ArticlePLoS neglected tropical diseases2026
Development of a predictive model for meglumine antimoniate treatment failure in patients with cutaneous leishmaniasis: Aretrospective cohort study.
Article in PLoS neglected tropical diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAlthough alternative therapies for cutaneous leishmaniasis (CL) are available, systemic meglumine antimoniate (MA) remains the first-line treatment in many endemic regions. Its use is nevertheless associated with serious adverse effects and a high risk of treatment failure (TF). This study aimed to identify clinical and sociodemographic risk factors for TF following systemic MA therapy, which may enhance therapeutic decision-making and improve clinical outcomes. METHODOLOGY: We evaluated a retrospective cohort of 296 patients with CL treated with MA between 2007 and 2024. A multivariable logistic regression model was performed using candidate variables selected via clinical relevance, biological plausibility, stepwise selection and least absolute shrinkage and selection operator (Lasso) regression. Model performance was assessed through discrimination, calibration, and internal validation. Results were reported as odds ratios, 95% confidence intervals and p-values. PRINCIPAL
findingsAll included patients received first line MA therapy and completed at least six months of follow-up. Independent predictors associated with TF were age, occupational activity, size, number and anatomical location of lesions, clinical form, regional lymphadenopathy, and prior history of leishmaniasis. The final model showed moderate overall performance with a Hosmer-Lemeshow p value = 0.667, AUC = 0.691, and a Brier score of 22.8. Internal validation yielded a Harrel C = 0.608.
conclusionsThe present study demonstrates an association between TF and socio-demographic, clinical variables. Identifying these risk factors may support clinical decision-making and contribute to optimizing treatment outcomes.
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