تعليقEffective and efficient TB diagnostic algorithms are key components of a diagnostic cascade that is designed to ensure that people with TB are diagnosed accurately and rapidly, and are promptly placed on appropriate therapy. In turn, that therapy should improve patient outcomes, reduce transmission and avoid development of drug resistance. This section presents a set of four model algorithms that incorporate the goals of the End TB Strategy and the most recent WHO recommendations for the diagnosis and treatment of TB and DR-TB. The model algorithms should be adapted to the local situation.
When selecting a diagnostic algorithm to implement, it is important to consider the characteristics of the population being served. The four algorithms are as follows:
- Algorithm 1 relies on mWRDs as the initial diagnostic tests and is appropriate for all settings, although the choice of which mWRD to use may differ in a setting with a high prevalence of MDR/RR-TB (e.g. a test that detects MTBC and RIF with or without INH resistance may be needed), HIV (e.g. a more sensitive test may be needed) or Hr-TB (e.g. a test that detects MTBC, RIF and INH resistance simultaneously will be needed).
- Algorithm 2 incorporates the most recent WHO recommendations for the use of the LF-LAM as an aid in the diagnosis of TB in PLHIV; it is most relevant to settings with a high prevalence of HIV. However, Algorithm 2 applies to any PLHIV who meet the testing criteria, regardless of the underlying prevalence of HIV in that setting.
- Algorithm 3 and Algorithm 4 are for follow-up testing, after TB is diagnosed, to detect additional drug resistance:
- Algorithms 3a and 3b are used when the purpose is to detect resistance to second-line drugs in people with RR-TB; and
- Algorithm 4 is used when the purpose is to detect resistance in individuals with RIFsusceptible TB at risk of having DR-TB and those with Hr-TB. Molecular testing is preferred and may use any existing WHO-recommended rapid test. Targeted NGS tests are the preferred test for Algorithm 4 because these tests can detect mutations associated with resistance to many anti-TB medicines, and are rapid molecular tests for people at high risk of having DR-TB (e.g. people in whom therapy is failing).
Algorithms 3 and 4 are appropriate for all settings; however, the resource requirements for follow-up testing may differ widely between settings with a high burden of DR-TB and settings with a low burden of DR-TB.
Each algorithm is accompanied by explanatory notes and a decision pathway that provides a detailed description of the various decisions in the algorithm. In general, the algorithms do not show a specific test; rather, they provide a flow of the expected outcomes of the test that are common across the algorithms (e.g. “TB detected”) and the follow-up action required.
Although the algorithms are presented separately, they are interlinked and cascade from one to the other. This is illustrated in the overview (Fig. 4.1), which lists the various diagnostic tests currently recommended within each algorithm. Algorithm 2 is complementary to Algorithm 1, and currently it includes only a single product option intended as an additional test for PLHIV.
The diagnostic pathway begins with a person identified as a presumptive TB person through assessment of signs and symptoms or screened through another approach. WHO has released updated recommendations on TB screening, and readers are encouraged to consult the latest guidance (33). People presumed to have TB may not always present with symptoms that match the latest screening guideline recommendations but still have an increased probability for TB disease requiring diagnostic testing. The modalities for screening, beyond the four-symptom screen, now include chest X-ray, an mWRD used as a screening tool or C-reactive protein in PLHIV. The addition of mWRD for screening of selected at-risk populations and settings goes beyond its primary use as an initial line diagnostic tool and the different uses should not be confused. However, priority should be given to ensuring universal access to mWRDs as a diagnostic test for TB and DR-TB before extending its use to screening. Furthermore, the use of mWRDs as a screening test would have large financial and operational implications, and should be carefully considered.
People who are referred for diagnostic evaluation after a positive screen for TB using modalities other than an mWRD should go through a clinical evaluation, including other relevant investigations if available (e.g. chest X-ray) and follow Algorithm 1 to reach a bacteriologically confirmed diagnosis. Among those who are referred for diagnostic evaluation after a positive screen for TB using an mWRD (33) – that is. used in a screening rather than a diagnostic context – the pretest probability is an important consideration in addition to the clinical picture when deciding to repeat the mWRD or proceed with treatment. Due to the high specificity even when used as a screening test (99%), a positive test is likely to be a true positive when the pretest probability is high.
In the scenario recommending mWRD for screening all PLHIV who are medical inpatients and where the prevalence of TB is at least 10%, the likelihood of the mWRD being a true positive is high and treatment should be considered if the clinical picture is in keeping with a diagnosis of TB. However, the detection of TB DNA does not necessarily indicate that the person has active TB. This may occur in a person with a history of prior TB treatment (<5 years) and is particularly true for the more sensitive molecular tests (Xpert Ultra); culture would aid in interpretation. Furthermore, in PLHIV who require hospital admission, more than one infection may be present, and such people should be fully investigated.
In the scenario where an mWRD is used as a screening tool in a community with a prevalence of 0.5%, the positive predictive value would only be 39.5%, despite having a specificity of 99%, meaning that more than half of the positive mWRDs may be false positive. In such a situation, a repeat mWRD is warranted but should follow a clinical evaluation, and other investigations should be considered. Treatment should be based on the totality of evidence for the particular person.
Fig. 4.1. Integrated pathway of the diagnostic algorithmsᵃ
Algorithm 1 is the starting point for the diagnostic pathway for most people suspected of having TB. The number of tests recommended for this purpose has increased from five to nine with the latest addition of the moderate complexity automated NAAT class. This is a great improvement compared with the past, when smear microscopy was the only option. Member States can now make choices that best fit their circumstances, with the ultimate objective being to serve patient needs. The initial test options can be split into those that provide a TB diagnosis only (TB-LAMP) and those that also provide at least detection of RIF resistance (simultaneously or as a two-step process): Xpert MTB/RIF, Xpert Ultra, Truenat MTB, Truenat MTB Plus, Truenat MTBRIF Dx and moderate complexity automated NAATs. RIF resistance detection is recommended as a target for universal DST. Among the tests that detect RIF resistance, a subgroup provides this as a two-step procedure, separating TB detection and detection of RIF resistance. This second step could also be used as a follow-on test to initial tests that do not offer RIF resistance detection, such as TB-LAMP.
The test options for Algorithm 1 vary in complexity. The Xpert MTB/RIF, Xpert Ultra, Truenat MTB and Truenat MTB Plus all require basic pipetting skills and are easy to decentralize but have limited throughput with the commonly used instruments. In contrast, although some of the moderate complexity automated NAATs have minimal hands-on time, they have large infrastructure requirements; also, most of these tests provide higher throughput and are suited to established laboratories with reliable sample referral networks, and they detect resistance to INH in addition to RIF. In practice, test needs and associated choices are likely to vary, depending on the setting within a country or province. Consideration should be given towards hybrid models using a combination of tests from different manufacturers; this has the added advantage of providing a safety mechanism in the event of an expected problem with a supplier.
Algorithm 2 may be the starting point for some people, primarily for symptomatic PLHIV who would benefit from a rapid point-of-care test to diagnose TB. It is recommended that testing using Algorithm 1 and Algorithm 2 is done in parallel.
Algorithms 3 and 4 follow on from Algorithm 1, with the split being based on the RIF result.
Algorithm 3 is for those with confirmed RR-TB, and is aimed at providing additional rapid DST for FQs and BDQ.
- Algorithm 3a relies on the use of the recently recommended targeted NGS tests as the primary diagnostic tool for all people with RR-TB. It focuses on the detection of BDQ, FQ and LZD resistance, to address key aspects of recently recommended BPaL and BPaLM regimens as well as other shorter regimens for the treatment of MDR/RR-TB.
- Algorithm 3b uses existing tools complemented with the use of targeted NGS (where available) to provide rapid and comprehensive DST to drugs in prioritized groups. This algorithm focuses on the rapid detection of FQ resistance for two reasons: rapid early diagnosis of FQ resistance among people with MDR/RR-TB is important for selecting treatment regimens, and low to moderate complexity molecular tests to detect FQ resistance are readily available in many countries. The options for detecting resistance to FQs are low complexity automated NAAT (e.g. Xpert MTB/XDR), FL-LPA and SL-LPA, and the recently recommended targeted NGS-based tests.
Algorithm 4 is a follow-on algorithm when the purpose is to detect resistance in individuals with RIF-susceptible TB at risk of having DR-TB, and in individuals with Hr-TB. Molecular testing is preferred and may use any existing WHO-recommended rapid test. Algorithm 4 also takes advantage of the ability of targeted NGS tests to detect mutations associated with resistance to many anti-TB medicines to provide rapid molecular tests for people at high risk of having DR-TB (e.g. people in whom therapy is failing). Currently, INH resistance detection is not widely implemented at initial diagnosis, but this will change with the adoption of moderate complexity automated NAATs that also detect INH resistance as initial tests. The adoption of low complexity automated NAATs that detect INH resistance for follow-on testing of RIF-susceptible TB could also increase the detection of INH resistance. It is estimated that the incidence of Hr-TB is greater than that of RR-TB, and both types of TB require modification of the treatment regimen. People with Hr-TB would need an FQ added to the therapy. In scenarios where the only diagnostic tests available are those for RIF resistance (e.g. Xpert MTB/RIF and Truenat MTB), follow-on testing in settings or subpopulations with high Hr-TB burden using the low complexity automated NAAT for INH resistance detection will be appropriate, being simpler and cheaper than performing the FL-LPA or targeted NGS test.