Predicting Virological Response to HIV Treatment Over Time: A Tool for Settings With Different Definitions of Virological Response
OBJECTIVE:Definitions of virological response vary from
Gespeichert in:
| Veröffentlicht in: | Journal of acquired immune deficiency syndromes (1999) 2019-06, Vol.81 (2), p.207-215 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 215 |
|---|---|
| container_issue | 2 |
| container_start_page | 207 |
| container_title | Journal of acquired immune deficiency syndromes (1999) |
| container_volume | 81 |
| creator | Revell, Andrew D Wang, Dechao Perez-Elias, Maria-Jesus Wood, Robin Tempelman, Hugo Clotet, Bonaventura Reiss, Peter van Sighem, Ard I Alvarez-Uria, Gerardo Nelson, Mark Montaner, Julio S G Lane, H Clifford Larder, Brendan A |
| description | OBJECTIVE:Definitions of virological response vary from |
| doi_str_mv | 10.1097/QAI.0000000000001989 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2191010766</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2239162492</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3849-419e488ab252cdb1c5037c279ac8e823377075b2dee75c754b7f818ad6d754253</originalsourceid><addsrcrecordid>eNp9kV1vFCEUhonR2Fr9B8aQeOPNVmBgAO82rdpNmtSPtV4ShjnTpTLDFhgbb_3lstlqTGPk5kDynOdw8iL0nJJjSrR8_XG5OiZ_HaqVfoAOqeZ8IZXiD-tdMLHgtBEH6EnO15VpOdeP0UFDVCuoag_Rzw8Jeu-Kn67wpU8xxCvvbMCfIG_jlAGXiM9Wl3idwJYRpoIvvkPCaz_CG7zE6xgDHmLCn6HsHBl_9WWDT_0wQNrRpzD4yRdfXTgO_xzxFD0abMjw7K4eoS_v3q5PzhbnF-9XJ8vzhWsU13UPDVwp2zHBXN9RJ0gjHZPaOgWKNY2URIqO9QBSOCl4JwdFle3bvj6YaI7Qq713m-LNDLmY0WcHIdgJ4pwNo5oSSmTbVvTlPfQ6zmmqvzOMNZq2jGtWKb6nXIo5JxjMNvnRph-GErPLyNSMzP2MatuLO_ncjdD_afodSgXUHriNoUDK38J8C8lswIay-b_7Fye3nQY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2239162492</pqid></control><display><type>article</type><title>Predicting Virological Response to HIV Treatment Over Time: A Tool for Settings With Different Definitions of Virological Response</title><source>MEDLINE</source><source>Journals@Ovid LWW Legacy Archive</source><source>Free E- Journals</source><creator>Revell, Andrew D ; Wang, Dechao ; Perez-Elias, Maria-Jesus ; Wood, Robin ; Tempelman, Hugo ; Clotet, Bonaventura ; Reiss, Peter ; van Sighem, Ard I ; Alvarez-Uria, Gerardo ; Nelson, Mark ; Montaner, Julio S G ; Lane, H Clifford ; Larder, Brendan A</creator><creatorcontrib>Revell, Andrew D ; Wang, Dechao ; Perez-Elias, Maria-Jesus ; Wood, Robin ; Tempelman, Hugo ; Clotet, Bonaventura ; Reiss, Peter ; van Sighem, Ard I ; Alvarez-Uria, Gerardo ; Nelson, Mark ; Montaner, Julio S G ; Lane, H Clifford ; Larder, Brendan A ; on behalf of the RDI study group</creatorcontrib><description>OBJECTIVE:Definitions of virological response vary from <50 up to 1000 copies of HIV-RNA/mL. Our previous models estimate the probability of HIV drug combinations reducing the viral load to <50 copies/mL, with no indication of whether higher thresholds of response may be achieved. Here, we describe the development of models that predict absolute viral load over time.
METHODS:Two sets of random forest models were developed using 50,270 treatment change episodes from more than 20 countries. The models estimated viral load at different time points following the introduction of a new regimen from variables including baseline viral load, CD4 count, and treatment history. One set also used genotypes in their predictions. Independent data sets were used for evaluation.
RESULTS:Both models achieved highly significant correlations between predicted and actual viral load changes (r = 0.67–0.68, mean absolute error of 0.73–0.74 log10 copies/mL). The models produced curves of virological response over time. Using failure definitions of <100, 400, or 1000 copies/mL, but not 50 copies/mL, both models were able to identify alternative regimens they predicted to be effective for the majority of cases where the new regimen prescribed in the clinic failed.
CONCLUSIONS:These models could be useful for selecting the optimum combination therapy for patients requiring a change in therapy in settings using any definition of virological response. They also give an idea of the likely response curve over time. Given that genotypes are not required, these models could be a useful addition to the HIV-TRePS system for those in resource-limited settings.</description><identifier>ISSN: 1525-4135</identifier><identifier>EISSN: 1944-7884</identifier><identifier>DOI: 10.1097/QAI.0000000000001989</identifier><identifier>PMID: 30865186</identifier><language>eng</language><publisher>United States: Copyright Wolters Kluwer Health, Inc. All rights reserved</publisher><subject>Adult ; AIDS/HIV ; Anti-Retroviral Agents - pharmacology ; Anti-Retroviral Agents - therapeutic use ; Antiretroviral drugs ; CD4 antigen ; CD4 Lymphocyte Count ; Drug Therapy, Combination ; Female ; Forest management ; Genotype ; Genotypes ; HIV ; HIV - drug effects ; HIV Infections - drug therapy ; HIV Infections - virology ; Human immunodeficiency virus ; Humans ; Male ; Models, Statistical ; Predictions ; Ribonucleic acid ; RNA ; RNA, Viral - blood ; Therapy ; Viral Load - drug effects</subject><ispartof>Journal of acquired immune deficiency syndromes (1999), 2019-06, Vol.81 (2), p.207-215</ispartof><rights>Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.</rights><rights>Copyright Lippincott Williams & Wilkins Ovid Technologies Jun 1, 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3849-419e488ab252cdb1c5037c279ac8e823377075b2dee75c754b7f818ad6d754253</citedby><cites>FETCH-LOGICAL-c3849-419e488ab252cdb1c5037c279ac8e823377075b2dee75c754b7f818ad6d754253</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30865186$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Revell, Andrew D</creatorcontrib><creatorcontrib>Wang, Dechao</creatorcontrib><creatorcontrib>Perez-Elias, Maria-Jesus</creatorcontrib><creatorcontrib>Wood, Robin</creatorcontrib><creatorcontrib>Tempelman, Hugo</creatorcontrib><creatorcontrib>Clotet, Bonaventura</creatorcontrib><creatorcontrib>Reiss, Peter</creatorcontrib><creatorcontrib>van Sighem, Ard I</creatorcontrib><creatorcontrib>Alvarez-Uria, Gerardo</creatorcontrib><creatorcontrib>Nelson, Mark</creatorcontrib><creatorcontrib>Montaner, Julio S G</creatorcontrib><creatorcontrib>Lane, H Clifford</creatorcontrib><creatorcontrib>Larder, Brendan A</creatorcontrib><creatorcontrib>on behalf of the RDI study group</creatorcontrib><title>Predicting Virological Response to HIV Treatment Over Time: A Tool for Settings With Different Definitions of Virological Response</title><title>Journal of acquired immune deficiency syndromes (1999)</title><addtitle>J Acquir Immune Defic Syndr</addtitle><description>OBJECTIVE:Definitions of virological response vary from <50 up to 1000 copies of HIV-RNA/mL. Our previous models estimate the probability of HIV drug combinations reducing the viral load to <50 copies/mL, with no indication of whether higher thresholds of response may be achieved. Here, we describe the development of models that predict absolute viral load over time.
METHODS:Two sets of random forest models were developed using 50,270 treatment change episodes from more than 20 countries. The models estimated viral load at different time points following the introduction of a new regimen from variables including baseline viral load, CD4 count, and treatment history. One set also used genotypes in their predictions. Independent data sets were used for evaluation.
RESULTS:Both models achieved highly significant correlations between predicted and actual viral load changes (r = 0.67–0.68, mean absolute error of 0.73–0.74 log10 copies/mL). The models produced curves of virological response over time. Using failure definitions of <100, 400, or 1000 copies/mL, but not 50 copies/mL, both models were able to identify alternative regimens they predicted to be effective for the majority of cases where the new regimen prescribed in the clinic failed.
CONCLUSIONS:These models could be useful for selecting the optimum combination therapy for patients requiring a change in therapy in settings using any definition of virological response. They also give an idea of the likely response curve over time. Given that genotypes are not required, these models could be a useful addition to the HIV-TRePS system for those in resource-limited settings.</description><subject>Adult</subject><subject>AIDS/HIV</subject><subject>Anti-Retroviral Agents - pharmacology</subject><subject>Anti-Retroviral Agents - therapeutic use</subject><subject>Antiretroviral drugs</subject><subject>CD4 antigen</subject><subject>CD4 Lymphocyte Count</subject><subject>Drug Therapy, Combination</subject><subject>Female</subject><subject>Forest management</subject><subject>Genotype</subject><subject>Genotypes</subject><subject>HIV</subject><subject>HIV - drug effects</subject><subject>HIV Infections - drug therapy</subject><subject>HIV Infections - virology</subject><subject>Human immunodeficiency virus</subject><subject>Humans</subject><subject>Male</subject><subject>Models, Statistical</subject><subject>Predictions</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>RNA, Viral - blood</subject><subject>Therapy</subject><subject>Viral Load - drug effects</subject><issn>1525-4135</issn><issn>1944-7884</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kV1vFCEUhonR2Fr9B8aQeOPNVmBgAO82rdpNmtSPtV4ShjnTpTLDFhgbb_3lstlqTGPk5kDynOdw8iL0nJJjSrR8_XG5OiZ_HaqVfoAOqeZ8IZXiD-tdMLHgtBEH6EnO15VpOdeP0UFDVCuoag_Rzw8Jeu-Kn67wpU8xxCvvbMCfIG_jlAGXiM9Wl3idwJYRpoIvvkPCaz_CG7zE6xgDHmLCn6HsHBl_9WWDT_0wQNrRpzD4yRdfXTgO_xzxFD0abMjw7K4eoS_v3q5PzhbnF-9XJ8vzhWsU13UPDVwp2zHBXN9RJ0gjHZPaOgWKNY2URIqO9QBSOCl4JwdFle3bvj6YaI7Qq713m-LNDLmY0WcHIdgJ4pwNo5oSSmTbVvTlPfQ6zmmqvzOMNZq2jGtWKb6nXIo5JxjMNvnRph-GErPLyNSMzP2MatuLO_ncjdD_afodSgXUHriNoUDK38J8C8lswIay-b_7Fye3nQY</recordid><startdate>20190601</startdate><enddate>20190601</enddate><creator>Revell, Andrew D</creator><creator>Wang, Dechao</creator><creator>Perez-Elias, Maria-Jesus</creator><creator>Wood, Robin</creator><creator>Tempelman, Hugo</creator><creator>Clotet, Bonaventura</creator><creator>Reiss, Peter</creator><creator>van Sighem, Ard I</creator><creator>Alvarez-Uria, Gerardo</creator><creator>Nelson, Mark</creator><creator>Montaner, Julio S G</creator><creator>Lane, H Clifford</creator><creator>Larder, Brendan A</creator><general>Copyright Wolters Kluwer Health, Inc. All rights reserved</general><general>Lippincott Williams & Wilkins Ovid Technologies</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>7T2</scope><scope>7T5</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>C1K</scope><scope>H94</scope><scope>K9.</scope><scope>7X8</scope></search><sort><creationdate>20190601</creationdate><title>Predicting Virological Response to HIV Treatment Over Time: A Tool for Settings With Different Definitions of Virological Response</title><author>Revell, Andrew D ; Wang, Dechao ; Perez-Elias, Maria-Jesus ; Wood, Robin ; Tempelman, Hugo ; Clotet, Bonaventura ; Reiss, Peter ; van Sighem, Ard I ; Alvarez-Uria, Gerardo ; Nelson, Mark ; Montaner, Julio S G ; Lane, H Clifford ; Larder, Brendan A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3849-419e488ab252cdb1c5037c279ac8e823377075b2dee75c754b7f818ad6d754253</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adult</topic><topic>AIDS/HIV</topic><topic>Anti-Retroviral Agents - pharmacology</topic><topic>Anti-Retroviral Agents - therapeutic use</topic><topic>Antiretroviral drugs</topic><topic>CD4 antigen</topic><topic>CD4 Lymphocyte Count</topic><topic>Drug Therapy, Combination</topic><topic>Female</topic><topic>Forest management</topic><topic>Genotype</topic><topic>Genotypes</topic><topic>HIV</topic><topic>HIV - drug effects</topic><topic>HIV Infections - drug therapy</topic><topic>HIV Infections - virology</topic><topic>Human immunodeficiency virus</topic><topic>Humans</topic><topic>Male</topic><topic>Models, Statistical</topic><topic>Predictions</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>RNA, Viral - blood</topic><topic>Therapy</topic><topic>Viral Load - drug effects</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Revell, Andrew D</creatorcontrib><creatorcontrib>Wang, Dechao</creatorcontrib><creatorcontrib>Perez-Elias, Maria-Jesus</creatorcontrib><creatorcontrib>Wood, Robin</creatorcontrib><creatorcontrib>Tempelman, Hugo</creatorcontrib><creatorcontrib>Clotet, Bonaventura</creatorcontrib><creatorcontrib>Reiss, Peter</creatorcontrib><creatorcontrib>van Sighem, Ard I</creatorcontrib><creatorcontrib>Alvarez-Uria, Gerardo</creatorcontrib><creatorcontrib>Nelson, Mark</creatorcontrib><creatorcontrib>Montaner, Julio S G</creatorcontrib><creatorcontrib>Lane, H Clifford</creatorcontrib><creatorcontrib>Larder, Brendan A</creatorcontrib><creatorcontrib>on behalf of the RDI study group</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of acquired immune deficiency syndromes (1999)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Revell, Andrew D</au><au>Wang, Dechao</au><au>Perez-Elias, Maria-Jesus</au><au>Wood, Robin</au><au>Tempelman, Hugo</au><au>Clotet, Bonaventura</au><au>Reiss, Peter</au><au>van Sighem, Ard I</au><au>Alvarez-Uria, Gerardo</au><au>Nelson, Mark</au><au>Montaner, Julio S G</au><au>Lane, H Clifford</au><au>Larder, Brendan A</au><aucorp>on behalf of the RDI study group</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting Virological Response to HIV Treatment Over Time: A Tool for Settings With Different Definitions of Virological Response</atitle><jtitle>Journal of acquired immune deficiency syndromes (1999)</jtitle><addtitle>J Acquir Immune Defic Syndr</addtitle><date>2019-06-01</date><risdate>2019</risdate><volume>81</volume><issue>2</issue><spage>207</spage><epage>215</epage><pages>207-215</pages><issn>1525-4135</issn><eissn>1944-7884</eissn><abstract>OBJECTIVE:Definitions of virological response vary from <50 up to 1000 copies of HIV-RNA/mL. Our previous models estimate the probability of HIV drug combinations reducing the viral load to <50 copies/mL, with no indication of whether higher thresholds of response may be achieved. Here, we describe the development of models that predict absolute viral load over time.
METHODS:Two sets of random forest models were developed using 50,270 treatment change episodes from more than 20 countries. The models estimated viral load at different time points following the introduction of a new regimen from variables including baseline viral load, CD4 count, and treatment history. One set also used genotypes in their predictions. Independent data sets were used for evaluation.
RESULTS:Both models achieved highly significant correlations between predicted and actual viral load changes (r = 0.67–0.68, mean absolute error of 0.73–0.74 log10 copies/mL). The models produced curves of virological response over time. Using failure definitions of <100, 400, or 1000 copies/mL, but not 50 copies/mL, both models were able to identify alternative regimens they predicted to be effective for the majority of cases where the new regimen prescribed in the clinic failed.
CONCLUSIONS:These models could be useful for selecting the optimum combination therapy for patients requiring a change in therapy in settings using any definition of virological response. They also give an idea of the likely response curve over time. Given that genotypes are not required, these models could be a useful addition to the HIV-TRePS system for those in resource-limited settings.</abstract><cop>United States</cop><pub>Copyright Wolters Kluwer Health, Inc. All rights reserved</pub><pmid>30865186</pmid><doi>10.1097/QAI.0000000000001989</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1525-4135 |
| ispartof | Journal of acquired immune deficiency syndromes (1999), 2019-06, Vol.81 (2), p.207-215 |
| issn | 1525-4135 1944-7884 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2191010766 |
| source | MEDLINE; Journals@Ovid LWW Legacy Archive; Free E- Journals |
| subjects | Adult AIDS/HIV Anti-Retroviral Agents - pharmacology Anti-Retroviral Agents - therapeutic use Antiretroviral drugs CD4 antigen CD4 Lymphocyte Count Drug Therapy, Combination Female Forest management Genotype Genotypes HIV HIV - drug effects HIV Infections - drug therapy HIV Infections - virology Human immunodeficiency virus Humans Male Models, Statistical Predictions Ribonucleic acid RNA RNA, Viral - blood Therapy Viral Load - drug effects |
| title | Predicting Virological Response to HIV Treatment Over Time: A Tool for Settings With Different Definitions of Virological Response |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-09T17%3A13%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Predicting%20Virological%20Response%20to%20HIV%20Treatment%20Over%20Time:%20A%20Tool%20for%20Settings%20With%20Different%20Definitions%20of%20Virological%20Response&rft.jtitle=Journal%20of%20acquired%20immune%20deficiency%20syndromes%20(1999)&rft.au=Revell,%20Andrew%20D&rft.aucorp=on%20behalf%20of%20the%20RDI%20study%20group&rft.date=2019-06-01&rft.volume=81&rft.issue=2&rft.spage=207&rft.epage=215&rft.pages=207-215&rft.issn=1525-4135&rft.eissn=1944-7884&rft_id=info:doi/10.1097/QAI.0000000000001989&rft_dat=%3Cproquest_cross%3E2239162492%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2239162492&rft_id=info:pmid/30865186&rfr_iscdi=true |