دورية أكاديمية
Non-lab and semi-lab algorithms for screening undiagnosed diabetes: A cross-sectional studyResearch in context
| العنوان: | Non-lab and semi-lab algorithms for screening undiagnosed diabetes: A cross-sectional studyResearch in context |
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| المؤلفون: | Wei Li, Bo Xie, Shanhu Qiu, Xin Huang, Juan Chen, Xinling Wang, Hong Li, Qingyun Chen, Qing Wang, Ping Tu, Lihui Zhang, Sunjie Yan, Kaili Li, Jimilanmu Maimaitiming, Xin Nian, Min Liang, Yan Wen, Jiang Liu, Mian Wang, Yongze Zhang, Li Ma, Hang Wu, Xuyi Wang, Xiaohang Wang, Jingbao Liu, Min Cai, Zhiyao wang, Lin Guo, Fangqun Chen, Bei Wang, Sandberg Monica, Per-Ola Carlsson, Zilin Sun |
| المصدر: | EBioMedicine, Vol 35, Iss , Pp 307-316 (2018) |
| بيانات النشر: | Elsevier, 2018. |
| سنة النشر: | 2018 |
| المجموعة: | LCC:Medicine LCC:Medicine (General) |
| مصطلحات موضوعية: | Medicine, Medicine (General), R5-920 |
| الوصف: | Background: The terrifying undiagnosed rate and high prevalence of diabetes have become a public emergency. A high efficiency and cost-effective early recognition method is urgently needed. We aimed to generate innovative, user-friendly nomograms that can be applied for diabetes screening in different ethnic groups in China using the non-lab or noninvasive semi-lab data. Methods: This multicenter, multi-ethnic, population-based, cross-sectional study was conducted in eight sites in China by enrolling subjects aged 20–70. Sociodemographic and anthropometric characteristics were collected. Blood and urine samples were obtained 2 h following a standard 75 g glucose solution. In the final analysis, 10,794 participants were included and randomized into model development (n = 8096) and model validation (n = 2698) group with a ratio of 3:1. Nomograms were developed by the stepwise binary logistic regression. The nomograms were validated internally by a bootstrap sampling method in the model development set and externally in the model validation set. The area under the receiver operating characteristic curve (AUC) was used to assess the screening performance of the nomograms. Decision curve analysis was applied to calculate the net benefit of the screening model. Results: The overall prevalence of undiagnosed diabetes was 9.8% (1059/10794) according to ADA criteria. The non-lab model revealed that gender, age, body mass index, waist circumference, hypertension, ethnicities, vegetable daily consumption and family history of diabetes were independent risk factors for diabetes. By adding 2 h post meal glycosuria qualitative to the non-lab model, the semi-lab model showed an improved Akaike information criterion (AIC: 4506 to 3580). The AUC of the semi-lab model was statistically larger than the non-lab model (0.868 vs 0.763, P |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2352-3964 05600626 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2352396418302950; https://doaj.org/toc/2352-3964 |
| DOI: | 10.1016/j.ebiom.2018.08.009 |
| URL الوصول: | https://doaj.org/article/056006266a904df0a831825d7de9a67c |
| رقم الأكسشن: | edsdoj.056006266a904df0a831825d7de9a67c |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text via ClinicalKey 
Full Text Finder | تدمد: | 23523964 05600626 |
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| DOI: | 10.1016/j.ebiom.2018.08.009 |