Cargando…
Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data
BACKGROUND: HIV-1 drug resistance can be measured with phenotypic drug-resistance tests. However, the output of these tests, the resistance factor (RF), requires interpretation with respect to the in vivo activity of the tested variant. Specifically, the dynamic range of the RF for each drug has to...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JAIDS Journal of Acquired Immune Deficiency Syndromes
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5351752/ https://www.ncbi.nlm.nih.gov/pubmed/27787339 http://dx.doi.org/10.1097/QAI.0000000000001198 |
_version_ | 1782514823579303936 |
---|---|
author | Pironti, Alejandro Walter, Hauke Pfeifer, Nico Knops, Elena Lübke, Nadine Büch, Joachim Di Giambenedetto, Simona Kaiser, Rolf Lengauer, Thomas |
author_facet | Pironti, Alejandro Walter, Hauke Pfeifer, Nico Knops, Elena Lübke, Nadine Büch, Joachim Di Giambenedetto, Simona Kaiser, Rolf Lengauer, Thomas |
author_sort | Pironti, Alejandro |
collection | PubMed |
description | BACKGROUND: HIV-1 drug resistance can be measured with phenotypic drug-resistance tests. However, the output of these tests, the resistance factor (RF), requires interpretation with respect to the in vivo activity of the tested variant. Specifically, the dynamic range of the RF for each drug has to be divided into a suitable number of clinically meaningful intervals. METHODS: We calculated a susceptible-to-intermediate and an intermediate-to-resistant cutoff per drug for RFs predicted by geno2pheno([resistance]). Probability densities for therapeutic success and failure were estimated from 10,444 treatment episodes. The density estimation procedure corrects for the activity of the backbone drug compounds and for therapy failure without drug resistance. For estimating the probability of therapeutic success given an RF, we fit a sigmoid function. The cutoffs are given by the roots of the third derivative of the sigmoid function. RESULTS: For performance assessment, we used geno2pheno([resistance]) RF predictions and the cutoffs for predicting therapeutic success in 2 independent sets of therapy episodes. HIVdb was used for performance comparison. On one test set (n = 807), our cutoffs and HIVdb performed equally well receiver operating characteristic curve [(ROC)–area under the curve (AUC): 0.68]. On the other test set (n = 917), our cutoffs (ROC–AUC: 0.63) and HIVdb (ROC–AUC: 0.65) performed comparatively well. CONCLUSIONS: Our method can be used for calculating clinically relevant cutoffs for (predicted) RFs. The method corrects for the activity of the backbone drug compounds and for therapy failure without drug resistance. Our method's performance is comparable with that of HIVdb. RF cutoffs for the latest version of geno2pheno([resistance]) have been estimated with this method. |
format | Online Article Text |
id | pubmed-5351752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | JAIDS Journal of Acquired Immune Deficiency Syndromes |
record_format | MEDLINE/PubMed |
spelling | pubmed-53517522017-03-28 Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data Pironti, Alejandro Walter, Hauke Pfeifer, Nico Knops, Elena Lübke, Nadine Büch, Joachim Di Giambenedetto, Simona Kaiser, Rolf Lengauer, Thomas J Acquir Immune Defic Syndr Clinical Science BACKGROUND: HIV-1 drug resistance can be measured with phenotypic drug-resistance tests. However, the output of these tests, the resistance factor (RF), requires interpretation with respect to the in vivo activity of the tested variant. Specifically, the dynamic range of the RF for each drug has to be divided into a suitable number of clinically meaningful intervals. METHODS: We calculated a susceptible-to-intermediate and an intermediate-to-resistant cutoff per drug for RFs predicted by geno2pheno([resistance]). Probability densities for therapeutic success and failure were estimated from 10,444 treatment episodes. The density estimation procedure corrects for the activity of the backbone drug compounds and for therapy failure without drug resistance. For estimating the probability of therapeutic success given an RF, we fit a sigmoid function. The cutoffs are given by the roots of the third derivative of the sigmoid function. RESULTS: For performance assessment, we used geno2pheno([resistance]) RF predictions and the cutoffs for predicting therapeutic success in 2 independent sets of therapy episodes. HIVdb was used for performance comparison. On one test set (n = 807), our cutoffs and HIVdb performed equally well receiver operating characteristic curve [(ROC)–area under the curve (AUC): 0.68]. On the other test set (n = 917), our cutoffs (ROC–AUC: 0.63) and HIVdb (ROC–AUC: 0.65) performed comparatively well. CONCLUSIONS: Our method can be used for calculating clinically relevant cutoffs for (predicted) RFs. The method corrects for the activity of the backbone drug compounds and for therapy failure without drug resistance. Our method's performance is comparable with that of HIVdb. RF cutoffs for the latest version of geno2pheno([resistance]) have been estimated with this method. JAIDS Journal of Acquired Immune Deficiency Syndromes 2017-04-15 2017-03-09 /pmc/articles/PMC5351752/ /pubmed/27787339 http://dx.doi.org/10.1097/QAI.0000000000001198 Text en Copyright © 2016 The Author(s). Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/) (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Clinical Science Pironti, Alejandro Walter, Hauke Pfeifer, Nico Knops, Elena Lübke, Nadine Büch, Joachim Di Giambenedetto, Simona Kaiser, Rolf Lengauer, Thomas Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data |
title | Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data |
title_full | Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data |
title_fullStr | Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data |
title_full_unstemmed | Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data |
title_short | Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data |
title_sort | determination of phenotypic resistance cutoffs from routine clinical data |
topic | Clinical Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5351752/ https://www.ncbi.nlm.nih.gov/pubmed/27787339 http://dx.doi.org/10.1097/QAI.0000000000001198 |
work_keys_str_mv | AT pirontialejandro determinationofphenotypicresistancecutoffsfromroutineclinicaldata AT walterhauke determinationofphenotypicresistancecutoffsfromroutineclinicaldata AT pfeifernico determinationofphenotypicresistancecutoffsfromroutineclinicaldata AT knopselena determinationofphenotypicresistancecutoffsfromroutineclinicaldata AT lubkenadine determinationofphenotypicresistancecutoffsfromroutineclinicaldata AT buchjoachim determinationofphenotypicresistancecutoffsfromroutineclinicaldata AT digiambenedettosimona determinationofphenotypicresistancecutoffsfromroutineclinicaldata AT kaiserrolf determinationofphenotypicresistancecutoffsfromroutineclinicaldata AT lengauerthomas determinationofphenotypicresistancecutoffsfromroutineclinicaldata |