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Hyperglycemia, with or without diabetes, is associated with complications in hospitalized patients with COVID-19. There is no information regarding this problem in our region. This study was aimed to compare the characteristics and in-hospital clinical course of patients with a probable diagnosis of COVID-19, with and without hyperglycemia during the hospitalization. This is a retrospective, observational study of clinical records review of hospitalized patients with COVID-19. The ISARIC-WHO form was used for data collection. Hyperglycemia was defined as a fasting value ≥140 mg/dL according to standard glycaemia targets in hospitalized patients. A total of 148 patients were included, 97 (65.5%) men and 51 (34.5%) women, with a mean age of 64.1±16.1 years; of which 42/148 (28.4%) patients reported previous diabetes, 60/148 (40.5%) patients had hyperglycemia during the hospitalization and 32/60 (53.3%) of these cases did not report previous diabetes. The patients with hyperglycemia were older, received more frequently systemic corticosteroids (96.6 vs 82.6%; p=0.01), and antibiotics (68.3 vs 44.3%; p=0.01), had worse baseline oxygenation parameters (SpO2 88.1±11.7%; vs 92.8±5.5%; p=0.02, PaO2/FiO2, 194.4±119.7 vs 270.9±118.3; p<0.001), higher total lung severity score in the chest CT (14.9±5.7 vs 11.1±6.3; p<0.001) and higher levels of baseline inflammatory markers (CRP 6.73±3.61 vs 5.08±4.21; p<0.01, LDH 342.9±118.4 vs 296.5±161.4; p=0.01 and Ferritin 687.7±373.2 vs 542.6±395.3; p=0.01). Mortality (34.5 vs 10.7%; p<0.001) and admission to ICU (43.3 vs 7.9%; p<0.001) were higher in patients with hyperglycemia. Hyperglycemia in hospitalized patients with COVID-19 is a marker of severe disease and poor prognosis., La hiperglucemia con o sin diabetes en pacientes hospitalizados con COVID-19 se asocia con complicaciones. No existe información del problema en nuestro entorno. El objetivo del estudio fue comparar las características y curso clínico intrahospitalario de los pacientes con diagnóstico de COVID-19 que presentaron o no hiperglucemia durante su hospitalización. Estudio retrospectivo, observacional, de revisión de historias clínicas de pacientes hospitalizados con COVID-19. Se usó el formulario desarrollado por ISARIC y OMS para recopilar datos. Siguiendo los objetivos glucémicos estándar en pacientes hospitalizados, se definió como hiperglucemia, al valor en ayunas igual o mayor de 140 mg/dL. Se incluyeron 148 pacientes, 97 (65,5%) hombres y 51 mujeres (34,5%), con edad promedio de 64,1±16,1 años, de los cuales 42 (28,4%), refirieron antecedente de diabetes, 60 (40,5%) presentaron hiperglucemia intrahospitalaria y 32 (53,3%) de estos casos no reportaron diabetes previa. Los pacientes con hiperglucemia tenían mayor edad, recibieron más corticoides sistémicos (96,6 vs 82,6%; p=0,01), antibióticos (68,3 vs 44,3%; p=0,01), presentaron mayor alteración en la oxigenación de ingreso (SpO2 88,1±11,7% vs 92,8±5,5%, p=0,02; PaO2/FiO2, 194,4±119,7 vs 270,9±118,3, p<0,001), mayor puntaje de extensión de lesión pulmonar en la tomografía de tórax (14,9±5,7 vs 11,1±6,3; p<0,001) y marcadores inflamatorios al ingreso más alterados (PCR 6,73±3,61 vs 5,08±4,21; p<0,01, LDH 342,9±118,4 vs 296,5±161,4; p=0,01 y ferritina 687,7±373,2 vs 542,6±395,3; p=0,01). La mortalidad (34,5 vs 10,7%; p<0,001) y admisión a la UCI (43,3 vs 7,9%; p<0,001) fue mayor en los pacientes con hiperglucemia. La hiperglucemia en pacientes hospitalizados por COVID-19 es un marcador de mayor gravedad y mal pronóstico.

The severity of lung involvement on chest tomography (CT) images in COVID-19 patients may have a prognostic value. This study assesses the type, severity and frequency of the different images of lung CT in hospitalized patients with COVID-19, and the differences in clinical characteristics and in-hospital outcomes, according to the CT severity score. This represents an observational study (retrospective cohort) of hospitalized patients with COVID-19. The ISARIC-WHO form was used to collect data. The type of lung lesions, affected lobes, and total CT severity score were determined at hospital admission. The first, second and third quartiles of the total CT score were calculated to divide the sample into four equal parts (Q1, Q2, Q3 and Q4). A total of 556 patients were included, 336 men (60.4%) and 220 women (39.6%), with a mean age of 61.9±15.8 years; and 532 of them had CT scan at admission. Patients in the more severe quartiles had more days of symptoms evolution (Q1 6.4±3.5, Q2 7.9±4.1, Q3 8.2±4.1, Q4 8.1±4.4), desaturation (Q1 95.3±3.7, Q2 94.4±3.1%, Q3 91.7±4.8%, Q4 86.5±9.1%), alterations of inflammatory markers, hospital stay (Q1 6.4±2.9, Q2 7.4±4.1, Q3 9.6±5.8, Q4 13.1±10.4 days), admission to ICU (Q1-2.5%, Q2-5.8%, Q3-12.5%, Q4-49.1%), mortality (Q1-3.8%, Q2-4.5%, Q3-9.4%, Q4-33.3%), mixed CT lesions (ground glass opacity-consolidation), linear opacities, crazy-paving pattern, reverse halo sign, and bronchiectasis. The total CT score significantly correlated with leukocyte, neutrophil and lymphocyte counts, and with other inflammatory markers. Semi-quantitative evaluation of pulmonary involvement in the initial chest CT can help to establish the severity of the case and predict relevant clinical outcomes in COVID-19 patients., La gravedad de las imágenes en la tomografía (TC) de tórax en pacientes con COVID-19 puede tener valor pronóstico. Este estudio evalúa el tipo, gravedad y frecuencia de las lesiones pulmonares de pacientes hospitalizados con COVID-19 y las diferencias en las características clínicas y desenlaces intrahospitalarios según la gravedad tomográfica. Se trata de un estudio observacional (cohorte retrospectiva) de pacientes hospitalizados con COVID-19. Se usó el formulario de ISARIC-OMS para recopilar datos. Se determinó el tipo de lesiones pulmonares, ló-bulos afectados y puntuación de gravedad total en la TC de ingreso. Se calcularon el primer, segundo y tercer cuartiles de la puntuación total, para dividir la muestra en cuatro partes iguales (Q1, Q2, Q3 y Q4). Se incluyeron 556 pacientes, 336 hombres (60,4%) y 220 mujeres (39,6%), con edad promedio 61,9±15,8 años y 532 tenían TC al ingreso. Los pacientes en los cuartiles más graves tenían más días de evolución de síntomas (Q1 6,4±3,5, Q2 7,9±4,1, Q3 8,2±4,1, Q4 8,1±4,4), desaturación (Q1 95,3±3,7%, Q2 94,4±3,1%, Q3 91,7±4,8%, Q4 86,5±9,1%), alteración de marcadores inflamatorios, días de hospitalización (Q1 6,4±2,9, Q2 7,4±4,1, Q3 9,6±5,8, Q4 13,1±10,4), admisión a UCI (Q1-2,5%, Q2-5,8%, Q3-12,5%, Q4-49,1%), mortalidad (Q1-3,8%, Q2-4,5%, Q3-9,4%, Q4-33,3%), lesiones combinadas (vidrio deslustrado-consolidado) en la TC, opacidades lineales, patrón-empedrado, halo-invertido y bronquiectasia. La puntuación de la TC se correlacionó significativamente con el recuento de leucocitos, neutrófilos, linfocitos y otros marcadores inflamatorios. La evaluación semicuantitativa del compromiso pulmonar en la TC de tórax, puede ayudar a establecer la gravedad y predecir desenlaces clínicos en pacientes con COVID-19.

Indoor Air Quality (IAQ) in libraries is influenced by the presence of specific factors which can impact on both paper storage as well as people health. Microclimatic conditions induce and support a biodiversity pattern involving environmental and anthropic microorganisms. We used a multidisciplinary monitoring model to characterize microflora biodiversity by Next Generation Sequencing (NGS). Biodiversity indexes were adapted to evaluate anthropic vs environmental pollution by combining microbial load (H), species representativeness (EH), human/environmental pollution ratio (SA) to better characterize the NGS output and acquire synthetic information on Indoor Air Microbial Biodiversity (IAMB). Results indicate a frequently low microbial load (IGCM/m3 <1000) characterized by different species (n=102), including several cellulose metabolizing bacteria. Workers and visitors appeared a relevant source of microbial contamination. Air biodiversity assayed by NGS seems a promising marker for studying IAQ.

Indoor Air Quality (IAQ) in libraries is influenced by the presence of specific factors which can impact on both paper storage as well as people health. Microclimatic conditions induce and support a biodiversity pattern involving environmental and anthropic microorganisms. We used a multidisciplinary monitoring model to characterize microflora biodiversity by Next Generation Sequencing (NGS). Biodiversity indexes were adapted to evaluate anthropic vs environmental pollution by combining microbial load (H), species representativeness (EH), human/environmental pollution ratio (SA) to better characterize the NGS output and acquire synthetic information on Indoor Air Microbial Biodiversity (IAMB). Results indicate a frequently low microbial load (IGCM/m3 <1000) characterized by different species (n=102), including several cellulose metabolizing bacteria. Workers and visitors appeared a relevant source of microbial contamination. Air biodiversity assayed by NGS seems a promising marker for studying IAQ.