Evidence map›Paper›PMID 25765105›Full record

ReviewThe Journal of infectious diseases2015

Target product profile of a molecular drug-susceptibility test for use in microscopy centers.

Claudia M Denkinger, David Dolinger, Marco Schito, William Wells, Frank Cobelens, Madhukar Pai, Matteo Zignol, Daniela Maria Cirillo, David Alland, Martina Casenghi and 3 more

Open access · bronzeAbstract readReview
In one paragraph

Review in The Journal of infectious diseases, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
2.7field-weighted citation impact, top 10% 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

25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Review
  7. Advances in Molecular Diagnosis of Tuberculosis.Journal of clinical microbiology · 2020
    Review
  8. Article
  9. Implementation of XpertPublic health action · 2019
    Article
  10. Article
  11. Article
  12. What will it take to eliminate drug-resistant tuberculosis?The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2019
    Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

13 authors at 10 institutions in 6 countries.

Claudia M DenkingerFIND Division of Infectious Disease, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
David DolingerFIND.
Marco SchitoDivision of AIDS, Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland.
William WellsTB Alliance, New York, New York.
Frank CobelensKNCV Tuberculosis Foundation, the Hague Amsterdam Institute for Global Health and Development, Academic Medical Center, Amsterdam, The Netherlands.
Madhukar PaiMcGill International TB Centre Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada.
Matteo ZignolWorld Health Organization.
Daniela Maria CirilloIRCCS San Raffaele Scientific Institute, Milan, Italy.
David AllandRutgers University, New Brunswick, New Jersey.
Martina CasenghiMédecins sans Frontières, Geneva, Switzerland.
Jim GallardaBill and Melinda Gates Foundation, Seattle, Washington.
Catharina C BoehmeFIND.
Mark D PerkinsFIND.
Beth Israel Deaconess Medical Center · USBill & Melinda Gates Foundation · USHenry M. Jackson Foundation · USIstituti di Ricovero e Cura a Carattere Scientifico · ITMcGill University · CAMédecins Sans Frontières · CHRutgers, The State University of New Jersey · USTB Alliance · USTuBerculosis Vaccine Initiative · NLWorld Health Organization - Pakistan · PK

Funding

NIAID NIH HHS HHSN272200800014CWellcome TrustWorld Health Organization 001
6 · The paper itself

Abstract

backgroundCurrent phenotypic testing for drug resistance in patients with tuberculosis is inadequate primarily with respect to turnaround time. Molecular tests hold the promise of an improved time to diagnosis.

methodsA target product profile for a molecular drug-susceptibility test (DST) was developed on the basis of a collaborative effort that included opinions gathered from researchers, clinicians, policy makers, and test developers on optimal clinical and operational characteristics in settings of intended use. In addition, the current diagnostic ecosystem and the diagnostic development landscape were mapped.

resultsMolecular DSTs for detecting tuberculosis in microscopy centers should ideally evaluate for resistance to rifampin, fluoroquinolones, isoniazid, and pyrazinamide and enable the selection of the most appropriate treatment regimen. Performance characteristics of DSTs need to be optimized, but compromises can be made that depend on the trade-off between a false-positive result and a false-negative result. The operational requirements of a test will vary depending on the site of implementation. However, the most-important considerations pertain to quality control, maintenance and calibration, and the ability to export data.

conclusionThis target product profile defines the needs as perceived by the tuberculosis stakeholder community and attempts to provide a means of communication with test developers to ensure that fit-for-purpose DSTs are being developed.

Indexed as

Antitubercular AgentsDiagnostic Tests, RoutineHumansMicrobial Sensitivity TestsMolecular Diagnostic TechniquesQuality ControlTime FactorsTuberculosisAntitubercular Agentsdiagnosticsmolecular testingpoint of caretuberculosis

Identifiers

PMID25765105
PMCPMC4425821
OpenAlexW2112768419

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.