Impact of Air Pollution on Asthma: A Scoping Review

Asthma is the most common chronic respiratory disease and a major public health problem. Although the causal relationship between air pollution and asthma remains controversial, a large number of studies have provided increasingly consistent evidence of the involvement of air pollutants in asthma onset and exacerbations. We conducted a keyword search-based literature review using PubMed, Scopus and Web of Science databases for studies with titles or abstracts containing predefined terms. This narrative review discusses the current evidence on the pathological effects of pollution throughout life and the mechanisms involved in the onset, development, and exacerbation of asthma, and presents current measures and interventions for pollution damage control. Further global efforts are still needed to improve air quality. Resumen: El asma es la enfermedad respiratoria crónica más común, y un importante problema de salud pública. Aunque la relación causal entre la contaminación del aire y el asma sigue siendo controvertida, una gran cantidad de estudios han proporcionado evidencia cada vez más consistente de la participación de los contaminantes del aire en el inicio y las exacerbaciones del asma. Realizamos una revisión de la literatura basada en búsqueda de palabras clave utilizando las bases de datos PubMed, Scopus y Web of Science para estudios con títulos o resúmenes que contienen términos predefinidos. Esta revisión narrativa analiza la evidencia actual sobre los efectos patológicos de la contaminación a lo largo de la vida y los mecanismos involucrados en el inicio, desarrollo y exacerbación del asma, y presenta las medidas e intervenciones actuales para el control de daños por contaminación. Todavía se necesitan más esfuerzos globales para mejorar la calidad del aire

Key Pulmonology and Thoracic Surgery Issues under Discussion in the COVID-19 Era

Para finalizar un año tan complejo como el 2020 a causa de la pandemia por SARS-CoV-2 (COVID-19), tuvimos la oportunidad de celebrar, bajo el amparo de la Sociedad Española de Neumología y Cirugía Torácica (SEPAR), la segunda edición del Foro de presidentes de las sociedades científicas autonómicas de neumología y cirugía torácica de España. Esta pandemia ha puesto en una situación crítica a los sistemas de salud de las diferentes comunidades autónomas españolas, cada uno de ellos con sus peculiaridades y modelos de gestión diferentes. Y en concreto, debido a la afinidad de la COVID-19 por el aparato respiratorio, los servicios de Neumología se han visto directamente involucrados, obligados a modificar sus estructuras y distribución de recursos en tiempo récord para poder proporcionar la mejor asistencia sanitaria posible a estos enfermos. Todo ello, ante una situación de enorme complejidad, no solo por el incremento en la carga asistencial que ha supuesto, sino también por la gran incertidumbre asociada en las fases más iniciales de la pandemia. El desconocimiento de la propia enfermedad y la limitación de recursos disponibles complicó aún más la gestión y la atención sanitaria en una situación sumamente difícil. Estas vivencias relacionadas con la pandemia han marcado esta nueva edición del Foro de presidentes autonómicos y nos ha permitido conocer diferentes puntos de vista y modelos de respuesta según los distintos sistemas de salud implicados, como ya ocurriera en la edición anterior1. No obstante, la vida continúa a pesar de los cambios provocados por el SARS-CoV-2. Por lo tanto, manteniendo el carácter inclusivo e innovador que dio lugar a la creación de este foro a través de la SEPAR, en esta nueva edición se abordaron 5 mesas de debate con temas de especial relevancia para el desarrollo de la Neumología y la Cirugía Torácica en España, cuyas conclusiones se resumen a continuación

Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n = 1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d = 148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient’s C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels –saturation Sp O2, quotients Sp O2/RR and arterial Sat O2/Fi O2–, the neutrophil-to-lymphocyte ratio (NLR) –to certain extent, also neutrophil and lymphocyte counts separately–, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives

Estudio de prevalencia de asma en población general en España

Resumen: Introducción: El asma es una enfermedad con elevada prevalencia, que afecta a todos los grupos de edad y genera elevados costes sociosanitarios. Estudios realizados en diversas poblaciones muestran gran variabilidad en su prevalencia, incluso en poblaciones cercanas geográficamente, con datos que sugieren una influencia relevante de factores socioeconómicos. Actualmente en población adulta de España no disponemos de datos poblacionales fiables sobre la prevalencia de esta enfermedad. Los objetivos de este estudio son estimar la prevalencia de asma en población española de 18-79 años, describir la variabilidad entre comunidades autónomas, estimar la prevalencia de infra y sobrediagnóstico, prevalencia de asma no controlada, de asma córticodependiente, conocer el consumo de recursos sanitarios, identificar los fenotipos más frecuentes y establecer un punto de partida para evaluar la tendencia temporal con estudios posteriores. Material y métodos: Se realizará un estudio transversal, bietápico, incluyendo pacientes de 50 áreas sanitarias. El estudio se desarrollará en tres fases: 1) cribado y confirmación en historia clínica, en la cual se identificarán los pacientes con diagnóstico previo correctamente establecido de asma; 2) diagnóstico de asma, evaluando a los pacientes en los cuales no está claro el diagnóstico de asma con los datos disponibles en la historia clínica; 3) caracterización del asma, analizando las características de estos pacientes e identificando los fenotipos más frecuentes. Discusión: Parece necesario y factible realizar un estudio epidemiológico del asma en España que permita identificar la prevalencia de asma, optimizar la planificación sanitaria, caracterizar los fenotipos más frecuentes de la enfermedad y evaluar los diagnósticos erróneos. Abstract: Introduction: Asthma is a disease with high prevalence, which affects all age groups and generates high health and social care costs. Studies carried out in a number of populations show great variability in its prevalence, even in geographically close populations, with data suggesting a relevant influence of socio-economic factors. At present, we do not have reliable data on the prevalence of this disease in the adult population of Spain. The objectives of this study are to estimate the prevalence of asthma in the Spanish population for those aged 18-79, to describe the variability between autonomous communities, to estimate the prevalence of under and overdiagnosis, to analyse the prevalence of uncontrolled asthma and steroid-dependent asthma, to evaluate the health care cost, to identify the most frequent phenotypes and to establish a starting point to evaluate the temporal trend with subsequent studies. Methods: A cross-sectional, two-stage study will be carried out, including patients from 50 catchment areas. The study will be carried out in 3 phases: 1) screening and confirmation in the clinical history, in which patients with a previously correctly established diagnosis of asthma will be identified; 2) diagnosis of asthma to evaluate patients without a confirmed or excluded diagnosis; 3) characterization of asthma, where the characteristics of the asthmatic patients will be analysed, identifying the most frequent phenotypes. Discussion: It seems necessary and feasible to carry out an epidemiological study of asthma in Spain to identify the prevalence of asthma, to optimize healthcare planning, to characterize the most frequent phenotypes of the disease, and to evaluate inaccurate diagnoses

Asthma Control in Patients with Severe Eosinophilic Asthma Treated with Reslizumab: Spanish Real-Life Data.

Reslizumab is an anti-interleukin 5 monoclonal antibody that has demonstrated to reduce the risk of severe exacerbations and to improve symptoms, lung function, and quality of life in randomized controlled trials that included patients with severe eosinophilic uncontrolled asthma (SEUA) and a history of severe exacerbations. The aim of the present study was to evaluate the effectiveness of add-on reslizumab in a cohort of patients with SEUA under real-life conditions. This was a multi-centre, retrospective, real-life study that included subjects with SEUA treated with reslizumab in 44 asthma units throughout Spain. Eligible patients were those who had received at least one dose of reslizumab as part of normal clinical practice. The primary endpoint was complete asthma control at 52 weeks, defined as absence of severe exacerbations, ACT ≥20 and no maintenance oral corticosteroids (OCS). Demographic, clinical, and functional data were collected at baseline (T0), after four to six months (T1); after 12 months (T2) and beyond 12 months of therapy (T3). Treatment with reslizumab achieved complete asthma control in 40% of the 208 included SEUA patients and led to a significant reduction in exacerbations (from 3.0; IQR: 2.0-4.0 at V0 to 0.0; IQR: 0.0-0.0 at V2), maintenance OCS use (from 54.8% (95% CI: 48.0-61.6 at T0 to 18.5% (95% CI: 12.5-24.5 at T2) and a meaningful improvement in symptoms in the entire treated population: ACT increased from 12.8 ± 4.5 at V0 to 20.0 ± 5.1 at V2 (p Reslizumab is an effective therapy for SEUA with adequate safety profile in real-life conditions

Features selected in ⩾80% cases by the stable MI filters: <i>n</i>_<i>FS</i> = 20 or 40.

(a) MI Classif—knn imputer: nFS = 20. (b) MI Regress—knn imputer: nFS = 20. (c) MI Classif—iterat imputer: nFS = 20. (d) MI Regress—iterat imputer: nFS = 20. (e) MI Classif—knn imputer: nFS = 40. (f) MI Regress—knn imputer: nFS = 40. (g) MI Classif—iterat imputer: nFS = 40. (h) MI Regress—iterat imputer: nFS = 40.</p

Stability for the filter algorithms.

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n = 1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d = 148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient’s C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels –saturation Sp O2, quotients Sp O2/RR and arterial Sat O2/Fi O2–, the neutrophil-to-lymphocyte ratio (NLR) –to certain extent, also neutrophil and lymphocyte counts separately–, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives.</div

Stability for the wrapper algorithms.

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n = 1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d = 148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient’s C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels –saturation Sp O2, quotients Sp O2/RR and arterial Sat O2/Fi O2–, the neutrophil-to-lymphocyte ratio (NLR) –to certain extent, also neutrophil and lymphocyte counts separately–, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives.</div