Comparing Child-Pugh, MELD, and FIB-4 to Predict Clinical Outcomes in Hepatitis C Virus-Infected Persons: Results From ERCHIVES
Background. Identifying hepatitis C virus (HCV)-positive persons at high risk of early complications can help prioritize treatment decisions. We conducted this study to compare Child-Turcotte-Pugh (CP), MELD, and FIB-4 scores for predicting clinical outcomes and to identify those at low risk of comp...
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| Veröffentlicht in: | Clinical infectious diseases 2017-07, Vol.65 (1), p.64-72 |
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| creator | Butt, Adeel A. Ren, Yanjie Re, Vincent Lo Taddei, Tamar H. Kaplan, David E. |
| description | Background. Identifying hepatitis C virus (HCV)-positive persons at high risk of early complications can help prioritize treatment decisions. We conducted this study to compare Child-Turcotte-Pugh (CP), MELD, and FIB-4 scores for predicting clinical outcomes and to identify those at low risk of complications. Methods. Within electronically retrieved cohort of HCV-infected veterans, we identified HCV-positive persons and excluded those with human immunodeficiency virus (HIV), hepatitis B surface antigen (HBsAg), prevalent hepatic decompensation (HD), hepatocellular carcinoma (HCC), and those treated for HCV. We calculated incidence rates for HD, HCC, and all-cause mortality at 1, 3, and 5 years after HCV diagnosis. Using receiver operating characteristic (ROC) curves, we determined the optimal cut-off values for each score for these outcomes. Results. Among 21 116 persons evaluated, 89.7% were CP Class-A, 79.9% had MELD |
| doi_str_mv | 10.1093/cid/cix224 |
| format | Article |
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Identifying hepatitis C virus (HCV)-positive persons at high risk of early complications can help prioritize treatment decisions. We conducted this study to compare Child-Turcotte-Pugh (CP), MELD, and FIB-4 scores for predicting clinical outcomes and to identify those at low risk of complications. Methods. Within electronically retrieved cohort of HCV-infected veterans, we identified HCV-positive persons and excluded those with human immunodeficiency virus (HIV), hepatitis B surface antigen (HBsAg), prevalent hepatic decompensation (HD), hepatocellular carcinoma (HCC), and those treated for HCV. We calculated incidence rates for HD, HCC, and all-cause mortality at 1, 3, and 5 years after HCV diagnosis. Using receiver operating characteristic (ROC) curves, we determined the optimal cut-off values for each score for these outcomes. Results. Among 21 116 persons evaluated, 89.7% were CP Class-A, 79.9% had MELD<9, and 43.4% had FIB-4<1.45. AUROC for HD at 1, 3, and 5 years was higher for FIB-4 (0.84–0.86) compared with MELD (0.70–0.76) (P < .001). AUROC for HCC at 1, 3, and 5 years was 0.81–0.82 for FIB-4 but 0.61–0.68 for CP and MELD scores. (P < .001) AUROC for all-cause mortality at 3 and 5 years was 0.65–0.68. The optimal cut-off scores to identify persons at low risk of complications were as follows: CP <5; MELD <8; FIB-4 <3 for HD and HCC, and <2 for all-cause mortality, below which <1.5% developed HD and HCC and ≥2.5% died at 3 years. Conclusions. FIB-4 score is a better predictor of HD and HCC in HCV-positive persons. A score of <3 is associated with a low risk of HD and HCC 1 and 3 years after HCV diagnosis.]]></description><identifier>ISSN: 1058-4838</identifier><identifier>EISSN: 1537-6591</identifier><identifier>DOI: 10.1093/cid/cix224</identifier><identifier>PMID: 28369305</identifier><language>eng</language><publisher>United States: Oxford University Press</publisher><subject>ARTICLES AND COMMENTARIES ; Clinical outcomes ; Cohort Studies ; Comparative analysis ; Complications ; Diagnosis ; Female ; Hepatitis ; Hepatitis B ; Hepatitis B surface antigen ; Hepatitis C ; Hepatitis C - epidemiology ; Hepatitis C - mortality ; Hepatocellular carcinoma ; HIV ; Human immunodeficiency virus ; Humans ; Incidence ; Kaplan-Meier Estimate ; Male ; Middle Aged ; Mortality ; Risk ; ROC Curve ; Severity of Illness Index ; Treatment Outcome ; Viruses</subject><ispartof>Clinical infectious diseases, 2017-07, Vol.65 (1), p.64-72</ispartof><rights>Copyright © 2017 Oxford University Press on behalf of the Infectious Diseases Society of America</rights><rights>The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com</rights><rights>Copyright Oxford University Press, UK Jul 1, 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c439t-d45dd92daaf89c3129d039637a21f81b9e3e4da53e6ade1a19c539a624abf2b3</citedby><cites>FETCH-LOGICAL-c439t-d45dd92daaf89c3129d039637a21f81b9e3e4da53e6ade1a19c539a624abf2b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26374879$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26374879$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>315,781,785,804,27926,27927,58019,58252</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28369305$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Butt, Adeel A.</creatorcontrib><creatorcontrib>Ren, Yanjie</creatorcontrib><creatorcontrib>Re, Vincent Lo</creatorcontrib><creatorcontrib>Taddei, Tamar H.</creatorcontrib><creatorcontrib>Kaplan, David E.</creatorcontrib><title>Comparing Child-Pugh, MELD, and FIB-4 to Predict Clinical Outcomes in Hepatitis C Virus-Infected Persons: Results From ERCHIVES</title><title>Clinical infectious diseases</title><addtitle>Clin Infect Dis</addtitle><description><![CDATA[Background. Identifying hepatitis C virus (HCV)-positive persons at high risk of early complications can help prioritize treatment decisions. We conducted this study to compare Child-Turcotte-Pugh (CP), MELD, and FIB-4 scores for predicting clinical outcomes and to identify those at low risk of complications. Methods. Within electronically retrieved cohort of HCV-infected veterans, we identified HCV-positive persons and excluded those with human immunodeficiency virus (HIV), hepatitis B surface antigen (HBsAg), prevalent hepatic decompensation (HD), hepatocellular carcinoma (HCC), and those treated for HCV. We calculated incidence rates for HD, HCC, and all-cause mortality at 1, 3, and 5 years after HCV diagnosis. Using receiver operating characteristic (ROC) curves, we determined the optimal cut-off values for each score for these outcomes. Results. Among 21 116 persons evaluated, 89.7% were CP Class-A, 79.9% had MELD<9, and 43.4% had FIB-4<1.45. AUROC for HD at 1, 3, and 5 years was higher for FIB-4 (0.84–0.86) compared with MELD (0.70–0.76) (P < .001). AUROC for HCC at 1, 3, and 5 years was 0.81–0.82 for FIB-4 but 0.61–0.68 for CP and MELD scores. (P < .001) AUROC for all-cause mortality at 3 and 5 years was 0.65–0.68. The optimal cut-off scores to identify persons at low risk of complications were as follows: CP <5; MELD <8; FIB-4 <3 for HD and HCC, and <2 for all-cause mortality, below which <1.5% developed HD and HCC and ≥2.5% died at 3 years. Conclusions. FIB-4 score is a better predictor of HD and HCC in HCV-positive persons. A score of <3 is associated with a low risk of HD and HCC 1 and 3 years after HCV diagnosis.]]></description><subject>ARTICLES AND COMMENTARIES</subject><subject>Clinical outcomes</subject><subject>Cohort Studies</subject><subject>Comparative analysis</subject><subject>Complications</subject><subject>Diagnosis</subject><subject>Female</subject><subject>Hepatitis</subject><subject>Hepatitis B</subject><subject>Hepatitis B surface antigen</subject><subject>Hepatitis C</subject><subject>Hepatitis C - epidemiology</subject><subject>Hepatitis C - mortality</subject><subject>Hepatocellular carcinoma</subject><subject>HIV</subject><subject>Human immunodeficiency virus</subject><subject>Humans</subject><subject>Incidence</subject><subject>Kaplan-Meier Estimate</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Mortality</subject><subject>Risk</subject><subject>ROC Curve</subject><subject>Severity of Illness Index</subject><subject>Treatment Outcome</subject><subject>Viruses</subject><issn>1058-4838</issn><issn>1537-6591</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkd9rFDEQx4NY7A998V0J9EWkq_m5l_hW17vewUmPWvq65JLZNsfu5kyyUJ_8141cVfBhmIH58GWGD0KvKflAieYfrXelHhkTz9AJlXxW1VLT52UmUlVCcXWMTlPaEUKpIvIFOmaK15oTeYJ-NmHYm-jHe9w8-N5Vm-n-4QJ_na-_XGAzOrxYfa4EzgFvIjhvM256P3prenw9ZRsGSNiPeAl7k332CTf4zscpVauxA5vB4Q3EFMb0Cd9Amvqc8CKGAc9vmuXqbv7tJTrqTJ_g1VM_Q7eL-W2zrNbXV6vmcl1ZwXWunJDOaeaM6ZS2nDLtCNc1nxlGO0W3GjgIZySH2jighmoruTY1E2bbsS0_Q-8OsfsYvk-Qcjv4ZKHvzQhhSi1VStBaUD0r6Pl_6C5McSzHtVQzzmoptSjU-wNlY0gpQtfuox9M_NFS0v620hYr7cFKgd8-RU7bAdxf9I-GArw5ALuUQ_y3Lw8KNdP8F-hikGo</recordid><startdate>20170701</startdate><enddate>20170701</enddate><creator>Butt, Adeel A.</creator><creator>Ren, Yanjie</creator><creator>Re, Vincent Lo</creator><creator>Taddei, Tamar H.</creator><creator>Kaplan, David E.</creator><general>Oxford University Press</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>7QL</scope><scope>7T2</scope><scope>7T7</scope><scope>7U7</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>M7N</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20170701</creationdate><title>Comparing Child-Pugh, MELD, and FIB-4 to Predict Clinical Outcomes in Hepatitis C Virus-Infected Persons: Results From ERCHIVES</title><author>Butt, Adeel A. ; Ren, Yanjie ; Re, Vincent Lo ; Taddei, Tamar H. ; Kaplan, David E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c439t-d45dd92daaf89c3129d039637a21f81b9e3e4da53e6ade1a19c539a624abf2b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>ARTICLES AND COMMENTARIES</topic><topic>Clinical outcomes</topic><topic>Cohort Studies</topic><topic>Comparative analysis</topic><topic>Complications</topic><topic>Diagnosis</topic><topic>Female</topic><topic>Hepatitis</topic><topic>Hepatitis B</topic><topic>Hepatitis B surface antigen</topic><topic>Hepatitis C</topic><topic>Hepatitis C - epidemiology</topic><topic>Hepatitis C - mortality</topic><topic>Hepatocellular carcinoma</topic><topic>HIV</topic><topic>Human immunodeficiency virus</topic><topic>Humans</topic><topic>Incidence</topic><topic>Kaplan-Meier Estimate</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Mortality</topic><topic>Risk</topic><topic>ROC Curve</topic><topic>Severity of Illness Index</topic><topic>Treatment Outcome</topic><topic>Viruses</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Butt, Adeel A.</creatorcontrib><creatorcontrib>Ren, Yanjie</creatorcontrib><creatorcontrib>Re, Vincent Lo</creatorcontrib><creatorcontrib>Taddei, Tamar H.</creatorcontrib><creatorcontrib>Kaplan, David E.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Clinical infectious diseases</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Butt, Adeel A.</au><au>Ren, Yanjie</au><au>Re, Vincent Lo</au><au>Taddei, Tamar H.</au><au>Kaplan, David E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparing Child-Pugh, MELD, and FIB-4 to Predict Clinical Outcomes in Hepatitis C Virus-Infected Persons: Results From ERCHIVES</atitle><jtitle>Clinical infectious diseases</jtitle><addtitle>Clin Infect Dis</addtitle><date>2017-07-01</date><risdate>2017</risdate><volume>65</volume><issue>1</issue><spage>64</spage><epage>72</epage><pages>64-72</pages><issn>1058-4838</issn><eissn>1537-6591</eissn><abstract><![CDATA[Background. Identifying hepatitis C virus (HCV)-positive persons at high risk of early complications can help prioritize treatment decisions. We conducted this study to compare Child-Turcotte-Pugh (CP), MELD, and FIB-4 scores for predicting clinical outcomes and to identify those at low risk of complications. Methods. Within electronically retrieved cohort of HCV-infected veterans, we identified HCV-positive persons and excluded those with human immunodeficiency virus (HIV), hepatitis B surface antigen (HBsAg), prevalent hepatic decompensation (HD), hepatocellular carcinoma (HCC), and those treated for HCV. We calculated incidence rates for HD, HCC, and all-cause mortality at 1, 3, and 5 years after HCV diagnosis. Using receiver operating characteristic (ROC) curves, we determined the optimal cut-off values for each score for these outcomes. Results. Among 21 116 persons evaluated, 89.7% were CP Class-A, 79.9% had MELD<9, and 43.4% had FIB-4<1.45. AUROC for HD at 1, 3, and 5 years was higher for FIB-4 (0.84–0.86) compared with MELD (0.70–0.76) (P < .001). AUROC for HCC at 1, 3, and 5 years was 0.81–0.82 for FIB-4 but 0.61–0.68 for CP and MELD scores. (P < .001) AUROC for all-cause mortality at 3 and 5 years was 0.65–0.68. The optimal cut-off scores to identify persons at low risk of complications were as follows: CP <5; MELD <8; FIB-4 <3 for HD and HCC, and <2 for all-cause mortality, below which <1.5% developed HD and HCC and ≥2.5% died at 3 years. Conclusions. FIB-4 score is a better predictor of HD and HCC in HCV-positive persons. A score of <3 is associated with a low risk of HD and HCC 1 and 3 years after HCV diagnosis.]]></abstract><cop>United States</cop><pub>Oxford University Press</pub><pmid>28369305</pmid><doi>10.1093/cid/cix224</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Oxford University Press Journals All Titles (1996-Current); EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection; JSTOR |
| subjects | ARTICLES AND COMMENTARIES Clinical outcomes Cohort Studies Comparative analysis Complications Diagnosis Female Hepatitis Hepatitis B Hepatitis B surface antigen Hepatitis C Hepatitis C - epidemiology Hepatitis C - mortality Hepatocellular carcinoma HIV Human immunodeficiency virus Humans Incidence Kaplan-Meier Estimate Male Middle Aged Mortality Risk ROC Curve Severity of Illness Index Treatment Outcome Viruses |
| title | Comparing Child-Pugh, MELD, and FIB-4 to Predict Clinical Outcomes in Hepatitis C Virus-Infected Persons: Results From ERCHIVES |
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