Evidence map›Paper›PMID 26195521›Full record

ArticleAntimicrobial agents and chemotherapy2015

Robust Algorithm for Systematic Classification of Malaria Late Treatment Failures as Recrudescence or Reinfection Using Microsatellite Genotyping.

Mateusz M Plucinski, Lindsay Morton, Mary Bushman, Pedro Rafael Dimbu, Venkatachalam Udhayakumar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Antimicrobial agents and chemotherapy, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

44 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

5 authors.

Mateusz M PlucinskiMalaria Branch, Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA President's Malaria Initiative, Atlanta, Georgia, USA mplucinski@cdc.gov.
Lindsay MortonMalaria Branch, Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Mary BushmanMalaria Branch, Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA Graduate Program in Population Biology, Ecology, and Evolution, Emory University, Atlanta, Georgia, USA.
Pedro Rafael DimbuNational Malaria Control Program, Ministry of Health, Luanda, Angola.
Venkatachalam UdhayakumarMalaria Branch, Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Routine therapeutic efficacy monitoring to measure the response to antimalarial treatment is a cornerstone of malaria control. To correctly measure drug efficacy, therapeutic efficacy studies require genotyping parasites from late treatment failures to differentiate between recrudescent infections and reinfections. However, there is a lack of statistical methods to systematically classify late treatment failures from genotyping data. A Bayesian algorithm was developed to estimate the posterior probability of late treatment failure being the result of a recrudescent infection from microsatellite genotyping data. The algorithm was implemented using a Monte Carlo Markov chain approach and was used to classify late treatment failures using published microsatellite data from therapeutic efficacy studies in Ethiopia and Angola. The algorithm classified 85% of the Ethiopian and 95% of the Angolan late treatment failures as either likely reinfection or likely recrudescence, defined as a posterior probability of recrudescence of <0.1 or >0.9, respectively. The adjusted efficacies calculated using the new algorithm differed from efficacies estimated using commonly used methods for differentiating recrudescence from reinfection. In a high-transmission setting such as Angola, as few as 15 samples needed to be genotyped in order to have enough power to correctly classify treatment failures. Analysis of microsatellite genotyping data for differentiating between recrudescence and reinfection benefits from an approach that both systematically classifies late treatment failures and estimates the uncertainty of these classifications. Researchers analyzing genotyping data from antimalarial therapeutic efficacy monitoring are urged to publish their raw genetic data and to estimate the uncertainty around their classification.

Indexed as

AlgorithmsAntimalarialsGenotypeHumansMalariaMicrosatellite RepeatsMonte Carlo MethodTreatment OutcomeAntimalarials

Identifiers

PMID26195521
PMCPMC4576024

What OpenQuestion holds

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Registered trials

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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.