The Predictors for Continuous Renal Replacement Therapy in Liver Transplant Recipients

Abstract Background Acute renal failure (ARF) after liver transplantation requiring continuous renal replacement therapy (CRRT) adversely affects patient survival. We suggested that postoperative renal failure can be predicted if a clinically simple nomogram can be developed, thus selecting potentia...

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Veröffentlicht in:Transplantation proceedings 2014, Vol.46 (1), p.184-191
Hauptverfasser: Kim, J.M, Jo, Y.Y, Na, S.W, Kim, S.I, Choi, Y.S, Kim, N.O, Park, J.E, Koh, S.O
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container_end_page 191
container_issue 1
container_start_page 184
container_title Transplantation proceedings
container_volume 46
creator Kim, J.M
Jo, Y.Y
Na, S.W
Kim, S.I
Choi, Y.S
Kim, N.O
Park, J.E
Koh, S.O
description Abstract Background Acute renal failure (ARF) after liver transplantation requiring continuous renal replacement therapy (CRRT) adversely affects patient survival. We suggested that postoperative renal failure can be predicted if a clinically simple nomogram can be developed, thus selecting potential risk factors for preventive strategy. Methods We retrospectively reviewed the medical records of 153 liver transplant recipients from January 2008 to December 2011 at Severance Hospital, Yonsei University Health System, in Seoul, Korea. There were 42 patients treated with CRRT (20 and 22 patients received transplants from living and deceased donors, respectively) and 115 were not. Univariate and stepwise logistic multivariate analyses were performed. A clinical nomogram to predict postoperative CRRT application was constructed and validated internally. Results Hepatic encephalopathy (HEP; odds ratio OR, 5.47), deceased donor liver donations (OR, 3.47), Model for End-Stage Liver Disease (MELD) score (OR, 1.09), intraoperative blood loss (L; OR, 1.16), and tumor (hepatocellular carcinoma) as the indication for liver transplantation (OR, 0.11) were identified as independent predictive factors for postoperative CRRT on multivariate analysis. A clinical prediction model constructed for calculating the probability of CRRT post-transplantation was 1.7000 × HEP + [−4.5427 + 1.2440 × (deceased donor) + 0.0830 × (MELD score) + 0.000149 × the amount of intraoperative bleeding (L) − 2.1785 × tumor]. The validation set discriminated well with an area under the curve (AUC) of 0.90 (95% confidence interval, 0.85–0.95). The predicted and the actual probabilities were calibrated with the clinical nomogram. Conclusions We developed a predictive model of postoperative CRRT in liver transplantation patients. Perioperative strategies to modify these factors are needed.
doi_str_mv 10.1016/j.transproceed.2013.07.075
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We suggested that postoperative renal failure can be predicted if a clinically simple nomogram can be developed, thus selecting potential risk factors for preventive strategy. Methods We retrospectively reviewed the medical records of 153 liver transplant recipients from January 2008 to December 2011 at Severance Hospital, Yonsei University Health System, in Seoul, Korea. There were 42 patients treated with CRRT (20 and 22 patients received transplants from living and deceased donors, respectively) and 115 were not. Univariate and stepwise logistic multivariate analyses were performed. A clinical nomogram to predict postoperative CRRT application was constructed and validated internally. Results Hepatic encephalopathy (HEP; odds ratio OR, 5.47), deceased donor liver donations (OR, 3.47), Model for End-Stage Liver Disease (MELD) score (OR, 1.09), intraoperative blood loss (L; OR, 1.16), and tumor (hepatocellular carcinoma) as the indication for liver transplantation (OR, 0.11) were identified as independent predictive factors for postoperative CRRT on multivariate analysis. A clinical prediction model constructed for calculating the probability of CRRT post-transplantation was 1.7000 × HEP + [−4.5427 + 1.2440 × (deceased donor) + 0.0830 × (MELD score) + 0.000149 × the amount of intraoperative bleeding (L) − 2.1785 × tumor]. The validation set discriminated well with an area under the curve (AUC) of 0.90 (95% confidence interval, 0.85–0.95). The predicted and the actual probabilities were calibrated with the clinical nomogram. Conclusions We developed a predictive model of postoperative CRRT in liver transplantation patients. Perioperative strategies to modify these factors are needed.</description><identifier>ISSN: 0041-1345</identifier><identifier>EISSN: 1873-2623</identifier><identifier>DOI: 10.1016/j.transproceed.2013.07.075</identifier><identifier>PMID: 24507049</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Acute Kidney Injury - etiology ; Acute Kidney Injury - therapy ; Adult ; Area Under Curve ; Female ; Hepatic Encephalopathy - physiopathology ; Humans ; Kaplan-Meier Estimate ; Liver Failure - mortality ; Liver Failure - surgery ; Liver Transplantation - adverse effects ; Male ; Middle Aged ; Multivariate Analysis ; Predictive Value of Tests ; Renal Replacement Therapy - methods ; Republic of Korea ; Retrospective Studies ; Risk Factors ; Sensitivity and Specificity ; Surgery ; Transplant Recipients ; Treatment Outcome</subject><ispartof>Transplantation proceedings, 2014, Vol.46 (1), p.184-191</ispartof><rights>Elsevier Inc.</rights><rights>2014 Elsevier Inc.</rights><rights>Copyright © 2014 Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c435t-929758f93f124a941b321e08f85470f2ceb364004c0e4a4870bdf42b0ac241aa3</citedby><cites>FETCH-LOGICAL-c435t-929758f93f124a941b321e08f85470f2ceb364004c0e4a4870bdf42b0ac241aa3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.transproceed.2013.07.075$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,4024,27923,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24507049$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kim, J.M</creatorcontrib><creatorcontrib>Jo, Y.Y</creatorcontrib><creatorcontrib>Na, S.W</creatorcontrib><creatorcontrib>Kim, S.I</creatorcontrib><creatorcontrib>Choi, Y.S</creatorcontrib><creatorcontrib>Kim, N.O</creatorcontrib><creatorcontrib>Park, J.E</creatorcontrib><creatorcontrib>Koh, S.O</creatorcontrib><title>The Predictors for Continuous Renal Replacement Therapy in Liver Transplant Recipients</title><title>Transplantation proceedings</title><addtitle>Transplant Proc</addtitle><description>Abstract Background Acute renal failure (ARF) after liver transplantation requiring continuous renal replacement therapy (CRRT) adversely affects patient survival. We suggested that postoperative renal failure can be predicted if a clinically simple nomogram can be developed, thus selecting potential risk factors for preventive strategy. Methods We retrospectively reviewed the medical records of 153 liver transplant recipients from January 2008 to December 2011 at Severance Hospital, Yonsei University Health System, in Seoul, Korea. There were 42 patients treated with CRRT (20 and 22 patients received transplants from living and deceased donors, respectively) and 115 were not. Univariate and stepwise logistic multivariate analyses were performed. A clinical nomogram to predict postoperative CRRT application was constructed and validated internally. Results Hepatic encephalopathy (HEP; odds ratio OR, 5.47), deceased donor liver donations (OR, 3.47), Model for End-Stage Liver Disease (MELD) score (OR, 1.09), intraoperative blood loss (L; OR, 1.16), and tumor (hepatocellular carcinoma) as the indication for liver transplantation (OR, 0.11) were identified as independent predictive factors for postoperative CRRT on multivariate analysis. A clinical prediction model constructed for calculating the probability of CRRT post-transplantation was 1.7000 × HEP + [−4.5427 + 1.2440 × (deceased donor) + 0.0830 × (MELD score) + 0.000149 × the amount of intraoperative bleeding (L) − 2.1785 × tumor]. The validation set discriminated well with an area under the curve (AUC) of 0.90 (95% confidence interval, 0.85–0.95). The predicted and the actual probabilities were calibrated with the clinical nomogram. Conclusions We developed a predictive model of postoperative CRRT in liver transplantation patients. Perioperative strategies to modify these factors are needed.</description><subject>Acute Kidney Injury - etiology</subject><subject>Acute Kidney Injury - therapy</subject><subject>Adult</subject><subject>Area Under Curve</subject><subject>Female</subject><subject>Hepatic Encephalopathy - physiopathology</subject><subject>Humans</subject><subject>Kaplan-Meier Estimate</subject><subject>Liver Failure - mortality</subject><subject>Liver Failure - surgery</subject><subject>Liver Transplantation - adverse effects</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Multivariate Analysis</subject><subject>Predictive Value of Tests</subject><subject>Renal Replacement Therapy - methods</subject><subject>Republic of Korea</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><subject>Sensitivity and Specificity</subject><subject>Surgery</subject><subject>Transplant Recipients</subject><subject>Treatment Outcome</subject><issn>0041-1345</issn><issn>1873-2623</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkVFP2zAQxy00BB3wFVC0p72k3NlOk-xh0lTYhlQJBIVXy3Evmrs0yewEqd-eKwVp4gnJsmXd_-7-9zshviBMEXB2sZ4OwbaxD50jWk0loJpCzic7EBMscpXKmVSfxARAY4pKZ8fic4xr4L_U6kgcS51BDrqciMflH0puA628G7oQk7oLybxrB9-O3RiTO2ptw3ffWEcbaoeE9cH228S3ycI_UUiWL14ay7E7cr73rIqn4rC2TaSz1_dEPPy8Ws5_p4ubX9fzH4vUaZUNaSnLPCvqUtUotS01VkoiQVEXmc6hlo4qNdNs2wFpq4scqlWtZQXWSY3WqhPxdV-XYfwbKQ5m46Ojhu0Q-zeoyxKVggJZ-m0vdaGLMVBt-uA3NmwNgtlxNWvzP1ez42og55Nx8vlrn7HacOwt9Q0kCy73AuJpnzwFEx2TcAw2kBvMqvMf6_P9XRnX-NY72_ylLcV1NwZeCM9lojRg7ncb3i0YFSBiLtUzjrmlJw</recordid><startdate>2014</startdate><enddate>2014</enddate><creator>Kim, J.M</creator><creator>Jo, Y.Y</creator><creator>Na, S.W</creator><creator>Kim, S.I</creator><creator>Choi, Y.S</creator><creator>Kim, N.O</creator><creator>Park, J.E</creator><creator>Koh, S.O</creator><general>Elsevier Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>2014</creationdate><title>The Predictors for Continuous Renal Replacement Therapy in Liver Transplant Recipients</title><author>Kim, J.M ; Jo, Y.Y ; Na, S.W ; Kim, S.I ; Choi, Y.S ; Kim, N.O ; Park, J.E ; Koh, S.O</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c435t-929758f93f124a941b321e08f85470f2ceb364004c0e4a4870bdf42b0ac241aa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Acute Kidney Injury - etiology</topic><topic>Acute Kidney Injury - therapy</topic><topic>Adult</topic><topic>Area Under Curve</topic><topic>Female</topic><topic>Hepatic Encephalopathy - physiopathology</topic><topic>Humans</topic><topic>Kaplan-Meier Estimate</topic><topic>Liver Failure - mortality</topic><topic>Liver Failure - surgery</topic><topic>Liver Transplantation - adverse effects</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Multivariate Analysis</topic><topic>Predictive Value of Tests</topic><topic>Renal Replacement Therapy - methods</topic><topic>Republic of Korea</topic><topic>Retrospective Studies</topic><topic>Risk Factors</topic><topic>Sensitivity and Specificity</topic><topic>Surgery</topic><topic>Transplant Recipients</topic><topic>Treatment Outcome</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, J.M</creatorcontrib><creatorcontrib>Jo, Y.Y</creatorcontrib><creatorcontrib>Na, S.W</creatorcontrib><creatorcontrib>Kim, S.I</creatorcontrib><creatorcontrib>Choi, Y.S</creatorcontrib><creatorcontrib>Kim, N.O</creatorcontrib><creatorcontrib>Park, J.E</creatorcontrib><creatorcontrib>Koh, S.O</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Transplantation proceedings</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, J.M</au><au>Jo, Y.Y</au><au>Na, S.W</au><au>Kim, S.I</au><au>Choi, Y.S</au><au>Kim, N.O</au><au>Park, J.E</au><au>Koh, S.O</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Predictors for Continuous Renal Replacement Therapy in Liver Transplant Recipients</atitle><jtitle>Transplantation proceedings</jtitle><addtitle>Transplant Proc</addtitle><date>2014</date><risdate>2014</risdate><volume>46</volume><issue>1</issue><spage>184</spage><epage>191</epage><pages>184-191</pages><issn>0041-1345</issn><eissn>1873-2623</eissn><abstract>Abstract Background Acute renal failure (ARF) after liver transplantation requiring continuous renal replacement therapy (CRRT) adversely affects patient survival. We suggested that postoperative renal failure can be predicted if a clinically simple nomogram can be developed, thus selecting potential risk factors for preventive strategy. Methods We retrospectively reviewed the medical records of 153 liver transplant recipients from January 2008 to December 2011 at Severance Hospital, Yonsei University Health System, in Seoul, Korea. There were 42 patients treated with CRRT (20 and 22 patients received transplants from living and deceased donors, respectively) and 115 were not. Univariate and stepwise logistic multivariate analyses were performed. A clinical nomogram to predict postoperative CRRT application was constructed and validated internally. Results Hepatic encephalopathy (HEP; odds ratio OR, 5.47), deceased donor liver donations (OR, 3.47), Model for End-Stage Liver Disease (MELD) score (OR, 1.09), intraoperative blood loss (L; OR, 1.16), and tumor (hepatocellular carcinoma) as the indication for liver transplantation (OR, 0.11) were identified as independent predictive factors for postoperative CRRT on multivariate analysis. A clinical prediction model constructed for calculating the probability of CRRT post-transplantation was 1.7000 × HEP + [−4.5427 + 1.2440 × (deceased donor) + 0.0830 × (MELD score) + 0.000149 × the amount of intraoperative bleeding (L) − 2.1785 × tumor]. The validation set discriminated well with an area under the curve (AUC) of 0.90 (95% confidence interval, 0.85–0.95). The predicted and the actual probabilities were calibrated with the clinical nomogram. Conclusions We developed a predictive model of postoperative CRRT in liver transplantation patients. Perioperative strategies to modify these factors are needed.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>24507049</pmid><doi>10.1016/j.transproceed.2013.07.075</doi><tpages>8</tpages></addata></record>
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subjects Acute Kidney Injury - etiology
Acute Kidney Injury - therapy
Adult
Area Under Curve
Female
Hepatic Encephalopathy - physiopathology
Humans
Kaplan-Meier Estimate
Liver Failure - mortality
Liver Failure - surgery
Liver Transplantation - adverse effects
Male
Middle Aged
Multivariate Analysis
Predictive Value of Tests
Renal Replacement Therapy - methods
Republic of Korea
Retrospective Studies
Risk Factors
Sensitivity and Specificity
Surgery
Transplant Recipients
Treatment Outcome
title The Predictors for Continuous Renal Replacement Therapy in Liver Transplant Recipients
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