Background 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) has emerged as a useful diagnostic tool for suspected infective endocarditis (IE) in patients with prosthetic valves or implantable devices. However, there is limited evidence regarding use of 18F-FDG-PET/CT for the diagnosis of native valve endocarditis (NVE). Methods Between 2014 and 2017, 303 episodes of left-sided suspected IE (188 prosthetic valves/ascending aortic prosthesis and 115 native valves) were studied. 18F-FDG-PET/CT accuracy was determined in the subgroups of patients with NVE and prosthetic valve endocarditis (PVE)/ascending aortic prosthesis infection (AAPI). Associations between inflammatory infiltrate patterns and 18F-FDG-PET/CT uptake were investigated in an exploratory ad hoc histological analysis. Results Among 188 patients with PVE/AAPI, the sensitivity, specificity, and positive and negative predictive values of 18F-FDG-PET/CT focal uptake were 93%, 90%, 89%, and 94%, respectively, while among 115 patients with NVE, the corresponding values were 22%, 100%, 100%, and 66%. The inclusion of abnormal 18F-FDG cardiac uptake as a major criterion at admission enabled a recategorization of 76% (47/62) of PVE/AAPI cases initially classified as “possible” to “definite” IE. In the histopathological analysis, a predominance of polymorphonuclear cell inflammatory infiltrate and a reduced extent of fibrosis were observed in the PVE group only. Conclusions Use of 18F-FDG-PET/CT at the initial presentation of patients with suspected PVE increases the diagnostic capability of the modified Duke criteria. In patients who present with suspected NVE, the use of 18F-FDG-PET/CT is less accurate and could only be considered a complementary diagnostic tool for a specific population of patients with NVE.

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.

COVID-19 is still placing a heavy health and financial burden worldwide. Impairment in patient screening and risk management plays a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study enrolled 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules related to the disease’s pathophysiology and several discriminating features to patient’s health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings with the disease’s pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using machine learning algorithms, transforming this screening approach in a tool with great potential for real-world application.

No abstract