Randomized phase II clinical trial of ruxolitinib plus simvastatin in COVID19 clinical outcome and cytokine evolution.

Background: Managing the inflammatory response to SARS-Cov-2 could prevent respiratory insufficiency. Cytokine profiles could identify cases at risk of severe disease. Methods: We designed a randomized phase II clinical trial to determine whether the combination of ruxolitinib (5 mg twice a day for 7 days followed by 10 mg BID for 7 days) plus simvastatin (40 mg once a day for 14 days), could reduce the incidence of respiratory insufficiency in COVID-19. 48 cytokines were correlated with clinical outcome. Participants: Patients admitted due to COVID-19 infection with mild disease. Results: Up to 92 were included. Mean age was 64 ± 17, and 28 (30%) were female. 11 (22%) patients in the control arm and 6 (12%) in the experimental arm reached an OSCI grade of 5 or higher (p = 0.29). Unsupervised analysis of cytokines detected two clusters (CL-1 and CL-2). CL-1 presented a higher risk of clinical deterioration vs CL-2 (13 [33%] vs 2 [6%] cases, p = 0.009) and death (5 [11%] vs 0 cases, p = 0.059). Supervised Machine Learning (ML) analysis led to a model that predicted patient deterioration 48h before occurrence with a 85% accuracy. Conclusions: Ruxolitinib plus simvastatin did not impact the outcome of COVID-19. Cytokine profiling identified patients at risk of severe COVID-19 and predicted clinical deterioration. Trial registration: https://clinicaltrials.gov/, identifier NCT04348695.

Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study.

The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N = 2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N = 242) and an academic center in Seoul, Republic of Korea (N = 336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83-0.87), 0.76 (0.70-0.82), and 0.95 (0.92-0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification, and/or optimization.

A multicenter randomized open-label clinical trial for convalescent plasma in patients hospitalized with COVID-19 pneumonia.

BACKGROUNDPassive immunotherapy with convalescent plasma (CP) is a potential treatment for COVID-19. Evidence from controlled clinical trials is inconclusive.METHODSWe conducted a randomized, open-label, controlled clinical trial at 27 hospitals in Spain. Patients had to be admitted for COVID-19 pneumonia within 7 days from symptom onset and not on mechanical ventilation or high-flow oxygen devices. Patients were randomized 1:1 to treatment with CP in addition to standard of care (SOC) or to the control arm receiving only SOC. The primary endpoint was the proportion of patients in categories 5 (noninvasive ventilation or high-flow oxygen), 6 (invasive mechanical ventilation or extracorporeal membrane oxygenation [ECMO]), or 7 (death) at 14 days. Primary analysis was performed in the intention-to-treat population.RESULTSBetween April 4, 2020, and February 5, 2021, 350 patients were randomly assigned to either CP (n = 179) or SOC (n = 171). At 14 days, proportion of patients in categories 5, 6, or 7 was 11.7% in the CP group versus 16.4% in the control group (P = 0.205). The difference was greater at 28 days, with 8.4% of patients in categories 5-7 in the CP group versus 17.0% in the control group (P = 0.021). The difference in overall survival did not reach statistical significance (HR 0.46, 95% CI 0.19-1.14, log-rank P = 0.087).CONCLUSIONCP showed a significant benefit in preventing progression to noninvasive ventilation or high-flow oxygen, invasive mechanical ventilation or ECMO, or death at 28 days. The effect on the predefined primary endpoint at 14 days and the effect on overall survival were not statistically significant.TRIAL REGISTRATIONClinicaltrials.gov, NCT04345523.FUNDINGGovernment of Spain, Instituto de Salud Carlos III.

Epigenome-wide association study of COVID-19 severity with respiratory failure.

BACKGROUND: Patients infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the coronavirus disease 2019 (COVID-19), exhibit a wide spectrum of disease behaviour. Since DNA methylation has been implicated in the regulation of viral infections and the immune system, we performed an epigenome-wide association study (EWAS) to identify candidate loci regulated by this epigenetic mark that could be involved in the onset of COVID-19 in patients without comorbidities. METHODS: Peripheral blood samples were obtained from 407 confirmed COVID-19 patients ≤ 61 years of age and without comorbidities, 194 (47.7%) of whom had mild symptomatology that did not involve hospitalization and 213 (52.3%) had a severe clinical course that required respiratory support. The set of cases was divided into discovery (n = 207) and validation (n = 200) cohorts, balanced for age and sex of individuals. We analysed the DNA methylation status of 850,000 CpG sites in these patients. FINDINGS: The DNA methylation status of 44 CpG sites was associated with the clinical severity of COVID-19. Of these loci, 23 (52.3%) were located in 20 annotated coding genes. These genes, such as the inflammasome component Absent in Melanoma 2 (AIM2) and the Major Histocompatibility Complex, class I C (HLA-C) candidates, were mainly involved in the response of interferon to viral infection. We used the EWAS-identified sites to establish a DNA methylation signature (EPICOVID) that is associated with the severity of the disease. INTERPRETATION: We identified DNA methylation sites as epigenetic susceptibility loci for respiratory failure in COVID-19 patients. These candidate biomarkers, combined with other clinical, cellular and genetic factors, could be useful in the clinical stratification and management of patients infected with the SARS-CoV-2. FUNDING: The Unstoppable campaign of the Josep Carreras Leukaemia Foundation, the Cellex Foundation and the CERCA Programme/Generalitat de Catalunya.

Presenting Characteristics, Treatment Patterns, and Outcomes among Patients with Venous Thromboembolism during Hospitalization for COVID-19.

Venous thromboembolism (VTE) is common in patients with coronavirus disease-2019 (COVID-19). However, limited data exist on patient characteristics, treatments, and outcomes. To describe the clinical characteristics, treatment patterns, and short-term outcomes of patients diagnosed with VTE during hospitalization for COVID-19. This is a prospective multinational study of patients with incident VTE during the course of hospitalization for COVID-19. Data were obtained from the Registro Informatizado de la Enfermedad TromboEmbólica (RIETE) registry. All-cause mortality, VTE recurrences, and major bleeding during the first 10 days were separately investigated for patients in hospital wards versus those in intensive care units (ICUs). As of May 03, 2020, a total number of 455 patients were diagnosed with VTE (83% pulmonary embolism, 17% isolated deep vein thrombosis) during their hospital stay; 71% were male, the median age was 65 (interquartile range, 55-74) years. Most patients (68%) were hospitalized in medical wards, and 145 in ICUs. Three hundred and seventeen (88%; 95% confidence interval [CI]: 84-91%) patients were receiving thromboprophylaxis at the time of VTE diagnosis. Most patients (88%) received therapeutic low-molecular-weight heparin, and 15 (3.6%) received reperfusion therapies. Among 420 patients with complete 10-day follow-up, 51 (12%; 95% CI: 9.3-15%) died, no patient recurred, and 12 (2.9%; 95% CI: 1.6-4.8%) experienced major bleeding. The 10-day mortality rate was 9.1% (95% CI: 6.1-13%) among patients in hospital wards and 19% (95% CI: 13-26%) among those in ICUs. This study provides characteristics and early outcomes of patients diagnosed with acute VTE during hospitalization for COVID-19. Additional studies are needed to identify the optimal strategies to prevent VTE and to mitigate adverse outcomes associated.