Longitudinal analysis of ARDS variability and biomarker predictive power in burn patients

• Published in: Scientific reports Vol. 14; no. 1; pp. 26376 - 13
• Main Authors: Yoon, Jaechul, Kym, Dohern, Cho, Yong Suk, Hur, Jun, Yoon, Dogeon
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.11.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308; 692/499; Accuracy; Albumin; ARDS; Biomarkers; Burn patients; Heterogeneity; Humanities and Social Sciences; Hypoxemia; Intensive care; Leukocytes; Mortality; multidisciplinary; Patients; Precision medicine; Prediction models; Respiratory distress syndrome; Retrospective cohort study; Science; Science (multidisciplinary); Heterogeneity; Precision medicine; Retrospective cohort study; Biomarkers; Clustering analysis; Burn patients; ARDS
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-77188-x

Acute Respiratory Distress Syndrome (ARDS) is a severe complication in burn patients, characterized by rapid lung inflammation and hypoxemia. Managing ARDS is challenging due to its high mortality rates and the complex interplay of various pathophysiological factors. This study aims to investigate the heterogeneity of ARDS in burn patients admitted to the Intensive Care Unit (ICU) and evaluate the predictive efficacy of biomarkers in comparison to the PaO2/FiO2 (PF) ratio. A retrospective cohort study was conducted at the Burn Intensive Care Unit of Hangang Sacred Heart Hospital in Seoul, Korea, from July 2010 to December 2022, involving 2,318 patients. Longitudinal k-means clustering was employed to identify patient subgroups based on PF ratio trajectories. Biomarkers, including albumin, white blood cell (WBC) count, and lactate, were assessed for their predictive accuracy using multivariable logistic regression, area under the curve (AUC) analysis, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Four distinct patient clusters were identified. Cluster A exhibited the highest mortality rate at 54.9%, while Cluster D showed the lowest at 7.9%. Albumin demonstrated significantly higher predictive accuracy in Cluster A (AUC: 0.905, NRI: 0.421) and Cluster C (AUC: 0.915, NRI: 0.845), outperforming the PF ratio in both groups. However, in Clusters B and D, biomarkers did not significantly improve upon the predictive power of the PF ratio. Across all clusters, the integration of biomarkers with the PF ratio led to modest improvements but did not consistently outperform the PF ratio as a standalone predictor. This study reveals substantial heterogeneity in ARDS progression among burn patients, with varying mortality rates and biomarker efficacy across clusters. While biomarkers such as albumin showed potential in specific subgroups, their overall contribution to predictive accuracy was limited. Further multicenter, prospective studies are required to validate these findings and develop more refined predictive models. Personalized treatment strategies, based on biomarker profiles and traditional clinical metrics, could enhance ARDS management in burn patients.