Syndromic algorithms for detection of gambiense human african trypanosomiasis in South Sudan.

Détails

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Etat: Serval
Version: Final published version
ID Serval
serval:BIB_DADB0E1D2839
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Syndromic algorithms for detection of gambiense human african trypanosomiasis in South Sudan.
Périodique
Plos Neglected Tropical Diseases
Auteur(s)
Palmer J.J., Surur E.I., Goch G.W., Mayen M.A., Lindner A.K., Pittet A., Kasparian S., Checchi F., Whitty C.J.
ISSN
1935-2735 (Electronic)
ISSN-L
1935-2727
Statut éditorial
Publié
Date de publication
2013
Volume
7
Numéro
1
Pages
e2003
Langue
anglais
Notes
Publication types: Journal Article Publication Status: ppublish PDF type: Article
Résumé
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.
Pubmed
Web of science
Open Access
Oui
Création de la notice
01/03/2013 19:11
Dernière modification de la notice
09/05/2019 2:06
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