Derivación y validación de una puntuación de riesgo de ingreso en la Unidad de Cuidados Intensivos para pacientes con COVID-19 / Derivation and validation of a risk score for admission to the Intensive Care Unit in patients with COVID-19

Fundamento: Identificar y validar una escala de riesgo de ingreso en las unidades de cuidados intensivos (UCI) en pacientes hospitalizados con enfermedad por coronavirus 2019 (COVID-19). Métodos: Realizamos una regla de derivación y otra de validación para ingreso en UCI, utilizando los datos de un registro nacional de cohortes de pacientes con infección confirmada por SARS-CoV-2 ingresados entre marzo y agosto del año 2020 (n = 16.298). Analizamos variables demográficas, clínicas, radiológicas y de laboratorio disponibles en el ingreso hospitalario. Evaluamos el rendimiento de la escala de riesgo mediante estimación del área bajo la curva de característica operativa del receptor (AROC). Utilizamos los coeficientes β del modelo de regresión para elaborar una puntuación (0 a 100 puntos) asociada con ingreso en UCI. Resultados: La edad media de los pacientes fue de 67 años; 57% varones. Un total de 1.420 (8,7%) pacientes ingresaron en la UCI. Las variables independientes asociadas con el ingreso en UCI fueron: edad, disnea, índice de comorbilidad de Charlson, cociente neutrófilos-linfocitos, lactato deshidrogenasa e infiltrados difusos en la radiografía de tórax. El modelo mostró un AROC de 0,780 (IC: 0,763-0,797) en la cohorte de derivación y un AROC de 0,734 (IC: 0,708-0,761) en la cohorte de validación. Una puntuación > 75 se asoció con una probabilidad de ingreso en UCI superior a un 30%, mientras que una puntuación < 50 redujo la probabilidad de ingreso en UCI al 15%. Conclusiós: Una puntuación de predicción simple proporcionó una herramienta útil para predecir la probabilidad de ingreso en la UCI con un alto grado de precisión (AU)

Background: This work aims to identify and validate a risk scale for admission to intensive care units (ICU) in hospitalized patients with coronavirus disease 2019 (COVID-19). Methods: We created a derivation rule and a validation rule for ICU admission using data from a national registry of a cohort of patients with confirmed SARS-CoV-2 infection who were admitted between March and August 2020 (n = 16,298). We analyzed the available demographic, clinical, radiological, and laboratory variables recorded at hospital admission. We evaluated the performance of the risk score by estimating the area under the receiver operating characteristic curve (AUROC). Using the β coefficients of the regression model, we developed a score (0 to 100 points) associated with ICU admission. Results: The mean age of the patients was 67 years; 57% were men. A total of 1,420 (8.7%) patients were admitted to the ICU. The variables independently associated with ICU admission were age, dyspnea, Charlson Comorbidity Index score, neutrophil-to-lymphocyte ratio, lactate dehydrogenase levels, and presence of diffuse infiltrates on a chest X-ray. The model showed an AUROC of 0.780 (CI: 0.763-0.797) in the derivation cohort and an AUROC of 0.734 (CI: 0.708-0.761) in the validation cohort. A score of greater than 75 points was associated with a more than 30% probability of ICU admission while a score of less than 50 points reduced the likelihood of ICU admission to 15%. Conclusion: A simple prediction score was a useful tool for forecasting the probability of ICU admission with a high degree of precision (AU)

Derivation and validation of a risk score for admission to the Intensive Care Unit in patients with COVID-19.

BACKGROUND: This work aims to identify and validate a risk scale for admission to intensive care units (ICU) in hospitalized patients with coronavirus disease 2019 (COVID-19). METHODS: We created a derivation rule and a validation rule for ICU admission using data from a national registry of a cohort of patients with confirmed SARS-CoV-2 infection who were admitted between March and August 2020 (N = 16,298). We analyzed the available demographic, clinical, radiological, and laboratory variables recorded at hospital admission. We evaluated the performance of the risk score by estimating the area under the receiver operating characteristic curve (AUROC). Using the ß coefficients of the regression model, we developed a score (0-100 points) associated with ICU admission. RESULTS: The mean age of the patients was 67 years; 57% were men. A total of 1420 (8.7%) patients were admitted to the ICU. The variables independently associated with ICU admission were age, dyspnea, Charlson Comorbidity Index score, neutrophil-to-lymphocyte ratio, lactate dehydrogenase levels, and presence of diffuse infiltrates on a chest X-ray. The model showed an AUROC of 0.780 (CI: 0.763-0.797) in the derivation cohort and an AUROC of 0.734 (CI: 0.708-0.761) in the validation cohort. A score of greater than 75 points was associated with a more than 30% probability of ICU admission while a score of less than 50 points reduced the likelihood of ICU admission to 15%. CONCLUSION: A simple prediction score was a useful tool for forecasting the probability of ICU admission with a high degree of precision.

[Derivation and validation of a risk score for admission to the Intensive Care Unit in patients with COVID-19]. / Derivación y validación de una puntuación de riesgo de ingreso en la Unidad de Cuidados Intensivos para pacientes con COVID-19.

BACKGROUND: This work aims to identify and validate a risk scale for admission to intensive care units (ICU) in hospitalized patients with coronavirus disease 2019 (COVID-19). METHODS: We created a derivation rule and a validation rule for ICU admission using data from a national registry of a cohort of patients with confirmed SARS-CoV-2 infection who were admitted between March and August 2020 (n = 16,298). We analyzed the available demographic, clinical, radiological, and laboratory variables recorded at hospital admission. We evaluated the performance of the risk score by estimating the area under the receiver operating characteristic curve (AUROC). Using the ß coefficients of the regression model, we developed a score (0 to 100 points) associated with ICU admission. RESULTS: The mean age of the patients was 67 years; 57% were men. A total of 1,420 (8.7%) patients were admitted to the ICU. The variables independently associated with ICU admission were age, dyspnea, Charlson Comorbidity Index score, neutrophil-to-lymphocyte ratio, lactate dehydrogenase levels, and presence of diffuse infiltrates on a chest X-ray. The model showed an AUROC of 0.780 (CI: 0.763-0.797) in the derivation cohort and an AUROC of 0.734 (CI: 0.708-0.761) in the validation cohort. A score of greater than 75 points was associated with a more than 30% probability of ICU admission while a score of less than 50 points reduced the likelihood of ICU admission to 15%. CONCLUSION: A simple prediction score was a useful tool for forecasting the probability of ICU admission with a high degree of precision.

Clinical characteristics of patients hospitalized with COVID-19 in Spain: Results from the SEMI-COVID-19 Registry. / Características clínicas de los pacientes hospitalizados con COVID-19 en España: resultados del Registro SEMI-COVID-19.

BACKGROUND: Spain has been one of the countries most affected by the COVID-19 pandemic. OBJECTIVE: To create a registry of patients with COVID-19 hospitalized in Spain, in order to improve our knowledge of the clinical, diagnostic, therapeutic, and prognostic aspects of this disease. METHODS: A multicentre retrospective cohort study, including consecutive patients hospitalized with confirmed COVID-19 throughout Spain. Epidemiological and clinical data, additional tests at admission and at seven days, treatments administered, and progress at 30 days of hospitalization were collected from electronic medical records. RESULTS: Up to June 30th 2020, 15,111 patients from 150 hospitals were included. Their median age was 69.4 years (range: 18-102 years) and 57.2% were male. Prevalences of hypertension, dyslipidemia, and diabetes mellitus were 50.9%, 39.7%, and 19.4%, respectively. The most frequent symptoms were fever (84.2%) and cough (73.5%). High values of ferritin (73.5%), lactate dehydrogenase (73.9%), and D-dimer (63.8%), as well as lymphopenia (52.8%), were frequent. The most used antiviral drugs were hydroxychloroquine (85.6%) and lopinavir/ritonavir (61.4%); 33.1% developed respiratory distress. Overall mortality rate was 21.0%, with a marked increase with age (50-59 years: 4.7%, 60-69 years: 10.5%, 70-79 years: 26.9%, ≥80 years: 46.0%). CONCLUSIONS: The SEMI-COVID-19 Network provides data on the clinical characteristics of patients with COVID-19 hospitalized in Spain. Patients with COVID-19 hospitalized in Spain are mostly severe cases, as one in three patients developed respiratory distress and one in five patients died. These findings confirm a close relationship between advanced age and mortality.

[Clinical characteristics of patients hospitalized with COVID-19 in Spain: Results from the SEMI-COVID-19 Registry]. / Características clínicas de los pacientes hospitalizados con COVID-19 en España: resultados del Registro SEMI-COVID-19.

BACKGROUND: Spain has been one of the countries most affected by the COVID-19 pandemic. OBJECTIVE: To create a registry of patients with COVID-19 hospitalized in Spain, in order to improve our knowledge of the clinical, diagnostic, therapeutic, and prognostic aspects of this disease. METHODS: A multicentre retrospective cohort study, including consecutive patients hospitalized with confirmed COVID-19 throughout Spain. Epidemiological and clinical data, additional tests at admission and at seven days, treatments administered, and progress at 30 days of hospitalization were collected from electronic medical records. RESULTS: Up to June 30th 2020, 15,111 patients from 150 hospitals were included. Their median age was 69.4 years (range: 18-102 years) and 57.2% were male. Prevalences of hypertension, dyslipidemia, and diabetes mellitus were 50.9%, 39.7%, and 19.4%, respectively. The most frequent symptoms were fever (84.2%) and cough (73.5%). High values of ferritin (73.5%), lactate dehydrogenase (73.9%), and D-dimer (63.8%), as well as lymphopenia (52.8%), were frequent. The most used antiviral drugs were hydroxychloroquine (85.6%) and lopinavir/ritonavir (61.4%); 33.1% developed respiratory distress. Overall mortality rate was 21.0%, with a marked increase with age (50-59 years: 4.7%, 60-69 years: 10.5%, 70-79 years: 26.9%, ≥ 80 years: 46.0%). CONCLUSIONS: The SEMI-COVID-19 Network provides data on the clinical characteristics of patients with COVID-19 hospitalized in Spain. Patients with COVID-19 hospitalized in Spain are mostly severe cases, as one in three patients developed respiratory distress and one in five patients died. These findings confirm a close relationship between advanced age and mortality.

[Hereditary and acquired angioedema: clinical characteristics in 8 patients and review of the literature]. / Angioedema hereditario y adquirido: características clínicas de ocho pacientes y revisión de la literatura.

C1 inhibitor disorders are a group of rare conditions in which the C1 inhibitor is deficient or defective. We present the clinical characteristics of 8 patients and a review of the literature. These are characterized by recurrent episodes of angioedema, which most often affect the skin or mucosal tissues of the upper respiratory and gastrointestinal tract. Laryngeal involvement may cause fatal asphyxiation. These disorders may be divided into two broad categories: hereditary angioedema (HAE) and acquired C1 inhibitor disorders. Indications for screening for HAE include: recurrent angioedema, episodic abdominal pain, laryngeal, a family background of angioedema, and a low C4 level. Acquired C1 inhibitor disorders are similar, but lack a family background. Treatment is divided into short and long-term prophylaxis with androgens, antifibrinolytics and C1 inhibitor replacement. First line therapy of acute attacks is C1 inhibitor.

Revisión de 11 casos de eritema indurado en un hospital de segundo nivel / Review of eleven cases of erythema induratum at a second level hospital

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Zoonosis emergentes / Emerging zoonosis

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