Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

An International Study on Psychological Coping During COVID-19: Towards a Meaning-Centered Coping Style

Background/Objective This study examined the role of different psychological coping mechanisms in mental and physical health during the initial phases of the COVID-19 crisis with an emphasis on meaning-centered coping. Method A total of 11,227 people from 30 countries across all continents participated in the study and completed measures of psychological distress (depression, stress, and anxiety), loneliness, well-being, and physical health, together with measures of problem-focused and emotion-focused coping, and a measure called the Meaning-centered Coping Scale (MCCS) that was developed in the present study. Validation analyses of the MCCS were performed in all countries, and data were assessed by multilevel modeling (MLM). Results The MCCS showed a robust one-factor structure in 30 countries with good test-retest, concurrent and divergent validity results. MLM analyses showed mixed results regarding emotion and problem-focused coping strategies. However, the MCCS was the strongest positive predictor of physical and mental health among all coping strategies, independently of demographic characteristics and country-level variables. Conclusions The findings suggest that the MCCS is a valid measure to assess meaning-centered coping. The results also call for policies promoting effective coping to mitigate collective suffering during the pandemic

Efficacy of a Combined Acceptance and Commitment Intervention to Improve Psychological Flexibility and Associated Symptoms in Cancer Patients: Study Protocol for a Randomized Controlled Trial.

Psychological flexibility is a key concept of acceptation and commitment therapy (ACT). This factor has been linked with psychological wellbeing and associated factors, such as quality of life, in cancer patients. These and other positive results of acceptation and commitment therapy in cancer patients found in previous research could be enhanced by using mhealth tools. A three-arm randomized superiority clinical trial, with a pre-post-follow-up repeated measures intergroup design with a 1:1:1 allocation ratio is proposed. A hundred and twenty cancer patients will be randomly assigned to one of the following interventions: (1) face-to-face ACT + mobile application (app), (2) face-to-face ACT, and (3) Waitlist control group. The primary expected outcome is to observe significant improvements in psychological flexibility acceptance and action questionnaire- II (AAQ-II) in the face-to-face ACT + app group, after comparing baseline and post-treatment scores, and the scores will remain stable in the two assessment points, 3 and 6 months after the intervention. Secondary expected outcomes are significant increasing scores in quality of life (EORTC QLQ C-30) and post-traumatic-growth (PTGI-SF), and significant decreasing scores in anxiety and depression (HADS), insomnia (ISI) and fatigue (BFI) at the same assessment points. Also, it is expected that the scores of this group will be higher than the scores of the face-to-face ACT group and the waitlist control group. This study aims to assess the efficacy of a combined intervention (face-to face ACT + app) for psychological flexibility and associated symptoms in cancer patients. The results of this protocol may help to consider the use of acceptation and commitment therapy and mhealth applications in cancer settings as a valid therapeutic choice. [www.ClinicalTrials.gov], identifier [NCT05126823]

Webscraping de Facebook con la liberia Selenium - Autobot

Este semestre 2022-2 en el semillero de mercadeo nos dedicamos a raspar la informacion de la red social Facebook con fines de mercadeo digital, para aplicar la libreria Selenium (lenguaje de programación Python) creando un auto-Bot2022-I

Efectividad de los glucocorticoides en pacientes hospitalizados por neumonía grave por SARS-CoV-2

Several studies have reported the beneficial effect of glucocorticoids in the treatment of cytokine storm that occurs in patients with severe COVID-19. Various glucocorticoids regimens have been proposed. Methods Retrospective observational study that includes patients with severe SARS-CoV-2 pneumonia and compares admission to an Intensive Care Unit (ICU) or death during hospitalization in three groups of patients: no glucocorticoids treatment, use of glucocorticoids doses equivalent to less than 250 mg of prednisone daily and use of equivalent doses greater than or equal to 250 mg of prednisone daily. Multivariate analysis was performed using logistic regression, using the propensity index as a covariant. Results Of the 259 patients enrolled in the study, 67 (25.9%) had an unfavorable evolution, dying or requiring ICU admission. Comparative analyzes between different glucocorticoids treatments and the association with ICU admission or death were: glucocorticoids treatment (any dose) versus no glucocorticoids treatment (OR: 0.71 [0.30–1.66]), treatment with glucocorticoids (≥250 mg prednisone daily) versus no glucocorticoids treatment (OR: 0.35 [0.11–1.08]) and glucocorticoids treatment (≥250 mg prednisone daily) versus patients with glucocorticoids doses <250 mg prednisone daily or without glucocorticoids treatment (OR: 0.30 [0.10–0.88]). Conclusion The results of this study show that patients with severe SARS-CoV-2 pneumonia treated with glucocorticoids pulses with equivalent doses of prednisone greater than or equal to 250 mg have a more favorable evolution (less mortality and less admission to ICU).Sin financiación3.200 JCR (2021) Q2, 75/172 Medicine, General & Internal0.325 SJR (2021) Q3, 1635/2489 Medicine (miscellaneous)No data IDR 2020UE

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts