Diagnostic and prognostic value of presepsin vs ...download.xuebalib.com/169dIRJO19GD.pdf · the diagnostic process and guide the therapy. Traditional biomarkers of illness severity,

Clin Chem Lab Med 2017; aop

Helena Brodska, Jiri Valenta, Kveta Pelinkova, Zdenek Stach, Robert Sachl, Martin Balik, Tomas Zima and Tomas Drabek*Diagnostic and prognostic value of presepsin vs. established biomarkers in critically ill patients with sepsis or systemic inflammatory response syndrome

https://doi.org/10.1515/cclm-2017-0839Received September 15, 2017; accepted October 22, 2017

Abstract

Background: Inflammatory biomarkers may aid to distin-guish between systemic inflammatory response syndrome (SIRS) vs. sepsis. We tested the hypotheses that (1) presep-sin, a novel biomarker, can distinguish between SIRS and sepsis, and (2) higher presepsin levels will be associated with increased severity of illness and (3) with 28-day mor-tality, outperforming traditional biomarkers.Methods: Procalcitonin (PCT), C-reactive protein (CRP), presepsin, and lactate were analyzed in 60 consecutive patients (sepsis and SIRS, n = 30 per group) on day 1 (D1) to D3 (onset sepsis, or after cardiac surgery). The systemic organ failure assessment (SOFA) score was determined daily.Results: There was no difference in mortality in sepsis vs. SIRS (12/30 vs. 8/30). Patients with sepsis had higher SOFA score vs. patients with SIRS (11 ± 4 vs. 8 ± 5; p = 0.023), higher presepsin (AUC = 0.674; p < 0.021), PCT (AUC = 0.791; p < 0.001), CRP (AUC = 0.903; p < 0.0001), but not lactate (AUC = 0.506; p = 0.941). Unlike other biomark-ers, presepsin did not correlate with SOFA on D1. All bio-markers were associated with mortality on D1: presepsin (AUC = 0.734; p = 0.0006; best cutoff = 1843 pg/mL), PCT (AUC = 0.844; p < 0.0001), CRP (AUC = 0.701; p = 0.0048), and lactate (AUC = 0.778; p < 0.0001). Multiple regression analyses showed independent associations of CRP with diagnosis of sepsis, and CRP and lactate with mortality.

Increased neutrophils (p = 0.002) and decreased lym-phocytes (p = 0.007) and monocytes (p = 0.046) were also associated with mortality.Conclusions: Presepsin did not outperform traditional sepsis biomarkers in diagnosing sepsis from SIRS and in prognostication of mortality in critically ill patients. Prese-psin may have a limited adjunct value for both diagnosis and an early risk stratification, performing independently of clinical illness severity.

Keywords: biomarker; mortality; outcome; sepsis; soluble cluster of differentiation 14 subtype; systemic inflamma-tory response syndrome.

IntroductionSepsis is one of the most prevalent causes of morbidity and mortality in hospitalized patients worldwide, with an estimated 19 million people treated for septic shock [1, 2]. The clinical definitions of sepsis and patho-physiologi-cally related systemic inflammatory response syndrome (SIRS) were refined over time but remain imprecise. Both conditions are intrinsically related to the broad array of causative agents and produced symptoms affecting het-erogeneous patient populations. While SIRS represents an orchestrated whole-body response to a non-infectious insult, definition of sepsis includes a known or suspected source of underlying infection [3].

Both SIRS and sepsis can initially manifest with similar clinical symptoms. However, the treatment could be substantially different. Hematological and biochemical biomarkers have historically been employed to facilitate the diagnostic process and guide the therapy. Traditional biomarkers of illness severity, e.g. lactate or leukocyte count may lack the specificity to distinguish these insults. From multiple biomarkers that have been tested, only a handful were adopted in a clinical setting. Specifically, C-reactive protein (CRP) and procalcitonin (PCT) [4, 5] seem to possess capacities to differentiate between infective vs. non-infective illness. As the complex pathophysiology of sepsis becomes gradually unveiled, novel biomarkers of early imprints of sepsis are explored for their capacities to

*Corresponding author: Tomas Drabek, MD, PhD, Department of Anesthesiology, University of Pittsburgh School of Medicine, UPMC Presbyterian Hospital, 200 Lothrop St. Suite C220, Pittsburgh, PA 15213, USA, Phone: +(1) 412-647-1687, E-mail: [email protected] Brodska, Kveta Pelinkova and Tomas Zima: Institute of Medical Biochemistry and Laboratory Diagnostics, 1st Faculty of Medicine, Charles University in Prague and General University Hospital in Prague, Prague, Czech RepublicJiri Valenta, Zdenek Stach, Robert Sachl and Martin Balik: Department of Anesthesiology and Intensive Care, 1st Faculty of Medicine, Charles University in Prague and General University Hospital in Prague, Prague, Czech Republic

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2 Brodska et al.: Presepsin in sepsis vs. SIRS

provide fast and accurate diagnosis to optimize and target therapy.

Presepsin – soluble cluster of differentiation 14 subtype (sCD14) – has recently gained significant attention as a promising 0 prognostic biomarker in sepsis. The biologi-cal role of sCD14 is very complex [6]. Membrane CD14 is a co-receptor for endotoxin, and its soluble form is cleaved from the monocyte/macrophage-specific CD14 receptor complex during systemic infections.

Presepsin is purported to possess superior diagnos-tic capacity for sepsis compared to PCT [7–10]. Presepsin was also found to be a valuable tool in prognostication of mortality [11–14]. However, its diagnostic and prognostic capacities in direct comparison to other biomarkers and physiologic parameters in critically ill patients yielded mixed results.

We tested the hypotheses that (1) presepsin can dis-tinguish between sepsis and SIRS; (2) presepsin will cor-relate with illness severity, and (3) higher presepsin levels will be associated with 28-day mortality. We hypothesized that presepsin would perform better than traditional bio-markers or hematological parameters assessed in criti-cally ill patients with SIRS or sepsis.

Materials and methodsThe study was approved by the Institutional Review Board (assur-ance # 1163/12 S-IV) of the General Teaching Hospital of Charles University, Prague, Czech Republic. A separate approval of the Insti-tutional Review Board (assurance # PRO12060499) of the University of Pittsburgh, Pittsburgh, PA, USA was granted for statistical evalua-tion of de-identified data.

The study was performed at the General Teaching Hospital of the First Medical Faculty, Prague, Czech Republic. Adult patients >18 years of age with expected intensive care unit (ICU) length of stay over 3 days were considered for enrollment. Exclusion criteria included patients with pre-existing immunosuppression, hemato-oncologic patients, patients with severe non-septic leucopenia, traumatic brain injury, intoxicated patients, pregnant women and prisoners. After receiving the informed consent from the patient or next-of-kin, respectively, 30 consecutive patients admitted for sepsis to the mixed medical-surgical ICU, Department of Anesthesiology and Intensive Care were enrolled. Concurrently, 30 patients sched-uled for a complex cardiac surgery at the 2nd Department of Cardiac Surgery were enrolled as a control SIRS group that underwent the same scoring and biomarkers assessment.

Physiologic condition on admission (day 1 – D1) was assessed using acute physiologic and chronic health evaluation (APACHE II) scoring system. The systemic organ failure assessment (SOFA) score was determined on D1 through D3.

Plasma levels of presepsin, PCT, CRP, lactate and creatinine were assessed on admission, which was considered D1, and then at 24 h (D2) and 48 h (D3).

Patients

SIRS, sepsis and its severity (uncomplicated sepsis/SIRS, severe sep-sis/SIRS, sepsis/SIRS with shock) were defined according to the 1992 criteria [15]. Acute kidney injury (AKI) was defined according to the AKI Network [16]. Acute lung injury (ALI) was defined according to previously published criteria [17, 18]. Oxygenation index was defined as paO2/FiO2.

Laboratory analyses

All routine biochemical parameters such as creatinine, lactate, CRP were determined by standard clinical chemistry methods using Unicel DXC 880i Beckman. Plasma supernatants were obtained after centrifugation of the blood samples for 10 min (RCF 2000), following which samples were stored in 0.5 mL aliquots at −80 °C before final assessment to avoid multiple freeze-thaw cycles. Plasma presepsin levels were measured by a chemiluminescent enzyme immunoassay system (Pathfast, Mitsubishi Chemical Medi-cine Corporation, Japan). PCT was measured by COBAS e411 ROCHE using electrochemiluminiscent imunoassay. The reference ranges used were as follows: PCT 0.0–0.05 μg/L, CRP 0.0–5.0 mg/L, cre-atinine 44–110 mmol/L, lactate 0.5–2.0 mmol/L. The reference lim-its of presepsin in healthy volunteers were previously determined to be 55–184 pg/mL [19]. Other authors report presepsin levels in healthy volunteers to be 259 ± 92 (min–max 111–425 pg/mL) [20] or 456 ± 25 pg/mL [21]. Total white blood count (WBC), neutrophils, lymphocytes and monocytes were determined by Sysmex XN 3000 via fluorescent flow cytometry.

Statistical analyses

Statistical software packages IBM SPSS Statistics Version 24 and MedCalc 17.7.2 were used. Initially, the Shapiro-Wilk test was used to determine the normality of data distribution. Student’s t-test or the Mann-Whitney U-test were then used to determine differences in individual data between groups as appropriate. A receiver opera-tor characteristic (ROC) curve was employed to determine the sen-sitivity and specificity of individual biomarkers to predict 28-day mortality. Comparison of ROC curves was used to evaluate the diagnostic performance of individual biomarkers [22]. Diagnostic accuracy was classified according to the area under the ROC curve (AUC) 0.9–1 = excellent; 0.80–0.90 = good; 0.70–0.80 = fair; 0.60–0.70 = poor; 0.50–0.60 = fail. AUC < 0.70 was not deemed acceptable for a diagnostic test [23]. Multiple logistic regression was used to test the independent association of biomarkers and diagnosis of sepsis, and 28-day mortality. Mortality was assessed using chi-square test. Survival time was assessed using the Kaplan-Meier survival plot, with additional analyses on individual biomarkers dichotomized over the median value to determine the effect on survival time using log-rank analysis. Pearson’s or Spearman’s rank correlation coefficient were used as a measure of linear relationship between two sets of data. Data are presented as mean ± standard deviation (SD) or median and 25%–75% interquartile ranges (IQR) for normally and non-normally distributed data, respectively. A value of p < 0.05 was considered sta-tistically significant.



Brodska et al.: Presepsin in sepsis vs. SIRS 3

Results

Pooled population of sepsis and SIRS patients

The baseline characteristics of the groups are displayed in Table 1. There were no differences in age, sex, survival or survival time between sepsis and SIRS. APACHE II was similar between groups contrasting SOFA score that was higher in the sepsis group. This was most likely driven by a higher incidence of ALI in sepsis patients. Correspond-ingly, the oxygenation index was also worse in the sepsis group but this trend failed to reach statistical significance. AKI and creatinine were similar between groups. There was no difference in the use of renal replacement therapy between groups on D1-3. Severity of illness was higher in the sepsis group.

Mortality and survival time were not different between groups (Table 1, Supplemental Figure 1). APACHE II score or age were not associated with mortality (p = 0.493 and p = 0.114, respectively), whereas higher SOFA score was (p = 0.013).

The overall evolution of biomarkers in individual groups is displayed in Supplemental Figure 2, showing raw data. For improved clarity, the same data were log-transformed and are displayed in Figure 1.

Table 1: Baseline characteristics of patient populations in sepsis vs. SIRS.

Sepsis (n = 30) SIRS (n = 30) p-Value

Age, years (median, IQR) 66 (53, 73) 68 (59, 73) 0.644Sex (m/f) 18/12 20/10 0.592Survival (yes/no) 18/12 22/8 0.273Survival, days (median, IQR) 28 (10, 28) 28 (15, 28) 0.550APACHE II (median, IQR) 33 (21, 35) 15 (10, 27) 0.114SOFA (mean ± SD) 11 ± 4 8 ± 5 0.023ALI (yes/no) 14/16 6/23 0.035Oxygenation index (mean ± SD)

238 ± 90 285 ± 102 0.067

AKI (yes/no) 14/16 9/21 0.184Creatinine (median, IQR) 134 (81, 221) 87 (75, 153) 0.160Severity of illness grade, n 4/5/21 14/3/13 0.019

Bold values are statistically significant i.e. p < 0.05. SIRS, systemic inflammatory response syndrome; APACHE, acute physiologic and chronic health evaluation; SOFA, systemic organ failure assess-ment; ALI, acute lung injury; AKI, acute kidney injury.

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Figure 1: Box plot of biomarkers on D1-3 sepsis vs. SIRS.Data are log transformed and displayed as mean ± SEM. Statistical differences between groups are not displayed to increase the clarity of the graphs. PCT, procalcitonin; CRP, C-reactive protein.




Sepsis vs. SIRS – which biomarkers can discriminate these conditions?

Presepsin, PCT and CRP, but not lactate were different between sepsis and SIRS patients on D1 and D2. Only prese-psin was higher in septic vs. SIRS group on D3 (Table 2). ROC analysis determined a strong correlation with the diagnosis of sepsis on D1 for CRP (AUC = 0.903, p < 0.001), superior to other studied biomarkers (PCT: AUC = 0.791, p < 0.001; presepsin: AUC = 0.674, p = 0.021; lactate: AUC = 0.506, p = 0.941). The correlation of presepsin with sepsis was still statistically significant on D3 but the correlation was poor (AUC = 0.695; p = 0.017). A multiple regression was run to differentiate sepsis vs. SIRS from CRP, PCT, presepsin and lactate. These variables statistically significantly differenti-ated sepsis from SIRS, F(4, 55) = 11.511, p < 0.001, R2 = 0.456. From the above-mentioned variables, only CRP added sta-tistically significantly to the analysis, p < 0.001.

Hematologic parameters except monocytes did not differ between groups (Supplemental Table 1). Creatinine was not different between groups in any timepoint (data not shown). Oxygenation index was decreased in the sepsis group on D2 (261 ± 97 vs. 324 ± 106; p = 0.026) and D3 (259 ± 61 vs. 340 ± 117; p = 0.004).

Which biomarkers can predict mortality?

Presepsin, PCT, CRP and lactate were higher in non-sur-vivors vs. survivors from the entire patient population on D1. Increased levels of individual biomarkers on D1 were associated with mortality irrespective of sepsis/SIRS status (Figure 2, Tables 3 and 4). Pairwise comparisons of ROC curves showed statistical difference only between CRP and PCT (p = 0.0332). On D2 and D3, presepsin, PCT and lactate but not CRP remained higher in non-survivors

Table 2: Biomarkers in sepsis vs. SIRS on D1-3.

CRP PCT PRE LAC

D1Sepsis 197 (127, 231) 7.8 (1.9, 16.8) 1692 (1017, 2523) 1.9 (1.2, 3.0)SIRS 5.8 (1.8, 5.8) 0.2 (0.1, 1.8) 884 (452, 1851) 2.0 (1.2, 2.9)p-Value 0.000 0.000 0.021 0.941D2Sepsis 202 (151, 281) 6.5 (1.6, 38) 1721 (840, 2588) 1.9 (1.0, 2.7)SIRS 98 (73, 154) 1.6 (0.2, 7.4) 881 (430, 1385) 1.6 (1.1, 2.4)p-Value 0.000 0.019 0.001 0.719D3Sepsis 185 (135, 293) 4.8 (1.0, 31.8) 1414 (896, 2568) 1.6 (1.0, 2.1)SIRS 189 (116, 238) 1.4 (0.4, 4.6) 773 (416, 1612) 1.1 (0.9, 1.4)p-Value 0.940 0.178 0.017 0.089

Bold values are statistically significant i.e. p < 0.05. Data are shown as median (IQR). SIRS, systemic inflammatory response syndrome; CRP, C-reactive protein; PCT, procalcitonin; PRE, presepsin; LAC, lactate.

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Figure 2: ROC curves for biomarkers and association with mortality.Area under the ROC curve (AUC) for sepsis and SIRS patients: CRP = 0.701; PCT = 0.844; PRE = 0.734; LAC = 0.778. p = 0.032 between CRP and PCT. For detailed evaluation please see Table 3. Area under the ROC curve for sepsis patients: CRP = 0.715; PCT = 0.729; PRE = 0.759; LAC = 0.731. No difference between area under the ROC curves. Area under the ROC curve for SIRS patients: CRP = 0.722; PCT = 0.955; PRE = 0.722; LAC = 0.861. PCT vs. CRP, p = 0.0091; PCT vs. PRE, p = 0.0301. ROC, receiver operator characteristics; SIRS, systemic inflamma-tory response syndrome; CRP, C-reactive protein; PCT, procalcitonin; PRE, presepsin; LAC, lactate.




Table 3: Area under the ROC curve analysis of association between biomarkers and mortality on D1.

CRP PCT PRE LAC

Area under the ROC curve 0.701 0.844 0.734 0.778Standard error 0.0711 0.0496 0.0677 0.062995% Confidence interval 0.569;0.812 0.727;0.925 0.604;0.840 0.652;0.875Significance level P 0.0048 <0.0001 0.0006 <0.0001Youden index J 0.3750 0.6500 0.5000 0.5500Associated criterion ≤42 ≤1.8 ≤1853 ≤1.8Sensitivity 47.50 65.00 75.00 60.00Specificity 90.00 100.00 75.00 90.00

ROC, receiver operator characteristics; CRP, C-reactive protein; PCT, procalcitonin; PRE, presepsin; LAC, lactate.

Table 4: Biomarkers in survivors vs. non-survivors on D1-3.

CRP PCT PRE LAC

Sepsis + SIRSD1Survivors 33 (3, 146) 0.3 (0.1, 3.6) 1017 (535, 1781) 1.6 (1.2, 3.0)Non-survivors 156 (77, 358) 10.8 (4.9, 62.2) 2228 (1299, 2808) 3.2 (2.4, 4.4)p-Value 0.012 0.000 0.003 0.000D2Survivors 133 (86, 199) 1.6 (0.6, 7.8) 1031 (552, 1937) 1.3 (0.9, 2.4)Non-survivors 161 (104, 228) 21.6 (3.7, 41.5) 2380 (1348, 3960) 2.1 (1.7, 8.6)p-Value 0.200 0.002 0.004 0.003D3Survivors 142 (92, 293) 1.4 (0.8, 8.6) 1103 (583, 1841) 1.0 (0.9, 1.4)Non-survivors 199 (139, 238) 7.6 (1.9, 16.8) 1843 (1110, 2940) 1.7 (1.2, 3.0)p-Value 0.489 0.027 0.047 0.002SepsisD1Survivors 166 (127, 214) 3.6 (0.9, 18.8) 1504 (1002, 2187) 1.6 (1.2, 3.0)Non-survivors 298 (159, 367) 26.6 (4.9, 93.6) 2808 (1519, 5594) 2.9 (2.4, 4.4)p-Value 0.048 0.035 0.017 0.035D2Survivors 176 (115, 259) 4.0 (1.4, 37.9) 1760 (916, 2518) 1.3 (0.8, 2.4)Non-survivors 214 (178, 231) 15.4 (3.0, 54.1) 3308 (1566, 6639) 2.6 (1.9, 9.6)p-Value 0.286 0.146 0.035 0.009D3Survivors 168 (96, 329) 3.6 (0.8, 22.9) 1390 (739, 2389) 1.2 (0.9, 1.7)Non-survivors 181 (99, 244) 5.0 (1.6, 54.0) 2541 (1105, 3377) 1.8 (1.3, 3.2)p-Value 0.849 0.338 0.144 0.030SIRSD1Survivors 5.5 (1.7, 65.2) 0.2 (0.1, 0.4) 805 (421, 1771) 1.6 (1.1, 2.8)Non-survivors 77 (18, 111) 9.4 (3.6, 41.0) 2106 (1161, 2236) 3.8 (3.2, 5.6)p-Value 0.070 0.000 0.070 0.002D2Survivors 101 (73, 167) 1.0 (0.2, 3.0) 723 (430, 1285) 1.4 (0.9, 2.4)Non-survivors 130 (97, 160) 21.6 (4.1, 27.9) 1652 (707, 2380) 1.8 (1.4, 9.8)p-Value 0.636 0.004 0.048 0.165D3Survivors 140 (92, 221) 1.0 (0.3, 3.6) 785 (419, 1710) 0.7 (0.6, 1.4)Non-survivors 212 (146, 237) 16.1 (4.5, 16.8) 1768 (828, 1848) 1.1 (0.9, 1.5)p-Value 0.192 0.051 0.303 0.051

Bold values are statistically significant i.e. p < 0.05. Data are shown as medians (IQR). SIRS, systemic inflammatory response syndrome; CRP, C-reactive protein; PCT, procalcitonin; PRE, presepsin; LAC, lactate.




vs. survivors (Table 4). A multiple regression showed that CRP, PCT, presepsin and lactate significantly predicted mortality, F(4, 55) = 4.282, p = 0.002, R2 = 0.260. However, only CRP (p = 0.026) and lactate (p = 0.007) added statisti-cally significantly to the prediction. Kaplan-Meier survival analyses on individual biomarkers showed that patients with higher-than-median PCT (p < 0.001), presepsin (p = 0.005), lactate (p = 0.002) but not CRP (p = 0.607) had shorter survival time (Supplemental Figure 3). Interest-ingly, two patients had abnormally high presepsin levels that decreased over time. One patient was from the sepsis group (#14) and one from the SIRS group (#34); both patients survived (Figure 3, Supplemental Figure 4).

Individual subpopulation of leukocytes but not WBC were significantly different in survivors vs. non-survivors only on D1 (Supplemental Table 2). Creatinine was higher in non-survivors vs. survivors on D1 (196 [73, 148] vs. 97 [73, 148]; p = 0.001).

Do biomarkers correlate with illness severity?

Lactate showed a superior, consistent strong correla-tion with the SOFA score on D1-3. CRP and PCT but not

presepsin correlated with SOFA score on D1. Lactate, CRP and PCT but not presepsin also correlated with the APACHE II score. Only CRP and PCT correlated with illness severity on D1 (Supplemental Table 3).

Separate analyses for patients in severe sepsis and septic shock only

We performed separate analyses of the capacities of bio-markers to discriminate between sepsis vs. SIRS and with respect to mortality in patients with higher degree of severity of illness, i.e. pooled population of patients with severe sepsis/SIRS or septic/SIRS shock.

In this more severely ill population, only CRP was dif-ferent between sepsis vs. SIRS on D1 (195 [141, 352] vs. 77 [11, 192]; p = 0.000) but not on D2-3. Other biomarkers were not statistically different between sepsis and SIRS on D1-3 (data not shown). Analysis of ROC curves for individual biomarkers shows the largest AUC for CRP (AUC = 0.879), followed by PCT (AUC = 0.679), lactate (AUC = 0.575) and presepsin (AUC = 0.522). Pairwise comparison of AUC of ROC curves showed statistical difference only between CRP and other individual biomarkers.

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Figure 3: Biomarkers in non-survivors vs. survivors on D1-3.Data are log transformed and displayed as mean ± SEM. Statistical differences between groups are not displayed to increase the clarity of the graphs. PCT, procalcitonin; CRP, C-reactive protein.




Higher levels of presepsin, PCT and lactate were asso-ciated with mortality, while CRP was not on D1-2. Only lactate was significantly different between non-survivors and survivors on D3 (Table 5). Analysis of ROC curves for individual biomarkers shows the largest AUC for lactate (AUC = 0.832), followed by PCT (AUC = 0.808), presepsin (AUC = 0.736) and CRP (AUC = 0.630). Pairwise comparison of AUC of ROC curves did not show statistical difference between AUC of individual ROC curves.

Separate analyses for sepsis and SIRS populations

In patients with sepsis, all biomarkers were higher in non-survivors vs. survivors on D1. On D2, only presepsin and lactate were higher. On D3, only lactate was higher. The association of biomarkers and mortality on D1 was the strongest for presepsin (AUC = 0.759; p = 0.018) followed by lactate (AUC = 0.731; p = 0.034), PCT (AUC = 0.729; p = 0.036) and CRP (AUC = 0.715; p = 0.049). There was no difference between survivors and non-survivors in any hematologic parameters on D1-3 (data not shown). Creati-nine was higher in non-survivors vs. survivors (213 [136, 235] vs. 129 [77, 176]; p = 0.035) on D1.

In patients with SIRS, only PCT and lactate were higher in non-survivors vs. survivors on D1. There were no differ-ences on D3. The association of biomarkers and mortality on D1 was the strongest for PCT (AUC = 0.955; p = 0.000) followed by lactate (AUC = 0.861; p = 0.003). Presepsin and CRP were not associated with mortality in patients with SIRS (both AUC = 0.722; p = 0.067) (Table 4). From hema-tologic parameters, neutrophil count was higher (88 [86, 93] vs. 83 [78, 84]; p = 0.001) and lymphocyte count lower

(4.0 [2.7, 11.8] vs. 4.9 [2.8, 10.3]; p = 0.002) in non-survivors vs. survivors. No differences were observed in monocytes. Creatinine was higher in non-survivors vs. survivors (184 [125, 206] vs. 87 [71, 124]; p = 0.012) on D1.

DiscussionIn our study, we tested the capacities of presepsin as a novel biomarker (1) to differentiate sepsis from SIRS, and (2) to predict mortality at 28 days. We compared presepsin to other established biomarkers, i.e. CRP, PCT and lactate, and selected hematologic parameters. We also performed a comparison of biomarkers with indices of the severity of illness, and evaluated separately patients in sepsis vs. SIRS.

Regarding diagnostic capacities, presepsin levels were higher in patients with sepsis vs. in patients with SIRS. However, analysis of the ROC curve showed a poor correlation with diagnosis of sepsis. Both CRP and PCT showed superior association with diagnosis of sepsis than presepsin whereas lactate was unable to differenti-ate between sepsis and SIRS. The diagnostic capacities to determine sepsis from SIRS were further decreased when patient population was limited to those with higher sever-ity of disease, i.e. in severe sepsis/SIRS or in shock from either sepsis or SIRS. In this subpopulation, only CRP was able to diagnose sepsis from SIRS. CRP outperformed other biomarkers in its ability to diagnose sepsis from SIRS. However, based on ROC curve analysis, the associa-tion of CRP levels with diagnosis of sepsis was still only fair [23].

Regarding prognostic capacities, presepsin levels were higher in non-survivors vs. survivors on D1-3. PCT

Table 5: Biomarkers in survivors vs. non-survivors in high severity subpopulation on D1-3.

CRP PCT PRE LAC

D1Survivors 136 (41, 190) 1.5 (0.3, 10.6) 1504 (943, 3187) 1.6 (1.1, 2.5)Non-survivors 168 (92, 358) 32.4 (6.5, 77.5) 2316 (2015, 4957) 3.7 (2.5, 5.5)p-Value 0.162 0.001 0.011 0.000D2Survivors 176 (101, 245) 2.2 (0.9, 30.0) 1616 (709, 2449) 1.4 (0.9, 2.4)Non-survivors 202 (102, 281) 26.9 (5.1, 49.5) 2667 (1623, 4200) 2.6 (2.0, 10.5)p-Value 0.871 0.026 0.018 0.001D3Survivors 227 (186, 275) 4.2 (0.9, 16.6) 1795 (1001, 2509) 1.2 (0.8, 1.5)Non-survivors 140 (92, 317) 16.1 (3.3, 41.9) 1853 (1191, 2940) 1.8 (1.4, 5.2)p-Value 0.357 0.136 0.522 0.005

Bold values are statistically significant i.e. p < 0.05. Data are shown as median (IQR). SIRS, systemic inflammatory response syndrome; CRP, C-reactive protein; PCT, procalcitonin; PRE, presepsin; LAC, lactate.




showed good correlation and lactate fair correlation with mortality, both biomarkers outperforming presepsin that was superior to CRP. These results were similar when tested in the population with higher severity of disease as previously described. In separate analyses for sepsis and SIRS patients, presepsin had the best correlation with mortality in patients with sepsis. In contrast, it was not associated with mortality in patients with SIRS.

Presepsin, in contrast to other biomarkers, did not correlate with severity of disease as determined by SOFA or APACHE II on D1.

Hematologic parameters were similar in sepsis vs. SIRS except monocytes being higher in patients with SIRS on D1-2. Higher neutrophil count and lower lymphocyte and monocyte count on D1 were associated with mortality.

Multiple biomarkers have been used to distinguish between infective and non-infective causes of critical illness to guide therapy. Traditional biomarkers, e.g. lactate showed strong association with outcome irrespec-tive of the underlying pathology [24]. CRP has become the first “gold standard” biomarker used in sepsis diagnosis. PCT [25] complements or has replaced CRP which was deemed less specific. However, the unequivocal evidence of superiority of PCT over CRP is still lacking [26].

The biological role of presepsin in the host-pathogen response and its diagnostic and prognostic value has been reviewed in detail previously [27]. Meta-analyses suggested a moderate (AUC = 0.86) [28] to a very good (AUC = 0.89) [29, 30] diagnostic capacities of presepsin. A follow-up analysis is currently under way [31].

In emergency department patients, presepsin values were significantly higher in sepsis vs. SIRS, with better per-formance of presepsin (AUC = 0.750) than CRP (AUC = 0.602) but worse than PCT (AUC = 0.787) [8]. A follow-up study confirmed diagnostic superiority of PCT (AUC = 0.815) over presepsin (AUC = 0.775) [7]. These findings are in concert with Ulla et al. [9] who reported better performance of PCT (AUC = 0.875) than presepsin (AUC = 0.701). Godnic et al. [32] reported better capacity for the detection of bacterial infection for presepsin (AUC = 0.705) than PCT (AUC = 0.630) but not CRP (AUC = 0.734). Other studies found a similar performance between presepsin and PCT, both outperforming CRP in diagnosing sepsis vs. SIRS [10, 33, 34]. In septic patients with community pneumonia, diagnostic accuracy of presepsin (AUC = 0.75) was similar to PCT (AUC = 0.80) [35]. Enguix-Armada et al. [36] showed an excellent association of presepsin (AUC = 0.948), PCT (AUC = 0.989) and CRP (AUC = 0.922) for diagnosis of sepsis vs. controls. These reports are contrasted by several studies in which presepsin outperformed other biomarkers in detecting sepsis [32, 37].

Several studies also explored predictive capacities of presepsin for mortality from sepsis. Masson et al. [38] found that admission levels of presepsin were increased in non-survivors vs. survivors, with levels almost identical to ours, providing further credibility to our study. Presep-sin was the only variable independently associated with ICU and 28-day mortality [38]. Kim et al. [13] identified an optimal cutoff value of presepsin for non-survivorship was 2455 pg/mL, corresponding to our result 2808 pg/mL. Interestingly, Ulla et al. [9] reported much higher values of presepsin on admission for both survivors (3451 pg/mL) and non-survivors (4232 pg/mL). Initial presepsin levels, but not PCT levels, significantly correlated with in-hospi-tal mortality of patients affected by sepsis, severe sepsis or septic shock [9]. Matera et al. [12] reported an excellent association of presepsin with 30-day mortality in critically ill septic patients (AUC = 0.933 on D1 and AUC = 0.993 on D3, respectively), outperforming PCT and CRP. Yu et al. reported a decreasing trend of presepsin levels in survi-vors but an increasing trend in non-survivors. Our results do not support this observation. This could be partially explained by the fact that our study was limited to D1-3, while Yu et al. [14] continued to D12, with the most marked difference in D5-12. Correlation between presepsin and mortality yielded conflicting results [35, 36].

A significant proportion of our patient population was diagnosed with AKI on admission. AKI could have a specific effect on individual biomarkers. Presepsin con-centrations increase with age and kidney dysfunction [39, 40]. In AKI patients with inflammation, both PCT and presepsin were found to be useful markers of bacte-rial infections, but different thresholds were advised to be applied to diagnose sepsis [41]. Creatinine levels, the incidence of AKI and the use of renal replacement therapy was similar between groups in our study. Thus, we did not adjust our statistical analyses for presence of AKI or use of continuous renal replacement therapy. However, interpre-tation of presepsin concentrations might be altered in the elderly or in patients with impaired renal function, espe-cially in those undergoing continuous renal replacement therapy [42]. Adapted thresholds may be needed in these specific populations.

Presepsin, aside from its role in sepsis, was studied in non-sepsis clinical scenarios. Increasing levels of pre-sepsin were associated with worse outcomes in cardiac surgery patients [43]. In our study, we chose cardiac surgery patients exposed to ECC as a control group with SIRS of similar illness severity. Both PCT [44, 45] and pre-sepsin [43] showed significant associations with illness severity and prognosis in cardiac surgery patients. The dynamics of PCT levels over time may be more important




than absolute values. This finding is similar to other ICU populations [46]. Our results document good correlation of presepsin with a SOFA score in SIRS patients on D2 (p = 0.023) and a non-significant trend on D3 (p = 0.088). This may represent a delayed response to ECC which was not apparent on D1. In contrast, presepsin was increased in non-surviving SIRS patients after ECC on D2 and corre-lated with increased severity of illness on D2-3. Our results are thus in agreement with previously reported data from cardiac surgery patients.

Our study was designed as a pragmatic exploratory trial that included a control group of non-septic patients exposed to ECC. However, the groups were not matched in terms of severity of illness. The limitations of our study include its relatively small size. However, other studies reported a similar or a smaller number of patients [12, 21, 32]. Our study was not powered to perform a sepa-rate analysis of biomarkers in patients in severe sepsis or septic shock. Performance of presepsin was reportedly superior to other biomarkers in this subgroup of critically ill patients [36]. We did not observe this effect.

We did not study a group of healthy individuals. Pre-viously reported presepsin levels from healthy controls vary considerably [19–21]. These individual variations may have contributed to the lack of statistically signifi-cant effect in selected comparisons in our study. We used sepsis criteria valid at the time of study. However, current sepsis-3 criteria correlate well with prior versions, identi-fying similar populations [47, 48].

In conclusion, presepsin could discriminate between SIRS and sepsis in D1-3 contrasting CRP and PCT that could do so only on D1-2. However, diagnostic accuracy of CRP and PCT was superior to presepsin. Lactate levels were similar in SIRS and sepsis. Presepsin did not corre-late with illness severity on D1 whereas other biomarkers did. Higher presepsin levels were associated with 28-day mortality, with strongest correlation in septic patients on D1 in whom it outperformed other biomarkers.

Taken altogether, our results provide evidence that presepsin may have a limited, modest value in diagnos-ing sepsis from SIRS in the early stages, and provide addi-tional useful prognostic information for patients with severe sepsis or septic shock. These preliminary findings suggest that presepsin may be of clinical importance for early risk stratification.

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.Research funding: Supported by the Research Project PROGRES Q 25 of the Charles University in Prague, Czech

Republic, by RVO VFN 64165 of the General University Hospital in Prague, Czech Republic, and by the Euro-pean Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 687697 (www.SmartDiagnos.eu).Employment or leadership: None declared.Honorarium: None declared.Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

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Supplemental Material: The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2017-0839).



https://doi.org/10.1515/cclm-2017-0839

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