Automated System Identifies Ventilated Patients at Risk

An automated system for detecting mechanically ventilated patients at risk of ventilator-associated events surpasses traditional surveillance methods, according to a new study.

Developed at Massachusetts General Hospital (MGH; Boston, USA), the Georgia Institute of Technology (Atlanta, USA), and Harvard Medical School (HMS; Boston, MA, USA), the automated process uses an algorithm that analyses physiologic data to detect increases in positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (FiO2); queries the electronic health record (EHR) for leukopenia or leukocytosis and antibiotic initiation data; and retrieves and interprets microbiology reports.

Based on the data, the algorithm can determine whether criteria were met for a ventilator-associated event and, if so, which level of event:

• A ventilator-associated condition (VAC), an increase in a patient's need for oxygen without evidence of infection.
• An infection-related ventilator-associated complication (IVAC), with increased oxygen need accompanied by signs of infection, such as fever, elevated white blood cell count or an antibiotic prescription.
• And possible ventilator-associated pneumonia (VAP), with evidence of bacterial growth in the respiratory system, along with the factors listed above.

A validation study to test the algorithm followed 1,234 patients admitted to the intensive care unit (ICU), 431 of who received ventilator support. More...