Influencia de microcalcificaciones en placas ateroscleróticas: Estudio paramétrico

Este proyecto refleja las conclusiones y tesituras del estudio de la influencia de las microcalcificaciones en placas ateroscleróticas. Una las líneas de investigación más recientes dentro de la ingeniería biomédica en el ámbito vascular es el estudio de vulnerabilidad en placas de ateroma, es decir, analizar la influencia de distintos factores mecánicos en la probabilidad de ruptura de dicho tipos de placa. Se ha demostrado que determinados factores geométricos establecen una especial predisposición a la ruptura de dichas placas, así mismo, se conoce que la aparición de microcalcificaciones dentro de la placa supone un riesgo añadido a su integridad. El objetivo de este trabajo consiste en realizar un estudio paramétrico, basándose en un modelo de deformación plana, de los factores geométricos en la vulnerabilidad de una placa aterosclerótica con la presencia de una microcalcificación en dicha capa fibrótica. Para ello se ha realizado un modelo de elementos finitos teniendo en cuenta la composición tanto de los tejidos de la arteria como los de la placa de ateroma (capas lipídica, fibrótica y microcalcificación), con el fin de establecer parámetros cualitativos del riesgo de rotura de la placa vulnerable. Los datos obtenidos nos permiten un diagnóstico más fiable y completo del riesgo de rotura

Jóvenes romaníes en asentamientos chabolistas: movilidad y contextos de exclusión en España y Francia

Este artículo de corte cualitativo tiene como objetivo realizar una investigación evaluativa y analítica de la intervención social con adolescentes de procedencia rumana y etnia gitana en los asentamientos de El Gallinero (Madrid) y Thiers-benauge (Burdeos), con el fin de establecer estrategias para proyectos de Trabajo Social con dichos colectivos. Los hallazgos han posibilitado por un lado, el conocimiento de una población estigmatizada y que vive en condiciones de vulnerabilidad social en la periferia de grandes ciudades, y por otro, el papel trascendental que alberga la movilidad laboral para dicha población. Promover un enfoque de intervención integral donde se creen redes colaborativas entre las administraciones públicas, las entidades sociales y la propia población, sería un modo de avanzar en la igualdad de oportunidades sobre todo en etapas como la adolescencia. This qualitative article aims to conduct an evaluative and analytical investigation of the social intervention with Romanian and gypsy teenagers in the settlements of El Gallinero (Madrid) and Thiers-Benauge (Bordeaux), in order to establish strategies for social work projects with these group. The finding have made possible, on the one hand, the knowledge of a stigmatized population living in conditions of social vulnerability in the periphery of large cities, and on the other, the transcendental role that houses labor mobility for said population. Promoting a comprehensive intervention approach where collaborative networks are created between public administrations, social entities and the population itself, would be a way to advance equality of opportunities especially in stages such as adolescence

Risk and temporal order of disease diagnosis of comorbidities in patients with COPD: a population health perspective

Introduction: Comorbidities in patients with chronic obstructive pulmonary disease (COPD) generate a major burden on ealthcare. Identification of costeffective strategies aiming at preventing and enhancing management of comorbid conditions in patients with COPD requires deeper knowledge on epidemiological patterns and on shared biological pathways xplaining cooccurrence of diseases. Methods: The study assesses the co-occurrence of several chronic conditions in patients with COPD using two different datasets: Catalan Healthcare Surveillance System (CHSS) (ES, 1.4 million registries) and Medicare (USA, 13 million registries). Temporal order of disease diagnosis was analysed in the CHSS dataset. Results The results demonstrate higher prevalence of most of the diseases, as comorbid conditions, in elderly (>65) patients with COPD compared with non-COPD subjects, an effect observed in both CHSS and Medicare datasets. Analysis of temporal order of disease diagnosis showed that comorbid conditions in elderly patients with COPD tend to appear after the diagnosis of the obstructive disease, rather than before it. Conclusion: The results provide a population health perspective of the comorbidity challenge in patients with COPD, indicating the increased risk of developing comorbid conditions in these patients. The research reinforces the need for novel approaches in the prevention and management of comorbidities in patients with COPD to effectively reduce the overall burden of the disease on these patients

Multimorbidity as a predictor of health service utilization in primary care: a registry-based study of the Catalan population

Background: Multimorbidity is highly relevant for both service commissioning and clinical decision-making. Optimization of variables assessing multimorbidity in order to enhance chronic care management is an unmet need. To this end, we have explored the contribution of multimorbidity to predict use of healthcare resources at community level by comparing the predictive power of four different multimorbidity measures. Methods: A population health study including all citizens ≥18 years (n = 6,102,595) living in Catalonia (ES) on 31 December 2014 was done using registry data. Primary care service utilization during 2015 was evaluated through four outcome variables: A) Frequent attendants, B) Home care users, C) Social worker users, and, D) Polypharmacy. Prediction of the four outcome variables (A to D) was carried out with and without multimorbidity assessment. We compared the contributions to model fitting of the following multimorbidity measures: i) Charlson index; ii) Number of chronic diseases; iii) Clinical Risk Groups (CRG); and iv) Adjusted Morbidity Groups (GMA). Results: The discrimination of the models (AUC) increased by including multimorbidity as covariate into the models, namely: A) Frequent attendants (0.771 vs 0.853), B) Home care users (0.862 vs 0.890), C) Social worker users (0.809 vs 0.872), and, D) Polypharmacy (0.835 vs 0.912). GMA showed the highest predictive power for all outcomes except for polypharmacy where it was slightly below than CRG. Conclusions: We confirmed that multimorbidity assessment enhanced prediction of use of healthcare resources at community level. The Catalan population-based risk assessment tool based on GMA presented the best combination of predictive power and applicability

Media representation of minors who migrate on their own: The 'MENA' in the Spanish press

This article analyses Spanish media treatment of a certain type of immigrant: the unaccompanied foreign minor ('MENA' in Spanish). The media play an important role in creating and disseminating ideas and images amongst the general public, thereby promoting the articulation of sets of meanings called discourses. The main goal of this research is to identify the discursive approaches that have been constructed around the term “MENA” in the main Spanish daily newspapers. To this end, we gathered and analysed all the news reports published between January 2017 and October 2019 by the digital editions of the four most widely-read newspapers in Spain (La Vanguardia, El País, El Mundo and ABC). This analysis was performed using text mining techniques (an important field in data science) such as term frequency, inverse document frequency, and correlation networks between words. Our results show that the term “MENA” evokes a criminalising, moralistic, welfare-dependent discourse that is articulated from an adult-centric, nationalist perspective. The study concluded that the conservative press uses the acronym more frequently than the left-wing media. However, no significant discursive differences were observed between conservative and progressive press in terms of the language used, which often had negative connotations that stigmatised the young people concerned

Informe sobre les característiques sociodemogràfiques, clíniques i els factors pronòstics dels pacients amb el diagnòstic de COVID-19 a Catalunya: resum executiu

Coronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Característiques sociodemogràfiques; Característiques clíniques; Factors pronòstics; PacientsCoronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Características sociodemográficas; Características clínicas; Factores pronósticos; PacientesCoronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Sociodemographic characteristics; Clinical features; Prognostic factors; PatientsInforme que descriu quines són les característiques de les persones afectades per la COVID-19 i quins són els factors que condicionen el seu pitjor pronòstic. Aquests factors són importants actualment, per exemple, per a definir el grup considerat vulnerable i al que s’ha de protegir amb l’ús de les mesures de confinament/distanciament físic que s’estan implementant per part dels governs

The adjusted morbidity groups (GMA): an exhaustive and severity-balanced tool for risk assessment

Grups morbiditat ajustada; GMA; Eina d'estratificació; Avaluació de riscosGrupos morbilidad ajustada; GMA; Herramienta de estratificación; Evaluación de riesgosAdjusted morbidity groups; GMA; Stratification tool; Risk assessmentEls GMA consisteixen en una eina que permet avaluar el risc en salut a partir de les característiques demogràfiques dels pacients, les seves malalties cròniques i aquelles situacions o malalties agudes que puguin tenir-hi impacte. Aquesta eina proporciona un índex de risc que es pot utilitzar com a factor d’ajust en models específics d’una determinada malaltia i a la vegada actua com un agrupament per estratificar la població en diferents nivells de risc.Los GMA consisten en una herramienta que permite evaluar el riesgo en salud a partir de las características demográficas de los pacientes, sus enfermedades crónicas y aquellas situaciones o enfermedades agudas que puedan tener impacto. Esta herramienta proporciona un índice de riesgo que se puede utilizar como factor de ajuste en modelos específicos de una determinada enfermedad y al mismo tiempo actúa como un agrupamiento para estratificar la población en diferentes niveles de riesgo.GMAs are a tool that assesses health risk based on the demographic characteristics of patients, their chronic diseases and those situations or acute diseases that may have an impact. This tool provides a risk index that can be used as an adjustment factor in specific models of a given disease and at the same time acts as a grouping to stratify the population at different levels of risk

Prevention of Unplanned Hospital Admissions in Multimorbid Patients Using Computational Modeling: Observational Retrospective Cohort Study

Background: Enhanced management of multimorbidity constitutes a major clinical challenge. Multimorbidity shows well-established causal relationships with the high use of health care resources and, specifically, with unplanned hospital admissions. Enhanced patient stratification is vital for achieving effectiveness through personalized postdischarge service selection. Objective: The study has a 2-fold aim: (1) generation and assessment of predictive models of mortality and readmission at 90 days after discharge; and (2) characterization of patients' profiles for personalized service selection purposes. Methods: Gradient boosting techniques were used to generate predictive models based on multisource data (registries, clinical/functional and social support) from 761 nonsurgical patients admitted in a tertiary hospital over 12 months (October 2017 to November 2018). K-means clustering was used to characterize patient profiles. Results: Performance (area under the receiver operating characteristic curve, sensitivity, and specificity) of the predictive models was 0.82, 0.78, and 0.70 and 0.72, 0.70, and 0.63 for mortality and readmissions, respectively. A total of 4 patients' profiles were identified. In brief, the reference patients (cluster 1; 281/761, 36.9%), 53.7% (151/281) men and mean age of 71 (SD 16) years, showed 3.6% (10/281) mortality and 15.7% (44/281) readmissions at 90 days following discharge. The unhealthy lifestyle habit profile (cluster 2; 179/761, 23.5%) predominantly comprised males (137/179, 76.5%) with similar age, mean 70 (SD 13) years, but showed slightly higher mortality (10/179, 5.6%) and markedly higher readmission rate (49/179, 27.4%). Patients in the frailty profile (cluster 3; 152/761, 19.9%) were older (mean 81 years, SD 13 years) and predominantly female (63/152, 41.4%, males). They showed medical complexity with a high level of social vulnerability and the highest mortality rate (23/152, 15.1%), but with a similar hospitalization rate (39/152, 25.7%) compared with cluster 2. Finally, the medical complexity profile (cluster 4; 149/761, 19.6%), mean age 83 (SD 9) years, 55.7% (83/149) males, showed the highest clinical complexity resulting in 12.8% (19/149) mortality and the highest readmission rate (56/149, 37.6%). Conclusions: The results indicated the potential to predict mortality and morbidity-related adverse events leading to unplanned hospital readmissions. The resulting patient profiles fostered recommendations for personalized service selection with the capacity for value generation

Population-based analysis of patients with COPD in Catalonia: a cohort study with implications for clinical management

BACKGROUND: Clinical management of patients with chronic obstructive pulmonary disease (COPD) shows potential for improvement provided that patients' heterogeneities are better understood. The study addresses the impact of comorbidities and its role in health risk assessment. OBJECTIVE: To explore the potential of health registry information to enhance clinical risk assessment and stratification. DESIGN: Fixed cohort study including all registered patients with COPD in Catalonia (Spain) (7.5 million citizens) at 31 December 2014 with 1-year (2015) follow-up. METHODS: A total of 264 830 patients with COPD diagnosis, based on the International Classification of Diseases (Ninth Revision) coding, were assessed. Performance of multiple logistic regression models for the six main dependent variables of the study: mortality, hospitalisations (patients with one or more admissions; all cases and COPD-related), multiple hospitalisations (patients with at least two admissions; all causes and COPD-related) and users with high healthcare costs. Neither clinical nor forced spirometry data were available. RESULTS: Multimorbidity, assessed with the adjusted morbidity grouper, was the covariate with the highest impact in the predictive models, which in turn showed high performance measured by the C-statistics: (1) mortality (0.83), (2 and 3) hospitalisations (all causes: 0.77; COPD-related: 0.81), (4 and 5) multiple hospitalisations (all causes: 0.80; COPD-related: 0.87) and (6) users with high healthcare costs (0.76). Fifteen per cent of individuals with highest healthcare costs to year ratio represented 59% of the overall costs of patients with COPD. CONCLUSIONS: The results stress the impact of assessing multimorbidity with the adjusted morbidity grouper on considered health indicators, which has implications for enhanced COPD staging and clinical management

Automatic deep learning-based pipeline for automatic delineation and measurement of fetal brain structures in routine mid-trimester ultrasound images

Introduction: The aim of this study was to develop a pipeline using state-of-the-art deep learning methods to automatically delineate and measure several of the most important brain structures in fetal brain ultrasound (US) images. Methods: The dataset was composed of 5,331 images of the fetal brain acquired during the routine mid-trimester US scan. Our proposed pipeline automatically performs the following three steps: brain plane classification (transventricular, transthalamic, or transcerebellar plane); brain structures delineation (9 different structures); and automatic measurement (from the structure delineations). The methods were trained on a subset of 4,331 images and each step was evaluated on the remaining 1,000 images. Results: Plane classification reached 98.6% average class accuracy. Brain structure delineation obtained an average pixel accuracy higher than 96% and a Jaccard index higher than 70%. Automatic measurements get an absolute error below 3.5% for the four standard head biometries (head circumference, biparietal diameter, occipitofrontal diameter, and cephalic index), 9% for transcerebellar diameter, 12% for cavum septi pellucidi ratio, and 26% for Sylvian fissure operculization degree. Conclusions: The proposed pipeline shows the potential of deep learning methods to delineate fetal head and brain structures and obtain automatic measures of each anatomical standard plane acquired during routine fetal US examination.The research leading to these results has received funding from the Cerebra Foundation for the Brain Injured Child (Carmarthen, Wales,UK) and ASISA foundation.Peer ReviewedPostprint (published version