Serum Inflammatory Cytokine Markers of Invasive Fungal Infection in Previously Immunocompetent Battle Casualties

Abstract

Background:

Invasive fungal infection (IFI) is described increasingly in individuals experiencing high-energy military trauma. Hallmarks of successful treatment involve aggressive surgical debridement and early initiation of systemic antimicrobial therapy. Currently, intravenous anti-fungal therapy commences based on appearance of wounds and patient's clinical course. Whereas some clinical protocols exist to predict which critically injured patients should receive anti-fungal therapies, there are no established serum markers associated with IFI. Our hypothesis is that serum inflammatory cytokines exist that can assist in identifying individuals at risk for IFI.

Methods:

This is a retrospective case control study at a single institution. Nine patients with IFI (Saksenaea vasiformis, Fusarium sp., Graphium sp., Scedosporium sp., Aspergillus sp., Mucor sp., and Alternaria sp.) after battlefield trauma were matched to nine individuals with similar injury patterns whose laboratory results were negative for IFI. The combination of serum inflammatory cytokines from the first and second debridements was examined with multiplex platform proteomic analysis. We defined statistical significance as a two-tailed α <0.05 after adjusting for multiple comparisons using the false discovery rate method. This model was refined further with correlation-based filter selection and the area under the curve of the receiver operating characteristics (AUROC) was tested.

Results:

Both groups had similar Injury Severity Scores (ISS) (mean±standard deviation [SD]) (26.8±15.5 vs. 29.2±16.8, p=0.766). Elevated RANTES (regulated on activation, normal T cell expressed and secreted) alone (10,492.8±4,450.1 vs. 5,333.3±4,162.2, p=0.006) correlated with IFI. Also, the combination of persistent elevations in RANTES, interleukin (IL)-2R, and IL-15 was a robust model for predicting IFI with the AUROC being 0.9.

Conclusions:

Elevation in serum cytokines, particularly RANTES, correlated with IFI in this small group of patients. This demonstrates the potential of future rapid serum testing for early initiation and guidance of anti-fungal therapies.

Angio-invasive fungal infections (IFI) are rare but devastating disease processes generally occurring in immunocompromised individuals with systemic diseases such as diabetes mellitus and acquired immune deficiency syndrome (AIDS) [1], or those pharmacologically immunosuppressed to prevent transplanted organ rejection [2,3]. However, after sustaining severe trauma (e.g., burns, farming accidents, natural disasters, etc.) previously immunocompetent individuals in whom IFI developed from Mucorales, Aspergillus, and Candida [4 –6]. These infections carry a high mortality rate regardless of immune status with 11%–38% mortality in trauma [7 –9].

The massive injuries sustained from high-energy explosive devices in the recent military engagements Operation Iraqi Freedom (OIF) and Operation Enduring Freedom (OEF) have provided additional examples of IFI occurring in otherwise immunocompetent young adults [9 –11]. Despite a devastating injury pattern, improved body armor, expedient casualty evacuation systems, and modern adaptive surgical/resuscitative measures contribute to improved survivability of patients with previously lethal injuries [12,13]. However, the degree of trauma, coupled with massive transfusion requirements, leads to suppression of previously robust immune systems [14] and potentially increasing susceptibility to IFI.

Current standard of care for patients with IFI involves aggressive surgical debridement coupled with systemic anti-fungal therapy. This is often combined with topical therapy [15] in which dilute (0.0025%) sodium hypochlorite (Dakin) solution [16] is delivered directly to each wound—a treatment that has been found to be effective in decreasing gross fungus contamination [17]. Use of voriconazole or amphotericin B-impregnated beads is also considered. Because of the rapidly progressive nature of fungal infections, early intervention and initiation of the modalities discussed above are paramount.

Through establishment of clinical practice guidelines [18], the military trauma system has been able to decrease time to diagnosis from 9.5 to 5 d from time of injury. However, clinical outcomes remain unchanged [19] leading to delays in recognition and initiation of treatment, which are associated with prolonged hospitalizations, protracted wound healing times, and postponement of rehabilitative therapy, or worse, higher amputation levels [20]. Cytokine analysis may provide a clue to IFI potential that could pre-empt the clinical manifestations.

Cytokine analysis has been performed in trauma patients [21,22] and also in those with fungal infections [23]. However, to our knowledge, there has been no direct comparison of those with IFI with those without. We hypothesize that a serum inflammatory cytokine profile may distinguish trauma patients with IFI from those without IFI. This information would then help guide decisions regarding similarly injured individuals and enhance algorithms for initiation of adequate treatment.

Patients and Methods

Patient selection

After Institutional Review Board approval, we searched our prospectively collected Combat Trauma Registry to identify patients with histologically confirmed IFI infections. A cohort of patients without IFI was also identified with matched injury pattern (blast and multiple amputations), age, and Injury Severity Scores (ISS). All study subjects were injured in combat operations abroad and were evacuated to our institution. Five were injured in support of OIF and 13 in support of OEF (Afghanistan). Of the IFI cohort, two were injured in Iraq.

Sample collection

Serum samples were obtained in a manner previously described [13,24]. Specifically, 8 mL of venous blood was collected in Red-Top BD Vacutainer^® (Becton Dickinson, Franklin Lakes, NJ) prior to each debridement in the operating suite. Blood was separated immediately using a centrifuge (Thermo-Electron Corporation, Waltham, MA) at 2,500g for 10 min. The serum was then transferred to Cryo-Loc^™ polypropylene tubes (Lake Charles Manufacturing, Lake Charles, LA) and stored at −80°C after flash-freezing in liquid nitrogen. These were thawed to room temperature at time of analysis. Whereas patients often underwent multiple operations prior to arrival in the United States, the first tested debridements occurred an average of 6.4 d after injury (standard deviation [SD] 2.24) and the second debridements occurred an average of 8.4 d after injury (SD 2.24).

Protein analysis

The serum samples were analyzed using a Luminex^® 100 IS xMAP Bead Array Platform (Millipore Corporation, Billerica, MA). A combination of 32 cytokines and chemokines was quantified using a Human Cytokine 30-Plex Panel supplemented with a custom Human 2-plex Kit (Life Technologies, Grand Island, NY). Evaluated cytokines included interleukin (IL)-1A, -1β, -1RA, -2, -2R, -3, -4, -5, -6, -7, -8, -10, -12, -13, -15, -17, granulocyte-macrophage colony-stimulating factor (GM-CSF), granulocyte colony-stimulating factor (G-CSF), interferon (INF)-γ, IFN-α, tumor necrosis factor (TNF)-α, epidermal growth factor (EGF), basic fibroblast growth factor (bFGF), hepatocyte growth factor (HGF), vascular endothelial growth factor (VEGF), eotaxin, monocyte chemotactic protein (MCP)-1, macrophage inflammatory proteins (MIP-1α, MIP-1β), chemokine (C-C motif) ligand 5 (regulated on activation, normal T cell expressed and secreted [RANTES]), chemokine (C-X-C motif) ligand 9 (MIG), and chemokine (C-X-C motif) ligand 10 (IP-10). Of the 32 analytes, the majority (17) fell within the linear portion of the standard curve and thus are the focus of the analysis herein.

Statistical analysis

Results were collected and organized using Office Excel 2007^® (Microsoft, Redmond, WA). Continuous variables are presented as mean (±SD) and categorical variables as n (%). The distribution of each continuous variable was compared with the normal distribution using the Shapiro-Wilk test. Equality of variance for continuous variables was determined using the Brown-Forsythe Levene test. Statistical differences between continuous variables versus the bivariate outcome variables (presence or absence of IFI) were evaluated using the Mann-Whitney U test and the post-hoc Tukey-Kramer assessment. The levels of significance for the proteomic data were adjusted using the false discovery rate method [25]. After a univariate screen of the variables using the methods describe above, those with p values<0.05 were included for the multivariable analysis.

All variables were then analyzed with a correlation-based feature selection multivariable filter method. The three variables that were selected were then evaluated with Nagelkerke R² test and then Hosmer and Lemeshow's for goodness of fit. Furthermore, the area under the receiver operating characteristic curve (AUROC) was also calculated. R^© version 3.0.2 (R Foundation for Statistical Computing, Vienna, Austria) was used for all statistical estimations.

Results

Nine servicemen with IFI were identified in our database from the years 2007–2013. Fungal infections were diagnosed as part of their clinical care based on histopathologic examination and culturing. Their injury patterns were noted and matched with nine non-IFI individuals with similar wounds. Demographic and injury data can be found in Table 1. By design, the mean ISS score of the IFI and non-IFI groups was similar (26.8±15.5 vs. 29.2±16.8, p=0.766), as was age (22.3±2.1 vs. 22.9±2.9, p=0.671). For IFI patients, adequate systemic anti-fungal therapy was only initiated after the sampling period covered in this study and once the diagnosis had been confirmed by histopathology or culture results.

Table 1.

Baseline Demographic Data of Invasive Fungal Infection Patients (1–9) and Non-Invasive Fungal Infection Patients (1a–9a)

Patient	Age	Injuries	ISS	In-theater blood products	Fungal isolates
1	21	Right open femur mid-shaft fracture; right above knee popliteal injury; right thigh soft tissue injury; right leg four-compartment fasciotomy; left wrist de-gloving injury with open ulnar fracture and vessel ligation; Right great toe amputation	9	22 packed cells 20 plasma 3 platelet 2 cryoprecipitate	Zygomycetes
2	24	Right hematocele; right testicular rupture; left below-knee amputation; right above-knee amputation; right hand blast injury with multiple fractures; right pneumothorax, multiple soft tissue fragment wounds	29	18 packed cells 19 plasma 2 platelet 2 cryoprecipitate	Alternaria sp. Aspergillus sp.
3	22	Left below-knee amputation; right through-knee amputation; closed left femoral neck fracture; open left elbow and right hand soft tissue injuries, right hand fractures	17	22 packed cells 17 plasma 4 platelet 1 cryoprecipitate	S. vasiformis
4	23	Soft tissue injury to left leg with significant defect	10	4 packed cells	Mucor sp. Aspergillus sp.
5	21	Bilateral above-knee amputations; Left elbow injury; multiple intra-abdominal injuries	41	29 Packed cells 40 whole blood 17 plasma 3 platelets 4 factor VII	Scedosporium sp. Graphium sp.
6	20	Bilateral above-knee amputations; left digit amputations; grade III–IV testicular injury; grade I rectal injury; bilateral pneumothoracies; bilateral tympanic membrane rupture	41	11 packed cells 4 whole blood 4 plasma	Aspergillus sp. Candida sp.
7	23	Bilateral high above-knee amputations; left arm and hand fractures; left testicular injury	57	159 packed cells 6 whole blood 118 plasma 24 platelets 113 cryoprecipitate	Aspergillus sp. Candida sp.
8	27	Left above-knee amputation; right through-knee amputation; right hand injuries; left scrotal/penile injuries	17	16 packed cells 8 whole blood 16 plasma 3 platelets 9 cryoprecipitate 1 TXA	Aspergillus sp.
9	20	Right below-knee amputation; left leg soft tissue injuries; right upper extremity soft tissue injuries; penile degloving, scrotal injury and testicular fracture	20	2 packed cells	Aspergillus sp.
1a	22	Right thigh soft tissue injury; right arm soft tissue injury	12	None	None
2a	21	Left below-knee amputation; right above-knee amputation	41	22 packed cells 22 plasma 1 factor VII	None
3a	24	Left through-knee amputation; right below-knee amputation	18	12 packed cells 11 plasma 1 platelet 1 cryoprecipitate	None
4a	20	Left lateral calf fasciotomy; grade IIIb tib/fib fracture	9	None	None
5a	20	Bilateral lower extremity soft tissue wounds with right femoral artery, femoral vein, and saphenous vein lacerations; right tibia fracture; left proximal ulna/radius fracture; left pneumothorax and axillary injuries including artery laceration	45	19 packed cells 15 whole blood 19 plasma 26 platelet 10 cryoprecipitate	None
6a	21	Left thigh degloving injury with femoral neck fracture; left hemidiaphragm rupture with splenic avulsion and distal pancreas transaction; left kidney infarction with arterial thrombosis; left elbow fracture and spinous process fractures	43	2 packed cells 4 plasma	None
7a	28	Right below-knee amputation; left femoral neck fracture; left lower extremity compartment syndrome; bilateral rib fractures; L radius/ulna fracture; L1 burst and multiple spinous process fractures; bilateral pneumothoracies	59	31 packed cells 26 plasma 4 platelets 20 cryoprecipitate 2 factor VII	None
8a	28	Right above-knee and left below-knee amputations	16	9 packed cells 9 plasma 6 platelets 10 cryoprecipitate	None
9a	22	Bilateral below-knee amputations; buttock soft tissue injury; right arm injury with segmental loss of ulna	20	None	None

ISS=Injury Severity Scores; TXA=tranexamic acid.

Seventeen cytokines and chemokines were analyzed by looking at their early time points prior to initiation of anti-fungal therapy (Table 2). This revealed four cytokines/chemokines of interest (IFN-α, IL-10, IL-15, and RANTES) that were further evaluated at each debridement point (Table 3). Serum concentrations of these markers were higher in patients with IFI compared with the non-IFI group.

Table 2.

Concentration of Serum Cytokines (pg/mL) in Invasive Fungal Infection and Non-Invasive Fungal Infection Patients during Both Debridements with False Discovery Rate-Adjusted p Values

Cytokine	Mean IFI	SD IFI	Mean non-IFI	SD non-IFI	p
EGF	103.1	67.0	71.3	52.8	0.225
Eotaxin	80.6	33.8	59.5	25.9	0.126
HGF	1787.4	3449.1	928.8	1126.5	0.342
IFN-α	88.4	74.0	30.1	14.0	0.030
IL-10	31.4	34.9	5.3	3.9	0.031
IL-12	217.9	145.6	122.1	55.3	0.680
IL-15	111.7	86.7	24.1	20.9	0.006
IL-1β	18.3	21.0	10.4	8.6	0.225
IL-2R	1104.7	1067.6	477.0	546.3	0.122
IL-8	352.6	516.4	149.9	305.0	0.225
IL-1RA	656.7	584.1	450.6	254.1	0.235
IP-10	121.0	103.0	86.4	106.0	0.342
MCP-1	1481.1	1753.4	760.7	763.4	0.225
MIG	91.1	71.5	49.9	74.3	0.225
MIP-1α	66.6	32.2	51.5	23.8	0.225
MIP-1β	101.5	52.6	80.4	54.6	0.292
RANTES	10492.8	4324.7	5333.3	3082.6	0.006

IFI=invasive fungal infection; SD=standard deviation; EGF=epidermal growth factor; HGF=hepatocyte growth factor; IFN=interferon; IL=interleukin; IP=chemokine (C-X-C motif) ligand 10; MCP=monocyte chemotactic protein; MIG=chemokine (C-X-C motif) ligand 9; RANTES=regulated on activation, normal T cell expressed and secreted.

Table 3.

Concentration of Serum Cytokines/Chemokines of Interest (pg/mL) in Invasive Fungal Infection and Non-Invasive Fungal Infection Patients during the First and Second Debridements with False Discovery Rate-Adjusted p Values

	IFI		Non-IFI
Debridement 1	Mean	SD	Mean	SD	p
IFN-α	89.4	73.2	32.4	12.1	0.060
IL-10	29.6	28.4	6.3	5.2	0.060
IL-15	142.7	95.8	32.4	25.4	0.039
RANTES	11250.0	4720.1	5825.6	3438.2	0.039

	IFI		Non-IFI
Debridement 2	Mean	SD	Mean	SD	p
IFN-α	87.4	74.8	27.8	15.3	0.075
IL-10	33.1	40.2	4.3	1.3	0.077
IL-15	80.7	62.8	15.9	9.4	0.039
RANTES	9735.6	3739.0	4841.1	2588.2	0.034

IFI=invasive fungal infection; SD=standard deviation; IFN=interferon; IL=interleukin; RANTES=regulated on activation, normal T cell expressed and secreted.

Using the correlation-based feature selection, IL-15, IL-2r, and RANTES comprised the best performing model. The logistic model parameter estimates are found in Table 4. Nagelkerke R² evaluation of the power to explain the variation for this multivariable logistic model demonstrates the majority of the variability is explained, given R=0.83. The goodness of fit for this model as described by Hosmer and Lemeshow's test was 0.40. The AUC was 0.94.

Table 4.

Logistic Model Parameter Estimates and Odds Ratios

Cytokine	Estimate	SE	z	p	OR	CI
IL-15	0.0281	0.0166	1.692	0.09	1.029	1.003–1.076
IL-2R	0.0010	0.0006	1.838	0.07	1.001	1.000–1.002
RANTES	0.0003	0.0001	2.113	0.03	1.000	1.000–1.001

SE=standard error; OR=odds ratio; CI=confidence interval; IL=interleukin; RANTES=regulated on activation, normal T cell expressed and secreted.

Discussion

Invasive fungal infection in the immunocompetent individual is a rare but devastating disease process. To date, successful treatment requires cohesive functioning of the entire health care team, so that systemic and topical therapy may be administered as soon as the diagnosis is made. With IFI rates in combat trauma patients being evacuated from Afghanistan reaching 3.5%, the Joint Trauma System has developed IFI protocols, designed to confirm the diagnosis as quickly as possible [15].

Current methods of assessment of fungal infection are dependent on clinical progression of IFI and are occurring at day five from injury [19]. As such the promise of a standardized means by which to risk stratify patients for IFI is appealing. This study is the first attempt to delineate a cytokine profile that would signal potential increased risk for IFI. Using a multiplex enzyme-linked immunosorbent assay (ELISA) plate, serum cytokine concentration can be reported in a few hours after obtaining a blood sample.

After trauma, especially in individuals subjected to high-energy forces resulting in multiple injuries, there is an immunologic response characterized by multiple cytokine and chemokine elevations [26]. Previous studies from our group have shown a correlation with wound healing and decreasing serum cytokine production [22]. Persistent elevations of these cytokines may be caused by infection of the wound itself or sepsis [22 –24,26–27]. Determination of the cause of these persistent elevations could help direct therapy.

Interleukin-10, an anti-inflammatory cytokine [28], is generously upregulated in trauma and infection. Previous studies have demonstrated that there is significant production of IL-10 in response to Aspergillus fumigatus in individuals who contain and suppress the disease whereas there is poor production in those with stable or worsening disease [29]. Certain allelic and genotypic differences in the promoter region of the IL-10 gene can confer susceptibility or resistance to invasive pulmonary aspergillosis [30]. Furthermore, immunocompromised individuals fail to mount an IL-10 response in the face of invasive fungal disease [31]. Our patients' high concentrations of IL-10 may represent an effective immune response given the 100% survival in this cohort.

Interleukin-15 has been demonstrated to provide activation of natural killer cells in the face of Candida albicans [32]. Human neutrophils are activated by IL-15 and have an increase in hydrogen peroxide-mediated killing activity against Paracoccidioides brasiliensis [33]. Evaluations of both acute and chronic paracoccidiodomycosis patient cohorts revealed that both groups had increased numbers of natural killer cells, but that their cytotoxic activity was diminished in comparison with healthy control groups [34]. Interleukin-2R serum concentrations are elevated in response to broncho-pulmonary aspergillosis in patients with asthma; however, its role in IFI has yet to be studied in depth [35].

The cytokine with statistically significant association with IFI in this pilot study is RANTES. Invasive fungal infection has been demonstrated previously to increase circulating RANTES levels. Lower levels, as observed in immunocompromised individuals, is associated with lower platelet counts and lower survival rates than those who maintain elevated circulating levels [36]. Other studies have found that certain mold structures activate RANTES release from platelets. The hyphae from A. fumigatus and not the conidia stimulate this process [37]. Interferon-α is not specifically associated with fungal infections in the literature. However, its role in activation of natural killer cells fits with anti-fungal attributes of the other significantly elevated cytokines.

Limitations

Limitations of this pilot study include its retrospective design and small number of available subjects, which limited the ability to find statistical significance among the many cytokine options. However, the AUROC is robust for the IL-2R, IL-15, RANTES combination, demonstrating the model's accurate fit to the data. Whereas the information was collected prospectively, this is a subset analysis of a small population in one dataset compared with matched control groups in the other. The serum was drawn prior to the first and second debridement while at a continental United States hospital. This translates into approximately six and eight days post-injury. Samples from in-theater combat support hospitals or a regional hospital, such as Landstuhl Regional Medical Center, where several debridements occurred prior to evacuation to the United States, were not collected. Future studies with downrange sampling to investigate the chronologic pattern of persistent cytokine elevation are warranted.

We do not have concomitant quantified bioburden data for each associated wound (e.g., for other infectious agents such as bacteria). As our group has described elsewhere, the majority (69%) of combat wounds from OEF do not show bacterial growth when biopsied on arrival at a U.S.-based hospital [38]. However, given the severity of the wounding patterns in the current cases, it is likely that these wounds would fall into the 31% that showed positive growth. Furthermore, critically colonized wounds display an increase in serum inflammatory profiles as shown previously [24]. Also, these wounds occurred in two separate theaters, which may have caused different care at point-of-injury prior to evacuation to Landstuhl Regional Medical Center.

Despite these limitations, this analysis provides the first evidence of serum cytokine differences between individuals with IFI and similarly traumatized patients without. The availability of a rapid cytokine analysis at reception at a trauma center would be beneficial for instituting systemic anti-fungal therapies. Hesitation to commit patients with no overt evidence of IFI to a regimen of potentially nephrotoxic anti-fungals may be mitigated by positive cytokine results. Local therapies that affect native cells along with fungus, such as Dakin's solution, could be started and stopped based on a cytokine profile. Further studies with larger patient populations are warranted to ascertain the full cytokine profile of IFI patients. Such data could be subjected to logistic regression or machine-learned Bayesian Belief Network analysis to discover first-order predictors of IFI and develop assays to quickly demonstrate potential for fungal infections in trauma patients.

Footnotes

Acknowledgments

We acknowledge Fred Gage, Tala Ghadimi, and Kathleen Curry for their invaluable help with procurement of data for this study, to Capt. James Dunne, MD for his support and sponsorship, and to Dr. Ying Cao for his assistance with the statistical evaluation.

Supported by Defense Health Program grant #0602115HP.

These data were presented in a poster format at the 71st annual meeting of the American Association for the Surgery of Trauma and Acute Care Surgery, September 2012.

Author contributions as follows: conception and design: J.R., J.F., E.E.; acquisition of data: J.R., T.B., F.L., C.R.; analysis and interpretation of data: J.R., T.B., J.F.; drafting of manuscript: J.R., F.L., J.F.; critical revision: T.B., J.F., C.R., E.E.; statistical expertise: T.B., J.F.; obtaining funding: E.E.; Supervision: J.F., E.E.

The views expressed in this manuscript are those of the authors and do not reflect the official policy of the Department of the Army, the Department of the Navy, the Department of Defense, or the United States Government.

Author Disclosure Statement

No competing financial interests exist.

This work was prepared as part of our official duties. Title 17 U.S.C. 105 provides that “Copyright protection under this title is not available for any work of the United States Government.” Title 17 U.S.C. 101 defines a U.S. Government work as a work prepared by a military service member or employee of the U.S. Government as part of that person's official duties.

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