Unveiling the Truth Behind “Cytokine Storm”: Machine Learning Analyzes the Real Cause of COVID-19 Patient Deaths

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In a recent study published in The Journal of Clinical Investigation, scientists from Northwestern University Feinberg School of Medicine have arrived at a conclusion that debunks a widely believed concept that death from COVID-19 is due to a “cytokine storm” – a condition which involves an excessive release of cytokines in the body. The scientists instead found that the key driver of death in COVID-19 patients is a secondary bacterial infection of the lungs called pneumonia, which affects nearly half of the patients who require mechanical ventilation.

To reach their conclusion, the scientists used a machine learning approach known as CarpeDiem that groups similar ICU patient-days into clinical states according to electronic health record data. The data was gathered from the SCRIPT study, an observational trial that focuses on identifying new biomarkers and therapies for people suffering from severe pneumonia, including those afflicted with COVID-19. The study found that the mortality rates caused by the virus itself is relatively low if a secondary bacterial pneumonia is promptly treated and cured.

Senior author of the study Dr. Benjamin Singer, a Northwestern Medicine pulmonary and critical care physician, said that the application of machine learning and artificial intelligence to clinical data will result in better ways to treat COVID-19 patients in Intensive Care Units. Co-first author Dr. Catherine Gao, an instructor in the Department of Medicine and a Northwestern Medicine physician, commented on the importance of treating secondary bacterial pneumonia in such patients. Co-author Richard Wunderink, who leads the Successful Clinical Response in Pneumonia Therapy Systems Biology Center at Northwestern, also pointed out the need to look for and aggressively treat secondary pneumonia in patients with severe COVID-19.

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Currently, the study authors are looking to further develop their investigation by using molecular data from the samples and incorporating it with machine learning to determine why some patients are cured from pneumonia and some are not. Their end goal is to bring the new predictions to bedside to improve the care of critically ill patients.

The team from Northwestern University Feinberg School of Medicine is an internationally renowned leader in the field of clinical research. With experts from the fields of pulmonary and critical care, biochemistry and molecular genetics, the school provides students and fellows opportunities to participate in groundbreaking research and clinical training. With its cutting edge collaboration on many initiatives, the school strives to result in better healthcare solutions for the society.

Dr. Benjamin Singer, the senior author of the study, is a pulmonary and critical care physician with a huge amount of experience in this field. He holds particular expertise in the diagnosis and treatment of many respiratory conditions, including chronic and acute lung disease. He has sought to improve treatments for patients across the globe by conducting research on various subjects, making him an invaluable asset to Northwestern University and the medical field as a whole.

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