酮症倾向2型糖尿病多变量风险预测模型的建立与验证
Abstract 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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-09-01
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| Series: | Journal of Diabetes |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/1753-0407.13407 |
| _version_ | 1797680680601124864 |
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| author | Jia Zheng Shiyi Shen Hanwen Xu Yu Zhao Ye Hu Yubo Xing Yingxiang Song Xiaohong Wu |
| author_facet | Jia Zheng Shiyi Shen Hanwen Xu Yu Zhao Ye Hu Yubo Xing Yingxiang Song Xiaohong Wu |
| author_sort | Jia Zheng |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-11T23:33:33Z |
| format | Article |
| id | doaj.art-2d6d1ef2af744c1b9941dcfe03951a3e |
| institution | Directory Open Access Journal |
| issn | 1753-0393 1753-0407 |
| language | English |
| last_indexed | 2024-03-11T23:33:33Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes |
| spelling | doaj.art-2d6d1ef2af744c1b9941dcfe03951a3e2023-09-20T06:15:15ZengWileyJournal of Diabetes1753-03931753-04072023-09-0115975376410.1111/1753-0407.13407酮症倾向2型糖尿病多变量风险预测模型的建立与验证Jia Zheng0Shiyi Shen1Hanwen Xu2Yu Zhao3Ye Hu4Yubo Xing5Yingxiang Song6Xiaohong Wu7Geriatric Medicine Center, Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Department of Endocrinology Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College) Hangzhou People's Republic of ChinaGeriatric Medicine Center, Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Department of Endocrinology Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College) Hangzhou People's Republic of ChinaGeriatric Medicine Center, Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Department of Endocrinology Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College) Hangzhou People's Republic of ChinaGeriatric Medicine Center, Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Department of Endocrinology Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College) Hangzhou People's Republic of ChinaGeriatric Medicine Center, Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Department of Endocrinology Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College) Hangzhou People's Republic of ChinaGeriatric Medicine Center, Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Department of Endocrinology Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College) Hangzhou People's Republic of ChinaGeriatric Medicine Center, Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Department of Endocrinology Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College) Hangzhou People's Republic of ChinaGeriatric Medicine Center, Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Department of Endocrinology Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College) Hangzhou People's Republic of ChinaAbstract 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.https://doi.org/10.1111/1753-0407.13407临床特征酮症倾向2型糖尿病列线图预测模型 |
| spellingShingle | Jia Zheng Shiyi Shen Hanwen Xu Yu Zhao Ye Hu Yubo Xing Yingxiang Song Xiaohong Wu 酮症倾向2型糖尿病多变量风险预测模型的建立与验证 Journal of Diabetes 临床特征 酮症倾向2型糖尿病 列线图 预测模型 |
| title | 酮症倾向2型糖尿病多变量风险预测模型的建立与验证 |
| title_full | 酮症倾向2型糖尿病多变量风险预测模型的建立与验证 |
| title_fullStr | 酮症倾向2型糖尿病多变量风险预测模型的建立与验证 |
| title_full_unstemmed | 酮症倾向2型糖尿病多变量风险预测模型的建立与验证 |
| title_short | 酮症倾向2型糖尿病多变量风险预测模型的建立与验证 |
| title_sort | 酮症倾向2型糖尿病多变量风险预测模型的建立与验证 |
| topic | 临床特征 酮症倾向2型糖尿病 列线图 预测模型 |
| url | https://doi.org/10.1111/1753-0407.13407 |
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