Andrea Camera, Tawhidul Islam, Reza Parvan, Søren Erik Pischke, Gustavo Jose Justo Silva, Kåre-Olav Stensløkken
{"title":"肝移植预后和诊断生物标志物:一项系统综述和荟萃分析。","authors":"Andrea Camera, Tawhidul Islam, Reza Parvan, Søren Erik Pischke, Gustavo Jose Justo Silva, Kåre-Olav Stensløkken","doi":"10.1097/LVT.0000000000000666","DOIUrl":null,"url":null,"abstract":"<p><p>Liver transplantation (LT) is a therapeutic option for patients suffering from end-stage liver diseases. Recent research has probed the prognostic significance of biomarkers to predict graft function and mortality post-transplant, yet few candidates are recommended in clinical practice. We employed a pipeline that integrates meta-analysis (PRISMA 2020), followed by Kaplan Meier (KM)-based individual patient data (IPD) analysis, aiming to identify potential novel prognostic biomarker panels for LT recipients. Ovid Medline, Embase, and Cochrane databases were searched. Twenty-one prognostic and eight diagnostic studies were eligible, pooling 34922 patients. Single biomarkers sampled at an early stage (≤15 d after LT) were significant associated with graft-related outcomes (HR/OR 0.95 (0.94-0.97)) but did not predict mortality (HR/OR 1.00 (0.97-1.04)) or composite outcomes (HR/OR 1.02 (0.98-1.07)). Biomarkers in combination (GGT/bilirubin ratio, ALT+AST or ALT+AST+bilirubin+INR) predicted composite outcomes (graft failure or mortality, aHR/aOR 4.37 (2.65-7.21)). Biomarkers assessed at late stage (>15) did not show association with mortality (HR/OR 1.02 (1.00-1.04)) or composite outcomes (HR/OR 1.00 (0.99-1.01)). KM-based IPD analysis showed that coagulation factor V combined with ALT predicted graft survival (HR 2.12 (1.44-3.12)), and coagulation factor V+Insulin-like Growth Factor 1 stratified the risk of patient survival (HR 2.97 (1.79-4.91)). Therefore, we were able to compare various scoring systems in predicting graft-related outcomes and mortality followed LT. Additionally, we identified novel combinations of biomarkers that exhibited prognostic value for LT patients. Finally, we demonstrate that combined analytical tools for assessing large clinical datasets effectively evaluate multi-modal markers for risk stratification of early and late outcomes for LT.</p>","PeriodicalId":520704,"journal":{"name":"Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prognostic and diagnostic biomarkers in liver transplantation: A systematic review and meta-analysis.\",\"authors\":\"Andrea Camera, Tawhidul Islam, Reza Parvan, Søren Erik Pischke, Gustavo Jose Justo Silva, Kåre-Olav Stensløkken\",\"doi\":\"10.1097/LVT.0000000000000666\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Liver transplantation (LT) is a therapeutic option for patients suffering from end-stage liver diseases. Recent research has probed the prognostic significance of biomarkers to predict graft function and mortality post-transplant, yet few candidates are recommended in clinical practice. We employed a pipeline that integrates meta-analysis (PRISMA 2020), followed by Kaplan Meier (KM)-based individual patient data (IPD) analysis, aiming to identify potential novel prognostic biomarker panels for LT recipients. Ovid Medline, Embase, and Cochrane databases were searched. Twenty-one prognostic and eight diagnostic studies were eligible, pooling 34922 patients. Single biomarkers sampled at an early stage (≤15 d after LT) were significant associated with graft-related outcomes (HR/OR 0.95 (0.94-0.97)) but did not predict mortality (HR/OR 1.00 (0.97-1.04)) or composite outcomes (HR/OR 1.02 (0.98-1.07)). Biomarkers in combination (GGT/bilirubin ratio, ALT+AST or ALT+AST+bilirubin+INR) predicted composite outcomes (graft failure or mortality, aHR/aOR 4.37 (2.65-7.21)). Biomarkers assessed at late stage (>15) did not show association with mortality (HR/OR 1.02 (1.00-1.04)) or composite outcomes (HR/OR 1.00 (0.99-1.01)). KM-based IPD analysis showed that coagulation factor V combined with ALT predicted graft survival (HR 2.12 (1.44-3.12)), and coagulation factor V+Insulin-like Growth Factor 1 stratified the risk of patient survival (HR 2.97 (1.79-4.91)). Therefore, we were able to compare various scoring systems in predicting graft-related outcomes and mortality followed LT. Additionally, we identified novel combinations of biomarkers that exhibited prognostic value for LT patients. Finally, we demonstrate that combined analytical tools for assessing large clinical datasets effectively evaluate multi-modal markers for risk stratification of early and late outcomes for LT.</p>\",\"PeriodicalId\":520704,\"journal\":{\"name\":\"Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1097/LVT.0000000000000666\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1097/LVT.0000000000000666","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prognostic and diagnostic biomarkers in liver transplantation: A systematic review and meta-analysis.
Liver transplantation (LT) is a therapeutic option for patients suffering from end-stage liver diseases. Recent research has probed the prognostic significance of biomarkers to predict graft function and mortality post-transplant, yet few candidates are recommended in clinical practice. We employed a pipeline that integrates meta-analysis (PRISMA 2020), followed by Kaplan Meier (KM)-based individual patient data (IPD) analysis, aiming to identify potential novel prognostic biomarker panels for LT recipients. Ovid Medline, Embase, and Cochrane databases were searched. Twenty-one prognostic and eight diagnostic studies were eligible, pooling 34922 patients. Single biomarkers sampled at an early stage (≤15 d after LT) were significant associated with graft-related outcomes (HR/OR 0.95 (0.94-0.97)) but did not predict mortality (HR/OR 1.00 (0.97-1.04)) or composite outcomes (HR/OR 1.02 (0.98-1.07)). Biomarkers in combination (GGT/bilirubin ratio, ALT+AST or ALT+AST+bilirubin+INR) predicted composite outcomes (graft failure or mortality, aHR/aOR 4.37 (2.65-7.21)). Biomarkers assessed at late stage (>15) did not show association with mortality (HR/OR 1.02 (1.00-1.04)) or composite outcomes (HR/OR 1.00 (0.99-1.01)). KM-based IPD analysis showed that coagulation factor V combined with ALT predicted graft survival (HR 2.12 (1.44-3.12)), and coagulation factor V+Insulin-like Growth Factor 1 stratified the risk of patient survival (HR 2.97 (1.79-4.91)). Therefore, we were able to compare various scoring systems in predicting graft-related outcomes and mortality followed LT. Additionally, we identified novel combinations of biomarkers that exhibited prognostic value for LT patients. Finally, we demonstrate that combined analytical tools for assessing large clinical datasets effectively evaluate multi-modal markers for risk stratification of early and late outcomes for LT.