.418 和 0.126。随机森林模型在训练集、验证集和测试集上的 AUC 分别为 0.95、0.88 和 0.88,在 10 倍交叉验证中的平均 AUC 为 0.886。布赖尔评分显示了良好的校准水平,训练集、验证集和测试集的评分分别为 0.104、0.143 和 0.142:结论:所开发的提名图有望成为临床诊断糖尿病周围神经病变的有效工具。
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来源期刊
BMC Endocrine Disorders
BMC Endocrine Disorders ENDOCRINOLOGY & METABOLISM-
CiteScore
4.40
自引率
0.00%
发文量
280
审稿时长
>12 weeks
期刊介绍: BMC Endocrine Disorders is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of endocrine disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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