A prediction model for diabetes complications using the Kokuho Database and its application to public health services in Japan

IF 3 3区 医学 Q2 ENDOCRINOLOGY & METABOLISM
Shumpei Chiba, Takaaki Itoga, Kazuo Asada, Daisuke Yabe
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Abstract

Aims/Introduction

This study aimed to improve efficiency of participant selection in Japan's Specific Health Guidance (SHG) program by developing a model to predict 3-year risk of diabetes complications. The targeted complications included macrovascular diseases (ischemic heart disease and cerebrovascular disease), microvascular diseases (diabetic nephropathy, diabetic neuropathy, diabetic retinopathy), and chronic kidney disease (CKD).

Materials and Methods

We utilized the Kokuho Database to analyze individuals in Saga Prefecture who underwent Specific Health Checkups from 2016 to 2019 without diabetes complications at baseline. To evaluate risk of the complications across all examinees, we excluded medicated individuals ineligible for SHG, while including those without diabetes in the prediction cohort. The outcomes of diabetes complications were derived from claims data. Explanatory variables included health checkup results, questionnaire responses, medical diagnoses, and prescription records. The predictive model was constructed using logistic regression, and its performance was evaluated using the Area Under the Curve (AUC) from fivefold cross-validation.

Results

Through model optimization techniques, including stratification, the incorporation of quadratic terms, and variable selection, the AUC exceeded 0.7 for all conditions. Notably, the AUC for microvascular complications and CKD surpassed 0.8, indicating high predictive accuracy. The model identified a higher risk among individuals who met the health guidance criteria established by the Ministry of Health, Labour and Welfare, demonstrating alignment with existing standards.

Conclusions

This predictive model has potential to enhance the targeting process for health guidance in Japan, enabling more timely medical intervention. Its implementation could significantly contribute to the prevention of severe diabetes complications through earlier detection and treatment.

Abstract Image

利用Kokuho数据库的糖尿病并发症预测模型及其在日本公共卫生服务中的应用
目的/简介:本研究旨在通过建立预测3年糖尿病并发症风险的模型,提高日本特定健康指导(SHG)项目参与者选择的效率。目标并发症包括大血管疾病(缺血性心脏病和脑血管疾病)、微血管疾病(糖尿病肾病、糖尿病神经病变、糖尿病视网膜病变)和慢性肾脏疾病(CKD)。材料和方法:我们利用Kokuho数据库分析了佐贺县2016年至2019年接受特定健康检查且基线时无糖尿病并发症的个体。为了评估所有考生的并发症风险,我们排除了不符合SHG条件的药物个体,同时在预测队列中包括了那些没有糖尿病的人。糖尿病并发症的结果来源于索赔数据。解释变量包括健康检查结果、问卷回答、医疗诊断和处方记录。采用logistic回归构建预测模型,并采用五重交叉验证的曲线下面积(AUC)对其性能进行评价。结果:通过分层、纳入二次项、变量选择等模型优化技术,各工况下AUC均超过0.7。值得注意的是,微血管并发症和CKD的AUC超过0.8,表明预测精度很高。该模型确定,符合厚生劳动省制定的健康指导标准的个人的风险较高,表明符合现有标准。结论:该预测模型有可能提高日本健康指导的靶向过程,使医疗干预更加及时。它的实施可以通过早期发现和治疗大大有助于预防严重的糖尿病并发症。
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来源期刊
Journal of Diabetes Investigation
Journal of Diabetes Investigation ENDOCRINOLOGY & METABOLISM-
CiteScore
6.50
自引率
9.40%
发文量
218
审稿时长
6-12 weeks
期刊介绍: Journal of Diabetes Investigation is your core diabetes journal from Asia; the official journal of the Asian Association for the Study of Diabetes (AASD). The journal publishes original research, country reports, commentaries, reviews, mini-reviews, case reports, letters, as well as editorials and news. Embracing clinical and experimental research in diabetes and related areas, the Journal of Diabetes Investigation includes aspects of prevention, treatment, as well as molecular aspects and pathophysiology. Translational research focused on the exchange of ideas between clinicians and researchers is also welcome. Journal of Diabetes Investigation is indexed by Science Citation Index Expanded (SCIE).
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