Journal of Critical Care xxx (2014) xxx–xxx

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Predictive value of plasma biomarkers for mortality and organ failure development in patients with acute respiratory distress syndrome☆,☆☆ Rodrigo Cartin-Ceba, MD, MSc a,⁎, Rolf D. Hubmayr, MD a, Rui Qin, PhD b, Steve Peters, MD a, Rogier M. Determann, MD, PhD c, Marcus J. Schultz, MD, PhD c, Ognjen Gajic, MD, MSc a a b c

Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN Laboratory of Experimental Intensive Care and Anesthesiology and Department of Intensive Care, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.

a r t i c l e

i n f o

Keywords: Biomarkers ARDS Prognosis Organ failures

a b s t r a c t Purpose: To evaluate the predictive value of 6 different biomarkers in the development of multiple-organ failure (MOF) and mortality in a contemporary prospective cohort of acute respiratory distress syndrome (ARDS). Methods: Patients with ARDS admitted to a tertiary referral center during an 8-month period were included. Plasma sample collection of 6 different biomarkers on days 1, 3, and 5 after ARDS onset was performed (von Willebrand factor, thrombin–antithrombin III complex, plasminogen activator inhibitor 1, interleukin 8, receptor for advanced glycation end-products, and club cell secretory protein). Main outcomes included hospital mortality and development of MOF. Logistic regression models for MOF and mortality prediction were created including biomarkers levels and clinical predictors. Results: One hundred patients were included in the study. Do-not-resuscitate status and McCabe score were independently associated with increased mortality. None of the 6 biomarkers measured at the time of ARDS diagnosis predicted hospital mortality. After adjustment for important clinical characteristics, elevated day-1 interleukin 8 levels were associated with the development of MOF. Conclusions: Addition of biomarkers did not improve mortality prediction in this cohort of ARDS. Association between elevated interleukin 8 levels and progression of organ failures suggests an important role of exaggerated inflammatory response in the development of MOF. © 2014 Elsevier Inc. All rights reserved.

1. Introduction The acute respiratory distress syndrome (ARDS) is an important critical care syndrome affecting approximately 200 000 patients each year in the United States [1]. The high mortality and morbidity associated with ARDS appear intimately linked to a devastating clinical pattern Abbreviations: APACHE, Acute Physiologic and Chronic Evaluation; APS, Acute Physiologic Score; AUC, area under the ROC curve; BMI, body mass index; CC16, club cell secretory protein; CI, confidence interval; DNR/DNI, do not resuscitate/do not intubate; eGFR, estimated glomerular filtration rate; ESRD, end-stage renal disease; IBW, ideal body weight; ICU, intensive care unit; IL-8, interleukin 8; IL-1β, interleukin 1β; IL-6, interleukin 6; IQR, interquartile range; LOS, length of stay; MOF, multiorgan failure; OR, odds ratio; PAI-1, plasminogen activator inhibitor 1; P/F, PaO2/FIO2; RAGE, receptor for advanced glycation end-products; ROC, receiver operating characteristic; SOFA, Sequential Organ Failure Score; TATc, thrombin–antithrombin III complex; TNF-α, tumor necrosis factor α; VFD, ventilation-free days; vWF, von Willebrand factor. ☆ Authors' contributions: Study design, acquisition of data, or analysis and interpretation of data: Cartin-Ceba, Hubmayr, Qin, Peters, Determann, Schultz, and Gajic; drafting of the article: Cartin-Ceba and Gajic; revising the article for important intellectual content: Hubmayr, Qin, Peters, Determann, and Schultz; final approval of the version to be published: Cartin-Ceba, Hubmayr, Qin, Peters, Determann, Schultz, and Gajic. ☆☆ Disclosures: The authors do not have any disclosures related to this study. ⁎ Corresponding author: Mayo Clinic, 200 First Street SW, Rochester, MN 55905. Tel.: + 1 507 284 2494; fax: + 1 507 266 4372. E-mail address: [email protected] (R. Cartin-Ceba).

known as multiple-organ dysfunction syndrome [2], and it is not clear why some patients with ARDS develop multiorgan failure (MOF). Multiple studies in ARDS have been performed to evaluate the predictive role of diverse biomarkers in relationship to different outcomes. However, several shortcomings have limited the interpretation of biomarkers as prognosticators of this syndrome. The largest studies have been conducted in patients who were enrolled in clinical trials that limit the external validity of the biomarker profile because patients were enrolled as late as 48 hours after the diagnosis of ARDS, and these trials have tested the effect and efficacy of specific interventions [3–6]. In addition, most of the early studies on biomarkers and ARDS have had a single pathway approach, although a single mechanism is unlikely to predict the outcome in a complex syndrome like ARDS [3–8]. In this prospectively assembled cohort of patients with ARDS, we aimed to determine the prognostic role of 6 different biomarkers in the development of MOF and in-hospital mortality. We included biomarkers that have been previously reported to represent different pathophysiological pathways involved in ARDS: endothelial marker (von Willebrand factor [vWF]), coagulation/fibrinolysis markers (thrombin–antithrombin III complex [TATc] and plasminogen activator inhibitor 1 [PAI-1]), inflammation marker (interleukin 8 [IL-8]), and epithelial markers (receptor for advanced glycation end-products [RAGE] and club cell secretory protein [CC16], formerly known as Clara cell secretory

http://dx.doi.org/10.1016/j.jcrc.2014.09.001 0883-9441/© 2014 Elsevier Inc. All rights reserved.

Please cite this article as: Cartin-Ceba R, et al, Predictive value of plasma biomarkers for mortality and organ failure development in patients with acute respiratory distress syndrome, J Crit Care (2014), http://dx.doi.org/10.1016/j.jcrc.2014.09.001

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R. Cartin-Ceba et al. / Journal of Critical Care xxx (2014) xxx–xxx

protein). Furthermore, we planned to evaluate the pattern of these biomarkers at the time of ARDS diagnosis, at 3 and 5 days, which can improve our understanding of why some patients develop MOF and others do not.

The detection limit of the assay is 10 pg/mL, and the recovery of CC16 in plasma is 91% to 98%.

2.4. Statistical analysis 2. Methods 2.1. Study design and subjects Prospectively assembled cohort of all consecutive patients with ARDS admitted to 3 intensive care units (ICUs; medical, surgical, and mixed medical-surgical) at Mayo Clinic, Rochester, Minn, from October 2005 to May 2006. Inclusion criteria included the following: age ≥18 years and development of ARDS after hospital admission. Exclusion criteria included the following: patients who already have ARDS at the time of hospital admission (unable to identify exact timing since ARDS diagnosis), comfort care measures before the development of ARDS, and patients who declined to have their medical records reviewed for research purposes. Plasma at baseline (day of ARDS identification), 3 and 5, days was obtained from wasted blood samples, and therefore, no informed consent was required. The Mayo Clinic Institutional Review Board approved the study (IRB No. 2235-05). 2.2. Definitions Demographics and predictor variables collected for this study, including predisposing conditions for development of ARDS and ARDS modifiers, are listed in the e-Appendix section. We initially included all cases of acute lung injury and ARDS as defined by the 1994 AmericanEuropean Consensus Conference on ARDS [9]; however, the new ARDS definition and criteria based on the ARDS Definition Task Force (Berlin definition) [10,11] were used for reporting purposes in this article. The outcomes assessed were development of MOF as defined by Vincent et al [12] and Moreno et al [13] and in-hospital mortality [14]. 2.3. Sample collection, biomarker selection, and assays Four milliliters of waste whole blood collected for clinical purposes on the day of diagnosis of ARDS, 3, and 5 days for every patient was obtained. Blood was collected at room temperature (18°C-25°C) into cell preparation tubes containing sodium citrate. After collection, tubes were immediately transferred for processing and centrifugation. Plasma was collected and placed in 2-mL cryogenic freezing tubes after barcoded labeling them and was subsequently stored at − 70°C to 80°C until processing. Based on the available literature at the time of the study, the following biomarkers were selected for analyses: endothelial marker (vWF), coagulation/fibrinolysis markers (TATc and PAI-1), inflammation marker (IL-8), and epithelial markers (RAGE and CC16). Interleukin 8, RAGE, vWF, TATc, and PAI-1 were measured using specific commercially available enzyme-linked immunosorbent assays according to the instructions of the manufacturer (IL-8 from PeliKinecompact kit, Sanquin, Amsterdam, the Netherlands; RAGE from R&D Systems, Minneapolis, Minn; vWF antibodies from Dako, Glostrup, Denmark; TATc from Siemens Healthcare Diagnostics, Marburg, Germany; PAI-1 from Hyphen BioMed, Andrésy, France). Levels of CC16 were measured with a homemade enzyme-linked immunosorbent assay. Well plates were coated with 25 ng of monoclonal antihuman CC16 antibody AY1E6 (HyCult, Uden, the Netherlands). Calibrator (Biovendor, Heidelberg, Germany) and samples were diluted as appropriate and incubated for 1 hour. Then the plates were washed and incubated for 1 hour with 10 ng polyclonal biotinylated antihuman CC16 detection antibody A0257 (Dako). After washing, streptavidin labeled with polyhorseradish peroxidase (Sanquin) was added and incubated for 30 minutes. Finally, after 3 washes, 100 μL of sodium acetate buffer (pH 5.5) containing 100 μg/mL tetramethylbenzidine and 0.003% H2O2 was added, and the color reaction was stopped by 2 mmol/L H2SO4.

All data are summarized as mean (SD), median (interquartile range [IQR]) or percentages. Unpaired Student t tests were used to compare continuous variables with normal distribution and Wilcoxon rank test for skewed distribution. For comparison of categorical variables, χ 2 tests were used if the number of elements in each cell was 5 or more; Fisher exact test was used otherwise. Biomarkers were not normally distributed, and therefore, we performed log10 transformation for the analyses. Repeated-measures analyses of biomarkers levels during the 3 different time points (days 1, 3, and 5) were performed with multivariate analysis of variance, including severity of disease (Acute Physiologic and Chronic Evaluation [APACHE] III) and baseline renal function (estimated glomerular filtration rate [eGFR]) as covariates to account for differences observed in the levels of the different biomarkers at baseline. We then examined the predictive role for mortality and MOF of the different biomarkers using logistic regression models. Variables were considered for the multivariate logistic regression models if they occurred before the development of the outcomes of interest, had less than 10% missing data, and met the following assumptions: (a) had P values less than .1 in the univariate analysis and (b) were clinically plausible. The final model was determined using both clinical and statistical criteria taking into consideration collinearity, interaction, and the number of patients who experienced the outcome of interest. In a case of collinearity, the variable with “stronger” association (based on forward selection process) was used in multivariate analysis. For the logistic regression analysis, biomarker levels were also log10 transformed; hence, the odds ratios (ORs) represent the increased risk of mortality or MOF per log10 change in biomarker level. Based on the rules previously outlined, for the clinical risk factor–based model of mortality, in order to avoid overfitting of the model, we incorporated only 3 covariates given the fact that only 36 mortality events were observed. McCabe score, the Acute Physiologic Score (APS) from the APACHE III score and the do-not-resuscitate (DNR) status were included in the clinical risk factor–based model. The APS was chosen instead of the full APACHE score to avoid collinearity with age and the McCabe score. For the clinical risk factor–based model of MOF, a similar approach was done given the 37 events observed and the McCabe score, the APS from the APACHE III score, and age where chosen as covariates. The individual contributions of biomarkers were tested using likelihood ratio testing. Markers that did not make a statistically significant contribution to the model were eliminated (P N .2). Model discrimination was assessed using receiver operating characteristic (ROC) curves. Model fit (calibration) was assessed using the Hosmer-Lemeshow goodnessof-fit test. A nonsignificant value for the Hosmer-Lemeshow χ2 test suggests an absence of biased fit. When appropriate, the OR and 95% confidence intervals (CIs) were calculated. Two sided P values less than .05 were considered statistically significant. JMP statistical software (version 9.0.1; SAS, Cary, NC) was used for all data analyses.

3. Results 3.1. Study population and outcomes A total of 182 patients developed ARDS during the study period, of which 100 patients were included in the study (Fig. 1). All patients were whites, 54 patients were male, the median age (IQR) was 62.5 (51-75) years, and the median (IQR) APACHE III score was 74 (58-96). Baseline clinical characteristics between groups are presented in Table 1. According to the Berlin definition of severity, 19 cases were mild, 50 were moderate, and 31 were severe.

Please cite this article as: Cartin-Ceba R, et al, Predictive value of plasma biomarkers for mortality and organ failure development in patients with acute respiratory distress syndrome, J Crit Care (2014), http://dx.doi.org/10.1016/j.jcrc.2014.09.001

R. Cartin-Ceba et al. / Journal of Critical Care xxx (2014) xxx–xxx

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3.4. Contribution of biomarkers to prediction models of mortality and MOF A clinical factor–based model of mortality and MOF is presented in Table 3. The McCabe score and the DNR status were independently associated with in-hospital mortality after adjustment for severity of disease (area under the ROC curve [AUC], 0.77; 95% CI, 0.73-0.81). Age, severity of disease, and the McCabe score were independently associated with the development of MOF (AUC, 0.88; 95% CI, 0.85-0.91; Table 3). To determine the contribution of biomarkers to predictive models for mortality and MOF, we measured the AUCs for the logistic regression models based on clinical risk factors (Table 3), biomarkers (Table 4), and the combination (Table 4). The areas under the ROC curve for biomarkers alone showed poor predictability for mortality and MOF, with the exception of IL-8 in predicting MOF (OR, 2.77; 95% CI, 1.79-4.75; P b .001; AUC, 0.79 [0.76-0.82]). An attempt was made to combine different biomarkers when building the multivariable models (at least one of each pathway evaluated); however, we did not identify any significant prediction improvement in the models or important interactions (data not shown). The addition of biomarkers to the clinical risk factor–based model for both mortality and MOF did not increase the predictive value of the models as compared with clinical risk factors alone (Tables 3 and 4). However, IL-8 levels made a significant contribution to the predictive model of MOF (P = .03 for the comparison between models). The Hosmer-Lemeshow goodness-of-fit P value was greater than .20 for all models presented in Tables 3 and 4.

4. Discussion

Fig. 1. Patients with ARDS who were included in the study during the study period.

3.2. Biomarkers concentration at presentation A comparison of the biomarkers levels obtained on day 1 of ARDS development was made according to different baseline clinical characteristics, as shown in Table 2. The comparison of biomarkers according to the predefined outcomes showed that no major differences were observed in survivors vs nonsurvivors in ARDS day 1 concentration of all 6 biomarkers tested (e-Appendix section, Figure e-1). Only IL-8 levels were significantly higher in ARDS patients who developed MOF vs those who did not (e-Appendix section, Figure e-2).

3.3. Biomarkers concentration over time Biomarkers concentrations over time are presented in Figs. 2 and 3. No statistical significant differences in the biomarkers concentrations over time were observed between survivors and nonsurvivors after adjustment for baseline characteristics (Fig. 2). For the development of MOF, although there is no significant change in the levels of IL-8 over time, there was significantly higher IL-8 levels in ARDS patients who developed MOF vs those who did not, after adjustment for baseline characteristics (P b .001; Fig. 3). No significant associations were identified between the other 5 biomarkers and the development of MOF.

In this prospective cohort of patients with ARDS, we evaluated the association of 6 different biomarkers with in-hospital mortality and with MOF. In this study, none of the 6 biomarkers measured at the time of diagnosis of ARDS were significantly associated with inhospital mortality. In addition, only IL-8 levels at the time of diagnosis of ARDS were significantly associated with the development of MOF, even after adjustment of important baseline characteristics. Furthermore, IL-8 levels remained significantly elevated overtime in patients with MOF. Levels of different biomarkers at the time of diagnosis of ARDS varied according to baseline characteristics including the cause of ARDS, severity of disease and presence of comorbidities such as renal failure or cirrhosis. Our study was performed in a contemporary cohort of ARDS patients during the era of lung-protective ventilation and was carried outside randomized controlled trials; therefore, we consider that it is representative of the day-to-day practice in the ICU. Most of the biomarker studies in ARDS have focused on prediction of ARDS in at-risk population or prediction of mortality; however, we also focused on the development of MOF given its significant impact on long-term outcomes of survivors [15]. Compared with our approach, biomarker profiles from patients enrolled in clinical trials differed because specific interventions are evaluated during those trials. Moreover; in our study, the baseline blood samples were collected at the time of diagnosis of ARDS as compared with many of the largest studies where patients were enrolled after 48 hours of the diagnosis of ARDS [3–6]. By evaluating different pathogenetic pathways in this study, we attempted to overcome some of the limitations of biomarker studies that had followed a single pathway approach, given that a single mechanism is unlikely to predict outcomes in a complex syndrome like ARDS [3–8]. We also considered that adjusting for important baseline characteristics was necessary given the clear differences observed in the levels of biomarkers when comorbidities such as renal or liver disease are present. Given the fact that withdrawal and withholding of life support are the most common modes of death in the ICU, it is critically important to take into consideration patient preference (DNR status). Indeed, in our study, DNR status was independently associated with increased mortality.

Please cite this article as: Cartin-Ceba R, et al, Predictive value of plasma biomarkers for mortality and organ failure development in patients with acute respiratory distress syndrome, J Crit Care (2014), http://dx.doi.org/10.1016/j.jcrc.2014.09.001

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Table 1 Baseline characteristics, interventions and outcomes of ARDS patients included in the study

Age (y), median (IQR) Male sex, n (%) Smoking history, n (%) Alcoholism, n (%) Diabetes mellitus, n (%) Coronary artery disease, n (%) BMI (kg/m2), median (IQR) McCabe score ≥1, n (%) DNR status, n (%) APACHE III, median (IQR) Predicted mortality (%), median (IQR) SOFA score day 1, median (IQR) Extrapulmonary ARDS, n (%) P/F ratio day 1, median (IQR) Oxygenation index, median (IQR) Median day 1 PEEP in cm H2O, median (IQR) Use of noninvasive ventilation before intubation, n (%) Fluid balance (L) day 1, median (IQR) HFOV use, n (%) Use of stress doses of corticosteroids, n (%) Neuromuscular blockade agents, n (%) Vasopressor use, n (%) Tidal volume (mL/kg IBW), median (IQR) Ventilator-free days, median (IQR) ICU LOS (d), median (IQR) Hospital LOS (dys), median (IQR)

MOF (n = 37)

No MOF (n = 63)

P

Survivors (n = 64)

Nonsurvivors (n = 36)

P

54 (46-63) 18 (49) 23 (62) 12 (32) 3 (8) 8 (22) 28 (22-31) 29 (78) 5 (13) 104 (66-114) 65 (26-76) 13 (10-15) 19 (51) 113 (81-150) 19 (12-29) 10 (5-11) 9 (24) 3.3 (0.5-6.2) 3 (8) 14 (38) 5 (13) 27 (73) 6.1 (5.8-6.8) 16 (0-23) 8 (5.8-13.4) 21.4 (11.5-36.4)

69 (54-78) 36 (57) 37 (59) 15 (24) 19 (30) 23 (36) 28 (24-34) 27 (43) 4 (6) 65 (54-86) 24 (12-40) 6 (4-8) 29 (46) 157 (101-207) 12 (8-22) 8 (5-12) 29 (46) 0.9 (−0.4-2.9) 1 (2) 19 (30) 6 (9) 18 (28) 6 (5.9-7) 21 (15-27) 4.7 (3-9.4) 14.1 (8.4-23.2)

.004 .53 .83 .36 .01 .17 .15 b.001 .28 b.001 b.001 b.001 .68 b.001 .01 .48 .02 b.001 .14 .11 .52 b.001 .76 .002 .001 .01

62 (50-72) 36 (56) 39 (61) 19 (29) 16 (25) 19 (30) 29 (23-34) 26 (41) 1 (2) 69 (58-93) 26 (14-48) 7 (5-11) 33 (52) 149 (97-191) 14 (10-24) 7 (5-10) 30 (47) 1.1 (0.6-2.9) 2 (3) 22 (34) 5 (8) 29 (45) 6.4 (5.9-7.2) 22 (18-26) 6.7 (3.2-11) 17 (10.3-24)

66 (53-80) 18 (50) 21 (28) 8 (22) 6 (17) 12 (33) 26 (23-31) 30 (83) 8 (22) 80 (59-105) 35 (23-69) 8 (6-13) 15 (42) 136 (92-181) 18 (10-31) 10 (5-13) 22 (61) 3.6 (0.5-7.4) 2 (6) 18 (50) 6 (16) 16 (44) 6.2 (5.9-7) 0 (0-21) 6.5 (3.7-9.6) 12.6 (8.2-33.8)

.09 .54 .79 .41 .45 .82 .16 b.001 b.001 .38 .07 .16 0.40 .42 .37 .08 .32 b.001 .61 .14 .51 .93 .18 b.001 .85 .54

BMI indicates body mass index; LOS, length of stay; P/F, PaO2/FIO2; SOFA, Sequential Organ Failure Score; IBW, ideal body weight; HFOV, high-frequency oscillatory ventilation; PEEP; positive end-expiratory pressure.

Table 2 Day 1 biomarker levels based on different baseline clinical characteristics Baseline characteristics

Age group ≥62 y (n = 54) b62 y (n = 46) P Sex Female (n = 46) Male (n = 54) P APACHE III score ≥74 (n = 51) b74 (n = 49) P ARDS cause Extrapulmonary (n = 48) Pulmonary (n = 52) P Sepsis (n = 66) No sepsis (n = 44) P ESRD No (n = 89) Yes (n = 11) P Acute kidney injury day 1 No (n = 69) Yes (n = 31) P Cirrhosis No (n = 91) Yes (n = 9) P McCabe category ≥1 No (n = 44) Yes (n = 56) P

Biomarkers day 1 (log10), mean (SD) vWF

IL-8

TATc

RAGE

CC-16

PAI-1

5.83 (0.46) 5.84 (0.39) .87

3.72 (1.22) 4.19 (1.63) .10

2.37 (0.71) 2.43 (0.68) .65

6.59 (0.94) 6.75 (1.14) .44

3.12 (0.79) 2.81 (0.90) .07

5.11 (0.85) 5.44 (1.36) .14

5.88 (0.41) 5.78 (0.44) .23

4 (1.4) 3.88 (1.47) .67

2.3 (0.76) 2.48 (0.63) .21

6.80 (1.1) 6.56 (0.96) .25

3.07 (0.81) 2.90 (0.89) .34

5.37 (1.13) 5.16 (1.11) .36

5.91 (0.06) 5.75 (0.06) .07

4.37 (1.55) 3.48 (1.15) .001

2.42 (0.72) 2.37 (0.68) .76

6.83 (1.13) 6.50 (0.90) .11

3.31 (0.74) 2.64 (0.83) b.001

5.41 (1.25) 5.11 (0.95) .16

5.74 (0.42) 5.91 (0.43) .06 5.84 (0.43) 5.80 (0.42) .63

4.29 (1.61) 3.61 (1.17) .01 4.06 (1.53) 3.68 (1.2) .17

2.55 (0.77) 2.25 (0.58) .03 2.36 (0.67) 2.46 (0.74) .54

6.41 (1.05) 6.95 (0.95) .007 6.63 (1.03) 6.75 (1.04) .58

2.76 (0.85) 3.22 (0.80) .006 2.90 (0.81) 3.14 (0.91) .20

5.33 (1.12) 5.19 (1.12) .54 5.19 (1.13) 5.39 (1.09) .38

5.80 (0.43) 6.01 (0.39) .09

3.87 (1.35) 4.36 (1.94) .40

2.41 (0.66) 2.33 (0.93) .76

6.62 (1.03) 7.01 (0.97) .19

2.83 (0.79) 4 (0.51) b.001

5.24 (1.13) 5.37 (1.08) .69

5.81 (0.45) 5.85 (0.36) .70

3.72 (1.36) 4.40 (1.52) .04

2.37 (0.69) 2.46 (0.72) .55

6.56 (0.98) 6.84 (1.10) .24

2.93 (0.86) 3.33 (0.75) .005

5.10 (1.03) 5.61 (1.25) .06

5.79 (0.43) 6.18 (0.24) .008

3.94 (1.45) 3.90 (1.28) .93

2.33 (0.65) 3.05 (0.86) .002

6.61 (1.01) 7.23 (1.22) .08

2.98 (0.85) 2.94 (0.93) .88

5.29 (1.16) 4.96 (0.31) .40

5.80 (0.44) 5.85 (0.42) .51

3.57 (1.25) 4.22 (1.51) .02

2.35 (0.68) 2.43 (0.71) .58

6.68 (0.93) 6.66 (1.11) .91

2.87 (0.86) 3.06 (0.84) .26

5.28 (1.18) 5.24 (1.08) .84

ESRD indicates end-stage renal disease.

Please cite this article as: Cartin-Ceba R, et al, Predictive value of plasma biomarkers for mortality and organ failure development in patients with acute respiratory distress syndrome, J Crit Care (2014), http://dx.doi.org/10.1016/j.jcrc.2014.09.001

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Fig. 2. Mean plasma concentration of 6 different biomarkers over time comparing survivors vs nonsurvivors of ARDS. Adjusted for baseline characteristics (severity of disease and renal function). 1 ➔ between-subject effect, 2 ➔ within-subject effect, 3 ➔ interaction effect. Biomarkers were log10 transformed. P values indicate the time * group effect.

In this study, only the inflammatory pathway showed a predictive role in the development of MOF, although no association was found with mortality. Previous small studies have found no association of IL8 and mortality [16–19]. However, others have found that IL-8 is an important predictor of mortality [7,20,21], including 2 large studies by Parsons et al [6] and Ware et al [22] in the setting of randomized controlled trials, where 781 and 528 patients were analyzed, respectively. In fact, a recent systematic review and meta-analysis found a strong association of IL-8 levels and mortality in ARDS patients (OR, 3.4; 95% CI, 2-5.7; P b .01) [23], although these results are mainly driven by the 2 large studies previously mentioned that were part of randomized controlled trials [6,22]. In the study by Parsons and colleagues [6], elevated baseline levels of IL-8 were also associated with decreased organ failure–free days, similar to the association found in our study of increased risk of MOF in patients with elevated baseline levels of IL-8. A small study had also previously found a strong association between baseline levels of IL-8 and MOF [24]. Some have identified higher levels of IL-8 in the setting of sepsis as compared with other causes of ARDS [7,20,24]. In our study, we found no such difference. Baseline levels of the epithelial markers RAGE and CC-16 were found to be significantly higher in patients with pulmonary causes of ARDS as compared with extrapulmonary causes, likely reflecting the initial magnitude of damage to the alveolar epithelial cells. The levels of CC-16 were also found to be significantly higher when the renal function was impaired both in end-stage renal disease and acute kidney injury

patients, an observation previously reported by others [8], and therefore, the importance of the adjustment performed in our analyses. Others have found an association of RAGE [25] and CC-16 [8] levels with mortality; however, we found no association of these 2 biomarkers with mortality or the development of MOF in our study. Elevated levels of the endothelial biomarker vWF have also been associated with poor outcomes including mortality and organ failures in adults [3,4] and children [26]. In our study, there was a trend toward higher baseline levels of the endothelial marker vWF in nonsurvivors, which did not reach statistical significance. The degree of alterations in coagulation and fibrinolysis may be another important pathogenetic and prognostic determinant of mortality and MOF in ARDS as documented by different studies evaluating TATc [27] and PAI-1 [5,21]. Neither TATc nor PAI-1 was associated with mortality or MOF in our cohort. Several observations in the literature point to the important role of inflammation in the pathogenesis of ARDS [28], particularly the role of IL-8 [20,29,30]. The presence of inflammatory mediators such as IL-8 in the circulation has been shown to play a critical role in the pathophysiology of MOF [31]. Meduri and coworkers [7] found persistent elevation of blood levels of TNF-α, IL-1β, IL-6, and IL-8 in 10 nonsurvivors of early ARDS. The complexities and interrelationships of different pathways make it difficult to define an organized, sequential response to injury whether it is sepsis or another different insult such as high-risk surgery. Despite multiple studies showing different associations of biomarkers with mortality or organ failure, it is not well known if these markers are

Please cite this article as: Cartin-Ceba R, et al, Predictive value of plasma biomarkers for mortality and organ failure development in patients with acute respiratory distress syndrome, J Crit Care (2014), http://dx.doi.org/10.1016/j.jcrc.2014.09.001

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Fig. 3. Mean plasma concentration of 6 different biomarkers over time comparing development of MOF in ARDS patients. Adjusted for baseline characteristics (severity of disease and renal function). 1 ➔ between-subject effect, 2 ➔ within-subject effect, 3 ➔ interaction effect. Biomarkers were log10 transformed. P values indicate the time * group effect.

truly the mediators of an unfavorable outcome or simply an effect of the process (an epiphenomenon) predicting poor outcomes. Our study has several limitations. This is a single-center study with predominantly white population and relatively small sample size with limited statistical power. The biomarkers were measured from samples collected during routine clinical care; however, the samples were processed within 2 hours of collection, and a recent study by our group validated the use of clinically collected waste blood as compared with dedicated research samples [32]. Our study was limited to 6 different biomarkers; it is possible that other biomarkers might have better

Table 3 Multivariable model examining the association of baseline clinical characteristics of ARDS patients and the development of MOF and in-hospital mortality

Mortality McCabe score APS DNR status MOF McCabe score APS Agea a

OR (95% CI)

P

AUC (95% CI)

6.14 (2.23-19.2) 1.01 (0.90-1.13) 12.7 (2.81-54.2)

b.001 .84 .02

0.77 (0.73-0.81)

3.96 (1.33-12.9) 1.05 (1.02-1.08) 0.92 (0.88-0.96)

.01 b.001 b.001

0.88 (0.85-0.91)

Per 10-year increase in age.

performance. There are also other factors that could explain the differences with other studies including timing of measurements, thawing process, and assay characteristics. These limitations are counterbalanced by the strengths of the study that include the following: prospectively assembled cohort designed specifically for assessment of risk factors and outcomes of ARDS, well defined population with detailed description of ARDS phenotypes and covariates, and adequate adjustment for important baseline characteristics. 5. Conclusions In this study, we found that the addition of biological markers representative of important pathophysiologic pathways did not improve mortality prediction in a contemporary cohort of patients with ARDS. Only IL-8 levels at the time of diagnosis of ARDS were significantly associated with the development of MOF. The presence of inflammatory mediators such as IL-8 in the circulation confirms an important role of exaggerated inflammatory response in the pathophysiology of MOF. Acknowledgments Dr Cartin-Ceba and Dr Ognjen Gajic take full responsibility for the content of the manuscript, including the data and analysis. The authors do not have any disclosures related to this study.

Please cite this article as: Cartin-Ceba R, et al, Predictive value of plasma biomarkers for mortality and organ failure development in patients with acute respiratory distress syndrome, J Crit Care (2014), http://dx.doi.org/10.1016/j.jcrc.2014.09.001

R. Cartin-Ceba et al. / Journal of Critical Care xxx (2014) xxx–xxx

7

Table 4 Association between biomarker levels and the risk of MOF and in-hospital mortality comparing a single biomarker model and multivariable models Mortality

Single biomarker model vWF IL-8 TATc RAGE CC-16 PAI-1 Multivariate modelsb vWF IL-8 TATc RAGE CC-16 PAI-1 a b

MOF

ORa (95% CI)

P

AUC (95% CI)

ORa (95% CI)

P

AUC (95% CI)

2.39 (0.88-7.02) 1.18 (0.89-1.59) 1.10 (0.61-1.99) 0.88 (0.59-1.32) 1.23 (0.76-2.02) 0.88 (0.60-1.28)

.09 .23 .73 .55 .39 .52

0.58 (0.51-0.64) 0.57 (0.52-0.61) 0.54 (0.48-0.60) 0.53 (0.45-0.61) 0.55 (0.49-0.62) 0.54 (0.48-0.63)

2.05 (0.77-5.85) 2.77 (1.79-4.75) 1.44 (0.80-2.64) 1.07 (0.72-1.60) 1.14 (0.70-1.86) 1.32 (0.91-1.93)

.15 b.001 .22 .73 .57 .13

0.56 (0.50-0.63) 0.79 (0.76-0.82) 0.58 (0.51-0.64) 0.52 (0.45-0.59) 0.53 (0.45-0.61) 0.56 (0.48-0.64)

2.93 (0.90-10.7) 1.08 (0.72-1.61) 1.05 (0.53-2.05) 0.81 (0.50-1.30) 1.09 (0.60-2.02) 0.96 (0.62-1.47)

.10 .70 .88 .39 .75 .86

0.78 (0.74-0.83) 0.76 (0.69-0.83) 0.77 (0.70-0.84) 0.77 (0.71-0.83) 0.76 (0.67-0.84) 0.76 (0.66-0.86)

1.19 (0.34-4.45) 2.26 (1.34-4.22) 1.31 (0.58-2.97) 0.70 (0.40-1.18) 0.53 (0.25-1.07) 1.05 (0.66-1.67)

.78 .004 .50 .20 .08 .82

0.88 (0.83-0.93) 0.91 (0.88-0.94) 0.88 (0.84-0.93) 0.88 (0.82-0.95) 0.88 (0.82-0.94) 0.88 (0.84-0.92)

The ORs represent the increased risk per log10 change in biomarker level. Mortality multivariate model included the APS from the APACHE calculation, DNR status, and McCabe score. MOF model included APS, age, and McCabe score.

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Please cite this article as: Cartin-Ceba R, et al, Predictive value of plasma biomarkers for mortality and organ failure development in patients with acute respiratory distress syndrome, J Crit Care (2014), http://dx.doi.org/10.1016/j.jcrc.2014.09.001