Developing and validating a clinical algorithm for the diagnosis of podoconiosis. (11th November 2020)

• Record Type: Journal Article
• Authors: Deribe, Kebede; Florence, Lyndsey; Kelemework, Abebe; Getaneh, Tigist; Tsegay, Girmay; Cano, Jorge; Giorgi, Emanuele; Newport, Melanie J; Davey, Gail
• Is Part Of: Transactions of the Royal Society of Tropical Medicine and Hygiene. Volume 114:Number 12(2020)
• Page Start: 916
• Page End: 925
• Publication Date: 2020-11-11
• Subjects: diagnosis -- clinical algorithm -- clinical decision algorithms -- Ethiopia -- lymphoedema -- podoconiosis; Tropical medicine -- Periodicals; 616.9883
• DOI: 10.1093/trstmh/traa074
• Languages: English
• ISSNs: 0035-9203

Abstract

Abstract: Background: Difficulties in reliably diagnosing podoconiosis have severely limited the scale-up and uptake of the World Health Organization–recommended morbidity management and disability prevention interventions for affected people. We aimed to identify a set of clinical features that, combined into an algorithm, allow for diagnosis of podoconiosis. Methods: We identified 372 people with lymphoedema and administered a structured questionnaire on signs and symptoms associated with podoconiosis and other potential causes of lymphoedema in northern Ethiopia. All individuals were tested for Wuchereria bancrofti –specific immunoglobulin G4 in the field using Wb123. Results: Based on expert diagnosis, 344 (92.5%) of the 372 participants had podoconiosis. The rest had lymphoedema due to other aetiologies. The best-performing set of symptoms and signs was the presence of moss on the lower legs and a family history of leg swelling, plus the absence of current or previous leprosy, plus the absence of swelling in the groin, plus the absence of chronic illness (such as diabetes mellitus or heart or kidney diseases). The overall sensitivity of the algorithm was 91% (95% confidence interval [CI] 87.6 to 94.4) and specificity was 95% (95% CI 85.45 to 100). Conclusions: We developed a clinical algorithm of clinical history and physical examination that could be used in areas suspected or endemic for podoconiosis. Use of this algorithm should enable earlier identification of podoconiosis cases and scale-up of interventions.