RESUMEN
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)
Asunto(s)
Humanos , Unidades de Cuidados Intensivos , Infecciones por Coronavirus , Neumonía Viral , Pandemias , Hospitalización , Estudios Retrospectivos , Factores de RiesgoRESUMEN
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.
Asunto(s)
COVID-19 , Anciano , Hospitalización , Humanos , Unidades de Cuidados Intensivos , Masculino , Estudios Retrospectivos , Factores de Riesgo , SARS-CoV-2RESUMEN
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.
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BACKGROUND AND OBJECTIVES: Burnout is a psychosocial syndrome caused by stressful working conditions and affects 30-60% of medical personnel. The aim of this study was to assess the burnout rate of Spanish internists and the factors related to its onset. MATERIAL AND METHODS: We conducted a survey of work conditions followed by the Maslach Burnout Inventory, which was disseminated through the email registry and social networks of the Spanish Society of Internal Medicine. We performed a descriptive study and a univariate and multivariate analysis assessing the variables associated with burnout syndrome. RESULTS: A total of 934 internists (58.8% women and a median age of 40.0 years) answered the survey. Some 55.0% of the internists indicated high emotional fatigue, 61.7% indicated a high sense of depersonalisation, and 58.6% indicated low personal fulfilment. Some 33.4% of the interns experienced burnout. Burnout syndrome was independently related to age (OR 0.96; 95% CI 0.94-0.98), poor work environment (OR 1.94; 95% CI 1.31-2.82), insufficient wages (OR 1.79; 95% CI 1.20-2.67), receiving threats (OR 1.703; 95% CI 1.204-2.410) and the feeling of a lack of professional progress (OR 2.83; 95% CI 1.92-4.17). CONCLUSIONS: Burnout syndrome affects 33.4% of internists in Spain, and its onset is independently related with age, poor work environment, a lack of professional progress, insufficient financial remuneration and experiencing threats by patients or colleagues.
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BACKGROUND: Specialist training is based on the gradual acquisition of expertise, skills and responsibilities. The aim of this study is to determine the opinion of residents regarding their training. MATERIAL AND METHODS: This was a cross-sectional descriptive study based on an online survey of 5th-year residents during February and March 2017. RESULTS: A total of 194 residents (62.8% of the total) responded to the survey, 62.9% of whom were women and 50% of whom were younger than 30years, representing hospitals from all levels and from the 17 autonomous communities. More than 80% of the residents choose the specialty once again and believed that the duration of the residence was appropriate; however, 76.3% would eliminate some of their rotations. Most of the residents did not know the objectives of each rotation, and 37.1% felt they were not adequately supervised. Some 82.5% would change the evaluation system, and 68.0% would favour performing an excellence test. Most of the residents had published at least one article or performed one presentation at a congress; however, only 27.8% had completed a doctoral thesis. Although 74.7% of the internists believed they would find employment, only 28.4% had an offer 1month after completing their residence. CONCLUSIONS: The residents are satisfied with their training, although there is still a deficit in supervision and dissatisfaction with the method of assessing their knowledge and the precarious job market during the first year for specialists.
