Early identification of patients with infective endocarditis (IE) at higher risk for in-hospital mortality is essential to guide management and improve prognosis. 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. 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. 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.

Background: Non-HACEK Gram-negative bacilli (NGNB) infective endocarditis (IE) has a growing frequency. We aimed to describe cases of NGNB IE and find associated risk factors. Methods: We conducted a prospective observational study of consecutive patients with definitive IE according to the modified Duke criteria in four institutions in Brazil. Results: Of 1154 adult patients enrolled, 38 (3.29%) had IE due to NGNB. Median age was 57 years, males predominated, accounting for 25/38 (65.8%). Most common etiologies were Pseudomonas aeruginosa and Klebsiella spp. (8 episodes, 21% each). Worsening heart failure occurred in 18/38 (47.4%). Higher prevalence of embolic events was found (55,3%), mostly to the central nervous system 7/38 (18.4%). Vegetations were most commonly on aortic valves 17/38 (44.7%). Recent healthcare exposure was found in 52.6% and a central venous catheter (CVC) in 13/38 (34.2%). Overall mortality was 19/38 (50%). Indwelling CVC (OR 5.93; 95% CI, 1.29 to 27.3; p = 0.017), hemodialysis (OR 16.2; 95% CI, 1.78 to 147; p = 0.008) and chronic kidney disease (OR 4.8; 95% IC, 1.2 to 19.1, p = 0.049) were identified as risk factors for mortality. Conclusions: The rate of IE due to NGNB was similar to that in previous studies. Enterobacterales and P. aeruginosa were the most common etiologies. NGNB IE was associated with central venous catheters, prosthetic valves, intracardiac devices and hemodialysis and had a high mortality rate.

Endocarditis is an extremely challenging disease, both regarding diagnosis and due to the diversity of its presentation, as well as its management, which requires an endocarditis team including professionals from different specialties. An infectious disease specialist, clinical microbiologist, cardiologist, cardiac surgeon, neurologist and imaging specialist are of the essence, and assistance from other specialists is also desirable depending on each case. Its incidence has been increasing in recent decades, especially due to population aging, the growing number of individuals undergoing

Background: Despite advances in diagnosis and treatment, the incidence and mortality of infective endocarditis (IE) have increased in recent decades despite advances in diagnosis and treatment. Studies on the risk factors for mortality in endocarditis in Latin America are scarce. Methods: This retrospective cohort study included 240 patients diagnosed with IE according to the modified Duke criteria who were admitted to two university hospitals in Rio de Janeiro, Brazil from January 2009 to June 2021. Poisson regression analysis was performed for trend tests. The multivariate Cox proportional hazards model was used to estimate the hazard ratio (HR) of predictors of in-hospital mortality. Findings: The median age was 55 years (IQR: 39-66 years), 57% were male, and 41% had a Charlson comorbidity index (CCI) score > 3. Healthcare-associated infective endocarditis (54%), left-sided native valve IE (77.5%), and staphylococcal IE (26%) predominated. Overall in-hospital mortality was 45.8%, and mortality was significantly higher in the following patients: aged ≥ 60 years (53%), CCI score ≥ 3 (60%), healthcare-associated infective endocarditis (HAIE) (53%), left-sided IE (51%), and enterococcal IE (67%). Poisson regression analysis showed no trend in in-hospital mortality per year. The adjusted multivariate model determined that age ≥ 60 years was an independent risk factor for in-hospital mortality (HR = 1.9; 95% CI 1.2-3.1; p = 0.008). Interpretation: In this 12-year retrospective cohort, there was no evidence of an improvement in survival in patients with IE. Since older age is a risk factor for mortality, consensus is needed for the management of IE in this group of patients.