DESIGN: A multicenter, retrospective, descriptive cohort study.

SETTING: Ten multidisciplinary PICUs in Asia.

PATIENTS: All mechanically ventilated children meeting the Pediatric Acute Lung Injury Consensus Conference criteria for PARDS between 2009 and 2015.

INTERVENTIONS: None.

MEASUREMENTS AND MAIN RESULTS: Data on epidemiology, ventilation, adjunct therapies, and clinical outcomes were collected. Patients were followed for 100 days post diagnosis of PARDS. A total of 373 patients were included. There were 89 (23.9%), 149 (39.9%), and 135 (36.2%) patients with mild, moderate, and severe PARDS, respectively. The most common risk factor for PARDS was pneumonia/lower respiratory tract infection (309 [82.8%]). Higher category of severity of PARDS was associated with lower ventilator-free days (22 [17-25], 16 [0-23], 6 [0-19]; p < 0.001 for mild, moderate, and severe, respectively) and PICU free days (19 [11-24], 15 [0-22], 5 [0-20]; p < 0.001 for mild, moderate, and severe, respectively). Overall PICU mortality for PARDS was 113 of 373 (30.3%), and 100-day mortality was 126 of 317 (39.7%). After adjusting for site, presence of comorbidities and severity of illness in the multivariate Cox proportional hazard regression model, patients with moderate (hazard ratio, 1.88 [95% CI, 1.03-3.45]; p = 0.039) and severe PARDS (hazard ratio, 3.18 [95% CI, 1.68, 6.02]; p < 0.001) had higher risk of mortality compared with those with mild PARDS.

DESIGN: This is a secondary analysis of a multicenter, retrospective, cohort study. Data on epidemiology, ventilation, therapies, and outcomes were collected and analyzed. Patients were classified into two mutually exclusive groups (extrapulmonary pediatric acute respiratory distress syndrome and pulmonary pediatric acute respiratory distress syndrome) based on etiologies. Primary outcome was PICU mortality. Cox proportional hazard regression was used to identify risk factors for mortality.

SETTING: Ten multidisciplinary PICUs in Asia.

PATIENTS: Mechanically ventilated children meeting the Pediatric Acute Lung Injury Consensus Conference criteria for pediatric acute respiratory distress syndrome between 2009 and 2015.

INTERVENTIONS: None.

MEASUREMENTS AND MAIN RESULTS: Forty-one of 307 patients (13.4%) and 266 of 307 patients (86.6%) were classified into extrapulmonary pediatric acute respiratory distress syndrome and pulmonary pediatric acute respiratory distress syndrome groups, respectively. The most common causes for extrapulmonary pediatric acute respiratory distress syndrome and pulmonary pediatric acute respiratory distress syndrome were sepsis (82.9%) and pneumonia (91.7%), respectively. Children with extrapulmonary pediatric acute respiratory distress syndrome were older, had higher admission severity scores, and had a greater proportion of organ dysfunction compared with pulmonary pediatric acute respiratory distress syndrome group. Patients in the extrapulmonary pediatric acute respiratory distress syndrome group had higher mortality (48.8% vs 24.8%; p = 0.002) and reduced ventilator-free days (median 2.0 d [interquartile range 0.0-18.0 d] vs 19.0 d [0.5-24.0 d]; p = 0.001) compared with the pulmonary pediatric acute respiratory distress syndrome group. After adjusting for site, severity of illness, comorbidities, multiple organ dysfunction, and severity of acute respiratory distress syndrome, extrapulmonary pediatric acute respiratory distress syndrome etiology was not associated with mortality (adjusted hazard ratio, 1.56 [95% CI, 0.90-2.71]).

METHODS: Patients with PARDS from 10 paediatric intensive care units across Asia from 2009 to 2015 were identified. Data on epidemiology and clinical outcomes were collected. Patients on HFOV were compared to patients on other modes of ventilation. The primary outcome was 28-day mortality and secondary outcomes were 28-day ventilator- (VFD) and intensive care unit- (IFD) free days. Genetic matching (GM) method was used to analyse the association between HFOV treatment with the primary outcome. Additionally, we performed a sensitivity analysis, including propensity score (PS) matching, inverse probability of treatment weighting (IPTW) and marginal structural modelling (MSM) to estimate the treatment effect.