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

Multivariable model of key HAT symptoms associated with unanimous expert referral, adjusted for age, sex and previous HAT treatment history (n = 407).

*<p>An additional symptom, body pains, was moderately significant in the final model (p-value 0.051).</p

Performance of previously published syndromic algorithms.

<p>Performance of previously published syndromic algorithms.</p

Modified algorithms from other HAT studies tested using Nimule Hospital data.

<p>Numbers indicate scores attributed to each symptom, if present.</p>*<p>Data on this symptom were not collected in this study.</p

Performance of expert referrers.

*<p>No decision for 52 patients. Pts: patients. Sens: sensitivity. Spec: specificity.</p

Receiver operating curve diagram of all candidate syndromic algorithms evaluated.

<p>Each point represents the sensitivity and 1-specificity of a single algorithm. Ideally, the highest performing algorithms would be located in the top left corner of the graph.</p

Presenting symptom data collected and used in algorithm construction.

<p>Presenting symptom data collected and used in algorithm construction.</p

Crude associations between the 13 symptoms used in algorithm construction and a positive HAT test.

*<p>Significantly associated with being identified as a case, at p<0.05. Kerendel's sign (painful tibia) was present in 33.3% of cases and independently significantly associated with a positive test outcome (individual OR 5.9, p-value <0.001) but was combined with other more rare symptoms into the larger category ‘neurological problems’. There were no significant differences in demographic characteristics (age, sex, residency status, location) between cases and non-cases (data not shown). OR: Odds ratio. CI: Confidence interval.</p