[Prevalence and clinical features of non-alcoholic steatohepatitis in a hypertensive population]

OBJECTIVE

Non-alcoholic fatty liver is a chronic liver disease in which fat is deposited in the liver, causing an inflammation called non-alcoholic steatohepatitis (NASH), and fibrosis. NASH is associated with metabolic syndrome (MS) and other cardiovascular risk factors. The aim of this study was to analyse the epidemiological features of NASH within a hypertensive population, with a high prevalence of MS, and to determine the features related to NASH.

METHODS

The computerised records were collected from 3,473 patients from Mostoles University Hospital's Hypertension Unit in order to perform a retrospective, cross-sectional study. NASH was considered as ultrasound-detected fatty liver disease along with serum levels of alanine aminotransferase or aspartate aminotransferase 1.5 times above normal values, having ruled out other causes of liver disease: alcohol abuse, autoimmune hepatitis, drug toxicity, virus and hemochromatosis. A univariate, multivariate, and ANOVA analysis were performed to assess the effect of the studied features on the response of interest.

RESULTS

The cohort included 2,242 patients (51.3% men). NASH was present in 255 patients (11.4%) of whom 71% were men. MS was detected in 52.6% of patients (69.4% in the NASH group, and 50.5% in the non-NASH group, P=.001). Prevalence of type 2 diabetes mellitus was 11.5% (16.5% in the NASH group, and 10.8% in the non-NASH group, P=.01). In a multivariate analysis, waist circumference, MS, body mass index, type 2 diabetes mellitus, age, fasting serum insulin, and serum ferritin were associated with NASH. ANOVA revealed that NASH and transaminases were also significantly associated with components of metabolic syndrome.

CONCLUSIONS

In the population studied, MS, type 2 diabetes mellitus, and several components of MS were independently associated with NASH. Therefore, NASH can be considered as the liver manifestation of MS in patients with arterial hypertension.

Symmetrical compression distance for arrhythmia discrimination in cloud-based big-data services

The current development of cloud computing is completely changing the paradigm of data knowledge extraction in huge databases. An example of this technology in the cardiac arrhythmia field is the SCOOP platform, a national-level scientific cloud-based big data service for implantable cardioverter defibrillators. In this scenario, we here propose a new methodology for automatic classification of intracardiac electrograms (EGMs) in a cloud computing system, designed for minimal signal preprocessing. A new compression-based similarity measure (CSM) is created for low computational burden, so-called weighted fast compression distance, which provides better performance when compared with other CSMs in the literature. Using simple machine learning techniques, a set of 6848 EGMs extracted from SCOOP platform were classified into seven cardiac arrhythmia classes and one noise class, reaching near to 90% accuracy when previous patient arrhythmia information was available and 63% otherwise, hence overcoming in all cases the classification provided by the majority class. Results show that this methodology can be used as a high-quality service of cloud computing, providing support to physicians for improving the knowledge on patient diagnosis.