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.

The finding of substantial subadditivity, coupled with the marked discordance in probability estimates, questions the applicability of the threshold approach. Physicians need guidance, explicit tools and formal training in probability estimation to optimize the use of this approach in clinical practice.

process is offered as explanation and strongly supported by leading biologists. I consider the high values of the energy of activation reported here as incompatible with this view and as a support for those theories which postulate chemical processes as being responsible for the specific changes in permeability of conducting membranes during activity. Detection, by in vitro serological techniques, of circulating antibodies directed against penicillin has not been reported.In the past few months, however, sera from certain individuals have been encountered which appear to react specifically against penicillin. It is the purpose of this report to describe the system in which this reaction is demonstrable and to report studies on the characteristics and specificity of the antibody.Addition of penicillin to erythrocyte suspensions is frequently a routine procedure in the preparation and preservation of red cells used in specificity panels in blood-bank laboratories. In August 1957, during routine testing, the serum of a prospective transfusion recipient was found to agglutinate all of such a panel process is offered as explanation and strongly supported by leading biologists. I consider the high values of the energy of activation reported here as incompatible with this view and as a support for those theories which postulate chemical processes as being responsible for the specific changes in permeability of conducting membranes during activity.

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Physicians intuitively apply pattern recognition when evaluating a patient. Rational diagnosis making requires that clinical patterns be put in the context of disease prior probability, yet physicians often exhibit flawed probabilistic reasoning. Difficulties in making a diagnosis are reflected in the high rates of deadly and costly diagnostic errors. Introduced 6 decades ago, computerized diagnosis support systems are still not widely used by internists. These systems cannot efficiently recognize patterns and are unable to consider the base rate of potential diagnoses. We review the limitations of current computer-aided diagnosis support systems. We then portray future diagnosis support systems and provide a conceptual framework for their development. We argue for capturing physician knowledge using a novel knowledge representation model of the clinical picture. This model (based on structured patient presentation patterns) holds not only symptoms and signs but also their temporal and semantic interrelations. We call for the collection of crowdsourced, automatically deidentified, structured patient patterns as means to support distributed knowledge accumulation and maintenance. In this approach, each structured patient pattern adds to a self-growing and -maintaining knowledge base, sharing the experience of physicians worldwide. Besides supporting diagnosis by relating the symptoms and signs with the final diagnosis recorded, the collective pattern map can also provide disease base-rate estimates and real-time surveillance for early detection of outbreaks. We explain how health care in resource-limited settings can benefit from using this approach and how it can be applied to provide feedback-rich medical education for both students and practitioners.

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OBJECTIVEOveractivity of the Forkhead transcription factor FoxO1 promotes diabetic hyperglycemia, dyslipidemia, and acute-phase response, whereas suppression of FoxO1 activity by insulin may alleviate diabetes. The reported efficacy of long-chain fatty acyl (LCFA) analogs of the MEDICA series in activating AMP-activated protein kinase (AMPK) and in treating animal models of diabesity may indicate suppression of FoxO1 activity.RESEARCH DESIGN AND METHODSThe insulin-sensitizing and anti-inflammatory efficacy of a MEDICA analog has been verified in guinea pig and in human C-reactive protein (hCRP) transgenic mice, respectively. Suppression of FoxO1 transcriptional activity has been verified in the context of FoxO1- and STAT3-responsive genes and compared with suppression of FoxO1 activity by insulin and metformin.RESULTSTreatment with MEDICA analog resulted in total body sensitization to insulin, suppression of lipopolysaccharide-induced hCRP and interleukin-6–induced acute phase reactants and robust decrease in FoxO1 transcriptional activity and in coactivation of STAT3. Suppression of FoxO1 activity was accounted for by its nuclear export by MEDICA-activated AMPK, complemented by inhibition of nuclear FoxO1 transcriptional activity by MEDICA-induced C/EBPβ isoforms. Similarly, insulin treatment resulted in nuclear exclusion of FoxO1 and further suppression of its nuclear activity by insulin-induced C/EBPβ isoforms. In contrast, FoxO1 suppression by metformin was essentially accounted for by its nuclear export by metformin-activated AMPK.CONCLUSIONSSuppression of FoxO1 activity by MEDICA analogs may partly account for their antidiabetic anti-inflammatory efficacy. FoxO1 suppression by LCFA analogs may provide a molecular rational for the beneficial efficacy of carbohydrate-restricted ketogenic diets in treating diabetes.