In this large, prospective, multinational cohort, more than one half of all cases of non-HACEK gram-negative bacillus endocarditis were associated with health care contact. Non-HACEK gram-negative bacillus endocarditis is not primarily a disease of injection drug users.
Low adherence is a major determinant of virologic failure, however different therapies have different adherence cutoffs determining a significant increment of risk.
The objective of this study was to investigate predisposing factors and outcomes of infective endocarditis (IE) caused by non-HACEK Gram-negative bacilli (GNB) in a contemporary multicenter cohort. Patients with IE due to GNB, prospectively observed in 26 Italian centers from 2004 to 2011, were analyzed. Using a case-control design, each case was compared to three age- and sex-matched controls with IE due to other etiologies. Logistic regression was performed to identify risk factors for IE due to GNB. Factors associated with early and late mortality were assessed by Cox regression analysis. The study group comprised 58 patients with IE due to GNB. We found that was the most common pathogen, followed by and The genitourinary tract as a source of infection (odds ratio [OR], 13.59; 95% confidence interval [CI], 4.63 to 39.93; < 0.001), immunosuppression (OR, 5.16; 95% CI, 1.60 to 16.24; = 0.006), and the presence of a cardiac implantable electronic device (CIED) (OR, 3.57; 95% CI, 1.55 to 8.20; = 0.003) were factors independently associated with IE due to GNB. In-hospital mortality was 13.8%, and mortality rose to 30.6% at 1 year. A multidrug-resistant (MDR) etiology was associated with in-hospital mortality (hazard ratio [HR], 21.849; 95% CI, 2.672 to 178.683; = 0.004) and 1-year mortality (HR, 4.408; 95% CI, 1.581 to 12.287; = 0.005). We conclude that the presence of a genitourinary focus, immunosuppressive therapy, and an indwelling CIED are factors associated with IE due to GNB. MDR etiology is the major determinant of in-hospital and long-term mortality.
BackgroundHost factors and complications have been associated with higher mortality in infective endocarditis (IE). We sought to develop and validate a model of clinical characteristics to predict 6‐month mortality in IE.Methods and ResultsUsing a large multinational prospective registry of definite IE (International Collaboration on Endocarditis [ICE]–Prospective Cohort Study [PCS], 2000–2006, n=4049), a model to predict 6‐month survival was developed by Cox proportional hazards modeling with inverse probability weighting for surgery treatment and was internally validated by the bootstrapping method. This model was externally validated in an independent prospective registry (ICE‐PLUS, 2008–2012, n=1197). The 6‐month mortality was 971 of 4049 (24.0%) in the ICE‐PCS cohort and 342 of 1197 (28.6%) in the ICE‐PLUS cohort. Surgery during the index hospitalization was performed in 48.1% and 54.0% of the cohorts, respectively. In the derivation model, variables related to host factors (age, dialysis), IE characteristics (prosthetic or nosocomial IE, causative organism, left‐sided valve vegetation), and IE complications (severe heart failure, stroke, paravalvular complication, and persistent bacteremia) were independently associated with 6‐month mortality, and surgery was associated with a lower risk of mortality (Harrell's C statistic 0.715). In the validation model, these variables had similar hazard ratios (Harrell's C statistic 0.682), with a similar, independent benefit of surgery (hazard ratio 0.74, 95% CI 0.62–0.89). A simplified risk model was developed by weight adjustment of these variables.ConclusionsSix‐month mortality after IE is ≈25% and is predicted by host factors, IE characteristics, and IE complications. Surgery during the index hospitalization is associated with lower mortality but is performed less frequently in the highest risk patients. A simplified risk model may be used to identify specific risk subgroups in IE.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.