AIMTo investigated the real-world effectiveness and safety of various regimens of interferon-free treatments in patients infected with hepatitis C virus (HCV).METHODSWe performed an observational study to analyze different antiviral treatments administered to 462 HCV-infected patients, of which 56.7% had liver cirrhosis. HCV RNA after 4 wk of treatment and at 12 wk after treatment sustained virologic response (SVR) as well as serious adverse events (SAEs) was analyzed first for the whole cohort and then separately in patients who met or did not meet the inclusion criteria of a clinical trial (CT-met and CT-unmet, respectively).RESULTSThe most frequently prescribed treatment was simeprevir/sofosbuvir (36.4%), followed by sofosbuvir/ledipasvir (24.9%) and ombitasvir/paritaprevir/ritonavir (r)/dasabuvir (19.9%). Ribavirin (RBV) was administered in 198 patients (42.9%). SVRs occurred in 437/462 patients (94.6%). The SVRs ranged between 93.3% and 100% for genotypes 1-4. SVRs were achieved in 96.2% patients in the CT-met group vs 91.9% patients in the CT-unmet group (P = 0.049). Undetectable HCV RNA at week 4 occurred in 72.9% of the patients. In the univariate analysis, the factors associated with SVRs were lower liver stiffness, absence of cirrhosis, higher platelet count, higher albumin levels, no RBV dose reduction, undetectable HCV RNA at week 4 and CT-met group. In the multivariate analysis, only albumin was an independent predictor of treatment failure (P = 0.04). Eleven patients (2.4%) developed SAEs; 5.2% and 0.7% of the patients in the CT-unmet and CT-met groups, respectively (P = 0.003).CONCLUSIONA high proportion of patients with HCV infection achieved SVRs. For patients who did not meet the CT criteria, treatment regimens must be optimized.
Background and Aims:
In patients with non-severe acute or chronic autoimmune hepatitis (AIH) without cirrhosis, clinical practice guidelines recommend indistinct use of prednisone or budesonide. However, budesonide is infrequently used in clinical practice. We aimed to describe its use and compare its efficacy and safety with prednisone as first-line options.
Approach and Results:
This was a retrospective, multicenter study of 105 naive AIH patients treated with budesonide as the first-line drug. The control group included 276 patients treated with prednisone. Efficacy was assessed using logistic regression and validated using inverse probability of treatment weighting propensity score. The median time to biochemical response (BR) was 3.1 months in patients treated with budesonide and 4.9 months in those with prednisone. The BR rate was significantly higher in patients treated with prednisone (87% vs. 49% of patients with budesonide, p < 0.001). The probability of achieving BR, assessed using the inverse probability of treatment weighting propensity score, was significantly lower in the budesonide group (OR = 0.20; 95% CI: 0.11–0.38) at any time during follow-up, and at 6 (OR = 0.51; 95% CI: 0.29–0.89) and 12 months after starting treatment (0.41; 95% CI: 0.23–0.73). In patients with transaminases <2 × upper limit of normal, BR was similar in both treatment groups. Prednisone treatment was significantly associated with a higher risk of adverse events (24.2% vs. 15.9%, p = 0.047).
Conclusions:
In the real-life setting, the use of budesonide as first-line treatment is low, and it is generally prescribed to patients with perceived less disease activity. Budesonide was inferior to prednisone as a first-line drug but was associated with fewer side effects.
A significant and very extended approach for Smart Cities is the use of sensors and the analysis of the data generated for the interpretation of phenomena. The proper sensor location represents a key factor for suitable data collection, especially for big data. There are different methodologies to select the places to install sensors. Such methodologies range from a simple grid of the area to the use of complex statistical models to provide their optimal number and distribution, or even the use of a random function within a set of defined positions. We propose the use of the same data generated by the sensor to locate or relocate them in real-time, through what we denominate as a ‘hot-zone’, a perimeter with significant data related to the observed phenomenon. In this paper, we present a process with four phases to calculate the best georeferenced locations for sensors and their visualization on a map. The process was applied to the Guadalajara Metropolitan Zone in Mexico where, during the last twenty years, air quality has been monitored through sensors in ten different locations. As a result, two algorithms were developed. The first one classifies data inputs in order to generate a matrix with frequencies that works along with a matrix of territorial adjacencies. The second algorithm uses training data with machine learning techniques, both running in parallel modes, in order to diagnose the installation of new sensors within the detected hot-zones.
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