Development and validation of a simple machine learning tool to predict mortality in leptospirosis

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorFederal University of Ceará-
Autor(es): dc.contributorHospital Geral de Fortaleza-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Fortaleza-
Autor(es): dc.creatorGaldino, Gabriela Studart-
Autor(es): dc.creatorde Sandes-Freitas, Tainá Veras-
Autor(es): dc.creatorde Andrade, Luis Gustavo Modelli-
Autor(es): dc.creatorAdamian, Caio Manuel Caetano-
Autor(es): dc.creatorMeneses, Gdayllon Cavalcante-
Autor(es): dc.creatorda Silva Junior, Geraldo Bezerra-
Autor(es): dc.creatorde Francesco Daher, Elizabeth-
Data de aceite: dc.date.accessioned2025-08-21T17:51:19Z-
Data de disponibilização: dc.date.available2025-08-21T17:51:19Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1038/s41598-023-31707-4-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/247031-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/247031-
Descrição: dc.descriptionPredicting risk factors for death in leptospirosis is challenging, and identifying high-risk patients is crucial as it might expedite the start of life-saving supportive care. Admission data of 295 leptospirosis patients were enrolled, and a machine-learning approach was used to fit models in a derivation cohort. The comparison of accuracy metrics was performed with two previous models—SPIRO score and quick SOFA score. A Lasso regression analysis was the selected model, demonstrating the best accuracy to predict mortality in leptospirosis [area under the curve (AUC-ROC) = 0.776]. A score-based prediction was carried out with the coefficients of this model and named LeptoScore. Then, to simplify the predictive tool, a new score was built by attributing points to the predictors with importance values higher than 1. The simplified score, named QuickLepto, has five variables (age > 40 years; lethargy; pulmonary symptom; mean arterial pressure < 80 mmHg and hematocrit < 30%) and good predictive accuracy (AUC-ROC = 0.788). LeptoScore and QuickLepto had better accuracy to predict mortality in patients with leptospirosis when compared to SPIRO score (AUC-ROC = 0.500) and quick SOFA score (AUC-ROC = 0.782). The main result is a new scoring system, the QuickLepto, that is a simple and useful tool to predict death in leptospirosis patients at hospital admission.-
Descrição: dc.descriptionMedical Sciences Postgraduate Program Federal University of Ceará, Rua Silva Jatahy 1000 ap 600, Ceará-
Descrição: dc.descriptionHospital Universitário Walter Cantídio Federal University of Ceará, Ceará-
Descrição: dc.descriptionHospital Geral de Fortaleza, Ceara-
Descrição: dc.descriptionBotucatu Medical School Universidade Estadual Paulista, São Paulo-
Descrição: dc.descriptionSchool of Medicine Medical Sciences and Public Health Postgraduate Programs University of Fortaleza, Ceará-
Descrição: dc.descriptionBotucatu Medical School Universidade Estadual Paulista, São Paulo-
Idioma: dc.languageen-
Relação: dc.relationScientific Reports-
???dc.source???: dc.sourceScopus-
Título: dc.titleDevelopment and validation of a simple machine learning tool to predict mortality in leptospirosis-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.