High-Dose Intravenous Immunoglobulins in the Treatment of Severe Acute Viral Pneumonia: The Known Mechanisms and Clinical Effects

The current outbreak of viral pneumonia, caused by novel coronavirus SARS-CoV-2, is the focus of worldwide attention. The WHO declared the COVID-19 outbreak a pandemic event on Mar 12, 2020, and the number of confirmed cases is still on the rise worldwide. While most infected individuals only experience mild symptoms or may even be asymptomatic, some patients rapidly progress to severe acute respiratory failure with substantial mortality, making it imperative to develop an efficient treatment for severe SARS-CoV-2 pneumonia alongside supportive care. So far, the optimal treatment strategy for severe COVID-19 remains unknown. Intravenous immunoglobulin (IVIg) is a blood product pooled from healthy donors with high concentrations of immunoglobulin G (IgG) and has been used in patients with autoimmune and inflammatory diseases for more than 30 years. In this review, we aim to highlight the known mechanisms of immunomodulatory effects of high-dose IVIg therapy, the immunopathological hypothesis of viral pneumonia, and the clinical evidence of IVIg therapy in viral pneumonia. We then make cautious therapeutic inferences about high-dose IVIg therapy in treating severe COVID-19. These inferences may provide relevant and useful insights in order to aid treatment for COVID-19.

High-dimensional therapeutic inference in the focally damaged human brain

See Thiebaut de Schotten and Foulon (doi:10.1093/brain/awx332) for a scientific commentary on this article.Though consistency across the population renders the extraordinarily complex functional anatomy of the human brain surveyable, the inverse inference-from common functional maps to individual behaviour-is constrained by marked individual deviation from the population mean. Such inference is fundamental to the evaluation of therapeutic interventions in focal brain injury, where the impact of an induced structural change in the brain is quantified by its behavioural consequences, inevitably refracted through the lens of lesion-outcome relations. Current therapeutic evaluations do not incorporate inferences to the individual outcome derived from a detailed specification of the lesion anatomy, relying only on reductive parameters such as lesion volume and crudely discretised location. Examining 1172 patients with anatomically registered focal brain lesions, here we show that such low-dimensional models are highly insensitive to therapeutic effects. In contrast, high-dimensional models supported by machine learning dramatically improve sensitivity by leveraging complex individuating patterns in the functional architecture of the brain. The failure to replicate in humans positive interventional effects in experimental animals is thus revealed to have a remediable inferential cause, forcing a radical re-evaluation of therapeutic inference in the human brain.