Corticosteroid pulses for hospitalized patients with COVID-19. Effects on mortality and in-hospital stay.

Background: COVID-19 has high mortality in hospitalized patients, and we need effective treatments. Our objective was to assess corticosteroid pulses influence on 60-days mortality in hospitalized patients with severe COVID-19, intensive care admission, and hospital stay. Methods: We designed a multicenter retrospective cohort study in three teaching hospitals of Castilla y Leon, Spain (865.096 people). We selected patients with confirmed COVID-19 and lung involvement with a pO2/FiO2 < 300, excluding those exposed to immunosuppressors before or during hospitalization, patients terminally ill at admission, or died the first 24 hours. We performed a propensity score matching (PSM) adjusting covariates that modify the probability of being treated. Then we used a Cox regression model in the PSM group to consider factors affecting mortality. Findings: From 2933 patients, 257 fulfilled the inclusion and exclusion criteria. One hundred and twenty-four patients were on corticosteroid pulses, and 133 were not. 30{middle dot}3% (37/122) of patients died in the corticosteroid pulses group and 42{middle dot}9% (57/133) in the non-exposed cohort. These differences (12{middle dot}6% CI95% [8{middle dot}54-16{middle dot}65]) were statically significant (log-rank 4{middle dot}72, p=0{middle dot}03). We performed PSM using the exact method. Mortality differences remained in the PSM group (log-rank 5{middle dot}31, p=0{middle dot}021) and were still significant after a Cox regression model (HR for corticosteroid pulses 0{middle dot}561, p= 0{middle dot}039). There were no significant differences in intensive care admission rate (p=0{middle dot}173). The hospital stay was longer in the corticosteroid group (p<0,001). Interpretation: This study provides evidence about treatment with corticosteroid pulses in severe COVID-19 that might significantly reduce mortality. Strict inclusion and exclusion criteria with that selection process set a reliable frame to compare mortality in both exposed and non-exposed groups. Funding: There was no funding provided.

Predicting clinical outcome with phenotypic clusters in COVID-19 pneumonia: 2 an analysis of 12,066 hospitalized patients from the Spanish registry SEMI-3 COVID-19.

(1) Background: This study aims to identify different clinical phenotypes in COVID-19 88 pneumonia using cluster analysis and to assess the prognostic impact among identified clusters in 89 such patients. (2) Methods: Cluster analysis including 11 phenotypic variables was performed in a 90 large cohort of 12,066 COVID-19 patients, collected and followed-up from March 1, to July 31, 2020, 91 from the nationwide Spanish SEMI-COVID-19 Registry. (3) Results: Of the total of 12,066 patients 92 included in the study, most were males (7,052, 58.5%) and Caucasian (10,635, 89.5%), with a mean 93 age at diagnosis of 67 years (SD 16). The main pre-admission comorbidities were arterial 94 hypertension (6,030, 50%), hyperlipidemia (4,741, 39.4%) and diabetes mellitus (2,309, 19.2%). The 95 average number of days from COVID-19 symptom onset to hospital admission was 6.7 days (SD 7). 96 The triad of fever, cough, and dyspnea was present almost uniformly in all 4 clinical phenotypes 97 identified by clustering. Cluster C1 (8,737 patients, 72.4%) was the largest, and comprised patients 98 with the triad alone. Cluster C2 (1,196 patients, 9.9%) also presented with ageusia and anosmia; 99 cluster C3 (880 patients, 7.3%) also had arthromyalgia, headache, and sore throat; and cluster C4 100 (1,253 patients, 10.4%) also manifested with diarrhea, vomiting, and abdominal pain. Compared to 101 each other, cluster C1 presented the highest in-hospital mortality (24.1% vs. 4.3% vs. 14.7% vs. 102 18.6%; p<0.001). The multivariate study identified phenotypic clusters as an independent factor for 103 in-hospital death. (4) Conclusion: The present study identified 4 phenotypic clusters in patients with 104 COVID-19 pneumonia, which predicted the in-hospital prognosis of clinical outcomes.

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

BACKGROUNDEfficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODSWe 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. RESULTSA 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. CONCLUSIONSThis machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.