Objective: To specify the degree of probative force of the statistical hypotheses in relation to mortality at 28 days and the threshold value of 17 J/min mechanical power (MP) in patients with respiratory failure secondary to SARS-CoV-2.
Design: Cohort study, longitudinal, analytical.
Setting: Intensive care unit of a third level hospital in Spain.
Patients: Patients admitted for SARS-CoV-2 infection with admission to the ICU between March 2020 and March 2022.
Interventions: Bayesian analysis with the beta binomial model.
Main Variables Of Interest: Bayes factor, mechanical power.
Results: A total of 253 patients were analyzed. Baseline respiratory rate (BF: 3.83 × 10), peak pressure value (BF: 3.72 × 10) and neumothorax (BF: 17,663) were the values most likely to be different between the two groups of patients compared. In the group of patients with MP < 17 J/min, a BF of 12.71 and a BF of 0.07 were established with an 95%CI of 0.27-0.58. For the group of patients with MP ≥ 17 J/min the BF was 36,100 and the BF of 2.77e-05 with an 95%CI of 0.42-0.72.
Conclusions: A MP ≥ 17 J/min value is associated with extreme evidence with 28-day mortality in patients requiring MV due to respiratory failure secondary to SARS-CoV-2 disease.
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http://dx.doi.org/10.1016/j.medine.2023.03.016 | DOI Listing |
PLoS Med
January 2025
Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Background: Globally, over one-third of pulmonary tuberculosis (TB) disease diagnoses are made based on clinical criteria after a negative bacteriological test result. There is limited information on the factors that determine clinicians' decisions to initiate TB treatment when initial bacteriological test results are negative.
Methods And Findings: We performed a systematic review and individual patient data meta-analysis using studies conducted between January 2010 and December 2022 (PROSPERO: CRD42022287613).
J Exp Psychol Gen
January 2025
Centre for Perception and Cognition, School of Psychology, University of Southampton.
It has been claimed that deliberately making errors while studying, even when the correct answers are provided, can enhance memory for the correct answers, a phenomenon termed the derring effect. Such deliberate erring has been shown to outperform other learning techniques, including copying and underlining, elaborative studying with concept mapping, and synonym generation. To date, however, the derring effect has only been demonstrated by a single group of researchers and in a single population of participants.
View Article and Find Full Text PDFStrong sex differences exist in sleep phenotypes and also cardiovascular diseases (CVDs). However, sex-specific causal effects of sleep phenotypes on CVD-related outcomes have not been thoroughly examined. Mendelian randomization (MR) analysis is a useful approach for estimating the causal effect of a risk factor on an outcome of interest when interventional studies are not available.
View Article and Find Full Text PDFHeliyon
January 2025
Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.
Depression is more than just feeling sad. It is a severe and multifaceted mental health condition that impacts millions of individuals around the globe. Regrettably, it can even be more prevalent in university students of underdeveloped and developing countries like Bangladesh because of academic pressure, family and societal expectations, financial limitations, stigmatized social and cultural norms, unemployment concerns, lack of mental health awareness, etc.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
Department of Physiology, School of Basic Medical Sciences, Chengdu Medical College, Sichuan, 610500 China.
Unlabelled: Parkinson's disease (PD) is a neurodegenerative disease with various clinical manifestations caused by multiple risk factors. However, the effect of different factors and relationships between different features related to PD and the extent of those factors leading to the incidence of PD remains unclear. we employed Bayesian network to construct a prediction model.
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