Syndromic Algorithms for Detection of Gambiense Human African Trypanosomiasis in South Sudan

Background: Active screening by mobile teams is considered the best method for detecting human African trypanosomiasis (HAT) caused by Trypanosoma brucei gambiense but the current funding context in many post-conflict countries limits this approach. As an alternative, non-specialist health care workers (HCWs) in peripheral health facilities could be trained to identify potential cases who need testing based on their symptoms. We explored the predictive value of syndromic referral algorithms to identify symptomatic cases of HAT among a treatment-seeking population in Nimule, South Sudan. Methodology/Principal Findings: Symptom data from 462 patients (27 cases) presenting for a HAT test via passive screening over a 7 month period were collected to construct and evaluate over 14,000 four item syndromic algorithms considered simple enough to be used by peripheral HCWs. For comparison, algorithms developed in other settings were also tested on our data, and a panel of expert HAT clinicians were asked to make referral decisions based on the symptom dataset. The best performing algorithms consisted of three core symptoms (sleep problems, neurological problems and weight loss), with or without a history of oedema, cervical adenopathy or proximity to livestock. They had a sensitivity of 88.9–92.6%, a negative predictive value of up to 98.8% and a positive predictive value in this context of 8.4–8.7%. In terms of sensitivity, these out-performed more complex algorithms identified in other studies, as well as the expert panel. The best-performing algorithm is predicted to identify about 9/10 treatment-seeking HAT cases, though only 1/10 patients referred would test positive. Conclusions/Significance: In the absence of regular active screening, improving referrals of HAT patients through other means is essential. Systematic use of syndromic algorithms by peripheral HCWs has the potential to increase case detection and would increase their participation in HAT programmes. The algorithms proposed here, though promising, should be validated elsewhere. Author Summary: Human African trypanosomiasis (HAT or sleeping sickness) is an almost always fatal disease affecting poor people in rural, conflict-affected areas of sub-Saharan Africa. It is difficult to diagnose. Effective treatment exists, but because diagnostic and treatment services are usually based only in hospitals, many HAT patients in rural areas are never detected. Control programmes aim periodically to extend testing services via mobile teams (active screening) but their expense and operational issues severely restrict their use. We explored the predictive value of different combinations of symptoms that were present in a treatment-seeking population to identify people infected with HAT. Through this approach, we identified a simple four-symptom referral algorithm that, if replicable, has the potential to identify one HAT patient for every ten patients referred through subsequent testing. It would identify most symptomatic HAT patients who seek treatment, if systematically applied by non-specialist healthcare workers already working in these areas. As these types of health workers are rarely included in formal HAT control efforts, teaching this algorithm also represents an opportunity to decentralise life-saving knowledge, and contribute to endemic populations' long-term empowerment and ability to help control this disease.