Ruiping Bai, Rui An, Siyu Chen, Wenkang Ding, Mengwen Xue, Ge Zhao, Qingyong Ma, Xin Shen
{"title":"肝移植术后新发糖尿病的风险因素和预测评分","authors":"Ruiping Bai, Rui An, Siyu Chen, Wenkang Ding, Mengwen Xue, Ge Zhao, Qingyong Ma, Xin Shen","doi":"10.1111/jdi.14204","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Aim</h3>\n \n <p>New-onset diabetes mellitus is a frequent and severe complication arising after liver transplantation (LT). We aimed to identify the risk factors for new-onset diabetes mellitus after liver transplantation (NODALT) and to develop a risk prediction score system for relevant risks.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We collected and analyzed data from all recipients who underwent liver transplantation at the First Affiliated Hospital of Xi'an Jiaotong University. The OR derived from a multiple logistic regression predicting the presence of NODALT was used to calculate the risk prediction score. The performance of the risk prediction score was externally validated in patients who were from the CLTR (China Liver Transplant Registry) database.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>A total of 468 patients met the outlined criteria and finished the follow-up. Overall, NODALT was diagnosed in 115 (24.6%) patients. Age, preoperative impaired fasting glucose (IFG), postoperative fasting plasma glucose (FPG), and the length of hospital stay were significantly associated with the presence of NODALT. The risk prediction score includes age, preoperative IFG, postoperative FPG, and the length of hospital stay. The risk prediction score of the area under the receiver operating curve was 0.785 (95% CI: 0.724–0.846) in the experimental population and 0.782 (95% CI: 0.708–0.856) in the validation population.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Age at the time of transplantation, preoperative IFG, postoperative FPG, and length of hospital stay were independent predictive factors of NODALT. The use of a simple risk prediction score can identify the patients who have the highest risk of NODALT and interventions may start early.</p>\n </section>\n </div>","PeriodicalId":51250,"journal":{"name":"Journal of Diabetes Investigation","volume":"15 8","pages":"1105-1114"},"PeriodicalIF":3.1000,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jdi.14204","citationCount":"0","resultStr":"{\"title\":\"Risk factors and prediction score for new-onset diabetes mellitus after liver transplantation\",\"authors\":\"Ruiping Bai, Rui An, Siyu Chen, Wenkang Ding, Mengwen Xue, Ge Zhao, Qingyong Ma, Xin Shen\",\"doi\":\"10.1111/jdi.14204\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Aim</h3>\\n \\n <p>New-onset diabetes mellitus is a frequent and severe complication arising after liver transplantation (LT). We aimed to identify the risk factors for new-onset diabetes mellitus after liver transplantation (NODALT) and to develop a risk prediction score system for relevant risks.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We collected and analyzed data from all recipients who underwent liver transplantation at the First Affiliated Hospital of Xi'an Jiaotong University. The OR derived from a multiple logistic regression predicting the presence of NODALT was used to calculate the risk prediction score. The performance of the risk prediction score was externally validated in patients who were from the CLTR (China Liver Transplant Registry) database.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>A total of 468 patients met the outlined criteria and finished the follow-up. Overall, NODALT was diagnosed in 115 (24.6%) patients. Age, preoperative impaired fasting glucose (IFG), postoperative fasting plasma glucose (FPG), and the length of hospital stay were significantly associated with the presence of NODALT. The risk prediction score includes age, preoperative IFG, postoperative FPG, and the length of hospital stay. The risk prediction score of the area under the receiver operating curve was 0.785 (95% CI: 0.724–0.846) in the experimental population and 0.782 (95% CI: 0.708–0.856) in the validation population.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Age at the time of transplantation, preoperative IFG, postoperative FPG, and length of hospital stay were independent predictive factors of NODALT. The use of a simple risk prediction score can identify the patients who have the highest risk of NODALT and interventions may start early.</p>\\n </section>\\n </div>\",\"PeriodicalId\":51250,\"journal\":{\"name\":\"Journal of Diabetes Investigation\",\"volume\":\"15 8\",\"pages\":\"1105-1114\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-04-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jdi.14204\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Diabetes Investigation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jdi.14204\",\"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 Investigation","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jdi.14204","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Risk factors and prediction score for new-onset diabetes mellitus after liver transplantation
Aim
New-onset diabetes mellitus is a frequent and severe complication arising after liver transplantation (LT). We aimed to identify the risk factors for new-onset diabetes mellitus after liver transplantation (NODALT) and to develop a risk prediction score system for relevant risks.
Methods
We collected and analyzed data from all recipients who underwent liver transplantation at the First Affiliated Hospital of Xi'an Jiaotong University. The OR derived from a multiple logistic regression predicting the presence of NODALT was used to calculate the risk prediction score. The performance of the risk prediction score was externally validated in patients who were from the CLTR (China Liver Transplant Registry) database.
Results
A total of 468 patients met the outlined criteria and finished the follow-up. Overall, NODALT was diagnosed in 115 (24.6%) patients. Age, preoperative impaired fasting glucose (IFG), postoperative fasting plasma glucose (FPG), and the length of hospital stay were significantly associated with the presence of NODALT. The risk prediction score includes age, preoperative IFG, postoperative FPG, and the length of hospital stay. The risk prediction score of the area under the receiver operating curve was 0.785 (95% CI: 0.724–0.846) in the experimental population and 0.782 (95% CI: 0.708–0.856) in the validation population.
Conclusions
Age at the time of transplantation, preoperative IFG, postoperative FPG, and length of hospital stay were independent predictive factors of NODALT. The use of a simple risk prediction score can identify the patients who have the highest risk of NODALT and interventions may start early.
期刊介绍:
Journal of Diabetes Investigation is your core diabetes journal from Asia; the official journal of the Asian Association for the Study of Diabetes (AASD). The journal publishes original research, country reports, commentaries, reviews, mini-reviews, case reports, letters, as well as editorials and news. Embracing clinical and experimental research in diabetes and related areas, the Journal of Diabetes Investigation includes aspects of prevention, treatment, as well as molecular aspects and pathophysiology. Translational research focused on the exchange of ideas between clinicians and researchers is also welcome. Journal of Diabetes Investigation is indexed by Science Citation Index Expanded (SCIE).