Mental State of Inpatients with COVID-19: a Computational Psychiatry Approach
Abstract
The overload of healthcare systems around the world and the danger of infection have limited the ability of researchers to obtain sufficient and reliable data on psychopathology in hospitalized patients with COVID-19. The relationship between severe SARS-CoV-2 infection and specific mental disturbances remains poorly understood.
Aim
to reveal the possibility of identifying the typology and frequency of psychiatric syndromes associated with acute COVID-19 using cluster analysis of discrete psychopathological phenomena.
Materials and methods
Descriptive data on the mental state of 55 inpatients with COVID-19 were obtained by young-career physicians with psychiatric backgrounds. Classification of observed clinical phenomena was performed with k-means cluster analysis of variables codded from the main psychopathological symptoms. Dispersion analysis with p-level 0.05 was used to reveal the cluster’s differences in demography, parameters of inflammation and respiration function collected on the basis of the original medical records.
Results
Three resulting clusters of patients were identified: persons with anxiety, disorders of fluency and tempo of thinking, mood, attention, motor-volitional sphere, reduced insight, and pessimistic plans for the future (n=11); persons without psychopathology (n=37); persons with disorientation, disorders of memory, attention, fluency, and tempo of thinking, reduced insight (n=7). The development of a certain type of impaired mental state was specifically associated with: age, lung lesions according to computed tomography, saturation, respiratory rate, C-reactive protein level, platelet count.
Conclusion
The prevalence and typology of psychiatric disorders in patients with acute COVID-19 were described using the computational psychiatry approach.
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