用于 2 型糖尿病肾病鉴别诊断的新型风险评分模型:一项多中心研究

IF 3.6 3区 医学 Q2 ENDOCRINOLOGY & METABOLISM Journal of Diabetes Research Pub Date : 2023-12-21 DOI:10.1155/2023/5514767
Yuetong Zhao, Lin Liu, Li Zuo, Xianghai Zhou, Song Wang, Hongwei Gao, Feng Yu, Xiaomei Zhang, Mi Wang, Ling Chen, Rui Zhang, Fang Zhang, Shuhong Bi, Qiong Bai, Jiaxiang Ding, Qinghua Yang, Sixu Xin, Sanbao Chai, Min Chen, Junqing Zhang
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Seventy percent of patients from PKUFH were randomly assigned to the training group, and the remaining 30% were assigned to the internal validation group. Patients from the other three centers were assigned to the external validation group. We used univariate and multivariate logistic regression analyses to identify independent risk factors of DN in the training group and conducted multivariate logistic regression analysis with these independent risk factors in the training group to find regression coefficients “<svg height=\"12.7178pt\" style=\"vertical-align:-3.42947pt\" version=\"1.1\" viewbox=\"-0.0498162 -9.28833 7.68094 12.7178\" width=\"7.68094pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"></path></g></svg>” to establish a risk score model. Finally, we conducted internal and external validation of the model with ROC curves. <i>Results</i>. Diabetic retinopathy, diabetes <span><svg height=\"10.4277pt\" style=\"vertical-align:-1.1198pt\" version=\"1.1\" viewbox=\"-0.0498162 -9.3079 56.425 10.4277\" width=\"56.425pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,6.707,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,13.61,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,18.433,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,23.892,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,27.923,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,31.407,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,37.88,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,48.794,0)\"></path></g></svg><span></span><svg height=\"10.4277pt\" style=\"vertical-align:-1.1198pt\" version=\"1.1\" viewbox=\"60.006183799999995 -9.3079 6.392 10.4277\" width=\"6.392pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,60.056,0)\"></path></g></svg></span> years, <span><svg height=\"13.7421pt\" style=\"vertical-align:-2.1507pt\" version=\"1.1\" viewbox=\"-0.0498162 -11.5914 41.145 13.7421\" width=\"41.145pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,5.525,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,14.82,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,21.697,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,33.514,0)\"></path></g></svg><span></span><svg height=\"13.7421pt\" style=\"vertical-align:-2.1507pt\" version=\"1.1\" viewbox=\"44.7261838 -11.5914 34.065 13.7421\" width=\"34.065pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,44.776,0)\"></path></g><g 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xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,109.888,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,116.128,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,119.092,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,125.334,0)\"><use xlink:href=\"#g113-52\"></use></g><g transform=\"matrix(.013,0,0,-0.013,133.749,0)\"><use xlink:href=\"#g190-110\"></use></g><g transform=\"matrix(.0091,0,0,-0.0091,144.436,-5.741)\"></path></g></svg>,</span></span> 24 h <span><svg height=\"12.1567pt\" style=\"vertical-align:-3.40336pt\" version=\"1.1\" viewbox=\"-0.0498162 -8.75334 36.264 12.1567\" width=\"36.264pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,9.568,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,17.589,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,28.633,0)\"><use xlink:href=\"#g117-94\"></use></g></svg><span></span><span><svg height=\"12.1567pt\" style=\"vertical-align:-3.40336pt\" version=\"1.1\" viewbox=\"39.8461838 -8.75334 14.674 12.1567\" width=\"14.674pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,39.896,0)\"><use xlink:href=\"#g113-52\"></use></g><g transform=\"matrix(.013,0,0,-0.013,48.312,0)\"></path></g></svg>,</span></span> and no hematuria were independent risk factors (<span><svg height=\"9.2729pt\" style=\"vertical-align:-0.6370001pt\" version=\"1.1\" viewbox=\"-0.0498162 -8.6359 19.289 9.2729\" width=\"19.289pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,11.658,0)\"><use xlink:href=\"#g117-91\"></use></g></svg><span></span><span><svg height=\"9.2729pt\" style=\"vertical-align:-0.6370001pt\" version=\"1.1\" viewbox=\"22.8711838 -8.6359 21.918 9.2729\" width=\"21.918pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,22.921,0)\"><use xlink:href=\"#g113-49\"></use></g><g transform=\"matrix(.013,0,0,-0.013,29.161,0)\"><use xlink:href=\"#g113-47\"></use></g><g transform=\"matrix(.013,0,0,-0.013,32.125,0)\"><use xlink:href=\"#g113-49\"></use></g><g transform=\"matrix(.013,0,0,-0.013,38.365,0)\"><use xlink:href=\"#g113-54\"></use></g></svg>),</span></span> and each factor scored 2, 1, 1, 1, and 1. We assigned the patients to a low-risk group (0-1 points), a medium-risk group (2-3 points), and a high-risk group (4-6 points), representing unlikely DN, possibly DN, and a high probability of DN, respectively. The AUCs were 0.860, 0.924, and 0.855 for the training, internal validation, and external validation groups, respectively. <i>Conclusion</i>. The risk score model could help differentiate DN and NDKD in a noninvasive manner, reduce the number of renal biopsies, and provide a reference for clinical treatment.","PeriodicalId":15576,"journal":{"name":"Journal of Diabetes Research","volume":"25 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Risk Score Model for the Differential Diagnosis of Type 2 Diabetic Nephropathy: A Multicenter Study\",\"authors\":\"Yuetong Zhao, Lin Liu, Li Zuo, Xianghai Zhou, Song Wang, Hongwei Gao, Feng Yu, Xiaomei Zhang, Mi Wang, Ling Chen, Rui Zhang, Fang Zhang, Shuhong Bi, Qiong Bai, Jiaxiang Ding, Qinghua Yang, Sixu Xin, Sanbao Chai, Min Chen, Junqing Zhang\",\"doi\":\"10.1155/2023/5514767\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<i>Introduction</i>. DN is a common complication of diabetes. However, diabetes combined with renal injury may involve DN or NDKD, with different treatment schemes. The purpose of our study was to determine the independent risk factors of DN and establish a risk score model to help differentiate DN and NDKD, providing a reference for clinical treatment. <i>Methods</i>. A total of 678 T2D patients who had undergone renal biopsy in four affiliated hospitals of Peking University were consecutively enrolled. Patients were assigned to the DN group and NDKD group according to histopathological results. Seventy percent of patients from PKUFH were randomly assigned to the training group, and the remaining 30% were assigned to the internal validation group. Patients from the other three centers were assigned to the external validation group. We used univariate and multivariate logistic regression analyses to identify independent risk factors of DN in the training group and conducted multivariate logistic regression analysis with these independent risk factors in the training group to find regression coefficients “<svg height=\\\"12.7178pt\\\" style=\\\"vertical-align:-3.42947pt\\\" version=\\\"1.1\\\" viewbox=\\\"-0.0498162 -9.28833 7.68094 12.7178\\\" width=\\\"7.68094pt\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"><g transform=\\\"matrix(.013,0,0,-0.013,0,0)\\\"></path></g></svg>” to establish a risk score model. Finally, we conducted internal and external validation of the model with ROC curves. <i>Results</i>. Diabetic retinopathy, diabetes <span><svg height=\\\"10.4277pt\\\" style=\\\"vertical-align:-1.1198pt\\\" version=\\\"1.1\\\" viewbox=\\\"-0.0498162 -9.3079 56.425 10.4277\\\" width=\\\"56.425pt\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"><g transform=\\\"matrix(.013,0,0,-0.013,0,0)\\\"></path></g><g transform=\\\"matrix(.013,0,0,-0.013,6.707,0)\\\"></path></g><g transform=\\\"matrix(.013,0,0,-0.013,13.61,0)\\\"></path></g><g transform=\\\"matrix(.013,0,0,-0.013,18.433,0)\\\"></path></g><g transform=\\\"matrix(.013,0,0,-0.013,23.892,0)\\\"></path></g><g transform=\\\"matrix(.013,0,0,-0.013,27.923,0)\\\"></path></g><g transform=\\\"matrix(.013,0,0,-0.013,31.407,0)\\\"></path></g><g transform=\\\"matrix(.013,0,0,-0.013,37.88,0)\\\"></path></g><g transform=\\\"matrix(.013,0,0,-0.013,48.794,0)\\\"></path></g></svg><span></span><svg height=\\\"10.4277pt\\\" style=\\\"vertical-align:-1.1198pt\\\" version=\\\"1.1\\\" 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xlink:href=\\\"#g113-48\\\"></use></g></svg><span></span><span><svg height=\\\"13.7421pt\\\" style=\\\"vertical-align:-2.1507pt\\\" version=\\\"1.1\\\" viewbox=\\\"109.83818380000001 -11.5914 39.778 13.7421\\\" width=\\\"39.778pt\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"><g transform=\\\"matrix(.013,0,0,-0.013,109.888,0)\\\"></path></g><g transform=\\\"matrix(.013,0,0,-0.013,116.128,0)\\\"></path></g><g transform=\\\"matrix(.013,0,0,-0.013,119.092,0)\\\"></path></g><g transform=\\\"matrix(.013,0,0,-0.013,125.334,0)\\\"><use xlink:href=\\\"#g113-52\\\"></use></g><g transform=\\\"matrix(.013,0,0,-0.013,133.749,0)\\\"><use xlink:href=\\\"#g190-110\\\"></use></g><g transform=\\\"matrix(.0091,0,0,-0.0091,144.436,-5.741)\\\"></path></g></svg>,</span></span> 24 h <span><svg height=\\\"12.1567pt\\\" style=\\\"vertical-align:-3.40336pt\\\" version=\\\"1.1\\\" viewbox=\\\"-0.0498162 -8.75334 36.264 12.1567\\\" width=\\\"36.264pt\\\" 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We assigned the patients to a low-risk group (0-1 points), a medium-risk group (2-3 points), and a high-risk group (4-6 points), representing unlikely DN, possibly DN, and a high probability of DN, respectively. The AUCs were 0.860, 0.924, and 0.855 for the training, internal validation, and external validation groups, respectively. <i>Conclusion</i>. The risk score model could help differentiate DN and NDKD in a noninvasive manner, reduce the number of renal biopsies, and provide a reference for clinical treatment.\",\"PeriodicalId\":15576,\"journal\":{\"name\":\"Journal of Diabetes Research\",\"volume\":\"25 1\",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2023-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Diabetes Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1155/2023/5514767\",\"RegionNum\":3,\"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 Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1155/2023/5514767","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0

摘要

简介DN是糖尿病的常见并发症。然而,糖尿病合并肾损伤可能涉及 DN 或 NDKD,治疗方案也不尽相同。我们的研究旨在确定 DN 的独立风险因素,并建立一个风险评分模型,以帮助区分 DN 和 NDKD,为临床治疗提供参考。研究方法我们连续纳入了在北京大学四家附属医院接受肾活检的 678 名 T2D 患者。根据组织病理学结果将患者分为 DN 组和 NDKD 组。北京大学人民医院70%的患者被随机分配到培训组,其余30%的患者被分配到内部验证组。其他三个中心的患者被分配到外部验证组。我们使用单变量和多变量逻辑回归分析来确定培训组中 DN 的独立风险因素,并对培训组中的这些独立风险因素进行多变量逻辑回归分析,找出回归系数"",从而建立风险评分模型。最后,我们利用 ROC 曲线对模型进行了内部和外部验证。结果我们将患者分为低风险组(0-1 分)、中风险组(2-3 分)和高风险组(4-6 分),分别代表不太可能发生 DN、可能发生 DN 和极有可能发生 DN。训练组、内部验证组和外部验证组的 AUC 分别为 0.860、0.924 和 0.855。结论风险评分模型有助于以无创方式区分 DN 和 NDKD,减少肾活检次数,并为临床治疗提供参考。
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A Novel Risk Score Model for the Differential Diagnosis of Type 2 Diabetic Nephropathy: A Multicenter Study
Introduction. DN is a common complication of diabetes. However, diabetes combined with renal injury may involve DN or NDKD, with different treatment schemes. The purpose of our study was to determine the independent risk factors of DN and establish a risk score model to help differentiate DN and NDKD, providing a reference for clinical treatment. Methods. A total of 678 T2D patients who had undergone renal biopsy in four affiliated hospitals of Peking University were consecutively enrolled. Patients were assigned to the DN group and NDKD group according to histopathological results. Seventy percent of patients from PKUFH were randomly assigned to the training group, and the remaining 30% were assigned to the internal validation group. Patients from the other three centers were assigned to the external validation group. We used univariate and multivariate logistic regression analyses to identify independent risk factors of DN in the training group and conducted multivariate logistic regression analysis with these independent risk factors in the training group to find regression coefficients “” to establish a risk score model. Finally, we conducted internal and external validation of the model with ROC curves. Results. Diabetic retinopathy, diabetes years, , 24 h , and no hematuria were independent risk factors (), and each factor scored 2, 1, 1, 1, and 1. We assigned the patients to a low-risk group (0-1 points), a medium-risk group (2-3 points), and a high-risk group (4-6 points), representing unlikely DN, possibly DN, and a high probability of DN, respectively. The AUCs were 0.860, 0.924, and 0.855 for the training, internal validation, and external validation groups, respectively. Conclusion. The risk score model could help differentiate DN and NDKD in a noninvasive manner, reduce the number of renal biopsies, and provide a reference for clinical treatment.
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来源期刊
Journal of Diabetes Research
Journal of Diabetes Research ENDOCRINOLOGY & METABOLISM-MEDICINE, RESEARCH & EXPERIMENTAL
CiteScore
8.40
自引率
2.30%
发文量
152
审稿时长
14 weeks
期刊介绍: Journal of Diabetes Research is a peer-reviewed, Open Access journal that publishes research articles, review articles, and clinical studies related to type 1 and type 2 diabetes. The journal welcomes submissions focusing on the epidemiology, etiology, pathogenesis, management, and prevention of diabetes, as well as associated complications, such as diabetic retinopathy, neuropathy and nephropathy.
期刊最新文献
Understanding Experiences of Diabetes Distress: A Systematic Review and Thematic Synthesis. Prevalence of Hypertension and Its Clinical and Psychological Factors in Type 2 Diabetes Patients in Ghana: A Secondary Analysis of a Cross-Sectional Study. Total Water-Soluble Flavonoids From Lithocarpus litseifolius (Hance) Chun (Sweet Tea) Improve Glucose Homeostasis Through Multitarget Signalling in GDM Mice. Lipid Accumulation Product as a Predictor of Prediabetes and Diabetes: Insights From NHANES Data (1999-2018). Visceral Adiposity as an Independent Risk Factor for Diabetic Peripheral Neuropathy in Type 2 Diabetes Mellitus: A Retrospective Study.
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