Abnormal quality detection and isolation in water distribution networks using simulation models

This paper proposes a model based detection and localisation method to deal with abnormal quality levels based on the chlorine measurements and chlorine sensitivity analysis in a water distribution network. A fault isolation algorithm which correlates on line the residuals (generated by comparing the available chlorine measurements with their estimations using a model) with the fault sensitivity matrix is used. The proposed methodology has been applied to a District Metered Area (DMA) in the Barcelona network

Effect of Hepatitis E Virus RNA Universal Blood Donor Screening, Catalonia, Spain, 2017-2020

Altres ajuts: Banc de Sang i Teixits.Hepatitis E virus (HEV) is the major cause of acute viral hepatitis in several countries in Europe. HEV is acquired mainly by consumption of contaminated pork but can also be transmitted through blood transfusion. HEV infection is usually self-limited but can become persistent in immunocompromised persons. During the fi rst 30 months of HEV RNA universal screening of blood donations in Catalonia, Spain, we identifi ed 151 HEV RNA-positive donations (1/4,341 blood donations). Most infected donors reported consumption of pates and sausages, and 58% were negative for HEV IgM and IgG. All HEV isolates belonged to genotype 3. All infected donors spontaneously resolved the infection, and no neurologic symptoms and reinfections were observed after 1 year of follow-up. Since the implementation of HEV RNA universal screening, no new cases of transfusion-transmitted HEV infection were reported. Our data indicate HEV screening of blood donations provides safer blood for all recipients, especially for immunosuppressed persons

A decision support system for on-line leakage localization

This paper describes a model-driven decision-support system (software tool) implementing a model-based methodology for on-line leakage detection and localization which is useful for a large class of water distribution networks. Since these methods present a certain degree of complexity which limits their use to experts, the proposed software tool focuses on the integration of a method emphasizing its use by water network managers as a decision support system. The proposed software tool integrates a model-based leakage localization methodology based on the use of on-line telemetry information, as well as a water network calibrated hydraulic model. The application of the resulting decision support software tool in a district metered area (DMA) of the Barcelona distribution network is provided and discussed. The obtained results show that the leakage detection and localization may be performed efficiently reducing the required time. © 2014 Elsevier Ltd.The authors wish to thank the support received by the AM0901 project funded by R+i Alliance (Suez Environnement) and by the EFFINET grant FP7-ICT-2012-318556 of the European Commission.Peer Reviewe

Deterioration of Health-Related Quality of Life After Withdrawal of Risankizumab Treatment in Patients with Moderate-to-Severe Plaque Psoriasis : A Machine Learning Predictive Model

Risankizumab has demonstrated efficacy in treating moderate-to-severe psoriasis. The phase-3 IMMhance trial (NCT02672852) examined the effect of continuing versus withdrawing from risankizumab treatment on psoriasis severity, including the Psoriasis Area and Severity Index (PASI) and static Physician Global Assessment (sPGA). However, the effect of withdrawal on health-related quality of life (HRQL) was not assessed. Therefore, this study was conducted to evaluate the impact of risankizumab withdrawal on HRQL measured by the Dermatology Life Quality Index (DLQI). Because DLQI was not measured beyond week 16 in IMMhance, a machine learning predictive model for DLQI was developed. A machine learning model for DLQI was fitted using repeated measures data from three phase-3 trials (NCT02684370, NCT02684357, NCT02694523) (pooled N = 1602). An elastic-net algorithm performed automated variable selection among candidate predictors including concurrent PASI and sPGA, demographics, and interaction terms. The machine learning model was used to predict DLQI at weeks 28-104 of IMMhance among patients re-randomized to continue (N = 111) or withdraw from (N = 225) risankizumab after achieving response (sPGA = 0/1) at week 28. The machine learning predictive model demonstrated good statistical fit during tenfold cross-validation and external validation against observed DLQI at weeks 0-16 of IMMhance (N = 507). Predicted improvements in DLQI from baseline were lower in the withdrawal versus the continuation cohort (mean DLQI change at week 104, −5.9 versus −11.5, difference [95% CI] = 5.6 [4.1, 7.3]). Predicted DLQI deteriorated more extensively than PASI (49.7% versus 36.4%) after treatment withdrawal. The predicted DLQI score deteriorated more rapidly after risankizumab withdrawal than the PASI score, an objective measure of disease. These findings suggest that the deterioration in HRQL reflects more substantial impacts after risankizumab discontinuation than those measured by PASI only. The online version contains supplementary material available at 10.1007/s13555-021-00550-8

Comparative Efficacy and Relative Ranking of Biologics and Oral Therapies for Moderate-to-Severe Plaque Psoriasis : A Network Meta-analysis

The clinical benefits of biologic and oral treatments for moderate-to-severe plaque psoriasis are well-established, but efficacy outcomes can vary across therapies. Comparative efficacy analysis can be highly informative in clinical settings with multiple therapeutic options. This study assessed the short-term and long-term comparative efficacy of biologic and oral treatments for moderate-to-severe psoriasis. A systematic literature review identified phase 2/3/4 randomized controlled trials (RCTs) through to 1 July 2020 for Food and Drug Administration- or European Medicines Agency-licensed treatments for moderate-to-severe psoriasis. Psoriasis Area and Severity Index (PASI) 75/90/100 response rates at the end of the primary response (short-term: 10-16 weeks from baseline) and maintenance periods (long-term: 48-52 weeks from baseline) were estimated using Bayesian network meta-analysis. Surfaces under the cumulative ranking curves (SUCRA) were estimated to present the relative ranking of treatments. In the short term (N = 71 RCTs), the PASI 90 response rates were highest for ixekizumab (72.9%, SUCRA 0.951), risankizumab (72.5%, 0.940), and brodalumab (72.0%, 0.930), which were significantly higher than those for guselkumab (65.0%, 0.795), secukinumab (65.0%, 0.794), infliximab (56.8%, 0.702), certolizumab (400 mg: 49.6%, 0.607; 200 mg: 42.2%, 0.389), ustekinumab (90 mg: 47.9%, 0.568; weight-based: 45.7%, 0.505; 45 mg: 44.6%, 0.460), adalimumab (43.0%, 0.410), tildrakizumab (200 mg: 39.7%, 0.327; 100 mg: 37.2%, 0.268), etanercept (18.0%, 0.171), apremilast (12.4%, 0.090), and dimethyl fumarate (12.2%, 0.092). The PASI 100 response rates were highest for ixekizumab (41.4%), risankizumab (40.8%), and brodalumab (40.3%). In the long term (N = 11 RCTs), the PASI 90 rate was highest for risankizumab (85.3%, SUCRA: 0.998), which were significantly higher than those for brodalumab (78.8%, 0.786), guselkumab (78.1%, 0.760), ixekizumab (72.1%, 0.577), secukinumab (67.0%, 0.450), ustekinumab (weight-based: 55.0%, 0.252), adalimumab (51.6%, 0.176), and etanercept (37.9%, 0.001). Risankizumab had the highest PASI 100 response rate (65.4%), followed by brodalumab (55.7%) and guselkumab (54.8%). Ixekizumab, risankizumab, and brodalumab had the highest short-term efficacy, and risankizumab had the highest long-term efficacy. The online version contains supplementary material available at 10.1007/s13555-021-00511-1

REGSTATTOOLS: freeware statistical tools for the analysis of disease population databases used in health and social studies

Background: The repertoire of statistical methods dealing with the descriptive analysis of the burden of a disease has been expanded and implemented in statistical software packages during the last years. The purpose of this paper is to present a web-based tool, REGSTATTOOLS http://regstattools.net intended to provide analysis for the burden of cancer, or other group of disease registry data. Three software applications are included in REGSTATTOOLS: SART (analysis of disease"s rates and its time trends), RiskDiff (analysis of percent changes in the rates due to demographic factors and risk of developing or dying from a disease) and WAERS (relative survival analysis). Results: We show a real-data application through the assessment of the burden of tobacco-related cancer incidence in two Spanish regions in the period 1995-2004. Making use of SART we show that lung cancer is the most common cancer among those cancers, with rising trends in incidence among women. We compared 2000-2004 data with that of 1995-1999 to assess percent changes in the number of cases as well as relative survival using RiskDiff and WAERS, respectively. We show that the net change increase in lung cancer cases among women was mainly attributable to an increased risk of developing lung cancer, whereas in men it is attributable to the increase in population size. Among men, lung cancer relative survival was higher in 2000-2004 than in 1995-1999, whereas it was similar among women when these time periods were compared. Conclusions: Unlike other similar applications, REGSTATTOOLS does not require local software installation and it is simple to use, fast and easy to interpret. It is a set of web-based statistical tools intended for automated calculation of population indicators that any professional in health or social sciences may require

Definition of a SNOMED CT pathology subset and microglossary, based on 1.17 million biological samples from the Catalan Pathology Registry

SNOMED CT terminology is not backed by standard norms of encoding among pathologists. The vast number of concepts ordered in hierarchies and axes, together with the lack of rules of use, complicates the functionality of SNOMED CT for coding, extracting, and analyzing the data. Defining subgroups of SNOMED CT by discipline could increase its functionality. The challenge lies in how to choose the concepts to be included in a subset from a total of over 300,000. Besides, SNOMED CT does not cover daily need, as the clinical reality is dynamic and changing. To adapt SNOMED CT to needs in a flexible way, the possibility exists to create extensions. In Catalonia, most pathology departments have been migrating from SNOMED II to SNOMED CT in a bid to advance the development of the Catalan Pathology Registry, which was created in 2014 as a repository for all the pathological diagnoses. This article explains the methodology used to: (a) identify the clinico-pathological entities and the molecular diagnostic procedures not included in SNOMED CT; (b) define the theoretical subset and microglossary of pathology; (c) describe the SNOMED CT concepts used by pathologists of 1.17 million samples of the Catalan Pathology Registry; and d) adapt the theoretical subset and the microglossary according to the actual use of SNOMED CT. Of the 328,365 concepts available for coding the diagnoses (326,732 in SNOMED CT and 1,576 in Catalan extension), only 2% have been used. Combining two axes of SNOMED CT, body structure and clinical findings, has enabled coding most of the morphologies