Development And Validation Of A Simple Machine Learning Tool To Predict Mortality In Leptospirosis

This study aimed to assess a hard and objectively measurable endpoint, and focus on the possibility of early intervention, by proposing a new score constructed using machine learning techniques to predict death based on admission variables in patients with leptospirosis. The study was a retrospective multicenter cohort study carried out from January 2005 to December 2019, including all patients with leptospirosis consecutively admitted to three tertiary reference hospitals in Fortaleza, state of Ceara, Brazil. The criteria for leptospirosis diagnosis included the presence of a positive serology result with a microscopic agglutination test (MAT) titer higher than 1:800, or ELISA assay for the detection of immunoglobulin M (IgM) antibodies associated with an epidemiological and clinical history compatible with leptospirosis.