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Rational use of Xpert testing in patients with presumptive TB: clinicians should be encouraged to use the test-treat threshold

Overview of attention for article published in BMC Infectious Diseases, October 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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4 tweeters

Citations

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6 Dimensions

Readers on

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37 Mendeley
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Title
Rational use of Xpert testing in patients with presumptive TB: clinicians should be encouraged to use the test-treat threshold
Published in
BMC Infectious Diseases, October 2017
DOI 10.1186/s12879-017-2798-6
Pubmed ID
Authors

Tom Decroo, Aquiles R. Henríquez-Trujillo, Anja De Weggheleire, Lutgarde Lynen

Abstract

A recently published Ugandan study on tuberculosis (TB) diagnosis in HIV-positive patients with presumptive smear-negative TB, which showed that out of 90 patients who started TB treatment, 20% (18/90) had a positive Xpert MTB/RIF (Xpert) test, 24% (22/90) had a negative Xpert test, and 56% (50/90) were started without Xpert testing. Although Xpert testing was available, clinicians did not use it systematically. Here we aim to show more objectively the process of clinical decision-making. First, we estimated that pre-test probability of TB, or the prevalence of TB in smear-negative HIV infected patients with signs of presumptive TB in Uganda, was 17%. Second, we argue that the treatment threshold, the probability of disease at which the utility of treating and not treating is the same, and above which treatment should be started, should be determined. In Uganda, the treatment threshold was not yet formally established. In Rwanda, the calculated treatment threshold was 12%. Hence, one could argue that the threshold was reached without even considering additional tests. Still, Xpert testing can be useful when the probability of disease is above the treatment threshold, but only when a negative Xpert result can lower the probability of disease enough to cross the treatment threshold. This occurs when the pre-test probability is lower than the test-treat threshold, the probability of disease at which the utility of testing and the utility of treating without testing is the same. We estimated that the test-treatment threshold was 28%. Finally, to show the effect of the presence or absence of arguments on the probability of TB, we use confirming and excluding power, and a log10 odds scale to combine arguments. If the pre-test probability is above the test-treat threshold, empirical treatment is justified, because even a negative Xpert will not lower the post-test probability below the treatment threshold. However, Xpert testing for the diagnosis of TB should be performed in patients for whom the probability of TB was lower than the test-treat threshold. Especially in resource constrained settings clinicians should be encouraged to take clinical decisions and use scarce resources rationally.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 27%
Student > Master 7 19%
Other 4 11%
Student > Ph. D. Student 4 11%
Student > Doctoral Student 1 3%
Other 3 8%
Unknown 8 22%
Readers by discipline Count As %
Medicine and Dentistry 17 46%
Agricultural and Biological Sciences 3 8%
Biochemistry, Genetics and Molecular Biology 1 3%
Mathematics 1 3%
Nursing and Health Professions 1 3%
Other 3 8%
Unknown 11 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 October 2017.
All research outputs
#12,649,287
of 22,026,693 outputs
Outputs from BMC Infectious Diseases
#2,993
of 7,469 outputs
Outputs of similar age
#139,874
of 299,944 outputs
Outputs of similar age from BMC Infectious Diseases
#12
of 16 outputs
Altmetric has tracked 22,026,693 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,469 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.4. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 299,944 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.