Evidence map›Paper›PMID 27787339›Full record

ArticleJournal of acquired immune deficiency syndromes (1999)2017

Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data.

Alejandro Pironti, Hauke Walter, Nico Pfeifer, Elena Knops, Nadine Lübke, Joachim Büch, Simona Di Giambenedetto, Rolf Kaiser, Thomas Lengauer, EuResist Network Study Group

Open access · hybridAbstract read
In one paragraph

Article in Journal of acquired immune deficiency syndromes (1999), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.4field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Nucleic acid testing and molecular characterization of HIV infections.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2019
    Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 4 institutions in 2 countries.

Alejandro Pironti*Department of Computational Biology and Applied Algorithmics, Max-Planck-Institut für Informatik, Saarbrücken, Germany; †Medizinisches Infektiologiezentrum Berlin, Berlin, Germany; ‡Medizinisches Labor Stendal, Stendal, Germany; §Institute for Virology, University Clinic of Cologne, Germany; ‖Institute of Virology, University of Düsseldorf, Germany; and ¶Clinic of Infectious Diseases, Catholic University of the Sacred Heart, Rome, Italy.
Hauke Walter
Nico Pfeifer
Elena Knops
Nadine Lübke
Joachim Büch
Simona Di Giambenedetto
Rolf Kaiser
Thomas Lengauer
EuResist Network Study Group
Max Planck Institute for Informatics · DEHeinrich Heine University Düsseldorf · DELabor Pachmann · DEUniversità Cattolica del Sacro Cuore · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHIV-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.

methodsWe 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.

resultsFor 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.

conclusionsOur 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.

Indexed as

Drug Resistance, ViralAnti-HIV AgentsArea Under CurveGenotypeHIV-1HIV InfectionsHumansMicrobial Sensitivity TestsROC CurveTreatment FailureAnti-HIV Agents

Identifiers

PMID27787339
PMCPMC5351752
OpenAlexW2541643513

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.