Department of Public Health, Mizan Aman College of Health Sciences, Mizan-Aman, Ethiopia.
Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
PLoS One. 2023 Aug 29;18(8):e0276472. doi: 10.1371/journal.pone.0276472. eCollection 2023.
Diabetic neuropathy is the most common complication in both Type-1 and Type-2 DM patients with more than one half of all patients developing nerve dysfunction in their lifetime. Although, risk prediction model was developed for diabetic neuropathy in developed countries, It is not applicable in clinical practice, due to poor data, methodological problems, inappropriately analyzed and reported. To date, no risk prediction model developed for diabetic neuropathy among DM in Ethiopia, Therefore, this study aimed prediction the risk of diabetic neuropathy among DM patients, used for guiding in clinical decision making for clinicians.
Development and validation of risk prediction model for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, 2005-2021.
A retrospective follow up study was conducted with a total of 808 DM patients were enrolled from January 1,2005 to December 30,2021 at two selected referral hospitals in Amhara regional state. Multi-stage sampling techniques were used and the data was collected by checklist from medical records by Kobo collect and exported to STATA version-17 for analysis. Lasso method were used to select predictors and entered to multivariable logistic regression with P-value<0.05 was used for nomogram development. Model performance was assessed by AUC and calibration plot. Internal validation was done through bootstrapping method and decision curve analysis was performed to evaluate net benefit of model.
The incidence proportion of diabetic neuropathy among DM patients was 21.29% (95% CI; 18.59, 24.25). In multivariable logistic regression glycemic control, other comorbidities, physical activity, hypertension, alcohol drinking, type of treatment, white blood cells and red blood cells count were statistically significant. Nomogram was developed, has discriminating power AUC; 73.2% (95% CI; 69.0%, 77.3%) and calibration test (P-value = 0.45). It was internally validated by bootstrapping method with discrimination performance 71.7 (95% CI; 67.2%, 75.9%). It had less optimism coefficient (0.015). To make nomogram accessible, mobile based tool were developed. In machine learning, classification and regression tree has discriminating performance of 70.2% (95% CI; 65.8%, 74.6%). The model had high net benefit at different threshold probabilities in both nomogram and classification and regression tree.
The developed nomogram and decision tree, has good level of accuracy and well calibration, easily individualized prediction of diabetic neuropathy. Both models had added net benefit in clinical practice and to be clinically applicable mobile based tool were developed.
糖尿病神经病变是 1 型和 2 型糖尿病患者最常见的并发症，超过一半的患者在其一生中会出现神经功能障碍。尽管已经为发达国家的糖尿病神经病变开发了风险预测模型，但由于数据不佳、方法学问题、分析和报告不当，该模型在临床实践中并不适用。迄今为止，还没有针对埃塞俄比亚糖尿病患者的糖尿病神经病变风险预测模型，因此，本研究旨在预测糖尿病患者中糖尿病神经病变的风险，为临床医生的临床决策提供指导。
在 2005 年至 2021 年期间，在埃塞俄比亚阿姆哈拉地区选定的转诊医院，开发和验证糖尿病患者中糖尿病神经病变的风险预测模型。
这是一项回顾性随访研究，共纳入了 808 名糖尿病患者，他们来自阿姆哈拉地区的两家选定转诊医院，时间为 2005 年 1 月 1 日至 2021 年 12 月 30 日。采用多阶段抽样技术，通过 Kobo 收集从病历中检查表收集数据，并将数据导出到 STATA 版本 17 进行分析。使用套索法选择预测因子，并将其输入多变量逻辑回归，P 值<0.05 用于列线图的开发。使用 AUC 和校准图评估模型性能。通过 bootstrap 方法进行内部验证，并通过决策曲线分析评估模型的净收益。
糖尿病患者中糖尿病神经病变的发生率为 21.29%（95%CI；18.59, 24.25）。多变量逻辑回归显示，血糖控制、其他合并症、身体活动、高血压、饮酒、治疗类型、白细胞和红细胞计数均有统计学意义。列线图得到了开发，具有判别能力 AUC；73.2%（95%CI；69.0%, 77.3%）和校准测试（P 值=0.45）。通过 bootstrap 方法进行内部验证，其判别性能为 71.7%（95%CI；67.2%, 75.9%）。它的最优系数较小（0.015）。为了使列线图易于使用，还开发了基于移动设备的工具。在机器学习中，分类和回归树的判别性能为 70.2%（95%CI；65.8%, 74.6%）。在不同的阈值概率下，列线图和分类与回归树都具有较高的净收益。
开发的列线图和决策树具有良好的准确性和校准水平，能够很好地个体化预测糖尿病神经病变。两种模型在临床实践中都具有额外的净收益，并开发了基于移动设备的临床适用工具。
