Prediction model to discriminate leptospirosis from hantavirus
Rev. Assoc. Med. Bras. (1992, Impr.)
; 67(8): 1102-1108, Aug. 2021. tab
Artigo
em Inglês
| LILACS
| ID: biblio-1346966
Biblioteca responsável:
BR1.1
ABSTRACT
SUMMARY OBJECTIVE:
The aim of this study was to build a prediction model to discriminate precociously hantavirus infection from leptospirosis, identifying the conditions and risk factors associated with these diseases.METHODS:
A logistic regression model in which the response variable was the presence of hantavirus or leptospirosis was adjusted.RESULTS:
As a result, the method selected the following variables that influenced the prediction formula sociodemographic variables, clinical manifestations, and exposure to environmental risks. All variables considered in the model presented statistical significance with a p<0.05 value. The accuracy of the model to differentiate hantavirus from leptospirosis was 88.7%.CONCLUSIONS:
Concluding that the development of statistical tools with high potential to predict the disease, and thus differentiate them precociously, can reduce hospital costs, speed up the patient's care, reduce morbidity and mortality, and assist health professionals and public managers in decision-making.
Texto completo:
Disponível
Coleções:
Bases de dados internacionais
Contexto em Saúde:
ODS3 - Saúde e Bem-Estar
Problema de saúde:
Meta 3.3: Acabar com as doenças tropicais negligenciadas e combater as doenças transmissíveis
Base de dados:
LILACS
Assunto principal:
Orthohantavírus
/
Infecções por Hantavirus
/
Leptospirose
Tipo de estudo:
Estudo de etiologia
/
Estudo prognóstico
/
Fatores de risco
Limite:
Humanos
Idioma:
Inglês
Revista:
Rev. Assoc. Med. Bras. (1992, Impr.)
Assunto da revista:
EducaÆo em Sa£de
/
GestÆo do Conhecimento para a Pesquisa em Sa£de
/
Medicina
Ano de publicação:
2021
Tipo de documento:
Artigo
País de afiliação:
Brasil
Instituição/País de afiliação:
Universidade de Franca/BR