Effect of intravenous pulses of methylprednisolone 250 mg versus dexamethasone 6 mg in hospitalised adults with severe COVID-19 pneumonia: An open-label randomised trial.

BACKGROUND: The efficacy and safety of high versus medium doses of glucocorticoids for the treatment of patients with COVID-19 has shown mixed outcomes in controlled trials and observational studies. We aimed to evaluate the effectiveness of methylprednisolone 250 mg bolus versus dexamethasone 6 mg in patients with severe COVID-19. METHODS: A randomised, open-label, controlled trial was conducted between February and August 2021 at four hospitals in Spain. The trial was suspended after the first interim analysis since the investigators considered that continuing the trial would be futile. Patients were randomly assigned in a 1:1 ratio to receive dexamethasone 6 mg once daily for up to 10 days or methylprednisolone 250 mg once daily for 3 days. RESULTS: Of the 128 randomised patients, 125 were analysed (mean age 60 ± 17 years; 82 males [66%]). Mortality at 28 days was 4.8% in the 250 mg methylprednisolone group versus 4.8% in the 6 mg dexamethasone group (absolute risk difference, 0.1% [95% CI, -8.8 to 9.1%]; p = 0.98). None of the secondary outcomes (admission to the intensive care unit, non-invasive respiratory or high-flow oxygen support, additional immunosuppressive drugs, or length of stay), or prespecified sensitivity analyses were statistically significant. Hyperglycaemia was more frequent in the methylprednisolone group at 27.0 versus 8.1% (absolute risk difference, -18.9% [95% CI, -31.8 to - 5.6%]; p = 0.007). CONCLUSIONS: Among severe but not critical patients with COVID-19, 250 mg/d for 3 days of methylprednisolone compared with 6 mg/d for 10 days of dexamethasone did not result in a decrease in mortality or intubation.

Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients.

BACKGROUND: Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODS: We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. RESULTS: A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. CONCLUSIONS: This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.