Accuracy of Five Algorithms to Diagnose Gambiense Human African Trypanosomiasis

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Abstract
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. 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 99.9%) except for the Republic of Congo, where confirmation did not rely on microscopic evidence, resulting in frequent false positives (but also higher sensitivity). Algorithms misclassified about one third of stage 1 cases as stage 2, but stage 2 classification was highly accurate. The use of serology alone for confirmation merits caution. HAT diagnosis could be made more sensitively by following up serological suspects and repeating microscopic examinations. Computer simulations can help to adapt algorithms to local conditions in each HAT programme, such as the prevalence of infection and operational constraints.

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