Ann Surg Treat Res.  2023 Feb;104(2):126-135. 10.4174/astr.2023.104.2.126.

Longitudinal profile of routine biomarkers for mortality prediction using unsupervised clustering algorithm in severely burned patients: a retrospective cohort study with prospectively collected data

• Yoon J
• Kym D
• Hur J
• Cho YS
• Chun W
• Yoon D

• KMID: 2539227
• DOI: http://doi.org/10.4174/astr.2023.104.2.126

Abstract

Purpose
Burn injury has high clinical heterogeneity and worse prognosis in severely burned patients. Clustering algorithms using unsupervised methods to identify groups with similar trajectories in heterogeneous disease patients can provide insight into mechanisms of disease pathogenesis. This study analyzed routinely collected biomarkers to evaluate mortality prediction, find clinical meanings for these or their subtypes, and evaluate patterns.
Methods
This retrospective cohort study included patients aged >18 years, between July 2012 and June 2021. All eligible patients received fluid resuscitation and survived for at least 7 days. Characteristics of clinical interest to the physician at 4 clinically important time points were evaluated.
Results
Eligible patients were divided into 4 subgroups according to these time points: from 1st week to 4th week. Total of 1,249 patients admitted within 2 days after burns and receiving fluid resuscitation were included. Mean Harrell’s C-index of pH was the highest (0.816), followed by platelets (0.807), creatinine (0.796), red cell distribution width (RDW, 0.778), and lactate (0.759). Longitudinal profiles among biomarkers were different.
Conclusion
The main predictors were pH, platelets, creatinine, RDW, and lactate. Creatinine and RDW showed consistent patterns. The other markers varied according to patient condition. Thus, these markers could provide clues into underlying mechanisms and predict mortality.

Keyword

Biomarkers; Burns; Cluster analysis; Longitudinal studies; Mortality

Figure

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  Fig. 1 Flowchart of enrolled patients. BICU, burn intensive care unit.

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  Fig. 2 Survival curve and Harrell’s C-index for each biomarkers in group 1. (A) Platelet, (B) red cell distribution width (RDW), (C) creatinine, (D) pH, and (E) lactate.

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  Fig. 3 Survival curve and Harrell’s C-index for each biomarkers in group 2. (A) Platelet, (B) red cell distribution width (RDW), (C) creatinine, (D) pH, and (E) lactate.

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  Fig. 4 The raincloud plot of pH for each clusters. (A) Group 1, (B) group 2, (C) group 3, and (D) group 4.

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  Fig. 5 The raincloud plot of platelet for each clusters. (A) Group 1, (B) group 2, (C) group 3, and (D) group 4.

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  Fig. 6 The raincloud plot of creatinine for each clusters. (A) Group 1, (B) group 2, (C) group 3, and (D) group 4.

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