7 results on '"Yuancheng Dai"'
Search Results
2. 2型糖尿病患者的随访频率和临床结局:一项基于多中心真实数据的前瞻性分析
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Qiubo Zhao, Hongwei Li, Qicheng Ni, Yuancheng Dai, Qidong Zheng, Yufan Wang, Tingyu Ke, Li Li, Dong Zhao, Qijuan Dong, Bangqun Ji, Juan Shi, Ying Peng, Yifei Zhang, Fengmei Xu, and Weiqing Wang
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2型糖尿病 ,随访频率 ,血糖控制 ,成本效益 ,Diseases of the endocrine glands. Clinical endocrinology ,RC648-665 - Abstract
Abstract Background To determine whether the follow‐up frequency for type 2 diabetes mellitus (T2DM) patients in the National Metabolic Management Centers (MMCs) leads to different clinical outcomes. Methods A total of 19 908 T2DM patients with at least 6 months of facility‐based follow‐up were recruited in MMCs between June 2017 and April 2021 and divided into lower‐frequency and higher‐frequency follow‐up (LFF and HFF) groups according to the median follow‐up frequency of 2.0 (interquartile range 1.2) times per year. Metabolic parameters at baseline and at the last follow‐up visit were analyzed. Multivariable linear regression models were performed to assess the relationship between follow‐up frequency and between‐group percentage changes, adjusting for the major covariables. Additional stratified analyses were conducted to evaluate the metabolic outcomes in the subgroups. Results The characteristics of the participants in the LFF and HFF groups were significantly different at baseline. Participants had significant improvements in multiple metabolic parameters after follow‐up. Patients with HFF showed significantly greater decrease in percentage changes of fasting blood glucose (−4.95% ± 37.96% vs −2.21% ± 43.08%, P 9%) at baseline (P for interaction more...
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- 2022
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3. 中国成年2型糖尿病患者的肾脏疾病参数、代谢目标实现情况和动脉硬化风险
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Chen Xu, Li Li, Juan Shi, Bangqun Ji, Qidong Zheng, Yufan Wang, Tingyu Ke, Dong Zhao, Yuancheng Dai, Fengmei Xu, Ying Peng, Yifei Zhang, Qijuan Dong, and Weiqing Wang
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2型糖尿病 ,尿白蛋白/肌酐比值(UACR) ,估计肾小球滤过率(eGFR) ,臂‐踝脉搏波传导速度(BaPWV) ,心血管疾病 ,代谢目标 ,Diseases of the endocrine glands. Clinical endocrinology ,RC648-665 - Abstract
Abstract Background To investigate the arterial stiffness (AS) risk within urinary albumin‐to‐creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) categories and the joint effect between kidney disease parameters and metabolic goal achievement on AS risk in adult people with type 2 diabetes (T2D). Methods A total of 27 439 Chinese participants with T2D from 10 National Metabolic Management Centers (MMC) were categorized into four albuminuria/decreased eGFR groups. The criteria for decreased eGFR and AS were eGFR the 75th percentile (1770.0 cm/s). Three metabolic goals were defined as glycated hemoglobin more...
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- 2022
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4. 基于多中心前瞻性真实世界数据的基础胰岛素和预混合胰岛素对2型糖尿病患者血糖控制的影响研究
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Ying Peng, Peihong Xu, Juan Shi, Yifei Zhang, Shujie Wang, Qidong Zheng, Yufan Wang, Tingyu Ke, Li Li, Dong Zhao, Yuancheng Dai, Qijuan Dong, Bangqun Ji, Fengmei Xu, Weiqiong Gu, and Weiqing Wang
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型糖尿病 ,预混胰岛素 ,基础胰岛素 ,糖化血红蛋白 ,体重指数 ,Diseases of the endocrine glands. Clinical endocrinology ,RC648-665 - Abstract
Abstract Background To investigate the different efficacies of glycemic control between basal and premixed insulin in participants with type 2 diabetes (T2DM) when non‐insulin medications fail to reach treatment targets. Methods This was a prospective, large‐scale, real‐world study at 10 diabetes centers in China. Between June 2017 and June 2021, we enrolled 1104 T2DM participants initiated with either once‐daily basal insulin or twice‐daily premixed insulin when the glycosylated hemoglobin (HbA1c) control target was not met after at least two non‐insulin agents were administered. A Cox proportional hazards regression model adjusting for multiple influencing factors was performed to compare the different effects of basal and premixed insulin on reaching the HbA1c control target. Results At baseline, basal insulin (57.3%) was prescribed more frequently than premixed insulin (42.7%). Patients with a higher body mass index (BMI) or higher HbA1c levels were more likely to receive premixed insulin than basal insulin (both p more...
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- 2022
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5. 久坐时间与2型糖尿病患者颈动脉粥样硬化斑块的关系
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Jing Ke, Kun Li, Tingyu Ke, Xu Zhong, Qidong Zheng, Yufan Wang, Li Li, Yuancheng Dai, Qijuan Dong, Bangqun Ji, Fengmei Xu, Juan Shi, Ying Peng, Yifei Zhang, Dong Zhao, and Weiqing Wang
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久坐 ,颈动脉斑块 ,2型糖尿病 ,Diseases of the endocrine glands. Clinical endocrinology ,RC648-665 - Abstract
Abstract Background Atherosclerosis is a common complication in patients with type 2 diabetes (T2DM). Multiple factors are involved in the development and progress of atherosclerosis. We evaluated the association of weekly sedentary time (WST) with carotid plaque formation. Methods After data cleaning, a total of 26 664 participants with T2DM from 10 National Metabolic Management Centers (MMCs) from June 2017 to April 2021 were enrolled. Self‐reported lifestyle data including WST, sleeping time, smoking and drinking information, carotid artery ultrasound, and biochemical parameters were obtained. The independent association of carotid plaue with sedentary and other lifestyle behaviors was evaluated using multivariable logistic regression models, and odds ratio (OR) with 95% confidence interval (CI) were reported. Moreover, stratified analysis was conducted to demonstrate the influence of confounding factors. Results The mean (SD) age of the participants was 54.0 (11.6) years, and the median (interquartile range) WST was 35.0 (21.0, 42.0) h. Comparing with participants in the first tertile of WST, those in the second or third tertile of WST were younger and with a shorter duration of diabetes. There were positive associations between longer sedentary time and odds of artery plaque after adjustment, with corresponding ORs in the second and third tertile were 1.40 (95% CI: 1.31–1.50) and 1.67 (95% CI: 1.56–1.79), respectively. However, the effect of WST on plaque in patients aged 18–40 years old had no statistical significance; the p value in the third tertile was 0.163. Conclusions In summary, higher WST appears to be associated with higher prevalence of carotid plaque in patients with T2DM, especially in aged populations. more...
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- 2022
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6. Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study
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Li Yan, Yifei Zhang, Juan Shi, Qidong Zheng, Zilong Wang, Shengyin Jiao, Kexin Qiu, Ziheng Zhou, Dong Zhao, Hongwei Jiang, Yuancheng Dai, Benli Su, Pei Gu, Heng Su, Qin Wan, Yongde Peng, Tingyu Ke, Fengmei Xu, Qijuan Dong, Demetri Terzopoulos, and Xiaowei Ding more...
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Diseases of the endocrine glands. Clinical endocrinology ,RC648-665 - Abstract
Introduction Early screening for diabetic retinopathy (DR) with an efficient and scalable method is highly needed to reduce blindness, due to the growing epidemic of diabetes. The aim of the study was to validate an artificial intelligence-enabled DR screening and to investigate the prevalence of DR in adult patients with diabetes in China.Research design and methods The study was prospectively conducted at 155 diabetes centers in China. A non-mydriatic, macula-centered fundus photograph per eye was collected and graded through a deep learning (DL)-based, five-stage DR classification. Images from a randomly selected one-third of participants were used for the DL algorithm validation.Results In total, 47 269 patients (mean (SD) age, 54.29 (11.60) years) were enrolled. 15 805 randomly selected participants were reviewed by a panel of specialists for DL algorithm validation. The DR grading algorithms had a 83.3% (95% CI: 81.9% to 84.6%) sensitivity and a 92.5% (95% CI: 92.1% to 92.9%) specificity to detect referable DR. The five-stage DR classification performance (concordance: 83.0%) is comparable to the interobserver variability of specialists (concordance: 84.3%). The estimated prevalence in patients with diabetes detected by DL algorithm for any DR, referable DR and vision-threatening DR were 28.8% (95% CI: 28.4% to 29.3%), 24.4% (95% CI: 24.0% to 24.8%) and 10.8% (95% CI: 10.5% to 11.1%), respectively. The prevalence was higher in female, elderly, longer diabetes duration and higher glycated hemoglobin groups.Conclusion This study performed, a nationwide, multicenter, DL-based DR screening and the results indicated the importance and feasibility of DR screening in clinical practice with this system deployed at diabetes centers.Trial registration number NCT04240652. more...
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- 2020
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7. Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study
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Qijuan Dong, Zilong Wang, Dong Zhao, Weiqing Wang, Juan Shi, Xun Xu, Jianjun Liu, Ying Peng, Yuancheng Dai, Yongde Peng, Zhiyun Zhao, Ling Hu, Heng Su, Fengmei Xu, Hongwei Jiang, Ziheng Zhou, Pei Gu, Kexin Qiu, Lei Chen, Yifei Zhang, Kun Liu, Qin Wan, Shengyin Jiao, Tingyu Ke, Xiaowei Ding, Demetri Terzopoulos, Li Yan, Benli Su, Guang Ning, and Qidong Zheng more...
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Adult ,Research design ,China ,medicine.medical_specialty ,Endocrinology, Diabetes and Metabolism ,Concordance ,Fundus (eye) ,Diseases of the endocrine glands. Clinical endocrinology ,diagnostic techniques and procedures ,03 medical and health sciences ,chemistry.chemical_compound ,0302 clinical medicine ,Artificial Intelligence ,Diabetes mellitus ,Internal medicine ,Epidemiology ,Diabetes Mellitus ,medicine ,Humans ,Mass Screening ,Prospective Studies ,030212 general & internal medicine ,Prospective cohort study ,Aged ,Diabetic Retinopathy ,business.industry ,Diabetic retinopathy ,clinical study ,Middle Aged ,medicine.disease ,RC648-665 ,chemistry ,030221 ophthalmology & optometry ,Epidemiology/Health services research ,epidemiology ,Female ,Glycated hemoglobin ,business - Abstract
IntroductionEarly screening for diabetic retinopathy (DR) with an efficient and scalable method is highly needed to reduce blindness, due to the growing epidemic of diabetes. The aim of the study was to validate an artificial intelligence-enabled DR screening and to investigate the prevalence of DR in adult patients with diabetes in China.Research design and methodsThe study was prospectively conducted at 155 diabetes centers in China. A non-mydriatic, macula-centered fundus photograph per eye was collected and graded through a deep learning (DL)-based, five-stage DR classification. Images from a randomly selected one-third of participants were used for the DL algorithm validation.ResultsIn total, 47 269 patients (mean (SD) age, 54.29 (11.60) years) were enrolled. 15 805 randomly selected participants were reviewed by a panel of specialists for DL algorithm validation. The DR grading algorithms had a 83.3% (95% CI: 81.9% to 84.6%) sensitivity and a 92.5% (95% CI: 92.1% to 92.9%) specificity to detect referable DR. The five-stage DR classification performance (concordance: 83.0%) is comparable to the interobserver variability of specialists (concordance: 84.3%). The estimated prevalence in patients with diabetes detected by DL algorithm for any DR, referable DR and vision-threatening DR were 28.8% (95% CI: 28.4% to 29.3%), 24.4% (95% CI: 24.0% to 24.8%) and 10.8% (95% CI: 10.5% to 11.1%), respectively. The prevalence was higher in female, elderly, longer diabetes duration and higher glycated hemoglobin groups.ConclusionThis study performed, a nationwide, multicenter, DL-based DR screening and the results indicated the importance and feasibility of DR screening in clinical practice with this system deployed at diabetes centers.Trial registration numberNCT04240652. more...
- Published
- 2020
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