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 nonsurvivors. 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 (BI) score. When the duration, age increased by 1 day, 1 year, BI decreased by 1 point, the mortality increased by 3.6%, 2.4%, and 0.9% respectively. Laboratory‐examination indicators including C‐reactive protein, white blood cell count, platelet count, fibrin degradation products, oxygenation index, 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.

Background: Emerging functional imaging studies suggest that schizophrenia is associated with aberrant spatiotemporal interaction which may result in aberrant global and local dynamic properties.Methods: We investigated the dynamic functional connectivity (FC) by using instantaneous phase method based on Hilbert transform to detect abnormal spatiotemporal interaction in schizophrenia. Based on resting-state functional magnetic resonance imaging, two independent datasets were included, with 114 subjects from COBRE [51 schizophrenia patients (SZ) and 63 healthy controls (HCs)] and 96 from OpenfMRI (36 SZ and 60 HCs). Phase differences and instantaneous coupling matrices were firstly calculated at all time points by extracting instantaneous parameters. Global [global synchrony and intertemporal closeness (ITC)] and local dynamic features [strength of FC (sFC) and variability of FC (vFC)] were compared between two groups. Support vector machine (SVM) was used to estimate the ability to discriminate two groups by using all aberrant features.Results: We found SZ had lower global synchrony and ITC than HCs on both datasets. Furthermore, SZ had a significant decrease in sFC but an increase in vFC, which were mainly located at prefrontal cortex, anterior cingulate cortex, temporal cortex and visual cortex or temporal cortex and hippocampus, forming significant dynamic subnetworks. SVM analysis revealed a high degree of balanced accuracy (85.75%) on the basis of all aberrant dynamic features.Conclusions: SZ has worse overall spatiotemporal stability and extensive FC subnetwork lesions compared to HCs, which to some extent elucidates the pathophysiological mechanism of schizophrenia, providing insight into time-variation properties of patients with other mental illnesses.