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Accuracy of five algorithms to diagnose gambiense human African trypanosomiasis.

ABSTRACT: BACKGROUND: Algorithms to diagnose gambiense human African trypanosomiasis (HAT, sleeping sickness) are often complex due to the unsatisfactory sensitivity and/or specificity of available tests, and typically include a screening (serological), confirmation (parasitological) and staging component. There is insufficient evidence on the relative accuracy of these algorithms. This paper presents estimates of the accuracy of five algorithms used by past Médecins Sans Frontières programmes in the Republic of Congo, Southern Sudan and Uganda. METHODOLOGY AND PRINCIPAL FINDINGS: The sequence of tests in each algorithm was programmed into a probabilistic model, informed by distributions of the sensitivity, specificity and staging accuracy of each test, constructed based on a literature review. The accuracy of algorithms was estimated in a baseline scenario and in a worst-case scenario introducing various near worst-case assumptions. In the baseline scenario, sensitivity was estimated as 85-90% in all but one algorithm, with specificity above 99.9% except for the Republic of Congo, where CATT serology was used as independent confirmation test: here, positive predictive value (PPV) was estimated at <50% in realistic active screening prevalence scenarios. Furthermore, most algorithms misclassified about one third of true stage 1 cases as stage 2, and about 10% of true stage 2 cases as stage 1. In the worst-case scenario, sensitivity was 75-90% and PPV no more than 75% at 1% prevalence, with about half of stage 1 cases misclassified as stage 2. CONCLUSIONS: Published evidence on the accuracy of widely used tests is scanty. Algorithms should carefully weigh the use of serology alone for confirmation, and could enhance sensitivity through serological suspect follow-up and repeat parasitology. Better evidence on the frequency of low-parasitaemia infections is needed. Simulation studies should guide the tailoring of algorithms to specific scenarios of HAT prevalence and availability of control tools.

SUBMITTER: Checchi F

PROVIDER: S-EPMC3130008 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Accuracy of five algorithms to diagnose gambiense human African trypanosomiasis.

Checchi Francesco F Chappuis François F Karunakara Unni U Priotto Gerardo G Chandramohan Daniel D

PLoS neglected tropical diseases 20110705 7

<h4>Background</h4>Algorithms to diagnose gambiense human African trypanosomiasis (HAT, sleeping sickness) are often complex due to the unsatisfactory sensitivity and/or specificity of available tests, and typically include a screening (serological), confirmation (parasitological) and staging component. There is insufficient evidence on the relative accuracy of these algorithms. This paper presents estimates of the accuracy of five algorithms used by past Médecins Sans Frontières programmes in t ...[more]

PMID: 21750745

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Project description:BackgroundSerological screening tests play a crucial role to diagnose gambiense human African trypanosomiasis (gHAT). Presently, they preselect individuals for microscopic confirmation, but in future "screen and treat" strategies they will identify individuals for treatment. Variability in reported specificities, the development of new rapid diagnostic tests (RDT) and the hypothesis that malaria infection may decrease RDT specificity led us to evaluate the specificity of 5 gHAT screening tests.MethodsDuring active screening, venous blood samples from 1095 individuals from Côte d'Ivoire and Guinea were tested consecutively with commercial (CATT, HAT Sero-K-SeT, Abbott Bioline HAT 2.0) and prototype (DCN HAT RDT, HAT Sero-K-SeT 2.0) gHAT screening tests and with a malaria RDT. Individuals with ≥ 1 positive gHAT screening test underwent microscopy and further immunological (trypanolysis with T.b. gambiense LiTat 1.3, 1.5 and 1.6; indirect ELISA/T.b. gambiense; T.b. gambiense inhibition ELISA with T.b. gambiense LiTat 1.3 and 1.5 VSG) and molecular reference laboratory tests (PCR TBRN3, 18S and TgsGP; SHERLOCK 18S Tids, 7SL Zoon, and TgsGP; Trypanozoon S2-RT-qPCR 18S2, 177T, GPI-PLC and TgsGP in multiplex; RT-qPCR DT8, DT9 and TgsGP in multiplex). Microscopic trypanosome detection confirmed gHAT, while other individuals were considered gHAT free. Differences in fractions between groups were assessed by Chi square and differences in specificity between 2 tests on the same individuals by McNemar.ResultsOne gHAT case was diagnosed. Overall test specificities (n = 1094) were: CATT 98.9% (95% CI: 98.1-99.4%); HAT Sero-K-SeT 86.7% (95% CI: 84.5-88.5%); Bioline HAT 2.0 82.1% (95% CI: 79.7-84.2%); DCN HAT RDT 78.2% (95% CI: 75.7-80.6%); and HAT Sero-K-SeT 2.0 78.4% (95% CI: 75.9-80.8%). In malaria positives, gHAT screening tests appeared less specific, but the difference was significant only in Guinea for Abbott Bioline HAT 2.0 (P = 0.03) and HAT Sero-K-Set 2.0 (P = 0.0006). The specificities of immunological and molecular laboratory tests in gHAT seropositives were 98.7-100% (n = 399) and 93.0-100% (n = 302), respectively. Among 44 reference laboratory test positives, only the confirmed gHAT patient and one screening test seropositive combined immunological and molecular reference laboratory test positivity.ConclusionsAlthough a minor effect of malaria cannot be excluded, gHAT RDT specificities are far below the 95% minimal specificity stipulated by the WHO target product profile for a simple diagnostic tool to identify individuals eligible for treatment. Unless specificity is improved, an RDT-based "screen and treat" strategy would result in massive overtreatment. In view of their inconsistent results, additional comparative evaluations of the diagnostic performance of reference laboratory tests are indicated for better identifying, among screening test positives, those at increased suspicion for gHAT.Trial registrationThe trial was retrospectively registered under NCT05466630 in clinicaltrials.gov on July 15 2022.

Project description:Gambiense human African trypanosomiasis (gHAT) is a virulent disease declining in burden but still endemic in West and Central Africa. Although it is targeted for elimination of transmission by 2030, there remain numerous questions about the drivers of infection and how these vary geographically. In this study we focus on the Democratic Republic of Congo (DRC), which accounted for 84% of the global case burden in 2016, to explore changes in transmission across the country and elucidate factors which may have contributed to the persistence of disease or success of interventions in different regions. We present a Bayesian fitting methodology, applied to 168 endemic health zones (∼100,000 population size), which allows for calibration of a mechanistic gHAT model to case data (from the World Health Organization HAT Atlas) in an adaptive and automated framework. It was found that the model needed to capture improvements in passive detection to match observed trends in the data within former Bandundu and Bas Congo provinces indicating these regions have substantially reduced time to detection. Health zones in these provinces generally had longer burn-in periods during fitting due to additional model parameters. Posterior probability distributions were found for a range of fitted parameters in each health zone; these included the basic reproduction number estimates for pre-1998 (R0) which was inferred to be between 1 and 1.14, in line with previous gHAT estimates, with higher median values typically in health zones with more case reporting in the 2000s. Previously, it was not clear whether a fall in active case finding in the period contributed to the declining case numbers. The modelling here accounts for variable screening and suggests that underlying transmission has also reduced greatly-on average 96% in former Equateur, 93% in former Bas Congo and 89% in former Bandundu-Equateur and Bandundu having had the highest case burdens in 2000. This analysis also sets out a framework to enable future predictions for the country.

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Accuracy of five algorithms to diagnose gambiense human African trypanosomiasis.

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Accuracy of five algorithms to diagnose gambiense human African trypanosomiasis.

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