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Updated: Oct 31, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Multi-dimensional and longitudinal systems profiling reveals predictive pattern of severe COVID-19
Marcel S Woo1, Friedrich Haag2, Axel Nierhaus3
1Institute of Neuroimmunology and Multiple Sclerosis (INIMS), Center for Molecular Neurobiology Hamburg (ZMNH), University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany.
Abstract:
COVID-19 is a respiratory tract infection that can affect multiple organ systems. Predicting the severity and clinical outcome of individual patients is a major unmet clinical need that remains challenging due to intra- and inter-patient variability. Here, we longitudinally profiled and integrated more than 150 clinical, laboratory, and immunological parameters of 173 patients with mild to fatal COVID-19. Using systems biology, we detected progressive dysregulation of multiple parameters indicative of organ damage that correlated with disease severity, particularly affecting kidneys, hepatobiliary system, and immune landscape. By performing unsupervised clustering and trajectory analysis, we identified T and B cell depletion as early indicators of a complicated disease course. In addition, markers of hepatobiliary damage emerged as robust predictor of lethal outcome in critically ill patients. This allowed us to propose a novel clinical COVID-19 SeveriTy (COST) score that distinguishes complicated disease trajectories and predicts lethal outcome in critically ill patients.
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