Usefulness of the Hepatocyte Growth Factor as a Predictor of Mortality in Patients Hospitalized With Acute Heart Failure Regardless of Ejection Fraction

Hepatocyte growth factor (HGF) plays a role in the improvement of cardiac function and remodeling. Their serum levels are strongly related with mortality in chronic systolic heart failure (HF). The aim of this study was to study prognostic value of HGF in acute HF, interaction with ejection fraction, renal function, and natriuretic peptides. We included 373 patients (age 76 ± 10 years, left ventricular ejection fraction [LVEF] 46 ± 14%, 48% men) consecutively admitted for acute HF. Blood samples were obtained at admission. All patients were followed up until death or close of study (>1 year, median 371 days). HGF concentrations were determined using a commercial enzyme-linked immunosorbent assay (human HGF immunoassay). The predictive power of HGF was estimated by Cox regression with calculation of Harrell C-statistic. HGF had a median of 1,942 pg/ml (interquartile rank 1,354). According to HGF quartiles, mortality rates (per 1,000 patients/year) were 98, 183, 375, and 393, respectively (p <0.001). In Cox regression analysis, HGF (hazard ratio1SD = 1.5, 95% confidence interval 1.1 to 2.1, p = 0.002) and N-terminal pro b-type natriuretic peptide (NT-proBNP; hazard ratio1SD = 1.8, 95% confidence interval 1.2 to 2.6, p = 0.002) were independent predictors of mortality. Interaction between HGF and LVEF, origin, and renal function was nonsignificant. The addition of HGF improved the predictive ability of the models (C-statistic 0.768 vs 0.741, p = 0.016). HGF showed a complementary value over NT-proBNP (p = 0.001): mortality rate was 490 with both above the median versus 72 with both below. In conclusion, in patients with acute HF, serum HGF concentrations are elevated and identify patients at higher risk of mortality, regardless of LVEF, ischemic origin, or renal function. HGF had independent and additive information over NT-proBNP

Semantic Middleware Architectures for IoT Healthcare Applications

International audienceThe adoption of the Internet of Things (IoT) in healthcare has received considerable interest in the past decade. Indeed, IoT-based solutions are poised to transform how we keep people safe and healthy especially as the demand for solutions to lower healthcare costs increases in the coming years. However, the heterogeneity of the things that can be connected in such environments makes interoperability among them a challenging problem. Moreover, the observations produced by these things are made available with various vocabularies and data formats. This heterogeneity prevents generic solutions from being adopted on a global scale and makes difficult to share and reuse data for other purposes than those for which they were initially set up. In this book chapter, we provide an overview of the different solutions from both technical and semantic perspectives that have been used recently to tackle the interoperability issue in such IoT environments and especially in healthcare domain. We also present an overview of semantic middleware solutions that have combined the technical and semantic techniques for a complete interoperable solution