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 SpO2, quotients SpO2/RR and arterial SatO2/FiO2 –, 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

Impact of outdoor air pollution on severity and mortality in COVID-19 pneumonia

The relationship between exposure to air pollution and the severity of coronavirus disease 2019 (COVID-19) pneumonia and other outcomes is poorly understood. Beyond age and comorbidity, risk factors for adverse outcomes including death have been poorly studied. The main objective of our study was to examine the relationship between exposure to outdoor air pollution and the risk of death in patients with COVID-19 pneumonia using individual-level data. The secondary objective was to investigate the impact of air pollutants on gas exchange and systemic inflammation in this disease. This cohort study included 1548 patients hospitalised for COVID-19 pneumonia between February and May 2020 in one of four hospitals. Local agencies supplied daily data on environmental air pollutants ($PM_{10}$, $PM_{2.5}$, $O_3$, $NO_2$, $NO$ and $NO_X$) and meteorological conditions (temperature and humidity) in the year before hospital admission (from January 2019 to December 2019). Daily exposure to pollution and meteorological conditions by individual postcode of residence was estimated using geospatial Bayesian generalised additive models. The influence of air pollution on pneumonia severity was studied using generalised additive models which included: age, sex, Charlson comorbidity index, hospital, average income, air temperature and humidity, and exposure to each pollutant. Additionally, generalised additive models were generated for exploring the effect of air pollution on C-reactive protein (CRP) level and Sp$O_2$/Fi$O_2$ at admission. According to our results, both risk of COVID-19 death and CRP level increased significantly with median exposure to $PM_{10}$, $NO_2$, $NO$ and $NO_X$, while higher exposure to $NO_2$, $NO$ and $NO_X$ was associated with lower Sp$O_2$/Fi$O_2$ ratios. In conclusion, after controlling for socioeconomic, demographic and health-related variables, we found evidence of a significant positive relationship between air pollution and mortality in patients hospitalised for COVID-19 pneumonia. Additionally, inflammation (CRP) and gas exchange (Sp$O_2$/Fi$O_2$) in these patients were significantly related to exposure to air pollution

Impacto cuantitativo de la contaminación en la probabilidad de muerte por neumonía por SARS-CoV-2

Introducción La evidencia científica disponible señala que la contaminación del aire exterior podría agravar la severidad de la COVID-19 y por ende, incrementar las probabilidades de fallecimiento. Material y métodos Estudio observacional longitudinal retrospectivo de cohortes, multicéntrico en 4 hospitales: 2 en Bizkaia (1 urbano, 1 urbano-rural), Valencia y Barcelona (urbanos). Se incluyeron ingresos por neumonía SARS-CoV-2 en el primer pico epidémico de COVID-19 (febrero-mayo 2020). Para determinar la exposición a contaminación por PM$_{10}$ y NO$_{2}$, se obtuvieron los datos publicados por los organismos autonómicos de calidad del aire, para 2019 y 1er semestre 2020. Se utilizó un Modelo Aditivo Generalizado (GAM) para estimar el nivel diario de contaminante en cada código postal, en función de las coordenadas geográficas y la altitud de las estaciones de medición [Figura 1]. Para determinar la exposición crónica, se calcularon media y máximo en 2019; la aguda se caracterizó por media y máximo en los 7 días anteriores al ingreso. Se estudió la razón de probabilidades (‘odds ratio’, OR) de muerte frente a supervivencia entre nuestra cohorte. Se modeló mediante un GAM con regresión logística, incorporando como efectos fijos sexo, edad y contaminante; hospital como efecto aleatorio e índice de comorbilidad de Charlson como función suave mediantes splines penalizados. Resultados De los 1548 pacientes reclutados, 243 (15.7%) fallecieron durante su hospitalización y/o 30 días postingreso. Según los modelos [Tabla 1], existe evidencia estadística significativa de que la exposición crónica a PM$_{10}$ y NO$_{2}$ incrementan la probabilidad de muerte por neumonía SARS-CoV-2. Compensando por sexo, edad y Charlson -todos factores relacionados positivamente con el OR de muerte- así como por hospital; por cada incremento de 10 μg/m$^{3}$ en el nivel de PM$_{10}$ (máximo anual) el OR aumenta en 10.5%, linealmente proporcional al incremento en la contaminación. Mientras, cada 10 μg/m$^{3}$ más de NO2 (media anual) aumentan OR en 35.7%; cada 10 μg/m$^{3}$ más en exposición aguda a NO2 (media semana pre-ingreso): 62.9%; y NO$_{2}$ (máximo semana): 34.4%. Conclusiones Se cuantificaron y compensaron los efectos de los factores sexo, edad, Charlson y hospital. A igualdad de estos, incrementos en la exposición crónica y aguda a PM$_{10}$ y NO$_{2}$ aumentan de manera lineal y estadísticamente significativa la probabilidad de muerte por neumonía SARS-CoV-2

Predicción de la gravedad de neumonías por SARS-CoV-2 a partir de información clínica y contaminación, mediante inteligencia artificial

Introducción La contaminación del aire exterior se ha relacionado con mayor gravedad de las infecciones respiratorias. Por tanto, su inclusión en algoritmos predictivos podrían añadir información para pronosticar la gravedad de neumonías SARS-CoV-2. Material y métodos Estudio observacional longitudinal retrospectivo de cohortes, multicéntrico en 4 hospitales. Se incluyeron ingresos por neumonía SARS-CoV-2 en el primer pico epidémico de COVID-19 (febrero-mayo 2020). Se recogieron hasta 93 variables clínicas, analíticas y radiológicas por cada paciente (sexo, edad, peso, comorbilidades, síntomas, variables fisiológicas en urgencias, sangre, gasometría, etc.). Además, se calcularon los niveles exposición a contaminación por PM$_{10}$, PM$_{2.5}$, O$_{3}$, NO$_{2}$, NO, NO$_{X}$, SO$_{2}$ y CO en su código postal. En función de la evolución clínica de la neumonía, se definieron 3 niveles de gravedad [Tabla 1]. Para predecir dicha gravedad, se desarrolló un algoritmo de inteligencia artificial (IA), tipo ‘Random Forest’ con balanceo y ajuste automático de sus parámetros internos. El algoritmo se entrenó y evaluó mediante 20 repeticiones de validación cruzada 10-fold (90% entrenamiento, 10% validación), estratificando aleatoriamente por hospital y gravedad. Resultados En los conjuntos de validación, el algoritmo alcanzó una capacidad predictiva (área bajo la curva ROC) promedio AUC=0.834 para gravedad nivel 0, AUC=0.724 para 1 y AUC=0.850 para 2 [Figura 1]. Sin la información de contaminantes, su capacidad predictiva se degradó ligeramente (AUCs = 0.829, 0.722, 0.844; respectivamente). Conclusiones Nuestro algoritmo IA es capaz de predecir de manera satisfactoria la evolución de la gravedad en la neumonía; en particular para los casos más leves y más severos. El algoritmo IA extrae las reglas más relevantes a partir principalmente de la información clínica, analítica y radiológica de cada individuo; no obstante, la incorporación de la exposición a contaminantes mejora ligeramente la capacidad predictiva. El impacto de la contaminación podría estar ya reflejado en las analíticas de sangre, a través de su efecto en los niveles de inflamación del paciente (PCT, PCR, LDH, etc.)

Residential greenspace and lung function decline over 20 years in a prospective cohort: the ECRHS study

Background The few studies that have examined associations between greenspace and lung function in adulthood have yielded conflicting results and none have examined whether the rate of lung function decline is affected. Objective We explored the association between residential greenspace and change in lung function over 20 years in 5559 adults from 22 centers in 11 countries participating in the population-based, international European Community Respiratory Health Survey. Methods Forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) were measured by spirometry when participants were approximately 35 (1990–1994), 44 (1999–2003), and 55 (2010–2014) years old. Greenness was assessed as the mean Normalized Difference Vegetation Index (NDVI) in 500 m, 300 m, and 100 m circular buffers around the residential addresses at the time of lung function measurement. Green spaces were defined as the presence of agricultural, natural, or urban green spaces in a circular 300 m buffer. Associations of these greenspace parameters with the rate of lung function change were assessed using adjusted linear mixed effects regression models with random intercepts for subjects nested within centers. Sensitivity analyses considered air pollution exposures. Results A 0.2-increase (average interquartile range) in NDVI in the 500 m buffer was consistently associated with a faster decline in FVC (−1.25 mL/year [95% confidence interval: −2.18 to −0.33]). These associations were especially pronounced in females and those living in areas with low PM10 levels. We found no consistent associations with FEV1 and the FEV1/FVC ratio. Residing near forests or urban green spaces was associated with a faster decline in FEV1, while agricultural land and forests were related to a greater decline in FVC. Conclusions More residential greenspace was not associated with better lung function in middle-aged European adults. Instead, we observed slight but consistent declines in lung function parameters. The potentially detrimental association requires verification in future studies

Impact of long-term exposure to ambient ozone on lung function over a course of 20 years (The ECRHS study): a prospective cohort study in adults

Background While the adverse effects of short-term ambient ozone exposure on lung function are well-documented, the impact of long-term exposure remains poorly understood, especially in adults. Methods We aimed to investigate the association between long-term ozone exposure and lung function decline. The 3014 participants were drawn from 17 centers across eight countries, all of which were from the European Community Respiratory Health Survey (ECRHS). Spirometry was conducted to measure pre-bronchodilation forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) at approximately 35, 44, and 55 years of age. We assigned annual mean values of daily maximum running 8-h average ozone concentrations to individual residential addresses. Adjustments were made for PM2.5, NO2, and greenness. To capture the ozone-related change in spirometric parameters, our linear mixed effects regression models included an interaction term between long-term ozone exposure and age. Findings Mean ambient ozone concentrations were approximately 65 μg/m³. A one interquartile range increase of 7 μg/m³ in ozone was associated with a faster decline in FEV1 of −2.08 mL/year (95% confidence interval: −2.79, −1.36) and in FVC of −2.86 mL/year (−3.73, −1.99) mL/year over the study period. Associations were robust after adjusting for PM2.5, NO2, and greenness. The associations were more pronounced in residents of northern Europe and individuals who were older at baseline. No consistent associations were detected with the FEV1/FVC ratio. Interpretation Long-term exposure to elevated ambient ozone concentrations was associated with a faster decline of spirometric lung function among middle-aged European adults over a 20-year period

Time and age trends in smoking cessation in Europe

Background Smoking is the main risk factor for most of the leading causes of death. Cessation is the single most important step that smokers can take to improve their health. With the aim of informing policy makers about decisions on future tobacco control strategies, we estimated time and age trends in smoking cessation in Europe between 1980 and 2010. Methods Data on the smoking history of 50,228 lifetime smokers from 17 European countries were obtained from six large population-based studies included in the Ageing Lungs in European Cohorts (ALEC) consortium. Smoking cessation rates were assessed retrospectively, and age trends were estimated for three decades (1980–1989, 1990–1999, 2000–2010). The analyses were stratified by sex and region (North, East, South, West Europe). Results Overall, 21,735 subjects (43.3%) quit smoking over a total time-at-risk of 803,031 years. Cessation rates increased between 1980 and 2010 in young adults (16–40 years), especially females, from all the regions, and in older adults (41–60 years) from North Europe, while they were stable in older adults from East, South and West Europe. In the 2000s, the cessation rates for men and women combined were highest in North Europe (49.9 per 1,000/year) compared to the other regions (range: 26.5–32.7 per 1,000/year). A sharp peak in rates was observed for women around the age of 30, possibly as a consequence of pregnancy-related smoking cessation. In most regions, subjects who started smoking before the age of 16 were less likely to quit than those who started later. Conclusions Our findings suggest an increasing awareness on the detrimental effects of smoking across Europe. However, East, South and West European countries are lagging behind North Europe, suggesting the need to intensify tobacco control strategies in these regions. Additional efforts should be made to keep young adolescents away from taking up smoking, as early initiation could make quitting more challenging during later life

Second-hand smoke exposure in adulthood and lower respiratory health during 20 year follow up in the European Community Respiratory Health Survey 11 Medical and Health Sciences 1102 Cardiorespiratory Medicine and Haematology 11 Medical and Health Sciences 1117 Public Health and Health Services

BACKGROUND: Early life exposure to tobacco smoke has been extensively studied but the role of second-hand smoke (SHS) for new-onset respiratory symptoms and lung function decline in adulthood has not been widely investigated in longitudinal studies. Our aim is to investigate the associations of exposure to SHS in adults with respiratory symptoms, respiratory conditions and lung function over 20 years. METHODS: We used information from 3011 adults from 26 centres in 12 countries who participated in the European Community Respiratory Health Surveys I-III and were never or former smokers at all three surveys. Associations of SHS exposure with respiratory health (asthma symptom score, asthma, chronic bronchitis, COPD) were analysed using generalised linear mixed-effects models adjusted for confounding factors (including sex, age, smoking status, socioeconomic status and allergic sensitisation). Linear mixed-effects models with additional adjustment for height were used to assess the relationships between SHS exposure and lung function levels and decline. RESULTS: Reported exposure to SHS decreased in all 26 study centres over time. The prevalence of SHS exposure was 38.7% at baseline (1990-1994) and 7.1% after the 20-year follow-up (2008-2011). On average 2.4% of the study participants were not exposed at the first, but were exposed at the third examination. An increase in SHS exposure over time was associated with doctor-diagnosed asthma (odds ratio (OR): 2.7; 95% confidence interval (95%-CI): 1.2-5.9), chronic bronchitis (OR: 4.8; 95%-CI: 1.6-15.0), asthma symptom score (count ratio (CR): 1.9; 95%-CI: 1.2-2.9) and dyspnoea (OR: 2.7; 95%-CI: 1.1-6.7) compared to never exposed to SHS. Associations between increase in SHS exposure and incidence of COPD (OR: 2.0; 95%-CI: 0.6-6.0) or lung function (β: - 49 ml; 95%-CI: -132, 35 for FEV1 and β: - 62 ml; 95%-CI: -165, 40 for FVC) were not apparent. CONCLUSION: Exposure to second-hand smoke may lead to respiratory symptoms, but this is not accompanied by lung function changes