Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative.

COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV-2 infection, which leads to a cytokine release syndrome also known as cytokine storm associated with the severity of the disease. Considering the importance of this event in the immunopathology of COVID-19, this study analyses cytokine levels of hospitalized patients to identify cytokine profiles associated with severity and mortality. Using a machine learning approach, 3 clusters of COVID-19 hospitalized patients were created based on their cytokine profile. Significant differences in the mortality rate were found among the clusters, associated to different CXCL10/IL-38 ratio. The balance of a CXCL10 induced inflammation with an appropriate immune regulation mediated by the anti-inflammatory cytokine IL-38 appears to generate the adequate immune context to overrule SARS-CoV-2 infection without creating a harmful inflammatory reaction. This study supports the concept that analyzing a single cytokine is insufficient to determine the outcome of a complex disease such as COVID-19, and different strategies incorporating bioinformatic analyses considering a broader immune profile represent a more robust alternative to predict the outcome of hospitalized patients with SARS-CoV-2 infection.

Inter-Rater Variability in the Evaluation of Lung Ultrasound in Videos Acquired from COVID-19 Patients

Lung ultrasound (LUS) allows for the detection of a series of manifestations of COVID-19, such as B-lines and consolidations. The objective of this work was to study the inter-rater reliability (IRR) when detecting signs associated with COVID-19 in the LUS, as well as the performance of the test in a longitudinal or transverse orientation. Thirty-three physicians with advanced experience in LUS independently evaluated ultrasound videos previously acquired using the ULTRACOV system on 20 patients with confirmed COVID-19. For each patient, 24 videos of 3 s were acquired (using 12 positions with the probe in longitudinal and transverse orientations). The physicians had no information about the patients or other previous evaluations. The score assigned to each acquisition followed the convention applied in previous studies. A substantial IRR was found in the cases of normal LUS (kappa = 0.74), with only a fair IRR for the presence of individual B-lines (kappa = 0.36) and for confluent B-lines occupying < 50% (kappa = 0.26) and a moderate IRR in consolidations and B-lines > 50% (kappa = 0.50). No statistically significant differences between the longitudinal and transverse scans were found. The IRR for LUS of COVID-19 patients may benefit from more standardized clinical protocols.

Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative

COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV-2 infection, which leads to a cytokine release syndrome also known as cytokine storm associated with the severity of the disease. Considering the importance of this event in the immunopathology of COVID-19, this study analyses cytokine levels of hospitalized patients to identify cytokine profiles associated with severity and mortality. Using a machine learning approach, 3 clusters of COVID-19 hospitalized patients were created based on their cytokine profile. Significant differences in the mortality rate were found among the clusters, associated to different CXCL10/IL-38 ratio. The balance of a CXCL10 induced inflammation with an appropriate immune regulation mediated by the anti-inflammatory cytokine IL-38 appears to generate the adequate immune context to overrule SARS-CoV-2 infection without creating a harmful inflammatory reaction. This study supports the concept that analyzing a single cytokine is insufficient to determine the outcome of a complex disease such as COVID-19, and different strategies incorporating bioinformatic analyses considering a broader immune profile represent a more robust alternative to predict the outcome of hospitalized patients with SARS-CoV-2 infection.

La externalización de los servicios de información turística en el contexto de la COVID-19

La legislación española permite a las administraciones públicas la externalización de determinados servicios a falta de medios propios para prestarlos. Los servicios de información turística son uno de los servicios que las administraciones suelen externalizar al requerir de una mayor flexibilidad horaria para atender las necesidades de los turistas. El objetivo general de este trabajo es analizar la tendencia a la externalización de los servicios de información turística y cómo la COVID-19 ha influido en la misma. Para ello se ha diseñado una investigación que analiza las licitaciones publicadas en la Plataforma de Contratación del Estado según el CPV 6351300 Servicios de información turística de 2018 a 2020. Los datos parecen sugerirnos la existencia de una tendencia positiva y que la COVID-19 ha influido levemente. También parece indicarnos que las administraciones públicas no utilizan de manera adecuada el código CPV en sus procesos de licitación aunque parece estar corrigiéndose.Alternate : Spanish legislation allows public authorities to outsource the provision of certain services when they lack their own means to do so Thus, tourist information services are one of the services that authorities tend to outsource because tourism’s broad and flexible time margins cannot always be covered by the public sector. In addition, tourism has been one of the economic sectors hardest hit by the recent COVID-19 pandemic, with significant effects on employment, demand and the arrival of international tourists, among others. Thus, the general objective of this work is focused on analysing the effect that the COVID-19 pandemic has had on the outsourcing of tourist information services. As such, an investigation has been designed that researches the tenders from the Spanish State Contract Platform from 2018 to 2020. The results seem to suggest a positive trend and that COVID-19 has had a slight influence. The research also seems to indicate that public administrations do not use the Common Procurement Vocabulary (CPV) code adequately in their tender processes, although it seems to be being corrected.

Prognostic factors in cancer patients infected with SARS-CoV-2: a Latin American country results.

PURPOSE: The aim of this study was to evaluate the demographic characteristics, clinical and pathological factors, and the outcome of cancer and COVID-19 patients in Mexico. PATIENTS AND METHODS: A prospective, multicentric study was performed through a digital platform to have a national registry of patients with cancer and positive SARS-CoV-2 test results through reverse transcription quantitative polymerase chain reaction (RT-qPCR). We performed the analysis through a multivariate logistic regression model and Cox proportional hazard model. RESULTS: From May to December 2020, 599 patients were registered with an average age of 56 years with 59.3% female; 27.2% had hypertension. The most frequent diagnoses were breast cancer (30.4%), lymphoma (14.7%), and colorectal cancer (14.0%); 72.1% of patients had active cancer and 23.5% of patients (141/599) were deceased, the majority of which were men (51.7%). This study found that the prognostic factors that reduced the odds of death were gender (OR = 0.42, p = 0.031) and oxygen saturation (OR = 0.90, p = 0.0001); meanwhile, poor ECOG (OR = 5.4, p = 0.0001), active disease (OR = 3.9, p = 0.041), dyspnea (OR = 2.5, p = 0.027), and nausea (OR = 4.0, p = 0.028) increased the odds of death. In the meantime, the factors that reduce survival time were age (HR = 1.36, p = 0.035), COPD (HR = 8.30, p = 0.004), having palliative treatment (HR = 10.70, p = 0.002), and active cancer without treatment (HR = 8.68, p = 0.008). CONCLUSION: Mortality in cancer patients with COVID-19 is determined by prognostic factors whose identification is necessary. In our cancer population, we have observed that being female, younger, non-COPD, with non-active cancer, good performance status, and high oxygen levels reduce the probability of death.