Scientists at Northwestern University Feinberg School of Medicine used machine learning to analyze medical records of patients with COVID-19 and found that almost half of them who required mechanical ventilation also developed a secondary bacterial infection of the lung (pneumonia). This secondary pneumonia was a key driver of death in patients with COVID-19 and may even exceed death rates from the viral infection itself. The study found that patients who were cured of their secondary pneumonia were more likely to live, while those whose pneumonia did not resolve were more likely to die. The researchers used a new machine learning approach called CarpeDiem to analyze how complications like bacterial pneumonia impacted the course of illness. The findings negate the cytokine storm theory and the next step in the research will be to use molecular data from the study samples and integrate it with machine learning approaches.
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