The Association Between Oxygenation Status at 24 h After Diagnosis of Pulmonary Acute Respiratory Distress Syndrome and the 30-Day Mortality among Pediatric Oncological Patients

BACKGROUND: Pediatric oncology patients with acute respiratory distress syndrome (ARDS) secondary to pneumonia are at high risk of mortality. Our aim was to describe the epidemiology of ARDS in this clinical population and to identify the association between the oxygenation status at 24 h after diagnosis and the 30-day mortality rates, stratified by the severity of ARDS. METHODS: This was a retrospective cohort study of 82 pediatric oncology patients, with a median age of 4 years, admitted to our pediatric intensive care unit with a diagnosis of ARDS between 2013 and 2021. Demographic and clinical factors were compared between the survivor (n = 52) and non-survivor (n = 30) groups. Univariate and multivariate Cox proportional hazards regression models were used to determine the association between the oxygenation status at 24 h after diagnosis and the 30-day mortality rates. RESULTS: The mean airway pressure at ARDS diagnosis, PaO(2)/FiO(2) (P/F) ratio, oxygenation index (OI) value, peak inspiratory pressure, and lactate level at 24 h after ARDS diagnosis, as well as complications (i.e., septicemia and more than two extrapulmonary organ failures) and adjunctive continuous renal replacement therapy, were significant mortality risk factors. After adjusting for other covariates, the oxygenation status P/F ratio (Hazard ratio [HR] = 0.98, 95% confidence interval [CI] = 0.96–1.00, P = 0.043) and OI value (HR = 1.12, 95% CI = 1.02–1.23, P = 0.016) at 24 h remained independent mortality risk factors. According to the Kaplan–Meier survival curve, a low P/F ratio (≤ 150) and high OI (>10) were associated with a higher risk of 30-day mortality (50.9 and 52.9%, respectively; both P < 0.05) CONCLUSION: The P/F ratio and OI value measured at 24 h after ARDS diagnosis can provide a better stratification of patients according to ARDS disease severity to predict the 30-day mortality risk.