ABSTRACT
Introducción y objetivo: El COVID-19 supuso una amenaza para la capacidad hospitalaria por el elevado número de ingresos, lo que llevó al desarrollo de diversas estrategias para liberar y crear nuevas camas hospitalarias. Dada la importancia de los corticoides sistémicos en esta enfermedad, se evaluó la eficacia de estos en la reducción de la duración de la estancia hospitalaria (LOS) y se comparó el efecto de tres corticosteroides diferentes sobre este resultado. Métodos: Se realizó un estudio en vida real de cohorte retrospectivo, controlado que analizó una base de datos hospitalaria que incluyó 3.934 pacientes hospitalizados diagnosticados con COVID-19 en un hospital terciario de abril a mayo de 2020. Se comparó un grupo de enfermos que recibieron corticosteroides sistémicos (CG) frente a un grupo de control que no recibió corticosteroides sistémicos (NCG) emparejado por edad, sexo y gravedad de la enfermedad mediante una puntuación de propensión. La decisión de prescribir CG dependía principalmente del criterio del médico responsable. Resultados: Se compararon un total de 199 pacientes hospitalizados en el GC con 199 en el GNC. La LOS fue más corta para el GC que para el NCG (mediana = 3 [rango intercuartílico = 0-10] vs. 5 [2-8,5];p = 0,005, respectivamente), mostrando un 43% más de probabilidad de ser hospitalizado ≤ 4 días que > 4 días cuando se usaron corticosteroides. Además, esta diferencia solo la mostraron aquellos tratados con dexametasona (76,3% hospitalizados ≤ 4 días vs. 23,7% hospitalizados > 4 días [p < 0,001]). Los niveles de ferritina sérica, glóbulos blancos y plaquetas fueron más elevados en el GC. No se observaron diferencias en la mortalidad ni en el ingreso a la unidad de cuidados intensivos. Conclusiones: El tratamiento con corticosteroides sistémicos se asocia con una disminución de la estancia hospitalaria en pacientes hospitalizados con diagnóstico de COVID-19. Esta asociación es significativa en aquellos tratados con dexametasona, no así en metilprednisolona o prednisona.
ABSTRACT
Background and Objective: The COVID-19 pandemic has posed a threat to hospital capacity due to the high number of admissions, which has led to the development of various strategies to release and create new hospital beds. Due to the importance of systemic corticosteroids in this disease, we assessed their efficacy in reducing the length of stay (LOS) in hospitals and compared the effect of 3 different corticosteroids on this outcome. Methods: We conducted a real-world, controlled, retrospective cohort study that analysed data from a hospital database that included 3934 hospitalised patients diagnosed with COVID-19 in a tertiary hospital from April to May 2020. Hospitalised patients who received systemic corticosteroids (CG) were compared with a propensity score control group matched by age, sex and severity of disease who did not receive systemic corticosteroids (NCG). The decision to prescribe CG was at the discretion of the primary medical team. Results: A total of 199 hospitalized patients in the CG were compared with 199 in the NCG. The LOS was shorter for the CG than for the NCG (median = 3 [interquartile range = 0–10] vs. 5 [2–8.5];p = 0.005, respectively), showing a 43% greater probability of being hospitalised ≤ 4 days than > 4 days when corticosteroids were used. Moreover, this difference was only noticed in those treated with dexamethasone (76.3% hospitalised ≤ 4 days vs. 23.7% hospitalised > 4 days [p < 0.001]). Serum ferritin levels, white blood cells and platelet counts were higher in the CG. No differences in mortality or intensive care unit admission were observed. Conclusions: Treatment with systemic corticosteroids is associated with reduced LOS in hospitalised patients diagnosed with COVID-19. This association is significant in those treated with dexamethasone, but no for methylprednisolone and prednisone.
ABSTRACT
Background: SARS-CoV-2 virus has caused tremendous burden on both patients and providers across the globe. Our focus is to develop a practical and easy to deploy system to predict the severe manifestation of disease in COVID-19 patients with an aim to assist clinicians in triage and treatment decisions.Methods: We used multiple cohorts from 14,172 COVID-19 patients with captured clinical outcomes from four healthcare systems across the globe. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 patient records. We focused on building a parsimonious model with the fewest possible number of input parameters to facilitate clinical deployment. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use, end organ damage, and mortality.Findings: Model computed severity risk scores using nine laboratory markers taken from 4,293 patients at the initial presentation and the age have the prediction accuracy with the area under the curve (AUC) of 0 · 77 95% CI: 0·76-0·78, and the negative predictive value NPV of 0·87 95% CI: 0·85-0·87 for the need to use a ventilator. Similarly, the model has an accuracy with AUC of 0·83 95% CI: 0·82-0·84, and the NPV of 0·93 95% CI: 0·92-0·94 for predicting in-hospital 30-day mortality.Interpretations: Our deep learning model has a promising predictive performance in using various laboratory markers taken from patients admitted due to COVID-19 at the initial encounter to directly inform clinical and resource management and allocations, respectively.Funding: Provided through research and collaboration grants from Siemens Healthineers, Laboratory Diagnostics, Tarrytown, New York, USA.Declaration of Interests: VS, DC, JS, ER, RM, SV, DC, and AK are employees of Siemens Healthcare, USA. All other authors have nothing to declare. Ethics Approval Statement: This study was approved by the ethics committees at Hospital University of La Paz, Emory University Hospital, and Houston Methodist Hospital.
Subject(s)
COVID-19ABSTRACT
BACKGROUND Since the confirmation of the first patient infected with SARS-CoV-2 in Spain in January 2020, the epidemic has grown rapidly, with the greatest impact on the Madrid region. This article describes the first 2226 consecutive adult patients with COVID-19 admitted to the La Paz University Hospital in Madrid. METHODS Our cohort included all consecutively admitted patients who were hospitalized and who had a final outcome (death or discharge) in a 1286-bed hospital of Madrid (Spain) from February 25th (first case admitted) to April 19th, 2020. Data was entered manually into an electronic case report form, which was monitored prior to the analysis. RESULTS We consecutively included 2226 adult patients admitted to the hospital who either died (460) or were discharged (1766). The patients median age was 61 years; 51.8% were women. The most common comorbidity was arterial hypertension (41.3%). The most common symptoms on admission were fever (71.2%). The median time from disease onset to hospital admission was 6 days. Overall mortality was 20.7% and was higher in men (26.6% vs 15.1%). Seventy-five patients with a final outcome were transferred to the ICU (3.4%). Most patients admitted to the ICU were men, and the median age was 64 years. Baseline laboratory values on admission were consistent with an impaired immune-inflammatory profile. CONCLUSIONS We provide a description of the first large cohort of hospitalized patients with COVID-19 in Europe. Advanced age, male gender, the presence of comorbidities and abnormal laboratory values were more common among the patients with fatal outcomes.