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DEVELOPMENT OF A MULTIVARIATE PREDICTIVE MODEL FOR DAPSONE ADVERSE DRUG EVENTS IN PEOPLE WITH LEPROSY UNDER STANDARD WHO MULTIDRUG THERAPY
Drug therapy
Forecasting
Medical risk factors
Skin diseases
Anemia
Hemolytic anemia
Outpatient clinics
Author summary: Adverse events (AE) produced by the drugs used to treat leprosy can hinder the successful completion of the therapeutic regimen. Well-known AE produced by dapsone (DDS) are related to liver problems, allergic reactions, or to the destruction of red and/or white blood cells, causing anemia. Helping the physician to recognize a patient that may develop these adverse reactions can be useful. Thus, we developed a model to predict AE in patients with leprosy receiving standard World Health Organization-recommended multidrug therapy (WHO/MDT). Our question was whether we could use sociodemographic and clinical variables to generate a predictive model for DDS-ADEs. The model developed in this study could be a useful tool to assist physicians in predicting DDS-ADEs when treating patients with standard WHO/MDT for leprosy, and thus, establish a safer therapeutic plan for patients with a greater ADE risk.
Author Contributions:
Conceptualization: Ana Carolina Galvão dos Santos de Araujo, Ximena Illarramendi, Sandra Maria Barbosa Durães, Maurício Lisboa Nobre, Milton Ozório Moraes, Anna Maria Sales, Gilberto Marcelo Sperandio da Silva.
Data curation: Ana Carolina Galvão dos Santos de Araujo, Anna Maria Sales, Gilberto Marcelo Sperandio da Silva.
Formal analysis: Ana Carolina Galvão dos Santos de Araújo, Mariana de Andrea Vilas-Boas Hacker, Anna Maria Sales, Gilberto Marcelo Sperandio da Silva.
Investigation: Ana Carolina Galvão dos Santos de Araujo.
Methodology: Ana Carolina Galvão dos Santos de Araújo, Mariana de Andrea Vilas-Boas Hacker, Anna Maria Sales, Gilberto Marcelo Sperandio da Silva.
Supervision: Anna Maria Sales, Gilberto Marcelo Sperandio da Silva.
Writing – original draft: Ana Carolina Galvão dos Santos de Araújo.
Writing – review & editing: Roberta Olmo Pinheiro, Ximena Illarramendi, Sandra Maria Barbosa Durães, Maurício Lisboa Nobre, Anna Maria Sales, Gilberto Marcelo Sperandio da Silva.
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Affilliation
Abstract
Background: 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. Methodology: 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). Results: 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. Conclusion: We propose a clinical model that could become a helpful tool for physicians in predicting ADEs in DDS-treated leprosy patients.
Keywords
LeprosyDrug therapy
Forecasting
Medical risk factors
Skin diseases
Anemia
Hemolytic anemia
Outpatient clinics
Publisher
Public Library of Science
Citation
ARAÚJO, Ana Carolina Galvão dos Santos de et al. Development of a multivariate predictive model for dapsone adverse drug events in people with leprosy under standard WHO multidrug therapy. PLoS Neglected Tropical Diseases, v. 18, n. 1, p. 1-14, Jan. 2024.DOI
10.1371/journal.pntd.0011901ISSN
1935-2727Notes
Produção científica do Laboratório de Hanseníase.Author summary: Adverse events (AE) produced by the drugs used to treat leprosy can hinder the successful completion of the therapeutic regimen. Well-known AE produced by dapsone (DDS) are related to liver problems, allergic reactions, or to the destruction of red and/or white blood cells, causing anemia. Helping the physician to recognize a patient that may develop these adverse reactions can be useful. Thus, we developed a model to predict AE in patients with leprosy receiving standard World Health Organization-recommended multidrug therapy (WHO/MDT). Our question was whether we could use sociodemographic and clinical variables to generate a predictive model for DDS-ADEs. The model developed in this study could be a useful tool to assist physicians in predicting DDS-ADEs when treating patients with standard WHO/MDT for leprosy, and thus, establish a safer therapeutic plan for patients with a greater ADE risk.
Author Contributions:
Conceptualization: Ana Carolina Galvão dos Santos de Araujo, Ximena Illarramendi, Sandra Maria Barbosa Durães, Maurício Lisboa Nobre, Milton Ozório Moraes, Anna Maria Sales, Gilberto Marcelo Sperandio da Silva.
Data curation: Ana Carolina Galvão dos Santos de Araujo, Anna Maria Sales, Gilberto Marcelo Sperandio da Silva.
Formal analysis: Ana Carolina Galvão dos Santos de Araújo, Mariana de Andrea Vilas-Boas Hacker, Anna Maria Sales, Gilberto Marcelo Sperandio da Silva.
Investigation: Ana Carolina Galvão dos Santos de Araujo.
Methodology: Ana Carolina Galvão dos Santos de Araújo, Mariana de Andrea Vilas-Boas Hacker, Anna Maria Sales, Gilberto Marcelo Sperandio da Silva.
Supervision: Anna Maria Sales, Gilberto Marcelo Sperandio da Silva.
Writing – original draft: Ana Carolina Galvão dos Santos de Araújo.
Writing – review & editing: Roberta Olmo Pinheiro, Ximena Illarramendi, Sandra Maria Barbosa Durães, Maurício Lisboa Nobre, Anna Maria Sales, Gilberto Marcelo Sperandio da Silva.
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