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In-hospital Outcomes of Infective Endocarditis from 1978 to 2015: Analysis Through Machine-Learning Techniques
Plinio Resende1, Claudio Querido Fortes2, Emilia Matos do Nascimento3
1Department of Cardiology/ICES, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
Insights
Machine learning models identified high-risk infective endocarditis (IE) patients. Peripheral stigmata, nosocomial IE, and surgery with neurologic complications predict fatal outcomes.
Area of Science:
- Cardiology
- Medical Informatics
- Public Health
Background:
- Early identification of infective endocarditis (IE) patients at high risk for in-hospital mortality is crucial for timely management and improved outcomes.
- Risk stratification aids in resource allocation and personalized treatment strategies for IE.
Purpose of the Study:
- To identify subgroups of patients with infective endocarditis (IE) at higher risk for in-hospital mortality using machine learning techniques.
- To analyze the association between clinical characteristics and in-hospital mortality in IE patients.
Main Methods:
- Retrospective analysis of 653 patients with infective endocarditis (IE) from 1978 to 2015, classified by modified Duke criteria.
- Application of machine learning algorithms, including classification trees and log-linear models, to assess mortality predictors.
- Evaluation of clinical parameters, echocardiographic data, and blood cultures for risk factor identification.
Main Results:
- In-hospital mortality for IE patients was 36.0%.
- Classification tree analysis identified higher mortality risk in community-acquired IE with peripheral stigmata and in nosocomial IE.
- Log-linear modeling indicated that surgery in patients with neurologic complications was associated with increased in-hospital mortality.
Conclusions:
- Machine learning models effectively identified high-risk IE patient subgroups for in-hospital mortality.
- Key predictors of fatal outcomes include peripheral stigmata, nosocomial IE, absence of vegetation, and surgery in the presence of neurologic complications.
Background:
Early identification of patients with infective endocarditis (IE) at higher risk for in-hospital mortality is essential to guide management and improve prognosis.
Methods:
A 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.
Results:
This 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.
Conclusions:
The 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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