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Ventilator-associated pneumonia is the second most common nosocomial infection in pediatric intensive care. The Centers for Disease Control and Prevention recently issued diagnosis criteria for pediatric ventilator-associated pneumonia and for ventilator-associated events in adults. The objectives of this pediatric study were to determine the prevalence of ventilator-associated pneumonia using these new Centers for Disease Control and Prevention criteria, to describe the risk factors and management of ventilator-associated pneumonia, and to assess a simpler method to detect ventilator-associated pneumonia with ventilator-associated event in critically ill children.Retrospective, observational, single-center.PICU in a tertiary-care university hospital.Consecutive critically ill children mechanically ventilated for greater than or equal to 48 hours between November 2013 and November 2015.None.Of 304 patients mechanically ventilated for greater than or equal to 48 hours, 284 were included. Among them, 30 (10.6%) met clinical and radiologic Centers for Disease Control and Prevention criteria for ventilator-associated pneumonia, yielding an prevalence of 7/1,000 mechanical ventilation days. Median time from mechanical ventilation onset to ventilator-associated pneumonia diagnosis was 4 days. Semiquantitative culture of tracheal aspirates was the most common microbiological technique. Gram-negative bacteria were found in 60% of patients, with a predominance of Haemophilus influenzae and Pseudomonas aeruginosa. Antibiotic therapy complied with adult guidelines. Compared with patients without ventilator-associated pneumonia, those with ventilator-associated pneumonia had significantly longer median durations of mechanical ventilation (15 vs 6 d; p0.001) and PICU stay (19 vs 9 d; p0.001). By univariate analysis, risk factors for ventilator-associated pneumonia were younger age, reintubation, acute respiratory distress syndrome, and continuous enteral feeding. Among the 30 patients with ventilator-associated pneumonia, 17 met adult ventilator-associated event's criteria (sensitivity, 56%).Ventilator-associated pneumonia is associated with longer times on mechanical ventilation and in the PICU. Using the ventilator-associated event criteria is of interest to rapidly screen for ventilator-associated pneumonia in children. However, sensitivity must be improved by adapting these criteria to children.

Our objective was to construct a prospective high-quality and high-frequency database combining patient therapeutics and clinical variables in real time, automatically fed by the information system and network architecture available through fully electronic charting in our PICU. The purpose of this article is to describe the data acquisition process from bedside to the research electronic database.Descriptive report and analysis of a prospective database.A 24-bed PICU, medical ICU, surgical ICU, and cardiac ICU in a tertiary care free-standing maternal child health center in Canada.All patients less than 18 years old were included at admission to the PICU.None.Between May 21, 2015, and December 31, 2016, 1,386 consecutive PICU stays from 1,194 patients were recorded in the database. Data were prospectively collected from admission to discharge, every 5 seconds from monitors and every 30 seconds from mechanical ventilators and infusion pumps. These data were linked to the patient's electronic medical record. The database total volume was 241 GB. The patients' median age was 2.0 years (interquartile range, 0.0-9.0). Data were available for all mechanically ventilated patients (n = 511; recorded duration, 77,678 hr), and respiratory failure was the most frequent reason for admission (n = 360). The complete pharmacologic profile was synched to database for all PICU stays. Following this implementation, a validation phase is in process and several research projects are ongoing using this high-fidelity database.Using the existing bedside information system and network architecture of our PICU, we implemented an ongoing high-fidelity prospectively collected electronic database, preventing the continuous loss of scientific information. This offers the opportunity to develop research on clinical decision support systems and computational models of cardiorespiratory physiology for example.