A protocol for the development and internal validation of a model to predict clinical response to antihistamines in urticaria patients

ABSTRACT: Chronic urticaria causes a significant limitation to quality of life. In the literature, various studies can be found that have reviewed several clinical and laboratory markers, but none of these variables alone is sufficient to predict the patient's prognosis. In this study, we prese...

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Autores:
Velásquez Lopera, Margarita María
Sánchez Caraballo, Jorge Mario
Jaimes Barragán, Fabián Alberto
Tipo de recurso:
Article of investigation
Fecha de publicación:
2020
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/43086
Acceso en línea:
https://hdl.handle.net/10495/43086
Palabra clave:
Chronic Urticaria
Urticaria Crónica
Cohort Studies
Estudios de Cohortes
Histamine Antagonists
Antagonistas de los Receptores Histamínicos
Logistic Models
Modelos Logísticos
Prognosis
Pronóstico
Validation Studies as Topic
Estudios de Validación como Asunto
https://id.nlm.nih.gov/mesh/D000080223
https://id.nlm.nih.gov/mesh/D015331
https://id.nlm.nih.gov/mesh/D006633
https://id.nlm.nih.gov/mesh/D016015
https://id.nlm.nih.gov/mesh/D011379
https://id.nlm.nih.gov/mesh/D054928
Rights
openAccess
License
http://creativecommons.org/licenses/by/2.5/co/
Description
Summary:ABSTRACT: Chronic urticaria causes a significant limitation to quality of life. In the literature, various studies can be found that have reviewed several clinical and laboratory markers, but none of these variables alone is sufficient to predict the patient's prognosis. In this study, we present a protocol to develop a prognostic model that can predict the clinical response of urticaria patients to antihistamines. This is a protocol for a bidirectional cohort study. Urticaria data will be routinely collected from a population of patients over 18 years old. A full multivariable logistic regression model will be fitted, following five steps: 1) Selection of predictive variables for the model; 2) Evaluation of the quality of the collected data and control of lost data; 3) Data statistical management; 4) Strategies to select the variables to include at the end of the model; 5) Evaluation of the performance of the different possible models (predictive accuracy) and selection of the best model. The performance and internal validation of the model will be assessed. Some clinical and paraclinical variables will be measured for further exploration.