Secondary bacterial infection of the lung (pneumonia) was extremely common in patients with COVID-19, affecting almost half the patients who required support from mechanical ventilation. By applying machine learning to medical record data, scientists at Northwestern University Feinberg School of Medicine have found that secondary bacterial pneumonia that does not resolve was a key driver of death in patients with COVID-19, results published in the Journal of Clinical Investigation.
Bacterial infections may even exceed death rates from the viral infection itself, according to the findings. The scientists also found evidence that COVID-19 does not cause a “cytokine storm,” so often believed to cause death.
The investigators found nearly half of patients with COVID-19 develop a secondary ventilator-associated bacterial pneumonia.
The study analyzed 585 patients in the intensive care unit (ICU) at Northwestern Memorial Hospital with severe pneumonia and respiratory failure, 190 of whom had COVID-19. The scientists developed a new machine learning approach called CarpeDiem, which groups similar ICU patient-days into clinical states based on electronic health record data. This novel approach, which is based on the concept of daily rounds by the ICU team, allowed them to ask how complications like bacterial pneumonia impacted the course of the illness.
These patients or their surrogates consented to enroll in the Successful Clinical Response to Pneumonia Therapy (SCRIPT) study, an observational trial to identify new biomarkers and therapies for patients with severe pneumonia. As part of SCRIPT, an expert panel of ICU physicians used state-of-the-art analysis of lung samples collected as part of clinical care to diagnose and adjudicate the outcomes of secondary pneumonia events.
The application of machine learning and artificial intelligence to clinical data can be used to develop better ways to treat diseases like COVID-19 and to assist ICU physicians managing these patients.
The importance of bacterial superinfection of the lung as a contributor to death in patients with COVID-19 has been underappreciated, because most centers have not looked for it or only look at outcomes in terms of presence or absence of bacterial superinfection, not whether treatment is successful or not.
The next step in the research will be to use molecular data from the study samples and integrate it with machine learning approaches to understand why some patients go on to be cured of pneumonia and some don’t. Investigators also want to expand the technique to larger datasets and use the model to make predictions that can be brought back to the bedside to improve the care of critically ill patients.