Yuancheng Li, Xingchao Liu, Chengcheng Zhang, Ran Tao, Bi Pan, Wei Liu, Di Jiang, Feng Hu, Zeliang Xu, Dehong Tan, Yanjiao Ou, Xun Li, Yuemei You, Leida Zhang
{"title":"一个简短的模型根据供体移植物和受体的匹配情况对肝移植后的结果进行了评估。","authors":"Yuancheng Li, Xingchao Liu, Chengcheng Zhang, Ran Tao, Bi Pan, Wei Liu, Di Jiang, Feng Hu, Zeliang Xu, Dehong Tan, Yanjiao Ou, Xun Li, Yuemei You, Leida Zhang","doi":"10.14309/ctg.0000000000000761","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>A precise model for predicting outcomes is needed to guide perioperative management. With the developments of liver transplantation (LT) discipline, previous models may become inappropriate or noncomprehensive. Thus, we aimed to develop a novel model integrating variables from donors and recipients for quick assessment of transplant outcomes.</p><p><strong>Methods: </strong>The risk model was based on Cox regression in a randomly selected derivation cohort and verified in a validation cohort. Perioperative data and overall survival were compared between stratifications grouped by X-tile. Receiver operating characteristic curve and decision curve analysis were used to compare the models. Violin and raincloud plots were generated to present post-LT complications distributed in different stratifications.</p><p><strong>Results: </strong>Overall, 528 patients receiving LT from 2 centers were included with 2/3 in the derivation cohort and 1/3 in the validation cohort. Cox regression analysis showed that cold ischemia time (CIT) (P=0.012) and the Model for End-Stage Liver Disease (MELD) (P=0.007) score were predictors of survival. After comparison with the logarithmic models, the primitive algorithms of CIT and MELD were defined as the CIT-MELD Index (CMI). CMI was stratified by X-tile (grade 1 ≤1.06, 1.06< grade 2 ≤1.87, grade 3 >1.87). In both cohorts, CMI performed better in calculating transplant outcomes than the balance of risk score, including perioperative incidents and prevalence of complications.</p><p><strong>Conclusions: </strong>Model integrating variables from graft and recipient made the prediction more accurate and available. CMI provided new sight in outcome evaluation and risk factor management of LT.</p>","PeriodicalId":10278,"journal":{"name":"Clinical and Translational Gastroenterology","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A brief model evaluated outcomes after liver transplantation based on the matching of donor graft and recipient.\",\"authors\":\"Yuancheng Li, Xingchao Liu, Chengcheng Zhang, Ran Tao, Bi Pan, Wei Liu, Di Jiang, Feng Hu, Zeliang Xu, Dehong Tan, Yanjiao Ou, Xun Li, Yuemei You, Leida Zhang\",\"doi\":\"10.14309/ctg.0000000000000761\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>A precise model for predicting outcomes is needed to guide perioperative management. With the developments of liver transplantation (LT) discipline, previous models may become inappropriate or noncomprehensive. Thus, we aimed to develop a novel model integrating variables from donors and recipients for quick assessment of transplant outcomes.</p><p><strong>Methods: </strong>The risk model was based on Cox regression in a randomly selected derivation cohort and verified in a validation cohort. Perioperative data and overall survival were compared between stratifications grouped by X-tile. Receiver operating characteristic curve and decision curve analysis were used to compare the models. Violin and raincloud plots were generated to present post-LT complications distributed in different stratifications.</p><p><strong>Results: </strong>Overall, 528 patients receiving LT from 2 centers were included with 2/3 in the derivation cohort and 1/3 in the validation cohort. Cox regression analysis showed that cold ischemia time (CIT) (P=0.012) and the Model for End-Stage Liver Disease (MELD) (P=0.007) score were predictors of survival. After comparison with the logarithmic models, the primitive algorithms of CIT and MELD were defined as the CIT-MELD Index (CMI). CMI was stratified by X-tile (grade 1 ≤1.06, 1.06< grade 2 ≤1.87, grade 3 >1.87). In both cohorts, CMI performed better in calculating transplant outcomes than the balance of risk score, including perioperative incidents and prevalence of complications.</p><p><strong>Conclusions: </strong>Model integrating variables from graft and recipient made the prediction more accurate and available. CMI provided new sight in outcome evaluation and risk factor management of LT.</p>\",\"PeriodicalId\":10278,\"journal\":{\"name\":\"Clinical and Translational Gastroenterology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical and Translational Gastroenterology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.14309/ctg.0000000000000761\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical and Translational Gastroenterology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.14309/ctg.0000000000000761","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
A brief model evaluated outcomes after liver transplantation based on the matching of donor graft and recipient.
Background: A precise model for predicting outcomes is needed to guide perioperative management. With the developments of liver transplantation (LT) discipline, previous models may become inappropriate or noncomprehensive. Thus, we aimed to develop a novel model integrating variables from donors and recipients for quick assessment of transplant outcomes.
Methods: The risk model was based on Cox regression in a randomly selected derivation cohort and verified in a validation cohort. Perioperative data and overall survival were compared between stratifications grouped by X-tile. Receiver operating characteristic curve and decision curve analysis were used to compare the models. Violin and raincloud plots were generated to present post-LT complications distributed in different stratifications.
Results: Overall, 528 patients receiving LT from 2 centers were included with 2/3 in the derivation cohort and 1/3 in the validation cohort. Cox regression analysis showed that cold ischemia time (CIT) (P=0.012) and the Model for End-Stage Liver Disease (MELD) (P=0.007) score were predictors of survival. After comparison with the logarithmic models, the primitive algorithms of CIT and MELD were defined as the CIT-MELD Index (CMI). CMI was stratified by X-tile (grade 1 ≤1.06, 1.06< grade 2 ≤1.87, grade 3 >1.87). In both cohorts, CMI performed better in calculating transplant outcomes than the balance of risk score, including perioperative incidents and prevalence of complications.
Conclusions: Model integrating variables from graft and recipient made the prediction more accurate and available. CMI provided new sight in outcome evaluation and risk factor management of LT.
期刊介绍:
Clinical and Translational Gastroenterology (CTG), published on behalf of the American College of Gastroenterology (ACG), is a peer-reviewed open access online journal dedicated to innovative clinical work in the field of gastroenterology and hepatology. CTG hopes to fulfill an unmet need for clinicians and scientists by welcoming novel cohort studies, early-phase clinical trials, qualitative and quantitative epidemiologic research, hypothesis-generating research, studies of novel mechanisms and methodologies including public health interventions, and integration of approaches across organs and disciplines. CTG also welcomes hypothesis-generating small studies, methods papers, and translational research with clear applications to human physiology or disease.
Colon and small bowel
Endoscopy and novel diagnostics
Esophagus
Functional GI disorders
Immunology of the GI tract
Microbiology of the GI tract
Inflammatory bowel disease
Pancreas and biliary tract
Liver
Pathology
Pediatrics
Preventative medicine
Nutrition/obesity
Stomach.