Comportamiento del efecto clúster hospital y los factores asociados a la mortalidad a largo plazo, después de un ingreso por exacerbación en epoc

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Medicina Preventiva y Salud Pública y Microbiología. Fecha de lectura: 23-09-2020INTRODUCCIÓN: Resultado del análisis exhaustivo de cohortes multicéntricas, históricas y periódicas de casos, usando aproximaciones multivariables multinivel, hemos abordado el tema de la variabilidad de los datos y encontrado, la presencia de un claro efecto clúster de hospital, que reduce drásticamente la variabilidad de los desenlaces encontrados (duración del ingreso, mortalidad y reingresos a 90 días(1)) en los datos crudos. Reconociendo la advertencia de Juan Merlo en su trabajo (1-3), referida a que la OR promedio es solo una aproximación inexacta y quizá no represente completamente la variabilidad geográfica real en áreas sanitarias, postulamos como hipótesis que el efecto clúster hospital, se mantiene a largo plazo sobre la mortalidad y que este efecto, en parte, se debe a factores asociados al contexto territorial y ambiental del Área de Salud, como la calidad del aire respirado. METODOLOGIA Con el objetivo de demostrar el efecto diferencial del clúster hospital, particularmente en relación con la mortalidad a largo plazo, en el paciente con EPOC, se plantea un estudio descriptivo observacional, con seguimiento prospectivo de mortalidad a largo plazo, para una cohorte de pacientes con EPOC, identificados durante un ingreso hospitalario por exacerbación de su enfermedad. La Tabla de datos contiene información disociada y mortalidad a largo plazo de 10.449 casos procedentes de 142 hospitales públicos españoles, a la que se han asociado datos agregados por localidad, de los registros diarios de emisiones obtenidos entre 2008 y 2011 (Período de reclutamiento de la cohorte) por las diferentes estaciones. La mortalidad a corto plazo (a 90 días del ingreso), fue informada por los responsables locales de investigación de la red de hospitales participantes, y contrastada con la información obtenida de los registros oficiales del índice nacional de defunciones (INDEF) desde octubre de 2008 a diciembre de 2015. Todas las variables fueron evaluadas respecto de la significancia (valor P) en la diferencia de su distribución por mortalidad intrahospitalaria, a 90 días, al año y a los 5 años, usando como estadísticos el chi-cuadrado de independencia y log-Rank test. Se construyó un modelo de supervivencia de riesgos proporcionales (Cox), y un modelo en regresión logística de mortalidad, calculando los coeficientes estandarizados y la curva ROC. RESULTADOS La media de seguimiento fue de 304·5 días posteriores al ingreso hospitalario, con un máximo de 7 años. Casi la mitad de la mortalidad total de la cohorte se produjo dentro de los 90 días posteriores al ingreso hospitalario a partir del cual fueron reclutados. La ponderación del efecto de cada uno de las variables finalmente retenidas por los modelos explicativos, a través de los coeficientes estandarizados obtenidos en la regresión, enfatiza el peso del perfil clínico grave (dimensión paciente), seguido de cerca por la exposición de micro partículas (dimensión local territorio) y las características del hospital (dimensión local hospital). El modelo obtenido logró discriminar la mortalidad a largo plazo, con un área de 0·71 y un IC 95% entre 0·69-0·72. CONCLUSIONES: Además de los determinantes clínicos de enfermedad, otros factores del contexto espacio/temporal externo al individuo, sumados a las condiciones de salud y atención sanitaria recibida, afectan la supervivencia/mortalidad a largo plazo y configuran lo que hemos llamado en nuestros trabajos previos efecto clúster hospitalEste trabajo ha sido financiado principalmente por fondos destinados al Grupo de investigación de la Red temática Enfermedades Respiratorias del Consorcio CIBER M. P, en el Hospital Universitario 12 de Octubre. También obtuvo financiación de ayudas a los proyectos FIS número PS 09/01763, PS 09/01787 y PS 09/00629 (Instituto de Salud Carlos III, Secretaría de Estado de Investigación, Desarrollo e Innovación y FEDER/FSE

Economic evaluation of health services costs during pandemic influenza A (H1N1) pdm09 infection in pregnant and non-pregnant women in Spain (2009)

Background: The healthcare and socio-economic burden resulting from influenza A (H1N1) pdm09 in Spain was considerable. Our aim was to estimate and compare the management (resource utilization) and economic healthcare impact in an at-risk group of unvaccinated pregnant women with an unvaccinated group of non-pregnant woman of childbearing age (15-44 yr old). Methods: We addressed this question with a longitudinal, observational, multicentre study. Inputs were the require-ments in managing both groups of women. Outcome measures were healthcare costs. Direct healthcare (including medical utilisation, prescriptions of antivirals, medication, diagnostic tests, and hospitalisation) costs and indirect (productivity loss) costs were considered. Unit of cost was attributed to the frequency of health service resources utili-sation. The mean cost per patient was calculated in this group of women. Results: We found that the influenza clinical pattern was worse in non-pregnant women as they had a high medical risk of 20.4% versus 6.1% of pregnant women. Non-pregnant required more antipyretics and antibiotics, and needed more health service resource utilisation (338 medical visits in non-pregnant women vs. 42 in pregnant women). The total cost of non-pregnant women was higher ( 4,689.4/non-pregnant and 2,945.07/pregnant). Conclusions: Cost per (H1N1) pdm09 was lower for pregnant women, probably due to more preventive measures adopted for their protection in Spain. The highest costs were incurred by hospitalisations/day and work absenteeism for non-pregnant than for pregnant women. These data will allow better future pandemic influenza planning

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Comparison of body mass index (BMI) with the CUN-BAE body adiposity estimator in the prediction of hypertension and type 2 diabetes

Background Obesity is a world-wide epidemic whose prevalence is underestimated by BMI measurements, but CUN-BAE (Clínica Universidad de Navarra - Body Adiposity Estimator) estimates the percentage of body fat (BF) while incorporating information on sex and age, thus giving a better match. Our aim is to compare the BMI and CUN-BAE in determining the population attributable fraction (AFp) for obesity as a cause of chronic diseases. Methods We calculated the Pearson correlation coefficient between BMI and CUN-BAE, the Kappa index and the internal validity of the BMI. The risks of arterial hypertension (AHT) and diabetes mellitus (DM) and the AFp for obesity were assessed using both the BMI and CUN-BAE. Results 3888 white subjects were investigated. The overall correlation between BMI and CUN-BAE was R2 = 0.48, which improved when sex and age were taken into account (R2 > 0.90). The Kappa coefficient for diagnosis of obesity was low (28.7 %). The AFp was 50 % higher for DM and double for AHT when CUN-BAE was used. Conclusions The overall correlation between BMI and CUN-BAE was not good. The AFp of obesity for AHT and DM may be underestimated if assessed using the BMI, as may the prevalence of obesity when estimated from the percentage of BF

Beam gas curtain monitor: Vacuum studies for LHC integration and operation

A beam gas curtain (BGC) monitor has been designed to obtain information about the relative position between the LHC proton beam and the hollow electron lens electron beam through a minimally invasive process. Its working principle relies on intersecting the path of both beams with a supersonic gas curtain, introduced transversely into the LHC beamline, to produce a fluorescence signal. As an intermediate project stage (phase II), a preliminary version of the BGC monitor has been installed into the LHC beamline. To ensure the successful integration of the monitor and subsequent operation under LHC ultrahigh vacuum conditions, a series of vacuum studies have been performed. These can be classified as follows: An off-line laboratory test campaign, to assess BGC behavior during pump down and gas injections; simulations and analytical calculations, to evaluate BGC behavior and estimate the impact of its installation and operation in the LHC. This document will briefly present the off-line tests campaign, followed by a more extensive description of the simulations performed

Social factors related to the clinical severity of influenza cases in Spain during the A(H1N1)2009 virus pandemic

Background During the 2009 influenza pandemic, a change in the type of patients most often affected by influenza was observed. The objective of this study was to assess the role of individual and social determinants in hospitalizations due to influenza A (H1N1) 2009 infection. Methods We studied hospitalized patients (cases) and outpatients (controls) with confirmed influenza A (H1N1) 2009 infection. A standardized questionnaire was used to collect data. Variables that might be related to the hospitalization of influenza cases were compared by estimation of the odds ratio (OR) and 95% confidence intervals (CI) and the variables entered into binomial logistic regression models. Results Hospitalization due to pandemic A (H1N1) 2009 influenza virus infections was associated with non-Caucasian ethnicity (OR: 2.18, 95% CI 1.17 − 4.08), overcrowding (OR: 2.84, 95% CI 1.20 − 6.72), comorbidity and the lack of previous preventive information (OR: 2.69, 95% CI: 1.50 − 4.83). Secondary or higher education was associated with a lower risk of hospitalization (OR 0.56, 95% CI: 0.36 − 0.87) Conclusions In addition to individual factors such as comorbidity, other factors such as educational level, ethnicity or overcrowding were associated with hospitalization due to A (H1N1) 2009 influenza virus infections