• Vitaly Herasevich, Mykola Tsapenko, Marija Kojicic, Adil Ahmed, Rachul Kashyap, Chakradhar Venkata, Khurram Shahjehan, Sweta J Thakur, Brian W Pickering, Jiajie Zhang, Rolf D Hubmayr, and Ognjen Gajic.
• Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, and Multidisciplinary Epidemiology and Translational Research in Intensive Care (METRIC), Mayo Clinic College of Medicine, Rochester, MN, USA. herasevich.vitaly@mayo.edu
• Crit. Care Med. 2011 Jan 1;39(1):34-9.

BackgroundTo improve the safety of ventilator care and decrease the risk of ventilator-induced lung injury, we designed and tested an electronic algorithm that incorporates patient characteristics and ventilator settings, allowing near-real-time notification of bedside providers about potentially injurious ventilator settings.MethodsElectronic medical records of consecutive patients who received invasive ventilation were screened in three Mayo Clinic Rochester intensive care units. The computer system alerted bedside providers via the text paging notification about potentially injurious ventilator settings. Alert criteria included a Pao2/Fio2 ratio of <300 mm Hg, free text search for the words "edema" or "bilateral + infiltrates" on the chest radiograph report, a tidal volume of >8 mL/kg predicted body weight (based on patient gender and height), a plateau pressure of >30 cm H2O, and a peak airway pressure of >35 cm H2O. Respiratory therapists answered a brief online satisfaction survey. Ventilator-induced lung injury risk was compared before and after the introduction of ventilator-induced lung injury alert.FindingsThe prevalence of acute lung injury was 42% (n = 490) among 1,159 patients receiving >24 hrs of invasive ventilation. The system sent 111 alerts for 80 patients, with a positive predictive value of 59%. The exposure to potentially injurious ventilation decreased after the intervention from 40.6 ± 74.6 hrs to 26.9 ± 77.3 hrs (p = .004).InterpretationsElectronic medical record surveillance of mechanically ventilated patients accurately detects potentially injurious ventilator settings and is able to influence bedside practice at moderate costs. Its implementation is associated with decreased patient exposure to potentially injurious mechanical ventilation settings.

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