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World Association for Veterinary Dermatology Consensus Statement for Diagnosis, and Evidence-Based Clinical Practice Guidelines for Treatment and Prevention of Canine Leishmaniosis

Saridomichelakis MN, Baneth G, Colombo S, Dantas-Torres F, Ferrer L, Fondati A, Miró G, Ordeix L et al.

Veterinary dermatology, 2025

11 citations

Abstract

Background: Canine leishmaniosis (CanL) due to Leishmania infantum remains common, and veterinarians do not always follow scientifically sound approaches for diagnosis, treatment and prevention.

Objectives: To provide consensus guidelines for diagnosis and evidence-based guidelines for treatment and prevention of CanL.

Methods and material: Clinical consensus guidelines for the diagnosis were structured based on literature and authors' experience. Three electronic databases were searched for randomised controlled trials, systematic reviews and meta-analyses on treatment and prevention.

Results, conclusions and clinical importance: Diagnosis should be based on compatible clinical signs and/or clinicopathologic abnormalities, exclusion of differentials, demonstration of infection and increased concentration of anti-Leishmania IgG (quantitative serology). Euthanasia for public health purposes is not recommended and drugs with anti-Leishmania activity should be avoided in subclinically infected dogs. Recommended treatments include meglumine antimoniate-allopurinol (first-line treatment), miltefosine-allopurinol (first-line treatment) and aminosidine-allopurinol (second-line treatment); marbofloxacin may be considered in dogs with advanced chronic kidney disease. In endemic areas, recommended measures for prevention include deltamethrin 4% collar, flumethrin 4.5%-imidacloprid 10% collar or permethrin 50%-imidacloprid 10% spot-on, not using infected blood products for transfusion,…

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Evidence Hub reads this as a guideline, done in animal, not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.

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