Limiting ventilator-induced lung injury through individual electronic medical record surveillance.
BACKGROUND
To 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.


METHODS
Electronic 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.


RESULTS
The 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).


CONCLUSIONS
Electronic 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.
Herasevich
Vitaly
V
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
Tsapenko
Mykola
M
Kojicic
Marija
M
Ahmed
Adil
A
Kashyap
Rachul
R
Venkata
Chakradhar
C
Shahjehan
Khurram
K
Thakur
Sweta J
SJ
Pickering
Brian W
BW
Zhang
Jiajie
J
Hubmayr
Rolf D
RD
Gajic
Ognjen
O
D000328:Adult; D000368:Aged; D016009:Chi-Square Distribution; D015331:Cohort Studies; D016638:Critical Illness / Q000628:therapy; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D007362:Intensive Care Units; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D019095:Point-of-Care Systems; D011237:Predictive Value of Tests; D011322:Primary Prevention / Q000379:methods; D011446:Prospective Studies; D012121:Respiration, Artificial / Q000009:adverse effects* / Q000379:methods; D055397:Ventilator-Induced Lung Injury / Q000517:prevention & control*