{"title":"Development and validation of a multivariable risk prediction model for identifying ketosis-prone type 2 diabetes 酮症倾向2型糖尿病多变量风险预测模型的建立与验证","authors":"Jia Zheng, Shiyi Shen, Hanwen Xu, Yu Zhao, Ye Hu, Yubo Xing, Yingxiang Song, Xiaohong Wu","doi":"10.1111/1753-0407.13407","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>To develop and validate a multivariable risk prediction model for ketosis-prone type 2 diabetes mellitus (T2DM) based on clinical characteristics.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>A total of 964 participants newly diagnosed with T2DM were enrolled in the modeling and validation cohort. Baseline clinical data were collected and analyzed. Multivariable logistic regression analysis was performed to select independent risk factors, develop the prediction model, and construct the nomogram. The model's reliability and validity were checked using the receiver operating characteristic curve and the calibration curve.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>A high morbidity of ketosis-prone T2DM was observed (20.2%), who presented as lower age and fasting C-peptide, and higher free fatty acids, glycated hemoglobin A<sub>1c</sub> and urinary protein. Based on these five independent influence factors, we developed a risk prediction model for ketosis-prone T2DM and constructed the nomogram. Areas under the curve of the modeling and validation cohorts were 0.806 (95% confidence interval [CI]: 0.760–0.851) and 0.856 (95% CI: 0.803–0.908). The calibration curves that were both internally and externally checked indicated that the projected results were reasonably close to the actual values.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Our study provided an effective clinical risk prediction model for ketosis-prone T2DM, which could help for precise classification and management.</p>\n </section>\n </div>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"15 9","pages":"753-764"},"PeriodicalIF":3.0000,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.13407","citationCount":"0","resultStr":"{\"title\":\"Development and validation of a multivariable risk prediction model for identifying ketosis-prone type 2 diabetes\\n 酮症倾向2型糖尿病多变量风险预测模型的建立与验证\",\"authors\":\"Jia Zheng, Shiyi Shen, Hanwen Xu, Yu Zhao, Ye Hu, Yubo Xing, Yingxiang Song, Xiaohong Wu\",\"doi\":\"10.1111/1753-0407.13407\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>To develop and validate a multivariable risk prediction model for ketosis-prone type 2 diabetes mellitus (T2DM) based on clinical characteristics.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>A total of 964 participants newly diagnosed with T2DM were enrolled in the modeling and validation cohort. Baseline clinical data were collected and analyzed. Multivariable logistic regression analysis was performed to select independent risk factors, develop the prediction model, and construct the nomogram. The model's reliability and validity were checked using the receiver operating characteristic curve and the calibration curve.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>A high morbidity of ketosis-prone T2DM was observed (20.2%), who presented as lower age and fasting C-peptide, and higher free fatty acids, glycated hemoglobin A<sub>1c</sub> and urinary protein. Based on these five independent influence factors, we developed a risk prediction model for ketosis-prone T2DM and constructed the nomogram. Areas under the curve of the modeling and validation cohorts were 0.806 (95% confidence interval [CI]: 0.760–0.851) and 0.856 (95% CI: 0.803–0.908). The calibration curves that were both internally and externally checked indicated that the projected results were reasonably close to the actual values.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Our study provided an effective clinical risk prediction model for ketosis-prone T2DM, which could help for precise classification and management.</p>\\n </section>\\n </div>\",\"PeriodicalId\":189,\"journal\":{\"name\":\"Journal of Diabetes\",\"volume\":\"15 9\",\"pages\":\"753-764\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2023-05-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.13407\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Diabetes\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/1753-0407.13407\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Diabetes","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/1753-0407.13407","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Development and validation of a multivariable risk prediction model for identifying ketosis-prone type 2 diabetes
酮症倾向2型糖尿病多变量风险预测模型的建立与验证
Background
To develop and validate a multivariable risk prediction model for ketosis-prone type 2 diabetes mellitus (T2DM) based on clinical characteristics.
Methods
A total of 964 participants newly diagnosed with T2DM were enrolled in the modeling and validation cohort. Baseline clinical data were collected and analyzed. Multivariable logistic regression analysis was performed to select independent risk factors, develop the prediction model, and construct the nomogram. The model's reliability and validity were checked using the receiver operating characteristic curve and the calibration curve.
Results
A high morbidity of ketosis-prone T2DM was observed (20.2%), who presented as lower age and fasting C-peptide, and higher free fatty acids, glycated hemoglobin A1c and urinary protein. Based on these five independent influence factors, we developed a risk prediction model for ketosis-prone T2DM and constructed the nomogram. Areas under the curve of the modeling and validation cohorts were 0.806 (95% confidence interval [CI]: 0.760–0.851) and 0.856 (95% CI: 0.803–0.908). The calibration curves that were both internally and externally checked indicated that the projected results were reasonably close to the actual values.
Conclusions
Our study provided an effective clinical risk prediction model for ketosis-prone T2DM, which could help for precise classification and management.
期刊介绍：
Journal of Diabetes (JDB) devotes itself to diabetes research, therapeutics, and education. It aims to involve researchers and practitioners in a dialogue between East and West via all aspects of epidemiology, etiology, pathogenesis, management, complications and prevention of diabetes, including the molecular, biochemical, and physiological aspects of diabetes. The Editorial team is international with a unique mix of Asian and Western participation.
The Editors welcome submissions in form of original research articles, images, novel case reports and correspondence, and will solicit reviews, point-counterpoint, commentaries, editorials, news highlights, and educational content.
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