Clinical prediction rules for adverse evolution in patients with COVID-19 by the Omicron variant

Objective: We identify factors related to SARS-CoV-2 infection linked to hospitalization, ICU admission, and mortality and develop clinical prediction rules. Methods: Retrospective cohort study of 380,081 patients with SARS-CoV-2 infection from March 1, 2020 to January 9, 2022, including a subsample of 46,402 patients who attended Emergency Departments (EDs) having data on vital signs. For derivation and external validation of the prediction rule, two different periods were considered: before and after emergence of the Omicron variant, respectively. Data collected included sociodemographic data, COVID-19 vaccination status, baseline comorbidities and treatments, other background data and vital signs at triage at EDs. The predictive models for the EDs and the whole samples were developed using multivariate logistic regression models using Lasso penalization. Results: In the multivariable models, common predictive factors of death among EDs patients were greater age; being male; having no vaccination, dementia; heart failure; liver and kidney disease; hemiplegia or paraplegia; coagulopathy; interstitial pulmonary disease; malignant tumors; use chronic systemic use of steroids, higher temperature, low O2 saturation and altered blood pressure-heart rate. The predictors of an adverse evolution were the same, with the exception of liver disease and the inclusion of cystic fibrosis. Similar predictors were found to be related to hospital admission, including liver disease, arterial hypertension, and basal prescription of immunosuppressants. Similarly, models for the whole sample, without vital signs, are presented. Conclusions: We propose risk scales, based on basic information, easily-calculable, high-predictive that also function with the current Omicron variant and may help manage such patients in primary, emergency, and hospital care. Keywords: COVID-19; Clinical decision rules; Health care; Outcome assessment; SARS-CoV-2

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

Proton tracking for medical imaging and dosimetry

For many years, silicon micro-strip detectors have been successfully used as tracking detectors for particle and nuclear physics experiments. A new application of this technology is to the field of particle therapy, where radiotherapy is carried out by use of charged particles such as protons or carbon ions. Such a treatment has been shown to have advantages over standard x-ray radiotherapy and as a result of this, many new centres offering particle therapy are currently under construction—including two in the U.K.. The characteristics of a new silicon micro-strip detector based system for this application will be presented. The array uses specifically designed large area sensors in several stations in an x-u-v co-ordinate configuration suitable for very fast proton tracking with minimal ambiguities. The sensors will form a tracker capable of giving information on the path of high energy protons entering and exiting a patient. This will allow proton computed tomography (pCT) to aid the accurate delivery of treatment dose with tuned beam profile and energy. The tracker will also be capable of proton counting and position measurement at the higher fluences and full range of energies used during treatment allowing monitoring of the beam profile and total dose. Results and initial characterisation of sensors will be presented along with details of the proposed readout electronics. Radiation tests and studies with different electronics at the Clatterbridge Cancer Centre and the higher energy proton therapy facility of iThemba LABS in South Africa will also be shown

A new silicon tracker for proton imaging and dosimetry

For many years, silicon micro-strip detectors have been successfully used as tracking detectors for particle and nuclear physics experiments. A new application of this technology is to the field of particle therapy where radiotherapy is carried out by use of charged particles such as protons or carbon ions. Such a treatment has been shown to have advantages over standard x-ray radiotherapy and as a result of this, many new centres offering particle therapy are currently under construction around the world today. The Proton Radiotherapy, Verification and Dosimetry Applications (PRaVDA) consortium are developing instrumentation for particle therapy based upon technology from high-energy physics. The characteristics of a new silicon micro-strip tracker for particle therapy will be presented. The array uses specifically designed, large area sensors with technology choices that follow closely those taken for the ATLAS experiment at the HL-LHC. These detectors will be arranged into four units each with three layers in an x–u–v configuration to be suitable for fast proton tracking with minimal ambiguities. The sensors will form a tracker capable of tracing the path of ~200 MeV protons entering and exiting a patient allowing a new mode of imaging known as proton computed tomography (pCT). This will aid the accurate delivery of treatment doses and in addition, the tracker will also be used to monitor the beam profile and total dose delivered during the high fluences used for treatment. We present here details of the design, construction and assembly of one of the four units that will make up the complete tracker along with its characterisation using radiation tests carried out using a 90 Sr source in the laboratory and a 60 MeV proton beam at the Clatterbridge Cancer Centre

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

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.)

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