To date, the coronavirus disease 2019 (COVID‐19) has a worldwide distribution. Risk factors for mortality in critically ill patients, especially detailed self‐evaluation indicators and laboratory‐examination indicators, have not been well described. In this paper, a total of 192 critically ill patients (142 were discharged and 50 died in the hospital) with COVID‐19 were included. Self‐evaluation indicators including demographics, baseline characteristics and symptoms and detailed lab‐examination indicators were extracted. Data were first compared between survivors and non‐survivors. Multivariate pattern analysis (MVPA) was performed to identify possible risk factors for mortality of COVID‐19 patients. MVPA achieved a relatively high classification accuracy of 93% when using both self‐evaluation indicators and laboratory‐examination indicators. Several self‐evaluation factors related to COVID‐19 were highly associated with mortality, including age, duration (time from illness onset to admission), and the Barthel index score. When the duration, age and Barthel index increased by one day, one year and one point, the mortality increased by 3.6%, 2.4% and 0.9% respectively. Laboratory‐examination indicators including C‐reactive protein (CRP), white blood cell (WBC) count, platelet count, fibrin degradation products (FDP), oxygenation index (OI), lymphocyte count and D‐dimer were also risk factors. Among them, duration was the strongest predictor of all‐cause mortality. Several self‐evaluation indicators that can simply be obtained by questionnaires and without clinical examination were the risk factors of all‐cause mortality in critically ill COVID‐19 patients. The prediction model can be used by individuals to improve health awareness, and by clinicians to identify high‐risk individuals.
This article is protected by copyright. All rights reserved.
Background: The functional dysconnectivity observed from functional magnetic resonance imaging (fMRI) studies in schizophrenia is also seen in unaffected siblings indicating its association with the genetic diathesis. We intended to apportion resting-state dysconnectivity into components that represent genetic diathesis, clinical expression or treatment effect, and resilience. Methods: fMRI data were acquired from 28 schizophrenia patients, 28 unaffected siblings, and 60 healthy controls. Based on Dosenbach’s atlas, we extracted time series of 160 regions of interest. After constructing functional network, we investigated between-group differences in strength and diversity of functional connectivity and topological properties of undirected graphs. Results: Using analysis of variance, we found 88 dysconnectivities. Post hoc t tests revealed that 62.5% were associated with genetic diathesis and 21.6% were associated with clinical expression. Topologically, we observed increased degree, clustering coefficient, and global efficiency in the sibling group compared to both patients and controls. Conclusion: A large portion of the resting-state functional dysconnectivity seen in patients represents a genetic diathesis effect. The most prominent network-level disruption is the dysconnectivity among nodes of the default mode and salience networks. Despite their predisposition, unaffected siblings show a pattern of resilience in the emergent connectomic topology. Our findings could potentially help refine imaging genetics approaches currently used in the pursuit of the pathophysiology of schizophrenia.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.