02771nas a2200241   4500000000100000008004100001260001200042100001600054700001300070700001500083700001900098700001400117700001200131700001300143700001200156700001500168245014400183856009900327300000900426490000700435520207300442022001402515       2024                            d  c01/20241 ade Araujo A1 aHacker M1 aPinheiro R1 aIllarramendi X1 aDurães S1 aNobre M1 aMoraes M1 aSales A1 ada Silva G00aDevelopment of a multivariate predictive model for dapsone adverse drug events in people with leprosy under standard WHO multidrug therapy.  uhttps://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0011901&type=printable  a1-140 v183 a<p><strong>Background: </strong>The occurrence of adverse drug events (ADEs) during dapsone (DDS) treatment in patients with leprosy can constitute a significant barrier to the successful completion of the standardized therapeutic regimen for this disease. Well-known DDS-ADEs are hemolytic anemia, methemoglobinemia, hepatotoxicity, agranulocytosis, and hypersensitivity reactions. Identifying risk factors for ADEs before starting World Health Organization recommended standard multidrug therapy (WHO/MDT) can guide therapeutic planning for the patient. The objective of this study was to develop a predictive model for DDS-ADEs in patients with leprosy receiving standard WHO/MDT.</p>

<p><strong>Methodology: </strong>This is a case-control study that involved the review of medical records of adult (≥18 years) patients registered at a Leprosy Reference Center in Rio de Janeiro, Brazil. The cohort included individuals that received standard WHO/MDT between January 2000 to December 2021. A prediction nomogram was developed by means of multivariable logistic regression (LR) using variables. The Hosmer-Lemeshow test was used to determine the model fit. Odds ratios (ORs) and their respective 95% confidence intervals (CIs) were estimated. The predictive ability of the LRM was assessed by the area under the receiver operating characteristic curve (AUC).</p>

<p><strong>Results: </strong>A total of 329 medical records were assessed, comprising 120 cases and 209 controls. Based on the final LRM analysis, female sex (OR = 3.61; 95% CI: 2.03-6.59), multibacillary classification (OR = 2.5; 95% CI: 1.39-4.66), and higher education level (completed primary education) (OR = 1.97; 95% CI: 1.14-3.47) were considered factors to predict ADEs that caused standard WHO/MDT discontinuation. The prediction model developed had an AUC of 0.7208, that is 72% capable of predicting DDS-ADEs.</p>

<p><strong>Conclusion: </strong>We propose a clinical model that could become a helpful tool for physicians in predicting ADEs in DDS-treated leprosy patients.</p>
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