Despite declining mortality, acute respiratory distress syndrome is still involved in up to one third of pediatric intensive care deaths. The recently convened Pediatric Acute Lung Injury Consensus Conference has outlined research priorities for the field, which include the need for accurate bedside risk stratification of patients. We aimed to develop a simple yet robust model of mortality risk among pediatric patients with acute respiratory distress syndrome to facilitate the targeted application of high-risk investigational therapies and stratification for enrollment in clinical trials.
Prospective, multicenter cohort.
Five academic PICUs.
Three hundred eight children greater than 1 month and less than or equal to 18 years old, admitted to the ICU, with bilateral infiltrates on chest radiograph and Pao2/Fio2 ratio less than 300 in the clinical absence of left atrial hypertension.
Measurements and Main Results:
Twenty clinical variables were recorded in the following six categories: demographics, medical history, oxygenation, ventilation, radiographic imaging, and multiple organ dysfunction. Data were measured 0–24 and 48–72 hours after acute respiratory distress syndrome onset (day 1 and 3) and examined for associations with hospital mortality. Among 308 enrolled patients, mortality was 17%. Children with a history of cancer and/or hematopoietic stem cell transplant had higher mortality (47% vs 11%; p < 0.001). Oxygenation index, the Pao2/Fio2 ratio, extrapulmonary organ dysfunction, Pediatric Risk of Mortality-3, and positive cumulative fluid balance were each associated with mortality. Using two statistical approaches, we found that a parsimonious model of mortality risk using only oxygenation index and cancer/hematopoietic stem cell transplant history performed as well as other more complex models that required additional variables.
In the PICU, oxygenation index and cancer/hematopoietic stem cell transplant history can be used on acute respiratory distress syndrome day 1 or day 3 to predict hospital mortality without the need for more complex models. These findings may simplify risk assessment for clinical trials, counseling families, and high-risk interventions such as extracorporeal life support.