ArticleCJC open2022
In-hospital Outcomes of Infective Endocarditis from 1978 to 2015: Analysis Through Machine-Learning Techniques.
Article in CJC open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Host-Bacterium Interaction Mechanisms inInternational journal of molecular sciences · 2023Pooled it
- Development and validation of a machine learning model for predicting 6-month mortality in patients with infective endocarditis.Biomedical engineering online · 2026Article
- Review
- Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review.Frontiers in cardiovascular medicine · 2025Review
- Clinical aspects and short-term prognosis in a cohort of patients with infective endocarditis, São Paulo, Brazil.Revista da Escola de Enfermagem da U S P · 2025Article
- Bivalvular Endocarditis Due to Polymicrobial Coinfection withMedicina (Kaunas, Lithuania) · 2024Review
- Bridging Molecular and Clinical Sciences to Achieve the Best Treatment ofMicroorganisms · 2023Review
- Infective Endocarditis in High-Income Countries.Metabolites · 2022Review
- Nuclear imaging studies in patients with an indeterminate diagnosis of infective endocarditis: A retrospective analysis of a case series.Indian heart journalArticle
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly identification of patients with infective endocarditis (IE) at higher risk for in-hospital mortality is essential to guide management and improve prognosis.
methodsA retrospective analysis was conducted of a cohort of patients followed up from 1978 to 2015, classified according to the modified Duke criteria. Clinical parameters, echocardiographic data, and blood cultures were assessed. Techniques of machine learning, such as the classification tree, were used to explain the association between clinical characteristics and in-hospital mortality. Additionally, the log-linear model and graphical random forests (GRaFo) representation were used to assess the degree of dependence among in-hospital outcomes of IE.
resultsThis study analyzed 653 patients: 449 (69.0%) with definite IE; 204 (31.0%) with possible IE; mean age, 41.3 ± 19.2 years; 420 (64%) men. Mode of IE acquisition: community-acquired (67.6%), nosocomial (17.0%), undetermined (15.4%). Complications occurred in 547 patients (83.7%), the most frequent being heart failure (47.0%), neurologic complications (30.7%), and dialysis-dependent renal failure (6.5%). In-hospital mortality was 36.0%. The classification tree analysis identified subgroups with higher in-hospital mortality: patients with community-acquired IE and peripheral stigmata on admission; and patients with nosocomial IE. The log-linear model showed that surgical treatment was related to higher in-hospital mortality in patients with neurologic complications.
conclusionsThe use of a machine-learning model allowed identification of subgroups of patients at higher risk for in-hospital mortality. Peripheral stigmata, nosocomial IE, absence of vegetation, and surgery in the presence of neurologic complications are predictors of fatal outcomes in machine learning-based analysis.
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