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Clinical Science Aspects

Clinical Importance of a Cytokine Network in Major Burns

Matsuura, Hiroshi*; Matsumoto, Hisatake*; Osuka, Akinori; Ogura, Hiroshi*; Shimizu, Kentaro*; Kang, Sujin; Tanaka, Toshio; Ueyama, Masashi; Shimazu, Takeshi*

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doi: 10.1097/SHK.0000000000001152
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Abstract

INTRODUCTION

Patients with major burns are some of the most challenging critically ill patients to treat, and they continue to face many serious complications after injury (1, 2). The immune responses caused by burn injury result in an acute inflammatory response that induces various inflammatory and anti-inflammatory cytokines. An inflammatory reaction is caused by major burn injury in the early hospital phase. Subsequently, the invasive burn wound infection exacerbates further inflammation (3, 4). The excessive release of inflammatory cytokines results in serious systemic inflammation that induces tissue damage and vascular endothelial injury and that can progress to multiple organ failure and eventually lead to the patient's death (5).

Cytokines bind to multiple target cells and cause various actions such as inflammatory response and cell differentiation. Based on the present evidence, cytokines are reported to play roles in this mutual interaction (6–8). Clarification of the cytokine networks in chronic inflammatory diseases such as rheumatoid arthritis has led to effective cytokine-targeted therapies. Therefore, it is important to focus on the cytokine network to reveal the complicated pathogenesis of inflammation (9).

Although several articles have reported the importance of cytokines in patients with burns, evaluation of the cytokine network and its relation with the severity and prognosis of a burn have not yet been clarified (10–15). The purpose of this study was to evaluate serial changes in the cytokine network and the association between the cytokine network and outcome and injury severity in patients with major burns.

PATIENTS AND METHODS

Patients

This single-center, prospective, observational study was conducted in the Department of Trauma, Critical Care Medicine and Burn Center, Japan Community Health Care Organization Chukyo Hospital (Nagoya, Japan) from April 2014 to December 2016. Sample size was computed based on Power and Sample Size Calculation program (Vanderbilt School of Medicine, Nashville, Tenn) (16) and previous report (17). The inclusion criteria were burn patients with a percentage of total body surface area (%TBSA) ≥ 20% and age over 16 years. Patients who were in cardiopulmonary arrest on admission or transferred to the hospital more than 24 h after the burn were excluded. No patients were excluded due to preclinical conditions such as a history of alcohol exposure at the time of injury. The patients were treated with a unified treatment strategy that included surgical intervention. Third-degree burns were debrided as soon as possible after fluid resuscitation. In most cases, the initial operation was performed during days 3 to day 5, burned eschar was removed by 2 weeks, and major grafting was completed by 1 month. Twelve healthy volunteers with no previous medical history also provided blood samples.

This study followed the principles of the Declaration of Helsinki and was approved by the institutional review board of Chukyo Hospital (Permit Number: 2014015). Written informed consent was obtained from all participants.

Blood samples

Blood samples were collected from the patients until discharge or death on day 1 (the day of injury) and day 2, days 3–5, 1 week, 2 weeks, and about 1 month after the burn injury (maximum of six time points per patient) and once from the healthy volunteers. Serum samples were stored at −40°C until analyzed.

Cytokine analysis

Serum levels of IFN-α, IFN-γ, IL-1β, IL-6, IL-8, IL-12/IL-23p40, IL-17A, TNF-α, MCP-1, IL-4, and IL-10 were measured using a cytometric bead array kit (BD Biosciences) by a FACSCanto II flow cytometer (BD Biosciences). The detectable concentration range for each cytokine was 9.77 pg/mL to 3,000 pg/mL.

Clinical severities and outcome

The %TBSA, burn index (BI) and prognostic burn index (PBI) were assessed on admission. %TBSA that is affected by the burn is used when we describe the extent of injury. The BI is used for the assessment of severity and prognosis and is calculated as (percentage of 2nd-degree burns)/2 + percentage of 3rd-degree or deeper burns (18). The PBI is the sum of the BI and age of the patient. It is used clinically particularly in Japan, where a PBI above a threshold of 85 is significantly associated with mortality (19). The Acute Physiology and Chronic Health Evaluation (APACHE) II score was evaluated on admission. This score is used to evaluate the severity of critically ill patients and consists of 12 physiological variables and underlying health status and can be predictive for critically ill patients (20). The Sequential Organ Failure Assessment (SOFA) score was measured at the same time points as blood sampling. This score consists of six variables related to organ systems (respiratory, cardiovascular, neurologic, hepatic, renal, and coagulation) and is useful for assessing organ dysfunctions and patient outcome (21).

The outcomes were evaluated within 28 days after admission. The major burns were separated into two phases: the early hospital phase (days 1–2) and the late hospital phase (day 3–1 month).

Statistical analysis

The 11 cytokines were transformed to a natural logarithm to normalize the distribution of the data before the analyses. The Dunnet test was used to evaluate the differences of each cytokine between the patients with burns and the healthy controls. The Student t test was used to assess differences between surviving and non-surviving patients. Correlations between the 11 cytokines were assessed by hierarchical clustering analysis on the basis of serial Pearson correlation coefficients. Network analysis was performed with Cytoscape software (www.cytoscape.org) version 3.5.1 (22).

The log2 fold changes were calculated by dividing average cytokine levels in the burn patients by the average levels in the healthy controls. The network was visualized on the basis of the significant Pearson correlation coefficients between the 11 cytokines with log2 fold change > 0.5. The correlation between each cytokine and the SOFA score was evaluated by Spearman correlation coefficients. Cox proportional hazards analysis with time-dependent covariates was conducted on the basis of the cytokine levels. The maximum cytokine levels from 2 days were used for the analysis to reflect the impact of the maximum cytokine levels in the early hospital phase (i.e., day 1: cytokine levels on day 1; day 2: maximum cytokine levels on day 1 or day 2). To investigate a new predictive biomarker of major burns, the area under the receiver operating characteristic (ROC) curve area under the curve (AUC) was measured. A P value < 0.05 was considered to indicate statistical significance. Statistical analyses were performed with JMP Pro 12.0 for Windows (SAS Institute Inc, Cary, NC) and R version 3.3.4 (R Foundation for Statistical Computing, Vienna, Austria).

RESULTS

Patient characteristics

We enrolled 38 patients with burns and 12 healthy controls in this study (Table 1). In total, 210 blood samples from the patients and 12 blood samples from the controls were analyzed. Among the burn patients, 26 were men and 12 were women. The mortality in these patients was 15.8%. The median (interquartile range [IQR]) of %TBSA, BI, PBI, APACHE II, SOFA, and time from burn injury to admission were 35% (30%–59%), 27.5 (18–42), 81 (66–100), 13 (8–17), 3 (0–5), and 10.5 h (3.5–17 h), respectively. The causes of the major burns were flame (n = 28), scald (n = 9), and chemical (n = 1). There were no significant differences regarding age and sex between the patients with major burns and the controls. There were also no gender-related differences in patient age or in severities such as %TBSA, BI, and APACHE II or SOFA score.

Table 1
Table 1:
Patient characteristics

Serial changes of 11 cytokines and SOFA scores

The median (IQR) of IL-8 [Log IL-8] and MCP-1 [Log MCP-1] levels in the healthy controls were 25.0 (14.6–33.6) [3.2 (2.7–3.5)] and 278.1 (158.4–372.8) [5.6 (5.1–5.9)]. The rest of the cytokines levels in the healthy controls showed less than minimum detectable levels (data not shown).

The median (IQR) of IL-6 [Log IL-6] levels on days 1, 2, 3–5, 1 week, 2 weeks and 1 month were 87.0 (31.5–402.9) [4.5 (3.5–6.0)], 102.7 (31.9–334.3) [4.6 (3.5–5.8)], 323.0 (116.9–716.3) [5.7 (4.8–6.6)], 159.3 (61.5–439.7) [5.1 (4.1–6.1)], 40.6 (11.0–176.1) [3.7 (2.3–5.2)], and 26.5 (5.0–66.6) [3.3 (1.6–4.2)], respectively. The median (IQR) of IL-8 [Log IL-8] levels on days 1, 2, 3–5, 1 week, 2 weeks, and 1 month were 67.3 (37.7–183.4) [4.2 (3.6–5.2)], 96.8 (37.7–247.1) [4.6 (3.6–5.5)], 234.8 (87.0–378.9) [5.5 (4.5–5.9)], 168.2 (96.0–380.0) [5.1 (4.6–5.9)], 134.4 (46.7–261.4) [4.9 (3.8–5.5)], and 68.6 (38.3–180.8) [4.2 (3.6–5.2)], respectively. The median (IQR) of IL-10 [Log IL-10] levels on days 1, 2 and days 3–5 were 30.3 (5–228.5) [3.4 (1.6–5.4)], 8.2 (5.0–25.1) [2.1 (1.6–3.2)] and 5.0 (5.0–36.5) [1.6 (1.6–1.9)], respectively. IL-10 levels at 1 week, 2 weeks, and 1 month showed less than minimum detectable levels. The median (IQR) of MCP-1 [Log MCP-1] levels on days 1, 2, 3–5, 1 week, 2 weeks, and 1 month were 344.3 (169.2–751.8) [5.8 (5.1–6.6)], 332.9 (156.9–593.0) [5.8 (5.1–6.4)], 334.9 (167.0–625.5) [5.8 (5.1–6.4)], 255.1 (115.6–541.5) [5.5 (4.7–6.43], 292.2 (119.6–511.0) [5.7 (4.5–6.2] and 256.5 (155.0–389.2) [5.5 (5.0–6.0)], respectively (Fig. 1A).

Fig. 1
Fig. 1:
Serial changes in the cytokines of the cytokine network and in SOFA scores.a, Serial changes in the levels of the four cytokines contained in the cytokine network. (A) Interleukin 6 (IL-6), (B) interleukin 8 (IL-8), (C) interleukin-10 (IL-10), and (D) monocyte chemotactic protein-1 (MCP-1). All data are expressed as means ± SD. Asterisks indicate a significant difference between control and cytokine levels on each day (P < 0.05). b, Serial changes of SOFA scores. The boxes indicate the lower and upper quartiles, the central line is the median, and the ends of the whiskers represent the maximum and minimum values. SOFA indicates Sequential Organ Failure Assessment.

IL-6 and IL-8 levels significantly increased in the patients with major burns in comparison with those of the controls over the study period. The levels of IL-10 (day 1, 2) increased significantly compared with those of the controls. There were no significant differences in the levels of IL-1β, IFN-α, IFN-γ, IL-12/IL-23p40, IL-17A, TNF-α, IL-4 and MCP-1 between the patients and controls. The levels of IL-6 and IL-8 peaked at days 3 to 5 and then gradually decreased, as did the SOFA score (Fig. 1, a and b).

Based on the evidence for gender difference in burns (23–25), we evaluated gender specificity. However, there were no clear gender-related differences in serial changes of the cytokine levels and SOFA scores (data not shown).

Cytokine levels in survivors and non-survivors

We compared the levels of cytokines IL-6, IL-8, MCP-1, and IL-10 included in the cytokine network between the survivors and non-survivors. The levels of these four cytokines in the non-survivors were significantly higher than those in the survivors on day 1, and the levels of IL-10 in the non-survivors were significantly higher than those in the survivors on day 2 (Fig. 2).

Fig. 2
Fig. 2:
Levels of cytokines in the survivors and non-survivors on days 1 and 2.The boxes indicate the lower and upper quartiles, the central line is the median, and the ends of the whiskers represent the maximum and minimum values. Asterisks indicate a significant difference between the survivors and non-survivors on each day (P < 0.05). IL-6 indicates interleukin-6; IL-8, interleukin-8; MCP-1, monocyte chemotactic protein-1; IL-10, interleukin-10.

Hierarchical clustering and network visualization among 11 cytokines

The hierarchical clustering of Pearson correlations among the 11 cytokines was assessed during the study period to clarify serial cytokine clustering. The common cluster formed by IL-6, IL-8, MCP-1, and IL-10 was found on day 1, day 2, 1 week, 2 weeks, and 1 month (Fig. 3). The network visualization indicated that serial cytokine networks mutually interacted between IL-6, IL-8, MCP-1, and IL-10 on days 1 and 2 and formed a network consisting of IL-6 and IL-8 over the study period (Fig. 4). We also evaluated gender-related differences in the cytokine network but could find no clear differences by gender in the serial cytokine networks (data not shown).

Fig. 3
Fig. 3:
Hierarchical clustering.Hierarchical clustering is based on the Pearson correlations between cytokines. The dendrogram above the figure shows that the clusters are formed in order. The green outlined boxes show the common cytokine network in burns (day 1, day 2, days 3–5, 1 week, 2 weeks, 1 month).
Fig. 4
Fig. 4:
Network visualization.The cytokine network was visualized using the significant correlations. The cytokines with log2 fold change (i.e., average cytokine levels in burn patients/average cytokine levels in healthy controls) > 0.5 were included. The size of each node was determined based on log2 fold change. Red and yellow colors indicate significant and non-significant increases, respectively, in the cytokines in comparison with those of the controls. The edge width indicates the degree of the relation based on Pearson correlation coefficients. IFN-α indicates interferon-α; IFN-γ, interferon-γ; IL-1β, interleukin-1 beta; IL-6, interleukin-6; IL-8, interleukin-8; IL-12/IL-23p40, interleukin-12/23p40; IL-17A, interleukin-17A; TNF-α, tumor necrosis factor-α; MCP-1, monocyte chemotactic protein-1; IL-4, interleukin-4; IL-10, interleukin-10.

Serial Spearman correlations between 11 cytokines and SOFA scores

Serial Spearman correlation coefficients were investigated between the 11 cytokines and SOFA scores (Fig. 5). Significant correlations were seen between SOFA scores and levels of IL-6, IL-8, IL-10, and MCP-1 (day 1, day 2, days 3–5, 1 month); IL-6, IL-8, IL-10, MCP-1, and IL-17A (days 3–5); and IL-6 and IL-8, MCP-1 (1 week, 2 weeks). There were no significant correlations between SOFA scores and levels of IL-1β, IL-4, IL-12/IL-23p40, TNF-α, IFN-α, and IFN-γ.

Fig. 5
Fig. 5:
Correlations between cytokines and SOFA score in the patients with major burns.The red color indicates a positive correlation, and the blue color indicates a negative correlation. The red-colored P values indicate statistical significance. SOFA indicates Sequential Organ Failure Assessment; IL-6, interleukin-6; IL-8, interleukin-8; IL-10, interleukin-10; MCP-1, monocyte chemotactic protein-1.

Association between cytokine levels on day 1 and maximum SOFA scores

We assessed the association between cytokine levels on day 1 and maximum SOFA scores over the study period to evaluate the relation between the cytokines on admission and disease progression. Levels of IL-6, IL-8, IL-10, and MCP-1 (day 1) correlated significantly with the maximum SOFA score (r = 0.499, P = 0.002; r = 0.409, P = 0.012; r = 0.402, P = 0.014; r = 0.350, P = 0.034, respectively) (Fig. 6).

Fig. 6
Fig. 6:
Association between the cytokine level on day 1 and the maximum SOFA score.Blue points indicate survivors and red points indicate non-survivors. The max SOFA scores obtained during the study period were used. SOFA indicates Sequential Organ Failure Assessment; IL-6, interleukin-6; IL-8, interleukin-8; MCP-1, monocyte chemotactic protein-1; IL-10, interleukin-10.

Cox proportional hazards analysis with time-dependent covariates for prognosis

We performed Cox proportional hazards analysis with time-dependent covariates to assess the association between each cytokine and patient prognosis. The maximum levels of each cytokine over 2 days were used as the time-dependent covariates to focus on the early hospital phase of major burns. The levels of IL-6, IL-8, and IL-10 were significantly related to patient prognosis (Table 2).

Table 2
Table 2:
Cox proportional hazards model coefficients in the patients with burns

Predictive value of cytokines and combined cytokine score including the cytokine network for mortality

Finally, we performed ROC analysis to assess clinically useful prognostic biomarkers. Each of cytokines IL-6, IL-8, MCP-1, and IL-10, which had increased in the early hospital phase, and the combined cytokine score, which is based on a previous report (26), were used. To evaluate the combined cytokine score, patients were split into two groups based on the 75th percentile of the plasma levels for each of the cytokines (75th percentile levels: IL-6, 350.1 pg/mL; IL-8, 184.7 pg/mL; MCP-1, 740.2 pg/mL; IL-10, 428.9 pg/mL). The patients with cytokine levels equivalent to or higher than the 75th percentile value were assigned the value “1,” and those with cytokine levels below the 75th percentile value were assigned the value “0.” The combined cytokine scores were calculated by adding the values of each of the cytokines (i.e., for the combination of two cytokine scores [Combined score A], the individual value is 0 or 1 or 2; for the combination of three cytokine scores [Combined score B], the individual value is 0 or 1 or 2 or 3; and for the combination of four cytokine scores [Combined score C], the individual value is 0 or 1 or 2 or 3 or 4). The AUCs were analyzed for each of the cytokines and the combined cytokine values. The AUCs of IL-6, IL-8, MCP-1, and IL-10 on day 1 were 0.914, 0.822, 0.861, and 0.855 respectively, and the AUCs of the combined scores were 0.919 (Combined score A [IL-6+ IL-8+IL-10+MCP-1]), 0.922 (Combined score B [IL-6+IL-8+IL-10]), and 0.927 (Combined score C [IL-6+IL-10)) (Fig. 7).

Fig. 7
Fig. 7:
ROC analysis using the cytokines, the combined cytokine scores, and the clinical scores in the patients with major burn.AUCs were calculated on day 1. AUC indicates area under the ROC curve; ROC, receiver operating characteristic; IL-6, interleukin-6; IL-8, interleukin-8; MCP-1, monocyte chemotactic protein-1; IL-10, interleukin-10; SOFA, Sequential Organ Failure Assessment; APACHE, Acute Physiology and Chronic Health Evaluation; PBI, prognostic burn index.

Furthermore, the AUCs for each prognostic clinical score (SOFA score, APACHE II score and PBI) and those with IL-6 or combined score were respectively analyzed to pursue the most predictive biomarker. The AUCs of the SOFA score, APACHE II score, and PBI on day 1 were 0.852, 0.935, and 0.938 respectively. The AUCs of the SOFA score + IL-6, APACHE II score + IL-6, and PBI + IL-6 were 0.892, 0.957, and 0.946 respectively. Those of the predictive model in which the clinical scores were added to the combined score C (IL-6 + IL-10) were 0.930 for SOFA + combined score C, 0.978 for APACHE II + combined score C, and 0.989 for PBI + combined score C, respectively (Fig. 7).

DISCUSSION

This study is the first report, to our knowledge, to evaluate the relation between the cytokine network and severity and prognosis in patients with major burns. It suggests that the cytokine network (IL-6, IL-8, IL-10, MCP-1) may play a crucial role in major burn injury.

The levels of IL-6, IL-8, and IL-10 on days 1 and 2 increased significantly in the patients with major burns compared with the controls (Fig. 1). The MCP-1 levels in the non-surviving group showed a significant increase compared with those of the control and surviving groups on day 1. Although there was no significance difference due to the small number of samples, the MCP-1 levels were elevated in the order of non-surviving group, surviving group and control on day 2.

The hierarchical clustering analysis identified the cytokine cluster formed by IL-6, IL-8, IL-10, and MCP-1 on days 1 and 2 and network analysis revealed a network mutually interacting between these cytokines (Figs. 3 and 4). Inflammatory cytokines such as TNFα, IL-1β, IL-6, IL-8, and MCP-1 are produced from activated immunocompetent cells such as monocytes, neutrophils and vascular endothelial cells after burn injury. Systemic inflammatory response syndrome (SIRS) is caused by excessive production of these inflammatory cytokines (27, 28). Our results suggest that the inflammatory cytokines IL-6, IL-8, and MCP-1 mutually interact with each other and play an important role in SIRS in the early hospital phase of major burn injury.

Both the inflammatory response and the anti-inflammatory response produced from activated immunocompetent cells are involved in the pathogenesis of acute inflammation (29). In the present study, the cytokine network was formed by inflammatory cytokines IL-6, IL-8, and MCP-1 and anti-inflammatory cytokine IL-10 on days 1 and 2. This suggests that there is a mutual relation between the inflammatory and anti-inflammatory responses in the early hospital phase of major burn injury.

The levels of IL-6 and IL-8 significantly increased in the patients with major burns compared with the control group after days 3 to 5. The levels of IL-10 and MCP-1 also were higher than those of the controls after days 3 to 5 (Fig. 1). The hierarchical cluster analysis and network visualization identified a cluster and network formed by IL-6 and IL-8 over the study period (Figs. 3 and 4). This indicates that these cytokines might be both interrelated and related to the pathogenesis of burn injury in the late and early hospital phases.

Each cytokine contained in the cytokine network (IL-6, IL-8, IL-10, MCP-1) was associated with SOFA scores on days 1 and 2 (Fig. 5). In addition, each of these cytokines on the day of injury was respectively associated with the maximum SOFA score over the clinical course (Fig. 6). In the early hospital phase, a burn injury causes systemic endothelial dysfunction and hypovolemic shock that lead to multiple organ failure (30). Burn wound infection can also cause sepsis and promote inflammation leading to multiple organ failure after the early hospital phase (31, 32). The results suggest that inflammatory cytokines IL-6, IL-8, and MCP-1 and anti-inflammatory cytokine IL-10 formed the cytokine network and were related to disease severity and progression. In particular, IL-6 and IL-8 correlated significantly with patient prognosis (Table 2), indicating that these cytokines play a pivotal role in the progression of major burns.

The inflammatory cytokines IL-6 and IL-8 in the cytokine network were significantly associated with SOFA scores between days 3 and 5 and at 1 month after burn injury (Fig. 5). This suggests that these inflammatory cytokines mutually interact with each other and are related to the severity of major burns even in the late hospital phase.

To explore clinically useful prognostic biomarkers, the AUC evaluated for each cytokine in the cytokine network (IL-6, IL-8, MCP-1, IL-10) and the combined cytokine scores were compared. The combined cytokine scores of IL-6 and IL-10 showed the highest ability to predict mortality among all of the AUCs. Interestingly, the use of IL-6 alone was almost as good as the combined cytokine scores including those of IL-6 and IL-10. Previously, Kraft et al. (10) showed that the serum IL-8 level could be a biomarker for mortality in pediatric severe burn patients with a TBSA of over 30%. Our finding might shed light on IL-6 as a simple and useful prognostic biomarker for major adult burn injury. Furthermore, we integrated the results of prognostic cytokine biomarkers with prognostic clinical data such as the SOFA score, APACHE II score, and PBI to pursue the most predictive biomarker. The AUCs of the SOFA score, APACHE II score and PBI on day 1 were 0.852, 0.935, and 0.938, respectively. The predictive model that combined the clinical scores and IL-6 score had greater power to predict mortality (AUC of PBI + IL-6: 0.946). Moreover, the predictive model that combined the clinical scores and the cytokine network was better able to predict mortality (AUC of PBI + IL-6 + IL-10: 0.989). This indicates that the combination of the clinical score and the cytokine network could be a more powerful prognostic system than the addition of a cytokine alone.

There are several limitations in this study. First is the small sample size and the use of data from a single center. Second, surgical intervention was performed at various times, and elevation of the cytokine levels was affected not only by burn injury but also by surgery. Third, the relation between the cytokine network and other adverse outcomes was not assessed. Further study is necessary to clarify the role of cytokine networks in patients with major burns. We expect that these findings will establish the importance of the cytokine network in the pathogenesis of major burns and the usefulness of the identified cytokines as biomarkers leading to the development of improved burn treatment.

CONCLUSIONS

Serial cytokine profiles were evaluated in patients with major burns. We showed a cytokine network comprised by IL-6, IL-8, MCP-1, and IL-10 and that these cytokines in the early hospital phase were associated with disease severity and patient prognosis, suggesting that this cytokine network might play a role in major burns.

ACKNOWLEDGMENTS

The authors are very grateful to Kouji Yamamoto (Department of Medical Statistics, Osaka City University Graduate School of Medicine) for support with the statistical analysis. The authors greatly appreciate the patients, families, and healthy volunteers involved in this study.

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Keywords:

Burns; cytokine profile; cytometric bead array; flow cytometry; network; SIRS; APACHE; Acute Physiology and Chronic Health Evaluation; AUC; area under the curve; BI; burn index; IFN; interferon; IL; interleukin; MCP-1; monocyte chemotactic protein-1; PBI; prognostic burn index; ROC; receiver operating characteristic; SOFA; Sequential Organ Failure Assessment; TBSA; total body surface area; TNF; tumor necrosis factor

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