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. 2016 Apr 5;10(4):e0004593.
doi: 10.1371/journal.pntd.0004593. eCollection 2016 Apr.

The "Buruli Score": Development of a Multivariable Prediction Model for Diagnosis of Mycobacterium ulcerans Infection in Individuals with Ulcerative Skin Lesions, Akonolinga, Cameroon

Yolanda K Mueller  1 , Mathieu Bastard  1 , Patrick Nkemenang  2 , Eric Comte  2 , Geneviève Ehounou  2 , Sara Eyangoh  3 , Barbara Rusch  2 , Earnest Njih Tabah  4 , Laurence Toutous Trellu  5 , Jean-Francois Etard  1   6
Affiliations

The "Buruli Score": Development of a Multivariable Prediction Model for Diagnosis of Mycobacterium ulcerans Infection in Individuals with Ulcerative Skin Lesions, Akonolinga, Cameroon

Yolanda K Mueller et al. PLoS Negl Trop Dis. .

Abstract

Background: Access to laboratory diagnosis can be a challenge for individuals suspected of Buruli Ulcer (BU). Our objective was to develop a clinical score to assist clinicians working in resource-limited settings for BU diagnosis.

Methododology/principal findings: Between 2011 and 2013, individuals presenting at Akonolinga District Hospital, Cameroon, were enrolled consecutively. Clinical data were collected prospectively. Based on a latent class model using laboratory test results (ZN, PCR, culture), patients were categorized into high, or low BU likelihood. Variables associated with a high BU likelihood in a multivariate logistic model were included in the Buruli score. Score cut-offs were chosen based on calculated predictive values. Of 325 patients with an ulcerative lesion, 51 (15.7%) had a high BU likelihood. The variables identified for the Buruli score were: characteristic smell (+3 points), yellow color (+2), female gender (+2), undermining (+1), green color (+1), lesion hyposensitivity (+1), pain at rest (-1), size >5cm (-1), locoregional adenopathy (-2), age above 20 up to 40 years (-3), or above 40 (-5). This score had AUC of 0.86 (95%CI 0.82-0.89), indicating good discrimination between infected and non-infected individuals. The cut-off to reasonably exclude BU was set at scores <0 (NPV 96.5%; 95%CI 93.0-98.6). The treatment threshold was set at a cut-off ≥4 (PPV 69.0%; 95%CI 49.2-84.7). Patients with intermediate BU probability needed to be tested by PCR.

Conclusions/significance: We developed a decisional algorithm based on a clinical score assessing BU probability. The Buruli score still requires further validation before it can be recommended for wide use.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Estimated probability of Buruli ulcer according to pattern of laboratory test results in latent class model (1 = positive result, 0 = negative result; test order ZN CPC, PCR, culture, ZN Akonolinga.
Dashed line: 70% probability of Buruli ulcer (predefined treatment threshold).
Fig 2
Fig 2. ROC curve of multivariable prediction model (A) and Buruli score (B).
Fig 3
Fig 3. Algorithm based on Buruli score applied to the patients included in the study.
See this image and copyright information in PMC

References

    1. Mensah-Quainoo E, Yeboah-Manu D, Asebi C, Patafuor F, Ofori-Adjei D, Junghanss T, et al. Diagnosis of Mycobacterium ulcerans infection (Buruli ulcer) at a treatment centre in Ghana: a retrospective analysis of laboratory results of clinically diagnosed cases. TropMedIntHealth. 2008;13(2):191–8. - PubMed
    1. Portaels F. Laboratory diagnosis of buruli ulcer: a manual for health care providers.: World Health Organization; 2014.
    1. Herbinger KH, Adjei O, wua-Boateng NY, Nienhuis WA, Kunaa L, Siegmund V, et al. Comparative study of the sensitivity of different diagnostic methods for the laboratory diagnosis of Buruli ulcer disease. ClinInfectDis. 2009;48(8):1055–64. - PubMed
    1. Dreyer A, Roltgen K, Dangy JP, Ruf MT, Scherr N, Bolz M, et al. Identification of the Mycobacterium ulcerans protein MUL_3720 as a promising target for the development of a diagnostic test for Buruli ulcer. PLoS neglected tropical diseases. 2015;9(2):e0003477 10.1371/journal.pntd.0003477 - DOI - PMC - PubMed
    1. Moons KG, Kengne AP, Woodward M, Royston P, Vergouwe Y, Altman DG, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart. 2012;98(9):683–90. 10.1136/heartjnl-2011-301246 . - DOI - PubMed

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