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Perioperative Medicine  |   April 2017
Presepsin (sCD14-ST) Is a Novel Marker for Risk Stratification in Cardiac Surgery Patients
Author Notes
  • From the Department of Anaesthesiology, Intensive Care Medicine and Pain Medicine (H.B., T.V., H.V.G.); Division of Nephrology and Hypertension, Department of Medicine (M.K.), Institute for Medical Biometry, Epidemiology, and Medical Informatics (S.W.), and Department of Thoracic and Cardiovascular Surgery (H.-J.S.), Saarland University, University Medical Centre, Homburg/Saar, Germany; DIAneering—Diagnostics Engineering & Research GmbH, Heidelberg, Germany (E.S.); and Department of Outcomes Research, Anesthesiology Institute, Cleveland Clinic, Cleveland, Ohio (D.I.S.).
  • Submitted for publication June 5, 2016. Accepted for publication December 16, 2016.
    Submitted for publication June 5, 2016. Accepted for publication December 16, 2016.×
  • This article is featured in “This Month in Anesthesiology,” page 1A.
    This article is featured in “This Month in Anesthesiology,” page 1A.×
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    This article has an audio podcast.×
  • Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are available in both the HTML and PDF versions of this article. Links to the digital files are provided in the HTML text of this article on the Journal’s Web site (www.anesthesiology.org).
    Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are available in both the HTML and PDF versions of this article. Links to the digital files are provided in the HTML text of this article on the Journal’s Web site (www.anesthesiology.org).×
  • Address correspondence to Dr. Groesdonk: Department of Anesthesiology, Intensive Care Medicine and Pain Medicine, Saarland University Medical Center, Kirrbergerstraße 1, 66421 Homburg/Saar, Germany. heinrich.groesdonk@uniklinikum-saarland.de. This article may be accessed for personal use at no charge through the Journal Web site, www.anesthesiology.org.
Article Information
Perioperative Medicine / Clinical Science / Cardiovascular Anesthesia
Perioperative Medicine   |   April 2017
Presepsin (sCD14-ST) Is a Novel Marker for Risk Stratification in Cardiac Surgery Patients
Anesthesiology 4 2017, Vol.126, 631-642. doi:10.1097/ALN.0000000000001522
Anesthesiology 4 2017, Vol.126, 631-642. doi:10.1097/ALN.0000000000001522
Abstract

Background: Presepsin (soluble cluster-of-differentiation 14 subtype [sCD14-ST]) is a humoral risk stratification marker for systemic inflammatory response syndrome and sepsis. It remains unknown whether presepsin can be used to stratify risk in elective cardiac surgery. The authors therefore determined the usefulness of presepsin for risk stratification in patients having elective cardiac surgery.

Methods: Eight hundred fifty-six cardiac surgical patients were prospectively studied. Preoperative plasma concentrations of presepsin, procalcitonin, N-terminal pro–hormone natriuretic peptide, cystatin C, and the additive European System of Cardiac Operative Risk Evaluation 2 were compared to mortality at 30 days (primary outcome), 6 months, and 2 yr. Discrimination was assessed with C statistic. Logistic regression analysis was used to calculate univariable and multivariable odds ratios.

Results: Thirty-day mortality was 3.2%, 6-month mortality was 6.1%, and 2-yr mortality was 10.4% across the population. Median preoperative presepsin concentrations were significantly greater in 30-day nonsurvivors than in survivors: 842 pg/ml (interquartile range, 306 to 1,246) versus 160 pg/ml (interquartile range, 122 to 234); difference, 167 pg/ml (interquartile range, 92 to 301; P < 0.001). The results were similar for 6-month and 2-yr mortality. Compared to the European System of Cardiac Operative Risk Evaluation 2, presepsin concentration provided better discrimination for postoperative mortality at all follow-up periods, including 30 days (C statistic 0.88 vs. 0.74), 6 months (0.87 vs. 0.76), and 2 yr (0.81 vs. 0.74). Presepsin also provided better discrimination than cystatin C, N-terminal pro–hormone natriuretic peptide, or procalcitonin. Elevated presepsin remained an independent risk predictor after adjustment for potential confounding factors.

Conclusions: Elevated preoperative plasma presepsin concentration is an independent predictor of postoperative mortality in elective cardiac surgery patients and is a stronger predictor than several other commonly used assessments.

What We Already Know about This Topic
  • Presepsin (soluble cluster-of-differentiation 14 subtype [sCD14-ST]) is a humoral risk stratification marker for systemic inflammatory response syndrome and sepsis. It remains unknown whether presepsin can be used to stratify risk in elective cardiac surgery.

  • This study determined the usefulness of presepsin for risk stratification in patients having elective cardiac surgery.

What This Article Tells Us That Is New
  • Elevated preoperative plasma presepsin concentration is an independent predictor of postoperative mortality in elective cardiac surgery patients and is a stronger predictor than several other commonly used assessments.

SOLUBLE cluster-of-differentiation 14 subtype (sCD14-ST) is a 13-kd protein that is a truncated N-terminal fragment of CD14.1,2  Cluster-of-differentiation 14 (CD14) is a high-affinity receptor for complexes of lipopolysaccharide and lipopolysaccharide-binding proteins that are expressed on the surface of various cells of the intrinsic and adaptive immune systems.3  Upon cellular activation, CD14 is shed from the cell membrane and released in the circulation as soluble CD14 (sCD14).1  During circulation, sCD14 is activated by plasma proteases that transform it into sCD14-ST, also known as presepsin.2 
Recent study suggests that immune activation contributes to the pathogenesis of cardiac diseases and heart failure,4,5  as well as to the vulnerability of atherosclerotic plaques.6  For example, Toll-like receptor 4 expression on monocytes and the expression of Toll-like receptor 4 on circulating peripheral CD14+ monocytes may play pivotal roles in the pathogenesis of coronary artery diseases. Macrophage colony-stimulating factor induces CD14++ monocytes to become CD14+CD16+ monocytes. During this process, CD14 is shed into the circulation as sCD14. sCD14 is thus an indicator of monocyte activation, possibly triggered by factors including lipopolysaccharide, heat-shock proteins, ischemia, tissue hypoxia, left ventricular distension, or increasing filling pressures.7 
Plasma presepsin concentrations are associated with the severity of sepsis and its outcome, suggesting that presepsin may be a candidate marker for prognosis and for monitoring therapeutic efficacy.8–10  Elevated presepsin concentrations are also associated with increased incidence of cardiovascular disease and all-cause mortality in the elderly,11  suggesting that presepsin is a marker of cardiovascular dysfunction.
In the cardiac surgery population, it is well established that cardiopulmonary bypass (CPB) activates various inflammatory pathways, resulting in systemic inflammatory response syndrome and consequent release of proinflammatory cytokines.12  Since presepsin is an inflammatory marker, it is unsurprising that the elevated presepsin concentrations predicted infectious complications and consequent mortality in a small cohort of patients recovering from cardiac surgery.13 
Our primary hypothesis was that increased preoperative presepsin concentration is associated with increased overall mortality after elective cardiac surgery. We thus initially evaluated the relationship between preoperative presepsin plasma concentration and 30-day, 6-month, and 2-yr mortality. We then determined whether presepsin concentrations add prognostic information to conventional risk stratification strategies. Specifically, we considered the extent to which presepsin might add information to the clinical grading system European System of Cardiac Operative Risk Evaluation 2 (EuroSCORE 2). Finally, we compared presepsin to established markers of cardiovascular dysfunction (N-terminal pro–hormone brain natriuretic peptide [NT-proBNP]), comorbidities (cystatin C), and inflammation (procalcitonin).14–16 
Materials and Methods
With approval of our local ethics committee (Landesärztekammer des Saarlandes [Saarbrücken, Germany]; Ref. ID: 199/09) and written consent from participants, we conducted a prospective cohort study to evaluate various risk models for prediction of postoperative morbidity and mortality in patients undergoing elective cardiac surgery. Patients were enrolled from January 1, 2010, to March 31, 2011.
Adults scheduled for cardiac surgery at our university medical center were screened for participation. The inclusion criterion was elective cardiac surgery with planned CPB. Exclusion criteria were refusal to participate, planned off-pump surgery, American Society of Anesthesiologists physical status score IV/V, and insufficient knowledge of the German language.
Anesthesia was induced with fentanyl (5 μg/kg) and etomidate (20 mg). All patients were given intravenous rocuronium (1 mg/kg) before tracheal intubation. Anesthesia was maintained by sevoflurane (minimum alveolar concentration fraction 0.4) and remifentanil (0.5 μg · kg−1 · min−1). During CPB, propofol was given continuously with 5 mg · kg−1 · h−1.
Clinical data were obtained during an initial patient interview and subsequent review of medical documentation. Patient demographic and perioperative data were entered into an electronic databank. Loss of sinus rhythm, need for external pacemaker, intraaortic balloon pump support, reexploration for bleeding, packed erythrocyte transfusion, and platelet transfusion were evaluated on the first postoperative day.
Laboratory Analyses
Blood was sampled for presepsin the day before surgery from a peripheral vein and on the first postoperative morning from a central venous catheter. Blood was collected in 2.7-ml EDTA tubes (Sarstedt AG and Co., Germany) and kept on ice no longer than 5 min before being centrifuged at 1,525g for 10 min at 4°C. Plasma was allocated into polypropylene tubes (Sarstedt AG and Co.) and stored at –80°C for subsequent analysis.
Blood samples were blinded before sending to the DIAneering laboratory (Heidelberg, Germany). Plasma presepsin concentrations were determined in the DIAneering laboratory using PATHFAST Presepsin (LSI Medience Corporation, Japan), a noncompetitive chemiluminescence enzyme immunoassay combined with MAGTRATION technology: during incubation of the sample with alkaline phosphatase-labeled anti–presepsin polyclonal antibody and anti–presepsin monoclonal antibody-coated magnetic particles, the presepsin of the sample binds to the anti–presepsin antibodies, forming an immunocomplex with enzyme-labeled antibody and antibody-coated magnetic particles.17  The assay is usable for quantitative measurement of presepsin in anticoagulated (via heparin or EDTA) whole blood, serum, or plasma. The assay range is 20 to 20,000 ng/l, and functional sensitivity is 57 ng/l. At 445 ng/l, the coefficient of variation is 4.4%; at 882 ng/l, the coefficient of variation is 4.0%. Presepsin concentrations were batch processed after patients were discharged from the hospital. The results thus were unavailable to the clinical teams and could not influence care decisions.
Other blood analysis was conducted by the central laboratory of the Saarland University Medical Center (Homburg/Saar, Germany) per clinical routine. NT-proBNP, cystatin C, procalcitonin, creatinine, gamma-glutamyl transpeptidase, aspartate aminotransferase, alanine aminotransferase, bilirubin, and albumin were determined using automated tests (cobas 8000 modular analyzer; Roche Diagnostics GmbH, Germany), according to the manufacturer’s instructions.
NT-proBNP concentration was determined using the Elecsys NT-proBNP II Test automated electrochemiluminescence assay (Roche Diagnostics GmbH). The assay range is 5 to 35,000 pg/ml, functional sensitivity is 50 pg/ml. At 126 pg/ml, the coefficient of variation is 2.4%; at 33,606 pg/ml, the coefficient of variation is 2.7%.
Cystatin C concentration was determined using the Tina-quant Cystatin C latex particle-enhanced immunoturbidimetric assay (Roche Diagnostics GmbH). The assay range is 0.40 to 6.80 mg/l, functional sensitivity is 0.40 mg/l. At 0.983 mg/l, the coefficient of variation is 0.9%; at 4.18 mg/L, the coefficient of variation is 0.7%.
Procalcitonin concentration was determined using the Elecsys BRAHMS procalcitonin automated electrochemiluminescence assay (BRAHMS AG, Germany).The assay range is 0.02 to 100 ng/ml, functional sensitivity is 0.06 ng/ml. At 0.060 ng/ml, the coefficient of variation is 8.8%; at 41 ng/ml, the coefficient of variation is 2.1%. Procalcitonin was not routinely determined during the study period in all patients. For about one quarter of the patients, concentrations of procalcitonin were determined only after discharge in the same laboratory and with the same method as described above.
Clinical Endpoints
Our primary clinical endpoint was 30-day mortality; our secondary endpoints were 6-month and 2-yr mortality. Patients were contacted via telephone between October and December 2012, corresponding to 2 to 3 yr after hospital discharge. When patients were deceased, a family member was contacted to provide the date and reason of death. When neither the patient nor a relative could be reached, we contacted the patient’s general practitioner, cardiologist, or nephrologist. If mortality status remained undetermined, we then queried the national registration office to determine vital status. Patients who were lost to follow-up were excluded from 6-month and 2-yr analyses.
Data Analysis
Categorical variables in survivors and nonsurvivors were compared with chi-square tests. Continuous variables were compared with Mann–Whitney U test.
C statistic was performed to evaluate the predictive power of preoperative plasma presepsin concentrations for mortality. This is also done for additional known risk factors. C statistics were compared using the tests of DeLong et al.18  Additionally, the Youden Index was used to calculate optimal thresholds to predict mortality.
The number needed to screen (NNS) was calculated to measure how many patients have to be screened with values larger than respective optimal thresholds to avoid one death in patients undergoing elective cardiac surgery.
Mortality risk was initially estimated via univariate analysis. Thereafter, risk was estimated in a primary model using multivariable logistic regression including EuroSCORE 2 as confounding factors. Several sensitivity analyses were performed, such as including the confounding factors listed in table 1 and Supplemental Digital Content 1, Table, http://links.lww.com/ALN/B370. To account for possible collinearity in multiple regressions, Pearson or Spearman correlation coefficients were calculated. Variables with a positive or negative bivariate correlation exceeding +0.5 or less than −0.5 were evaluated for interactions. The goodness of fit was assessed by Hosmer–Lemeshow tests. In addition, we calculated the Bayesian Information Criterion (BIC) for adjusted logistic regression models.
Table 1.
Population Characteristics
Population Characteristics×
Population Characteristics
Table 1.
Population Characteristics
Population Characteristics×
×
Patients were additionally categorized into terciles of preoperative presepsin concentrations. A Kaplan–Meier survival curve was plotted to examine the relationship between terciles of presepsin and mortality. The difference of presepsin (postoperatively minus preoperatively) in survivors and nonsurvivors was evaluated with Mann–Whitney U test. Data analysis was performed using NCSS version 9.0.5 (NCSS, LLC, USA) and SPSS Statistics 19 (IBM, Germany). Continuous variables are expressed as median and interquartile range (IQR); categorical variables are presented as absolute and relative frequencies, respectively. Statistical significance was accepted at a two-sided significance level of α = 0.05. Statistical analyses performed in our study were planned before accessing the study data, including univariate analysis (chi-square and Mann–Whitney U test), C statistic, multivariable logistic regression analysis, Kaplan–Meier methods, and log-rank test. After accessing study data, post hoc calculation of BIC and tests of DeLong et al.18  were performed. No priori power calculation was conducted.
Results
During the study period, 1,272 patients had cardiac surgery with extracorporeal circulation. Among these, 407 patients did not meet inclusion criteria due to American Society of Anesthesiologists physical status IV/V or denying consent for the study. Nine additional patients were excluded because blood for preoperative presepsin was not obtained. The final study population thus included 856 patients, of whom 27 died during the initial 30 postoperative days. Among 856 discharged patients, 47 patients were lost during the 2-yr follow-up period (Supplemental Digital Content 2, Figure, flow chart of the case selection, http://links.lww.com/ALN/B369). Of the remaining 809 patients, 49 died during the initial 6 months and 84 during the initial 2 yr after surgery.
Patients who died within 30 days, 6 months, or 2 yr were generally older, had a higher EuroSCORE 2, and had poorer renal function. They also had more comorbidities, used more medications, had more pathologic preoperative and postoperative laboratory findings, and longer operations (table 1; Supplemental Digital Content 1, Table, which is a table with additional population characteristics, http://links.lww.com/ALN/B370). None of the patients had preoperative signs of infection, fever, or positives bacterial cultures. Nonsurvivors had more complications than survivors by the first postoperative day (table 1).
Preoperative Presepsin Concentrations
Median preoperative plasma presepsin concentrations were significantly greater in patients who died within 30 days (842 pg/ml [IQR, 306 to 1,246] vs. 160 pg/ml [IQR, 122 to 234; P < 0.001]), 6 months (468 pg/ml [IQR, 267 to 1,141] vs. 157 pg/ml [IQR, 121 to 225; P < 0.001]), and 2 yr (352 pg/ml [IQR, 219 to 751] vs. 156 pg/ml [IQR, 120 to 219; P < 0.001]). Presepsin concentrations were not significantly different between patients with and without statins.
C statistic showed that preoperative presepsin concentrations had a high discrimination for prediction of 30-day mortality (optimal threshold of 293 pg/ml, C-statistic 0.88, sensitivity 82%, and specificity 83%; fig. 1), 6-month mortality (optimal threshold of 289 pg/ml, C-statistic of 0.87, sensitivity 76%, and specificity 84%), and 2-yr mortality (optimal threshold of 215 pg/ml, C-statistic of 0.81, sensitivity 77%, and specificity 74%). The NNS, the positive predictive value (PPV), and the negative predictive value (NPV) were calculated for 30-day mortality using the relevant optimal thresholds (NNS: 7.9, PPV: 13%, and NPV: 99%), 6-month mortality (NNS: 4.7, PPV: 23%, and NPV: 98%), and 2-yr mortality (NNS: 4.5, PPV: 26%, and NPV: 97%).
Fig. 1.
Preoperative presepsin and C-statistic to predict mortality. Box-and-whisker plot with the tenth and ninetieth percentiles. Solid line inside box = median values of presepsin; plus symbol = mean values of presepsin; blue solid line inside box = optimal threshold. Receiver operating characteristic curves were constructed to evaluate the predictive power of presepsin for 30-day, 6-month, and 2-yr mortality. The Youden Index was used to calculate optimal threshold for presepsin in prediction of 30-day, 6-month, and 2-yr mortality. Samples were taken in the afternoon the day before surgery.
Preoperative presepsin and C-statistic to predict mortality. Box-and-whisker plot with the tenth and ninetieth percentiles. Solid line inside box = median values of presepsin; plus symbol = mean values of presepsin; blue solid line inside box = optimal threshold. Receiver operating characteristic curves were constructed to evaluate the predictive power of presepsin for 30-day, 6-month, and 2-yr mortality. The Youden Index was used to calculate optimal threshold for presepsin in prediction of 30-day, 6-month, and 2-yr mortality. Samples were taken in the afternoon the day before surgery.
Fig. 1.
Preoperative presepsin and C-statistic to predict mortality. Box-and-whisker plot with the tenth and ninetieth percentiles. Solid line inside box = median values of presepsin; plus symbol = mean values of presepsin; blue solid line inside box = optimal threshold. Receiver operating characteristic curves were constructed to evaluate the predictive power of presepsin for 30-day, 6-month, and 2-yr mortality. The Youden Index was used to calculate optimal threshold for presepsin in prediction of 30-day, 6-month, and 2-yr mortality. Samples were taken in the afternoon the day before surgery.
×
Outcomes as a function of optimal threshold of preoperative presepsin threshold concentrations are reported in table 2. Interestingly, 30-day mortality for patients below the median preoperative concentration of 164 pg/ml was 0.2 versus 6% above the median; at 6 months, mortality below the median of 164 pg/ml was 0.5 versus 12% at higher concentrations; at 2 yr, mortality below the median of 164 pg/ml was 2.5 versus 18% at higher concentrations.
Table 2.
Outcome Related to Threshold Values of Preoperative Presepsin
Outcome Related to Threshold Values of Preoperative Presepsin×
Outcome Related to Threshold Values of Preoperative Presepsin
Table 2.
Outcome Related to Threshold Values of Preoperative Presepsin
Outcome Related to Threshold Values of Preoperative Presepsin×
×
The risk factors with the highest odds ratios were age, duration of surgery, and the need for postoperative renal replacement therapy. Patients with presepsin concentrations above the thresholds had an increased risk of mortality within 30 days (threshold of 293 pg/ml; crude odds ratio [OR], 20.8; 95% CI, 7.7 to 55.7; P < 0.001), 6 months (threshold of 289 pg/ml; crude OR, 16.3; 95% CI, 8.3 to 32.1; P < 0.001), and 2 yr (threshold of 215 pg/ml; crude OR, 9.3; 95% CI, 5.5 to 15.7; P < 0.001) after surgery. Even after adjustment for EuroSCORE 2, presepsin concentrations remained an independent risk factor for mortality. This remained true in several sensitivity analyses including preoperative, intraoperative, and postoperative confounders (table 3). Goodness of fit for each adjusted model was assessed by Hosmer–Lemeshow tests and was not statistically significant. The BIC was lower with presepsin included in any of the adjusted models in table 3.
Table 3.
Logistic Regression Analysis for Preoperative Presepsin Thresholds and Mortality
Logistic Regression Analysis for Preoperative Presepsin Thresholds and Mortality×
Logistic Regression Analysis for Preoperative Presepsin Thresholds and Mortality
Table 3.
Logistic Regression Analysis for Preoperative Presepsin Thresholds and Mortality
Logistic Regression Analysis for Preoperative Presepsin Thresholds and Mortality×
×
Thirty-day mortality was attributed to cardiovascular causes (n = 5; median preoperative presepsin concentration 686 pg/ml), systemic inflammatory response syndrome or sepsis complicated by multiorgan failure (n = 18; 1,002 pg/ml), and others (including neurologic; n = 4; 235 pg/ml). Only five patients with a preoperative presepsin concentration below the optimal 30-day threshold of 293 pg/ml died within a month of surgery. The causes of death were stroke during surgery of mitral valve repair (96 pg/ml), asystole by displacement of pacemaker wire (179 pg/ml), unexpected abdominal aortic aneurysm rupture (215 pg/ml), and death on the first postoperative day consequent to intraoperative complications (228 and 241 pg/ml), in particular of refractory postcardiotomy shock.
Six-month mortality was attributed to cardiovascular causes (n = 15; median preoperative presepsin concentration 405 pg/ml), systemic inflammatory response syndrome or sepsis complicated by multiorgan failure (n = 20; 884 pg/ml), others (including neurologic; n = 7; 241 pg/ml), and unknown causes (n = 7; 412 pg/ml).
Two-year mortality was attributed to cardiovascular causes (n = 29; median preoperative presepsin concentration 353 pg/ml), systemic inflammatory response syndrome or sepsis complicated by multiorgan failure (n = 24; 675 pg/ml), oncologic (n = 7; 223 pg/ml), others (including neurologic; n = 12; 250 pg/ml), and unknown causes (n = 12; 365 pg/ml).
The 809 patients with follow-up data were categorized into terciles according to preoperative presepsin concentrations. One patient in the first tercile died within 6 months (0.35% of the tercile); 8 patients in the second tercile died (2.8%); and 40 patients in the third tercile died (14.0%; P < 0.001; fig. 2).
Fig. 2.
Kaplan–Meier survival plots for terciles of presepsin during long-term follow-up.
Kaplan–Meier survival plots for terciles of presepsin during long-term follow-up.
Fig. 2.
Kaplan–Meier survival plots for terciles of presepsin during long-term follow-up.
×
Comparisons between Presepsin and Other Predictors of Mortality
Postoperative presepsin concentrations were missing in four patients at 30 days and in three patients at 6 months and 2 yr. The group for 30 days thus included 852 patients, while there were 806 patients at 6 months and 2 yr. All other patients had complete values for all variables.
C statistic showed that elevated preoperative presepsin concentrations had the highest discrimination to predict 30-day and 6-month mortality compared to all other preoperative risk factors (table 4). Interestingly, for 6-month mortality, preoperative presepsin concentrations showed also significantly higher discrimination compared with operation time and postoperative procalcitonin concentrations. Presepsin showed the smallest NNS compared to all other risk factors. Other than NT-proBNP, no risk factor had a higher PPV and NPV than presepsin. However, the C statistic revealed a significantly higher discrimination to predict 30-day, 6-month, and 2-yr mortality for presepsin compared to NT-proBNP.
Table 4.
Comparison of Presepsin with Further Risk Factors
Comparison of Presepsin with Further Risk Factors×
Comparison of Presepsin with Further Risk Factors
Table 4.
Comparison of Presepsin with Further Risk Factors
Comparison of Presepsin with Further Risk Factors×
×
Postoperative Presepsin Concentrations
Presepsin concentrations increased significantly after surgery in all patients. The absolute increase was significantly higher in nonsurvivors than in survivors (fig. 3).
Fig. 3.
Preoperative versus postoperative plasma presepsin concentrations. Box-and-whisker plot with the tenth and ninetieth percentiles. Solid line inside box = median values; plus symbol = mean values. Samples were taken in the afternoon the day before and in the morning after surgery.
Preoperative versus postoperative plasma presepsin concentrations. Box-and-whisker plot with the tenth and ninetieth percentiles. Solid line inside box = median values; plus symbol = mean values. Samples were taken in the afternoon the day before and in the morning after surgery.
Fig. 3.
Preoperative versus postoperative plasma presepsin concentrations. Box-and-whisker plot with the tenth and ninetieth percentiles. Solid line inside box = median values; plus symbol = mean values. Samples were taken in the afternoon the day before and in the morning after surgery.
×
Discussion
Presepsin, a subtype of sCD14, is an inflammatory marker,8–10  which largely reflects monocytic activation.1,2  Consequently, it is unsurprising that presepsin provides prognostic information in patients with severe sepsis or septic shock.19  Inflammation is also associated with various cardiac conditions including coronary heart diseases, ischemia, left ventricular distension, and increasing filling pressures.4–7,20  Reiner et al.11  demonstrated a strong association between the precursor of presepsin (sCD14) and the incidence of cardiovascular disease and all-cause mortality in the elderly. Additionally, Popov et al.13  recently studied the prognostic value of presepsin as a predictor of postoperative complications in patients having surgery for acquired heart diseases. Although the study cohort was small, they were able to show that pre- and postoperative presepsin identified patients at risk for infectious complications and even mortality. We extend these results to a large study population having cardiac surgery and report that a single preoperative presepsin measurement predicts mortality after elective cardiac surgery better than the EuroSCORE 2.21 
Patients who died after cardiac surgery had distinctly greater preoperative presepsin plasma concentrations, suggesting that the monocytic system is already activated preoperatively in patients who subsequently have adverse postoperative outcomes. As might be expected given the substantial inflammatory stress of bypass,12,22  presepsin concentrations increased postoperatively.
Operative and CPB times were significantly longer in patients with elevated presepsin concentrations, although the surgery procedures were similar in both groups. The number of redo surgeries thus cannot explain prolonged operation times. Other reasons might include increased need for cardioversion, failure to wean from CPB, or diffuse bleeding due to coagulation disorders. Unfortunately, we did not record these details and are thus unable to distinguish among these and other possible explanations. However, nonsurvivors with increased serum presepsin concentrations were more likely to have loss of sinus rhythm, need an external pacemaker, require intraaortic balloon pump support, need reexploration for bleeding, and be transfused with packed erythrocytes and platelets. In particular, heart failure (intraaortic balloon pump support) and bleeding were strongly associated with increases in these patients.
Overall mortality was 3% at 30 days and 6% at 6 months, which is consistent with previous reports.21,23,24  Presepsin well discriminated patients who died from those who survived (C statistic 0.88). But unsurprisingly, preoperative presepsin failed to predict unexpected causes of death like displacement of a pacemaker wire, embolic stroke consequent to severe mitral valve calcification, or other intraoperative complications.
Numerous preoperative factors have been proposed as predictors of postoperative morbidity and mortality after cardiac surgery. Among these, factors describing renal or cardiac impairment appear to be especially strong predictors.14,15,21  It is thus unsurprising that cystatin C and NT-proBNP predict mortality in patients undergoing cardiac and noncardiac surgery.14,15,25  Presepsin concentrations are largely an indicator of inflammation, but also increase with renal compromise and severe cardiovascular disease.11,13,25,26  A multimodal diagnostic and therapeutic approach should thus be considered, perhaps including estimation of additional inflammatory and cardiac biomarkers such as procalcitonin or NT-proBNP as well as an extended clinical investigation.
Neither cystatin C nor NT-proBNP are routinely used for risk stratification in cardiac surgery or part of the EuroSCORE 2 although NT-proBNP probably should be.21  In our study, cystatin C and NT-proBNP were significantly less predictive than presepsin, suggesting that presepsin may help identify patients who might benefit from preoperative optimization. We caution, although, that there is currently no evidence that intervening to normalize any biomarker reduces perioperative morbidity and mortality.
Immune defenses are hardly monolithic. Instead, the immune system is subtle with innumerable components that can presumably be specifically activated as needed (or pathologically) under various circumstances. There is thus no particular reason to expect that various markers of immunologic activation will reliably predict death after cardiac surgery. And in fact, preoperative leukocytes counts and procalcitonin plasma concentrations failed to predict 30-day mortality, whereas presepsin plasma concentration was an excellent predictor. A corollary of immune specificity is that “sledge hammer” approaches to immune modulation may not prove helpful. Consistent with this theory, moderate-dose steroids do not improve outcomes after cardiac surgery.26 
Our major limitation is that we built our models from a single population. Furthermore, the population was too small to split into development and validation sets. Confirmation of our findings in distinct populations is absolutely required. Similarly, it would be of considerable interest to evaluate the ability of preoperative presepsin concentrations to predict myocardial injury and mortality after noncardiac surgery.
In summary, preoperative and postoperative presepsin concentrations were elevated in nonsurvivors after elective cardiac surgery, indicating that preoperative presepsin concentration is an independent risk factor for mortality. Presepsin appears to be superior to a well-regarded risk score and several validated biomarkers. Nevertheless, these findings of a single-center investigation need to be confirmed in a much larger multicenter validation study.
Research Support
Supported by the DIAneering—Diagnostics Engineering and Research consulted to Axis-Shield Diagnostics Ltd. (Dundee, Scotland), LSI Medience Corporation (Tokyo, Japan), Mitsubishi Chemical Europe (Düsseldorf, Germany), Radiometer GmbH (Willich, Germany), Roche Diagnostics Deutschland GmbH (Mannheim, Germany), Saladax Biomedical Inc. (Bethlehem, Pennsylvania), and Shanghai Kehua Bio-engineering Co. Ltd. (Shanghai, China).
Competing Interests
The authors declare no competing interests.
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Fig. 1.
Preoperative presepsin and C-statistic to predict mortality. Box-and-whisker plot with the tenth and ninetieth percentiles. Solid line inside box = median values of presepsin; plus symbol = mean values of presepsin; blue solid line inside box = optimal threshold. Receiver operating characteristic curves were constructed to evaluate the predictive power of presepsin for 30-day, 6-month, and 2-yr mortality. The Youden Index was used to calculate optimal threshold for presepsin in prediction of 30-day, 6-month, and 2-yr mortality. Samples were taken in the afternoon the day before surgery.
Preoperative presepsin and C-statistic to predict mortality. Box-and-whisker plot with the tenth and ninetieth percentiles. Solid line inside box = median values of presepsin; plus symbol = mean values of presepsin; blue solid line inside box = optimal threshold. Receiver operating characteristic curves were constructed to evaluate the predictive power of presepsin for 30-day, 6-month, and 2-yr mortality. The Youden Index was used to calculate optimal threshold for presepsin in prediction of 30-day, 6-month, and 2-yr mortality. Samples were taken in the afternoon the day before surgery.
Fig. 1.
Preoperative presepsin and C-statistic to predict mortality. Box-and-whisker plot with the tenth and ninetieth percentiles. Solid line inside box = median values of presepsin; plus symbol = mean values of presepsin; blue solid line inside box = optimal threshold. Receiver operating characteristic curves were constructed to evaluate the predictive power of presepsin for 30-day, 6-month, and 2-yr mortality. The Youden Index was used to calculate optimal threshold for presepsin in prediction of 30-day, 6-month, and 2-yr mortality. Samples were taken in the afternoon the day before surgery.
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Fig. 2.
Kaplan–Meier survival plots for terciles of presepsin during long-term follow-up.
Kaplan–Meier survival plots for terciles of presepsin during long-term follow-up.
Fig. 2.
Kaplan–Meier survival plots for terciles of presepsin during long-term follow-up.
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Fig. 3.
Preoperative versus postoperative plasma presepsin concentrations. Box-and-whisker plot with the tenth and ninetieth percentiles. Solid line inside box = median values; plus symbol = mean values. Samples were taken in the afternoon the day before and in the morning after surgery.
Preoperative versus postoperative plasma presepsin concentrations. Box-and-whisker plot with the tenth and ninetieth percentiles. Solid line inside box = median values; plus symbol = mean values. Samples were taken in the afternoon the day before and in the morning after surgery.
Fig. 3.
Preoperative versus postoperative plasma presepsin concentrations. Box-and-whisker plot with the tenth and ninetieth percentiles. Solid line inside box = median values; plus symbol = mean values. Samples were taken in the afternoon the day before and in the morning after surgery.
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Table 1.
Population Characteristics
Population Characteristics×
Population Characteristics
Table 1.
Population Characteristics
Population Characteristics×
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Table 2.
Outcome Related to Threshold Values of Preoperative Presepsin
Outcome Related to Threshold Values of Preoperative Presepsin×
Outcome Related to Threshold Values of Preoperative Presepsin
Table 2.
Outcome Related to Threshold Values of Preoperative Presepsin
Outcome Related to Threshold Values of Preoperative Presepsin×
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Table 3.
Logistic Regression Analysis for Preoperative Presepsin Thresholds and Mortality
Logistic Regression Analysis for Preoperative Presepsin Thresholds and Mortality×
Logistic Regression Analysis for Preoperative Presepsin Thresholds and Mortality
Table 3.
Logistic Regression Analysis for Preoperative Presepsin Thresholds and Mortality
Logistic Regression Analysis for Preoperative Presepsin Thresholds and Mortality×
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Table 4.
Comparison of Presepsin with Further Risk Factors
Comparison of Presepsin with Further Risk Factors×
Comparison of Presepsin with Further Risk Factors
Table 4.
Comparison of Presepsin with Further Risk Factors
Comparison of Presepsin with Further Risk Factors×
×