Literature DB >> 23485767

Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings.

A D Revell1, D Wang, R Wood, C Morrow, H Tempelman, R L Hamers, G Alvarez-Uria, A Streinu-Cercel, L Ene, A M J Wensing, F DeWolf, M Nelson, J S Montaner, H C Lane, B A Larder.   

Abstract

OBJECTIVES: Genotypic HIV drug-resistance testing is typically 60%-65% predictive of response to combination antiretroviral therapy (ART) and is valuable for guiding treatment changes. Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART without a genotype and evaluated their potential as a treatment support tool in RLSs.
METHODS: Random forest models were trained to predict the probability of response to ART (≤400 copies HIV RNA/mL) using the following data from 14 891 treatment change episodes (TCEs) after virological failure, from well-resourced countries: viral load and CD4 count prior to treatment change, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. Models were assessed by cross-validation during development, with an independent set of 800 cases from well-resourced countries, plus 231 cases from Southern Africa, 206 from India and 375 from Romania. The area under the receiver operating characteristic curve (AUC) was the main outcome measure.
RESULTS: The models achieved an AUC of 0.74-0.81 during cross-validation and 0.76-0.77 with the 800 test TCEs. They achieved AUCs of 0.58-0.65 (Southern Africa), 0.63 (India) and 0.70 (Romania). Models were more accurate for data from the well-resourced countries than for cases from Southern Africa and India (P < 0.001), but not Romania. The models identified alternative, available drug regimens predicted to result in virological response for 94% of virological failures in Southern Africa, 99% of those in India and 93% of those in Romania.
CONCLUSIONS: We developed computational models that predict virological response to ART without a genotype with comparable accuracy to genotyping with rule-based interpretation. These models have the potential to help optimize antiretroviral therapy for patients in RLSs where genotyping is not generally available.

Entities:  

Keywords:  HIV drug resistance; antiretroviral therapy; computer models; predictions; resource-limited settings.; treatment outcomes

Mesh:

Substances:

Year:  2013        PMID: 23485767      PMCID: PMC3654223          DOI: 10.1093/jac/dkt041

Source DB:  PubMed          Journal:  J Antimicrob Chemother        ISSN: 0305-7453            Impact factor:   5.790


  24 in total

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Authors:  Brendan A Larder; Andrew Revell; Joann M Mican; Brian K Agan; Marianne Harris; Carlo Torti; Ilaria Izzo; Julia A Metcalf; Migdalia Rivera-Goba; Vincent C Marconi; Dechao Wang; Daniel Coe; Brian Gazzard; Julio Montaner; H Clifford Lane
Journal:  AIDS Patient Care STDS       Date:  2011-01       Impact factor: 5.078

2.  The development of artificial neural networks to predict virological response to combination HIV therapy.

Authors:  Brendan Larder; Dechao Wang; Andrew Revell; Julio Montaner; Richard Harrigan; Frank De Wolf; Joep Lange; Scott Wegner; Lidia Ruiz; Maria Jésus Pérez-Elías; Sean Emery; Jose Gatell; Antonella D'Arminio Monforte; Carlo Torti; Maurizio Zazzi; Clifford Lane
Journal:  Antivir Ther       Date:  2007

3.  A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy.

Authors:  Dechao Wang; Brendan Larder; Andrew Revell; Julio Montaner; Richard Harrigan; Frank De Wolf; Joep Lange; Scott Wegner; Lidia Ruiz; María Jésus Pérez-Elías; Sean Emery; Jose Gatell; Antonella D'Arminio Monforte; Carlo Torti; Maurizio Zazzi; Clifford Lane
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4.  Accumulation of drug resistance and loss of therapeutic options precede commonly used criteria for treatment failure in HIV-1 subtype-C-infected patients.

Authors:  Roos E Barth; Susan C Aitken; Hugo Tempelman; Sibyl P Geelen; Erik M van Bussel; Andy I M Hoepelman; Rob Schuurman; Annemarie M J Wensing
Journal:  Antivir Ther       Date:  2011-12-02

5.  Unnecessary antiretroviral treatment switches and accumulation of HIV resistance mutations; two arguments for viral load monitoring in Africa.

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Journal:  J Acquir Immune Defic Syndr       Date:  2011-09-01       Impact factor: 3.731

6.  Antiretroviral treatment of adult HIV infection: 2010 recommendations of the International AIDS Society-USA panel.

Authors:  Melanie A Thompson; Judith A Aberg; Pedro Cahn; Julio S G Montaner; Giuliano Rizzardini; Amalio Telenti; José M Gatell; Huldrych F Günthard; Scott M Hammer; Martin S Hirsch; Donna M Jacobsen; Peter Reiss; Douglas D Richman; Paul A Volberding; Patrick Yeni; Robert T Schooley
Journal:  JAMA       Date:  2010-07-21       Impact factor: 56.272

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8.  Virological monitoring and resistance to first-line highly active antiretroviral therapy in adults infected with HIV-1 treated under WHO guidelines: a systematic review and meta-analysis.

Authors:  Ravindra K Gupta; Andrew Hill; Anthony W Sawyer; Alessandro Cozzi-Lepri; Viktor von Wyl; Sabine Yerly; Viviane Dias Lima; Huldrych F Günthard; Charles Gilks; Deenan Pillay
Journal:  Lancet Infect Dis       Date:  2009-07       Impact factor: 25.071

9.  Routine versus clinically driven laboratory monitoring of HIV antiretroviral therapy in Africa (DART): a randomised non-inferiority trial.

Authors:  P Mugyenyi; A S Walker; J Hakim; P Munderi; D M Gibb; C Kityo; A Reid; H Grosskurth; J H Darbyshire; F Ssali; D Bray; E Katabira; A G Babiker; C F Gilks; H Grosskurth; P Munderi; G Kabuye; D Nsibambi; R Kasirye; E Zalwango; M Nakazibwe; B Kikaire; G Nassuna; R Massa; K Fadhiru; M Namyalo; A Zalwango; L Generous; P Khauka; N Rutikarayo; W Nakahima; A Mugisha; J Todd; J Levin; S Muyingo; A Ruberantwari; P Kaleebu; D Yirrell; N Ndembi; F Lyagoba; P Hughes; M Aber; A Medina Lara; S Foster; J Amurwon; B Nyanzi Wakholi; J Whitworth; K Wangati; B Amuron; D Kajungu; J Nakiyingi; W Omony; K Fadhiru; D Nsibambi; P Khauka; P Mugyenyi; C Kityo; F Ssali; D Tumukunde; T Otim; J Kabanda; H Musana; J Akao; H Kyomugisha; A Byamukama; J Sabiiti; J Komugyena; P Wavamunno; S Mukiibi; A Drasiku; R Byaruhanga; O Labeja; P Katundu; S Tugume; P Awio; A Namazzi; G T Bakeinyaga; H Katabira; D Abaine; J Tukamushaba; W Anywar; W Ojiambo; E Angweng; S Murungi; W Haguma; S Atwiine; J Kigozi; L Namale; A Mukose; G Mulindwa; D Atwiine; A Muhwezi; E Nimwesiga; G Barungi; J Takubwa; S Murungi; D Mwebesa; G Kagina; M Mulindwa; F Ahimbisibwe; P Mwesigwa; S Akuma; C Zawedde; D Nyiraguhirwa; C Tumusiime; L Bagaya; W Namara; J Kigozi; J Karungi; R Kankunda; R Enzama; A Latif; J Hakim; V Robertson; A Reid; E Chidziva; R Bulaya-Tembo; G Musoro; F Taziwa; C Chimbetete; L Chakonza; A Mawora; C Muvirimi; G Tinago; P Svovanapasis; M Simango; O Chirema; J Machingura; S Mutsai; M Phiri; T Bafana; M Chirara; L Muchabaiwa; M Muzambi; J Mutowo; T Chivhunga; E Chigwedere; M Pascoe; C Warambwa; E Zengeza; F Mapinge; S Makota; A Jamu; N Ngorima; H Chirairo; S Chitsungo; J Chimanzi; C Maweni; R Warara; M Matongo; S Mudzingwa; M Jangano; K Moyo; L Vere; N Mdege; I Machingura; E Katabira; A Ronald; A Kambungu; F Lutwama; I Mambule; A Nanfuka; J Walusimbi; E Nabankema; R Nalumenya; T Namuli; R Kulume; I Namata; L Nyachwo; A Florence; A Kusiima; E Lubwama; R Nairuba; F Oketta; E Buluma; R Waita; H Ojiambo; F Sadik; J Wanyama; P Nabongo; J Oyugi; F Sematala; A Muganzi; C Twijukye; H Byakwaga; R Ochai; D Muhweezi; A Coutinho; B Etukoit; C Gilks; K Boocock; C Puddephatt; C Grundy; J Bohannon; D Winogron; D M Gibb; A Burke; D Bray; A Babiker; A S Walker; H Wilkes; M Rauchenberger; S Sheehan; C Spencer-Drake; K Taylor; M Spyer; A Ferrier; B Naidoo; D Dunn; R Goodall; J H Darbyshire; L Peto; R Nanfuka; C Mufuka-Kapuya; P Kaleebu; D Pillay; V Robertson; D Yirrell; S Tugume; M Chirara; P Katundu; N Ndembi; F Lyagoba; D Dunn; R Goodall; A McCormick; A Medina Lara; S Foster; J Amurwon; B Nyanzi Wakholi; J Kigozi; L Muchabaiwa; M Muzambi; I Weller; A Babiker; S Bahendeka; M Bassett; A Chogo Wapakhabulo; J H Darbyshire; B Gazzard; C Gilks; H Grosskurth; J Hakim; A Latif; C Mapuchere; O Mugurungi; P Mugyenyi; C Burke; S Jones; C Newland; G Pearce; S Rahim; J Rooney; M Smith; W Snowden; J-M Steens; A Breckenridge; A McLaren; C Hill; J Matenga; A Pozniak; D Serwadda; T Peto; A Palfreeman; M Borok; E Katabira
Journal:  Lancet       Date:  2009-12-08       Impact factor: 79.321

10.  Population-level effect of HIV on adult mortality and early evidence of reversal after introduction of antiretroviral therapy in Malawi.

Authors:  Andreas Jahn; Sian Floyd; Amelia C Crampin; Frank Mwaungulu; Hazzie Mvula; Fipson Munthali; Nuala McGrath; Johnbosco Mwafilaso; Venance Mwinuka; Bernard Mangongo; Paul E M Fine; Basia Zaba; Judith R Glynn
Journal:  Lancet       Date:  2008-05-10       Impact factor: 79.321

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  8 in total

1.  2018 update to the HIV-TRePS system: the development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings.

Authors:  Andrew D Revell; Dechao Wang; Maria-Jesus Perez-Elias; Robin Wood; Dolphina Cogill; Hugo Tempelman; Raph L Hamers; Peter Reiss; Ard I van Sighem; Catherine A Rehm; Anton Pozniak; Julio S G Montaner; H Clifford Lane; Brendan A Larder
Journal:  J Antimicrob Chemother       Date:  2018-08-01       Impact factor: 5.790

2.  An update to the HIV-TRePS system: the development and evaluation of new global and local computational models to predict HIV treatment outcomes, with or without a genotype.

Authors:  Andrew D Revell; Dechao Wang; Robin Wood; Carl Morrow; Hugo Tempelman; Raph L Hamers; Peter Reiss; Ard I van Sighem; Mark Nelson; Julio S G Montaner; H Clifford Lane; Brendan A Larder
Journal:  J Antimicrob Chemother       Date:  2016-06-20       Impact factor: 5.790

3.  An update to the HIV-TRePS system: the development of new computational models that do not require a genotype to predict HIV treatment outcomes.

Authors:  Andrew D Revell; Dechao Wang; Robin Wood; Carl Morrow; Hugo Tempelman; Raph Hamers; Gerardo Alvarez-Uria; Adrian Streinu-Cercel; Luminita Ene; Annemarie Wensing; Peter Reiss; Ard I van Sighem; Mark Nelson; Sean Emery; Julio S G Montaner; H Clifford Lane; Brendan A Larder
Journal:  J Antimicrob Chemother       Date:  2013-11-24       Impact factor: 5.790

4.  Potential impact of a free online HIV treatment response prediction system for reducing virological failures and drug costs after antiretroviral therapy failure in a resource-limited setting.

Authors:  Andrew D Revell; Gerardo Alvarez-Uria; Dechao Wang; Anton Pozniak; Julio S Montaner; H Clifford Lane; Brendan A Larder
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5.  Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa.

Authors:  Andrew Revell; Paul Khabo; Lotty Ledwaba; Sean Emery; Dechao Wang; Robin Wood; Carl Morrow; Hugo Tempelman; Raph L Hamers; Peter Reiss; Ard van Sighem; Anton Pozniak; Julio Montaner; H Clifford Lane; Brendan Larder
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Journal:  Health Policy Technol       Date:  2022-08-13       Impact factor: 5.211

7.  2021 update to HIV-TRePS: a highly flexible and accurate system for the prediction of treatment response from incomplete baseline information in different healthcare settings.

Authors:  Andrew D Revell; Dechao Wang; Maria-Jesus Perez-Elias; Robin Wood; Dolphina Cogill; Hugo Tempelman; Raph L Hamers; Peter Reiss; Ard van Sighem; Catherine A Rehm; Brian Agan; Gerardo Alvarez-Uria; Julio S G Montaner; H Clifford Lane; Brendan A Larder
Journal:  J Antimicrob Chemother       Date:  2021-06-18       Impact factor: 5.790

8.  A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients.

Authors:  Kuteesa R Bisaso; Susan A Karungi; Agnes Kiragga; Jackson K Mukonzo; Barbara Castelnuovo
Journal:  BMC Med Inform Decis Mak       Date:  2018-09-04       Impact factor: 2.796

  8 in total

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