Evidence map›Paper›PMID 30455529›Full record

ArticleBulletin of the World Health Organization2018

Accuracy of diabetes screening methods used for people with tuberculosis, Indonesia, Peru, Romania, South Africa.

Daniel Grint, Bachti Alisjhabana, Cesar Ugarte-Gil, Anca-Leila Riza, Gerhard Walzl, Fiona Pearson, Rovina Ruslami, David A J Moore, Mihai Ioana, Susan McAllister and 10 more

Abstract readMulticenter Study
In one paragraph

Article in Bulletin of the World Health Organization, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–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

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Evaluation of point-of-care algorithms to detect diabetes during screening for latent TB infection.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2021
    Article
  6. Review
  7. Article
  8. Article
  9. Tuberculosis, Human Immunodeficiency Virus, and the Association With Transient Hyperglycemia in Periurban South Africa.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2020
    Article
  10. Article
  11. Article
  12. 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

20 authors.

Daniel GrintTropical Epidemiology Group, London School of Hygiene & Tropical Medicine, London, England.
Bachti AlisjhabanaInfectious Disease Research Centre, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia.
Cesar Ugarte-GilFacultad de Medicina Alberto Hurtado, Universidad Peruana Cayetano Heredia, Lima, Peru.
Anca-Leila RizaDepartment of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands.
Gerhard WalzlDivision of Molecular Biology and Human Genetics, Stellenbosch University, Cape Town, South Africa.
Fiona PearsonPopulation Health Research Institute, St George's University of London, Cranmer Terrace, London SW17 0RE, England.
Rovina RuslamiInfectious Disease Research Centre, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia.
David A J MooreLaboratorio de Investigación y Desarrollo, Universidad Peruana Cayetano Heredia, San Martin de Porres, Peru.
Mihai IoanaHuman Genomics Laboratory, Universitatea de Medicina si Farmacie din Craiova, Craiova, Romania.
Susan McAllisterCentre for International Health, University of Otago, Dunedin, New Zealand.
Katharina RonacherMater Medical Research, Translational Research Institute, University of Queensland, Brisbane, Australia.
Raspati C KoeseomadinataInfectious Disease Research Centre, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia.
Sarah R Kerry-BarnardPopulation Health Research Institute, St George's University of London, Cranmer Terrace, London SW17 0RE, England.
Jorge CoronelLaboratorio de Investigación y Desarrollo, Universidad Peruana Cayetano Heredia, San Martin de Porres, Peru.
Stephanus T MalherbeDivision of Molecular Biology and Human Genetics, Stellenbosch University, Cape Town, South Africa.
Hazel M DockrellDepartment of Immunology and Infection, London School of Hygiene & Tropical Medicine, London, England.
Philip C HillCentre for International Health, University of Otago, Dunedin, New Zealand.
Reinout Van CrevelDepartment of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands.
Julia A CritchleyPopulation Health Research Institute, St George's University of London, Cranmer Terrace, London SW17 0RE, England.
TANDEM consortium

Funding

Biology and Biosignatures of Anti-Tuberculosis Treatment ResponseU01AI115619 · NIAID · COLORADO STATE UNIVERSITY · PI BELISLE, JOHN T · 2015 to 2019
$3.1M
Medical Research Council MR/K012126/1NIAID NIH HHS U01 AI115619
6 · The paper itself

Abstract

objectiveTo evaluate the performance of diagnostic tools for diabetes mellitus, including laboratory methods and clinical risk scores, in newly-diagnosed pulmonary tuberculosis patients from four middle-income countries.

methodsIn a multicentre, prospective study, we recruited 2185 patients with pulmonary tuberculosis from sites in Indonesia, Peru, Romania and South Africa from January 2014 to September 2016. Using laboratory-measured glycated haemoglobin (HbA1c) as the gold standard, we measured the diagnostic accuracy of random plasma glucose, point-of-care HbA1c, fasting blood glucose, urine dipstick, published and newly derived diabetes mellitus risk scores and anthropometric measurements. We also analysed combinations of tests, including a two-step test using point-of-care HbA1cwhen initial random plasma glucose was ≥ 6.1 mmol/L.

findingsThe overall crude prevalence of diabetes mellitus among newly diagnosed tuberculosis patients was 283/2185 (13.0%; 95% confidence interval, CI: 11.6-14.4). The marker with the best diagnostic accuracy was point-of-care HbA1c (area under receiver operating characteristic curve: 0.81; 95% CI: 0.75-0.86). A risk score derived using age, point-of-care HbA1c and random plasma glucose had the best overall diagnostic accuracy (area under curve: 0.85; 95% CI: 0.81-0.90). There was substantial heterogeneity between sites for all markers, but the two-step combination test performed well in Indonesia and Peru.

conclusionRandom plasma glucose followed by point-of-care HbA1c testing can accurately diagnose diabetes in tuberculosis patients, particularly those with substantial hyperglycaemia, while reducing the need for more expensive point-of-care HbA1c testing. Risk scores with or without biochemical data may be useful but require validation.

Indexed as

AdultAge FactorsBlood GlucoseBody Weights and MeasuresDiabetes Mellitus, Type 2FemaleGlycated HemoglobinHumansIndonesiaMaleMass ScreeningMiddle AgedPeruPoint-of-Care TestingProspective StudiesRisk FactorsBlood GlucoseGlycated Hemoglobin

Identifiers

PMID30455529
PMCPMC6239004

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.