An automated system for identifying patients at risk for complications associated with the use of mechanical ventilators provided significantly more accurate results than did traditional surveillance methods, which rely on manual recording and interpretation of individual patient data. In their paper published in Infection Control & Hospital Epidemiology, a Massachusetts General Hospital (MGH) research team report that their system—using an algorithm developed through a collaboration among the hospital’s Division of Infectious Diseases, Infection Control Unit, and the Clinical Data Animation Center (CDAC)—was 100% accurate in identifying at-risk patients when provided with necessary data.

“Ventilator-associated pneumonia is a very serious problem that is estimated to develop in up to half the patients receiving mechanical ventilator support,” says Brandon Westover, MD, PhD, of the MGH Department of Neurology, director of the CDAC and co-senior author of the report. “Many patients die each year from ventilator-associated pneumonia, which can be prevented by following good patient care practices, such as keeping the head of the bed elevated and taking measures to prevent the growth of harmful bacteria in patients’ airways.”

Traditional surveillance of patients receiving mechanical ventilation involves manual recording every 12 hours, usually by a respiratory therapist, of ventilator settings—which are adjusted throughout the day to accommodate the patient’s needs. Those settings, which reflect the pressure required to keep a patient’s lungs open at the end of a breath and the percentage of oxygen being delivered to the patient, are reviewed by an infection control practitioner for signs that indicate possible ventilator-associated pneumonia.

Lead and corresponding author Erica Shenoy, MD, PhD, of the MGH Division of Infectious Diseases, the Infection Control Unit, and hospital epidemiology lead for CDAC says, “In our study, manual surveillance made many more errors than automated surveillance—including false-positives; reporting cases that on review, did not meet criteria for what are called ventilator-associated events; misclassifications, reporting an event as more or less serious than it really was; and failure to detect and report cases that, on closer inspection, actually met criteria. In contrast, so long as the necessary electronic data were available, the automated method performed perfectly.”