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Updated: Aug 6, 2026

Cutaneous Leishmaniasis in the Dorsal Skin of Hamsters: a Useful Model for the Screening of Antileishmanial Drugs
Published on: April 21, 2012
Development of a predictive model for meglumine antimoniate treatment failure in patients with cutaneous
Susana Ríos-Echavarría1,2, Gilma Hernández Herrera2, Hector Ivan García García2
1Group of Clinical Trials PECET, University of Antioquia, Medellín, Colombia.
Background:
Although 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 Findings:
All 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.
Conclusions:
The 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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