In-hospital Outcomes of Infective Endocarditis from 1978 to 2015: analysis through machine learning techniques

Abstract 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 Retrospective analysis 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 classification tree (CT), were used to explain the association between clinical characteristics and in-hospital mortality. Additionally, the log-linear model and GRaFo representation were used to assess the dependence degree between 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%), neurological complications (30.7%), and dialysis-dependent renal failure (6.5%). In-hospital mortality was 36.0%. The CT analysis identified subgroups with higher in-hospital mortality: patients with community-acquired IE and peripheral stigmata on admission; and patients with nosocomial IE. On the log-linear model, surgical treatment was related to higher in-hospital mortality in patients with neurological complications. Conclusion The use of a machine learning model allowed the 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 neurological complications are predictors of fatal outcomes in machine learning-based analysis.