Yonsei Med J.  2023 Jun;64(6):384-394. 10.3349/ymj.2022.0653.

Radiologic Assessment of Lung Edema Score as a Predictor of Clinical Outcome in Children with Acute Respiratory Distress Syndrome

• Han CH
• Park M
• Kim H
• Roh YY
• Kim SY
• Kim JD
• Kim MJ
• Lee YJ
• Kim KW
• Kim YH

• KMID: 2542609
• DOI: http://doi.org/10.3349/ymj.2022.0653

Abstract

Purpose
The radiographic assessment of lung edema (RALE) score enables objective quantification of lung edema and is a valuable prognostic marker of adult acute respiratory distress syndrome (ARDS). We aimed to evaluate the validity of RALE score in children with ARDS.
Materials and Methods
The RALE score was measured for its reliability and correlation to other ARDS severity indices. ARDSspecific mortality was defined as death from severe pulmonary dysfunction or the need for extracorporeal membrane oxygenation therapy. The C-index of the RALE score and other ARDS severity indices were compared via survival analyses.
Results
Among 296 children with ARDS, 88 did not survive, and there were 70 ARDS-specific non-survivors. The RALE score showed good reliability with an intraclass correlation coefficient of 0.809 [95% confidence interval (CI), 0.760–0.848]. In univariable analysis, the RALE score had a hazard ratio (HR) of 1.19 (95% CI, 1.18–3.11), and the significance was maintained in multivariable analysis adjusting with age, ARDS etiology, and comorbidity, with an HR of 1.77 (95% CI, 1.05–2.91). The RALE score was a good predictor of ARDS-specific mortality, with a C-index of 0.607 (95% CI, 0.519–0.695).
Conclusion
The RALE score is a reliable measure for ARDS severity and a useful prognostic marker of mortality in children, especially for ARDS-specific mortality. This score provides information that clinicians can use to decide the proper time of aggressive therapy targeting severe lung injury and to appropriately manage the fluid balance of children with ARDS.

Keyword

Acute respiratory distress syndrome; children; critical care outcome; mechanical ventilator; mortality prediction