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

被引:8
作者
Decroo, Tom [1 ]
Henriquez-Trujillo, Aquiles R. [2 ]
De Weggheleire, Anja [1 ]
Lynen, Lutgarde [1 ]
机构
[1] Inst Trop Med, Dept Clin Sci, Natl Str 155, B-2000 Antwerp, Belgium
[2] Univ Las Amer, Fac Hlth Sci, One Hlth Res Grp, Quito, Ecuador
关键词
Tuberculosis; Clinical decision-making; Empirical treatment; Molecular diagnostic techniques; Treatment threshold; TUBERCULOSIS; DIAGNOSIS;
D O I
10.1186/s12879-017-2798-6
中图分类号
R51 [传染病];
学科分类号
100401 ;
摘要
Background: 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. Conclusion: 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.
引用
收藏
页数:6
相关论文
共 13 条
[1]   Why are clinicians reluctant to treat smear-negative tuberculosis? An inquiry about treatment thresholds in Rwanda [J].
Basinga, Paulin ;
Moreira, Juan ;
Bisoffi, Zeno ;
Bisig, Bettina ;
Van den Ende, Jef .
MEDICAL DECISION MAKING, 2007, 27 (01) :53-60
[2]   Why do clinical trials of Xpert® MTB/RIF fail to show an effect on patient relevant outcomes? [J].
Boyles, T. H. .
INTERNATIONAL JOURNAL OF TUBERCULOSIS AND LUNG DISEASE, 2017, 21 (03) :249-250
[3]   An Algorithm for Tuberculosis Screening and Diagnosis in People with HIV [J].
Cain, Kevin P. ;
McCarthy, Kimberly D. ;
Heilig, Charles M. ;
Monkongdee, Patama ;
Tasaneeyapan, Theerawit ;
Kanara, Nong ;
Kimerling, Michael E. ;
Chheng, Phalkun ;
Thai, Sopheak ;
Sar, Borann ;
Phanuphak, Praphan ;
Teeratakulpisarn, Nipat ;
Phanuphak, Nittaya ;
Nguyen Huy Dung ;
Hoang Thi Quy ;
Le Hung Thai ;
Varma, Jay K. .
NEW ENGLAND JOURNAL OF MEDICINE, 2010, 362 (08) :707-716
[4]   Refining clinical diagnosis with likelihood ratios [J].
Grimes, DA ;
Schulz, KF .
LANCET, 2005, 365 (9469) :1500-1505
[5]   Treatment decisions and mortality in HIV-positive presumptive smear-negative TB in the Xpert™ MTB/RIF era: a cohort study [J].
Hermans, Sabine M. ;
Babirye, Juliet A. ;
Mbabazi, Olive ;
Kakooza, Francis ;
Colebunders, Robert ;
Castelnuovo, Barbara ;
Sekaggya-Wiltshire, Christine ;
Parkes-Ratanshi, Rosalind ;
Manabe, Yukari C. .
BMC INFECTIOUS DISEASES, 2017, 17
[6]  
Hunink MGM, 2014, DECISION MAKING IN HEALTH AND MEDICINE: INTEGRATING EVIDENCE AND VALUES, 2ND EDITION, P1, DOI 10.1017/CBO9781139506779
[7]   THE THRESHOLD APPROACH TO CLINICAL DECISION-MAKING [J].
PAUKER, SG ;
KASSIRER, JP .
NEW ENGLAND JOURNAL OF MEDICINE, 1980, 302 (20) :1109-1117
[8]   THERAPEUTIC DECISION-MAKING - COST-BENEFIT ANALYSIS [J].
PAUKER, SG ;
KASSIRER, JP .
NEW ENGLAND JOURNAL OF MEDICINE, 1975, 293 (05) :229-234
[9]  
Steingart KR, 2014, COCHRANE DB SYST REV, DOI [10.1002/14651858.CD009593.pub2, 10.1002/14651858.CD009593.pub3]
[10]   The trouble with likelihood ratios [J].
Van den Ende, J ;
Moreira, J ;
Basinga, P ;
Bisoffi, Z .
LANCET, 2005, 366 (9485) :548-548