ArticleScientific reports2023
Development and validation of a simple machine learning tool to predict mortality in leptospirosis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review.JMIR medical informatics · 2025Pooled it
- Clinical Predictors of Fatal Outcomes from Human Leptospirosis, Thailand, 2015-2024.Emerging infectious diseases · 2026Observational
- Early diagnosis and treatment of leptospirosis: Optimizing clinical outcomes.The Journal of infection · 2026Review
- Leptospira and Leptospirosis: A Review of Species Classifications, Genomes, Morphological Structures, Antimicrobial Resistances, Transmissions, and Clinical Manifestations.Current microbiology · 2026Review
- The Characteristics of Patients That Develop Severe Leptospirosis: A Scoping Review.Pathogens (Basel, Switzerland) · 2025Article
- Presentation and Clinical Course of Leptospirosis in a Referral Hospital in Far North Queensland, Tropical Australia.Pathogens (Basel, Switzerland) · 2025Article
- Leptospirosis.Nature reviews. Disease primers · 2025Review
- Factors associated with the lethality of human leptospirosis in Brazil.Cadernos de saude publica · 2025Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting 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.
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