In-hospital Outcomes of Infective Endocarditis from 1978 to 2015: Analysis Through Machine-Learning Techniques.

Autor:	Resende P Jr; Department of Cardiology/ICES, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil., Fortes CQ; Department of Infectious Diseases, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil., do Nascimento EM; Department of Statistics/UEZO, State University Center of West Zone, Rio de Janeiro, Brazil., Sousa C; Faculty of Medicine, University of Lisbon, Lisbon, Portugal., Querido Fortes NR; Clementino Fraga Filho Hospital, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil., Thomaz DC; School of Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil., de Bragança Pereira B; Department of Statistics/ICES, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil., Pinto FJ; Faculty of Medicine, University of Lisbon, Lisbon, Portugal., de Oliveira GMM; Department of Cardiology/ICES, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
Jazyk:	angličtina
Zdroj:	CJC open [CJC Open] 2021 Sep 11; Vol. 4 (2), pp. 164-172. Date of Electronic Publication: 2021 Sep 11 (Print Publication: 2022).
DOI:	10.1016/j.cjco.2021.08.017
Abstrakt:	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. (© 2021 The Authors.)
Databáze:	MEDLINE
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