Prognostic value of presepsin in adult patients with sepsis: Systematic review and meta-analysis
Prognostic value of presepsin in adult patients with sepsis: Systematic review and meta- analysis
Hyun Suk Yang 0 1
Mina Hur 0
Ahram Yi 0
Hanah Kim 0
Seungho Lee 0
Soo-Nyung Kim 0
0 Editor: Chiara Lazzeri , Azienda Ospedaliero Universitaria Careggi , ITALY
1 Department of Cardiovascular Medicine, Research Institute of Medical Science, Konkuk University School of Medicine , Seoul , Korea , 2 Department of Laboratory Medicine, Konkuk University School of Medicine , Seoul , Korea , 3 School of Public Health, Seoul National University , Seoul , Korea , 4 Department of Obstetrics and Gynecology, Konkuk University School of Medicine , Seoul , Korea
Presepsin is a novel biomarker to diagnose sepsis but its prognostic value has not been comprehensively reviewed. We conducted this meta-analysis to evaluate the mortality prediction value of presepsin in sepsis. We searched comprehensive electronic databases from PubMed, EMBASE, and Cochrane Library through September 2017 using the key words of (`presepsin' or `sCD14-ST' or `soluble CD14 subtype') and (`sepsis' or `septic shock') and (`prognosis' or `prognostic value' or `prognostic biomarker' or `mortality'). We extracted the presepsin levels in survivors and non-survivors from each individual study and evaluated the standardized mean difference (SMD) using a web-based meta-analysis with the R statistical analysis program. A total of 10 studies and 1617 patients were included. Presepsin levels in the first sampling (within 24 hours) were significantly lower among survivors as compared with non-survivors: the pooled SMD between survivors and non-survivors was 0.92 (95% CI: 0.62±1.22) in the random effects model (I2 = 79%, P< 0.01). In subgroups, divided by the sepsis severity or study site, pooled SMD was consistently noting higher presepsin levels in non-survivals (P< 0.05).
This meta-analysis demonstrates some mortality prediction value in presepsin in patients
with sepsis. Further studies are needed to define the optimal cut-off point to predict mortality
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response
to infection [
]. It's still a clinically challenging syndrome with a mortality range of 10% to
52% [2±4]. To promptly recognize and manage higher risk patients, several risk stratification
models have been adopted such as Sequential Organ Failure Assessment (SOFA) [
Physiology And Chronic Health Evaluation (APACHE) [
]or Mortality in Emergency
Department Sepsis (MEDS) [
] scores. For still more robust prognostication, multiple biomarkers
have been suggested including presepsin.
Presepsin, also known as soluble CD14 subtype, is a 13-kDa glycoprotein cleavage
N-terminal fragment of CD14, released into circulation after activation of a pro-inflammatory signal
cascade on contact with infectious agents . Presepsin can be detected by biochemical
methods and has been considered an emergent biomarker of infection. In 2002, presepsin was first
discovered as a blood biomarker in patients with sepsis in Japan [
]. In 2015, its diagnostic
accuracy in sepsis was confirmed by meta-analysis [10±12], but the prognostic accuracy of
presepsin in sepsis was only reported in individual clinical studies, some showing significantly
lower early presepsin levels in survivors compared with non-survivors [13±21], others not [
Therefore, we conducted a comprehensive systematic review and meta-analysis to evaluate
the mortality prediction value of presepsin in adult patients with sepsis.
We performed a comprehensive electronic search of PubMed, EMBASE, and the Cochrane
Library without language limitations through September 2017. The search terms used were:
(`presepsin' or `sCD14-ST' or `soluble CD14 subtype') and (`sepsis' or `septic shock') and
(`prognosis' or `prognostic value' or `prognostic biomarker' or `mortality'). References from
relevant articles were also reviewed.
A study was eligible for this meta-analysis if it was a clinical study conducted in patients
suffering from sepsis (including severe sepsis and septic shock) according to the international sepsis
definition [1, 24±27], and showed the presepsin levels in survivors and non-survivors within
24 hours of the diagnosis of sepsis. Published abstracts were also reviewed if they carried the
pertinent information. The studies considered ineligible for this meta-analysis were review
articles, editorials, case reports, studies on pediatrics, and studies with insufficient information
to discern the mean and standard deviation of presepsin levels. No restrictions have been
applied regarding the study setting or comorbidities for data collection. In case of multiple
publications with the same or over-lapping cohort, only the published report with the largest
series was included. All data selections were completed by two reviewers (HSY, MH)
independently, and any discrepancies were resolved by consensus discussion or consulting a third
We extracted the following data from each eligible study: year of publication, site of study,
severity of sepsis, time of first sampling, definition of non-survivor, and presepsin levels in
survivors and non-survivors (expressed as mean and SD or median and range). In case of multiple
presepsin sampling within 24 hours, we chose the first sampling result for the meta-analysis. If
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the presepsin levels were provided as median and range, the mean and SD were estimated by
Hozo et al's method [
], or estimated as mean = (2m + a + b)/4, where m is the median and a
and b are the 25th and 75th percentiles, as SD = IQR/1.35 by the Cochrane handbook formula
], unless we were able to obtain additional data information from the original authors.
Studies were evaluated for methodological quality utilizing elements from the QUADAS 2
], systematically noting 4 criteria: whether (1) the study included consecutive
patients (selection bias), (2) the professionals who influenced the outcomes were blinded to
the presepsin result at study entry (confusion bias), (3) the timing of blood sample was within
24 hours after diagnosis (information bias), and (4) the study excluded comorbidities
potentially influencing presepsin levels and accuracy (confounding bias). Each of these four criteria
was evaluated independently by two reviewers (HSY, MH), then any disagreement was
resolved after discussion and reevaluation by a third reviewer (SNK).
We performed statistical analysis using web-based meta-analysis with R (http://web-r.org).
Heterogeneity was explored using the statistic I2: a significant heterogeneity exists when
I2>56%. The pooled standardized mean difference (SMD) and the 95% confidence interval
(95% CI) were calculated using the random effects model. Pooled SMD was considered
significant if P < 0.05. We performed subgroup analyses to explore the prognostic value of presepsin
in different clinical settings such as in the intensive care unit (ICU) or in the emergency
department (ED), and with different sepsis severities, as post hoc analyses. The publication
bias was explored using the Eggar test via a funnel plot, with P< 0.05 indicating a significant
Characteristics of included studies
A total 10 studies with 1617 patients were included in this systematic review and meta-analysis
followed by the systematic selection flow diagram (Fig 1). The characteristics of included
studies are in Table 1. Six studies were prospectively observational [13, 17±20, 22], while three were
retrospective cohort studies from other prospectively-collected registries (Mannheim sepsis
], Surviving Sepsis Campaign 2012 [
], Original cohort of sepsis in University
Hospital Brno [
]), and one was a retrospective caseÐcontrol study from the multi-center
Albumin Italian Outcome Sepsis trial [
]. The publications ranged in date from 2013 to 2017;
actual study recruitment time varied from October 2011 [
] to June 2015 [
]. Four studies
were conducted in Europe [14±16, 19], three in Eastern Asia [
13, 21, 22
], two in Northwest
], and one in South America . All except one study were written in English; it
was written in Czech [
] but contains an English abstract and table, and ultimately the full
text was reviewed after English translation. Five studies were performed in the ICU [14, 15,
18±20], three in the ED [
13, 17, 22
], one in both the ICU and ED (60%, 30% respectively) [
and one was in-hospital without specifically mentioning the ICU or ED [
In Table 1, the included patients' quantities and severities (sepsis severity, SOFA score,
APACHE II score) are based on the patients analyzed as survivors or non-survivors; therefore,
in 4 studies [
14, 16, 18, 19
] those numbers are different from the original study numbers. In all
studies, identification of infection was performed on the bases of clinical features, laboratory
findings, microbiological evidence and imaging tests [13±22]: some studies explicitly included
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Fig 1. A flow diagram of the study selection process.
patients with either proven or suspected infection [
13, 17, 20
], and one accepted only those
with a blood culture positive infection . The source of sepsis was explored in 7 studies [
14, 15, 17, 19, 21, 22
], including 1504 patients: pulmonary in 58.6%, abdominal in 23.3%,
urinary in 10.0%, meningitis in 2.0%, catheter-related in 0.8%, skin in 0.8%, others in 3.9% and
unknown in 0.4%. In all 10 studies, presepsin measurements were performed with the
PATHFAST system (Mitsubish Chemical or LSI Medience Corporation, Tokyo Japan) based on a
chemi-luminescent enzyme immunoassay.
Study quality and publication bias
The total methodological quality scores by 4 criteria are presented in Table 1. All studies
performed blood sampling within 24 hours or in the first day of diagnosis. Four studies [
] enrolled consecutive patients to avoid selection bias; one retrospective case-control
study  had inherent selection bias. Three studies [
14, 19, 20
] mentioned the blinding of
professionals who influenced the outcome. Six studies [
13, 14, 16, 19, 20, 22
] excluded patients
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on rv )
. ta )n 895
s P (
d u e i
u o it h
lc C S C
Fig 2. A funnel plot of the included studies. The Egger's regression test with 95% confidence limits, Random-effect model, SMD (Standardized mean
difference), SE (Standard error).
with comorbidities likely to influence presepsin levels such as terminal stage liver or kidney
disease or traumatic or post-operative status. The funnel plot showed a trend that smaller
studies are associated with larger effects; smaller studies not showing a significant effect may be
less likely to be published. Even so, the linear regression test of funnel plot asymmetry
demonstrates no significant publication bias (Fig 2) (P = 0.175).
In this meta-analysis, 1617 patients from 10 studies were analyzed, with 580 non-survivors and
1037 survivors. The weighted pooled SMD of the first sampling of presepsin between
non-survivors and survivors was 0.92 (0.62±1.22) by the random effects model with significant
heterogeneity (I2 = 79%, P< 0.01) (Fig 3A); for the short-term follow-up interval studies (ICU,
inhospital, 28 or 30 day mortality) [13, 15±21], it was 1.09 (0.78±1.41) by the random effects
model (I2 = 74%, P< 0.01) (Fig 3B).
Subgroup meta-analysis by sepsis severity. Six studies [
14, 15, 18, 19, 21, 22
contain severe sepsis or septic shock (which means prior severe sepsis or septic shock, or the
3rd international consensus definition of sepsis and septic shock ). The pooled SMD
between the non-survivors (n = 246) and survivors (n = 327) was 0.81 (0.36±1.27) by the
random effects model (I2 = 82%, P< 0.01) (Fig 4A). In the studies with a SOFA score 8 [
], the pooled SMD between the non-survivors (n = 193) and survivors (n = 190) was 0.81
(0.36±1.27) by the random effects model (I2 = 79%, P< 0.01) (Fig 4B).
Subgroup meta-analysis by study site. In 5 studies conducted in the ICU [14, 15,
18±20], the pooled SMD between the non-survivor (n = 158) and survivor (n = 180) was
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Fig 3. Forest plot of presepsin levels to predict mortality in sepsis. (A) Overall mortality with the 10 included studies. (B) Mortality with the short-term
follow-up interval studies (a intensive care unit, b In-hospital, c 28-days, d 30-days).
1.04 (0.61±1.47) (I2 = 64%, P = 0.02); in 3 studies conducted in the ED [
13, 17, 22
] the pooled
SMD between two was 0.57 (0.12±1.02) by the random effects model (I2 = 82%, P< 0.01)
Prediction of mortality
Seven studies [13±15, 17, 18, 20, 21] presented a receiver operating characteristic (ROC)
analysis for the ability to use presepsin in prediction of mortality, but only 6 studies [
13, 15, 17, 18,
] provided the cutoff values with sensitivity and specificity (Table 2).
This is the first meta-analysis, to our knowledge, to demonstrate the mortality prediction value
of presepsin in sepsis. The first-day presepsin levels were significantly higher in non-survivors
as compared with survivors: a weighted pooled SMD of 0.92 (0.62±1.22) for over-all mortality
and 1.09 (0.78±1.41) for in-hospital or 30-days mortality (P< 0.01). This pattern was consistent
in patients with severe sepsis or septic shock, even in the ICU or ED (all, P< 0.05).
Because sepsis is not a simple infectious disease but rather an aberrant host response with
complex inflammatory pathophysiologic processes triggered by infection, no single pathogen
or inflammatory biomarker has been enough to explain sepsis mortality. Clinically, three
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Fig 4. Subgroup analyses investigating presepsin to predict sepsis mortality according to the sepsis severity. (A) Studies with patient selection limited to severe
sepsis and septic shock (Group I, n = 6) and studies without such a limitation (Group II, n = 4), (B) Studies with SOFA score 8 (Group I, n = 4) and studies with
SOFA score <8 or not reported (Group II, n = 6).
inflammatory biomarkers have been applied in patients with sepsis: C-reactive protein (CRP),
procalcitonin, and presepsin. The major drawback of CRP is lack of specificity, and ultimately
prognostication in sepsis is controversial [
]; on the other hand, Procalcitonin has clearly
showed mortality prediction value in meta-analysis [
]. Presepsin, however, is a relatively
new sepsis biomarker with no prior meta-analysis of its prognostic value; our meta-analysis
assessed its mortality prediction in 10 studies with 1617 cases. There was a significant overall
heterogeneity (I2 = 79%), and subsequent subgroup analysis divided by sepsis severity or
conduction site could not resolve it, so we analyzed all of the sub-groups with the random effects
model. In terms of the measurement method, it was homogeneous: all 10 studies used just the
same PATHFAST kit. The cutoff values might possibly be generalized, but a wide range of
cutoffs in various clinical situations (Table 2) makes this difficult. For kinetics, the peak presepsin
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Fig 5. Subgroup analyses investigating presepsin to predict sepsis mortality according to the study location. (A) Studies conducted in an intensive care unit (ICU)
or (B) emergency department (ED).
concentration was displayed on day 1±3 of sepsis diagnosis [
]. Due to early elevation, in our
study, the first sampling (within 24 hours) of presepsin might be an effective sepsis biomarker
to predict mortality. It can be a good candidate for the multi-biomarker approach to timely
prediction of sepsis mortality [
Regarding the sepsis definition, 8 of 10 studies used the previous definition [1, 24±27]; only
two studies [
] used the third international consensus definitions of sepsis and septic
shock . According to the new guideline [
], SIRS with infection is not sepsis anymoreÐ
organ dysfunction should be counted by an increase of SOFA score of 2 points or more
correspond to severe sepsis in previous definitions. Therefore, we could say that the current 3rd
definition sepsis group (Fig 4, group I) also demonstrated significantly higher presepsin levels in
non-survivors compared with survivors (P< 0.01). The degree of SMD tends to be modest in
the severe group (Fig 4. group I or Fig 5. group I) as compared with mixed severity group (Fig
4 group II or Fig 5 group II), because the later groups possibly contain low presepsin levels in
patients with infection-positive SIRS without organ dysfunction. A very high high mortality
rate (72% in 6 months follow-up) was observed in the study that exclusively analyzed patients
with septic shock [
Presepsin also has been shown to have prognostic value for situations other than sepsis
such as cardiac surgery [
], hemophagocytic syndrome [
], or renal failure [
particular, presepsin levels depend on renal function due to a 13-kDa small protein being filtered by
the kidney [
]. In our included studies, only half of them exclude comorbidities that might
influence presepsin levels, which involves some possible confounding bias. But in real world,
renal dysfunction is one of the sepsis-related organ failures and co-morbidities are not always
We acknowledge limitations to our systematic review and meta-analysis that are inherent
in the data availability. First, not all the studies are prospective designs addressing concerns
on selection bias that affect study quality. However, except for one case-control study, the
original registries, even if retrospective in nature, were prospectively collected for sepsis and
conducted with no interference in the routine clinical practice. Second, the authors' efforts
to contact the original article authors to get missing information were not as successful as
hoped so this meta-analysis information depends on already-published data. Finally, there is
possibly a publication bias against smaller and non-positive studies as suggested by a visual
inspection of the funnel plot. For generalization of these meta-analysis results, greater access
to original missing data or a larger number of prospective consecutive studies might be
In conclusion, first day presepsin levels had prognostic value to predict mortality in
adult patients with sepsis, especially to predict in-hospital or 30-day mortality, regardless of
sepsis severity or study location. Further controlled research is warranted for unified clinical
S1 Checklist. Prisma checklist.
S1 Table. Details of the quality assessment for each study.
Conceptualization: Mina Hur.
Data curation: Hyun Suk Yang, Mina Hur, Soo-Nyung Kim.
Formal analysis: Hyun Suk Yang, Mina Hur, Seungho Lee, Soo-Nyung Kim.
Methodology: Ahram Yi, Seungho Lee.
Resources: Ahram Yi, Hanah Kim.
Software: Seungho Lee, Soo-Nyung Kim.
Supervision: Soo-Nyung Kim.
Visualization: Ahram Yi, Hanah Kim.
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Writing ± original draft: Hyun Suk Yang.
Writing ± review & editing: Mina Hur, Soo-Nyung Kim.
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1. Singer M , Deutschman CS , Seymour CW , Shankar-Hari M , Annane D , Bauer M , et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) . JAMA. 2016 ; 315 ( 8 ): 801 ± 10 . https://doi.org/10.1001/jama. 2016 .0287 PMID: 26903338 .
2. Kaukonen KM , Bailey M , Suzuki S , Pilcher D , Bellomo R . Mortality related to severe sepsis and septic shock among critically ill patients in Australia and New Zealand, 2000 ± 2012 . JAMA. 2014 ; 311 ( 13 ): 1308 ± 16 . https://doi.org/10.1001/jama. 2014 .2637 PMID: 24638143 .
3. Pavon A , Binquet C , Kara F , Martinet O , Ganster F , Navellou JC , et al. Profile of the risk of death after septic shock in the present era: an epidemiologic study . Crit Care Med . 2013 ; 41 ( 11 ): 2600 ±9. https:// doi.org/10.1097/CCM.0b013e31829a6e89 PMID: 23963127 .
4. Martin GS , Mannino DM , Eaton S , Moss M. The epidemiology of sepsis in the United States from 1979 through 2000 . N Engl J Med . 2003 ; 348 ( 16 ): 1546 ± 54 . https://doi.org/10.1056/NEJMoa022139 PMID: 12700374 .
5. Vincent JL , de Mendonca A , Cantraine F , Moreno R , Takala J , Suter PM , et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study . Working group on "sepsis-related problems" of the European Society of Intensive Care Medicine . Crit Care Med . 1998 ; 26 ( 11 ): 1793 ± 800 . PMID: 9824069 .
6. Knaus WA , Draper EA , Wagner DP , Zimmerman JE . APACHE II: a severity of disease classification system . Crit Care Med . 1985 ; 13 ( 10 ): 818 ± 29 . PMID: 3928249 .
7. Shapiro NI , Wolfe RE , Moore RB , Smith E , Burdick E , Bates DW . Mortality in Emergency Department Sepsis (MEDS) score: a prospectively derived and validated clinical prediction rule . Crit Care Med . 2003 ; 31 ( 3 ): 670 ±5. https://doi.org/10.1097/01.CCM. 0000054867 .01688.D1 PMID: 12626967 .
Wright SD , Ramos RA , Tobias PS , Ulevitch RJ , Mathison JC . CD14, a receptor for complexes of lipopolysaccharide (LPS) and LPS binding protein . Science . 1990 ; 249 ( 4975 ): 1431 ±3. https://doi.org/10.
1126/science.1698311 PMID: 1698311 .
9. Okamura Y. [ Usefulness of Presepsin Measurement: A New Biomarker for Sepsis] . Rinsho byori . 2015 ; 63 ( 1 ): 62 ± 71 . PMID: 26524880 .
10. Zhang J , Hu ZD , Song J , Shao J . Diagnostic Value of Presepsin for Sepsis: A Systematic Review and Meta-Analysis . Medicine . 2015 ; 94 ( 47 ):e2158. https://doi.org/10.1097/MD.0000000000002158 PMID: 26632748 .
11. Wu J , Hu L , Zhang G , Wu F , He T. Accuracy of Presepsin in Sepsis Diagnosis: A Systematic Review and Meta-Analysis . PLoS One . 2015 ; 10 ( 7 ):e0133057. https://doi.org/10.1371/journal.pone.0133057 PMID: 26192602 .
12. Zhang X , Liu D , Liu YN , Wang R , Xie LX . The accuracy of presepsin (sCD14-ST) for the diagnosis of sepsis in adults: A meta-analysis . Crit Care . 2015 ; 19 ( 1 ).
13. Liu B , Chen YX , Yin Q , Zhao YZ , Li CS . Diagnostic value and prognostic evaluation of Presepsin for sepsis in an emergency department . Crit Care . 2013 ; 17 ( 5 ):R244. https://doi.org/10.1186/cc13070 PMID: 24138799 .
14. Behnes M , Bertsch T , Lepiorz D , Lang S , Trinkmann F , Brueckmann M , et al. Diagnostic and prognostic utility of soluble CD 14 subtype (presepsin) for severe sepsis and septic shock during the first week of intensive care treatment . Crit Care . 2014 ; 18 ( 5 ): 507 . https://doi.org/10.1186/s13054-014-0507-z PMID: 25190134 .
15. Masson S , Caironi P , Spanuth E , Thomae R , Panigada M , Sangiorgi G , et al. Presepsin (soluble CD14 subtype) and procalcitonin levels for mortality prediction in sepsis: data from the Albumin Italian Outcome Sepsis trial . Crit Care . 2014 ; 18 ( 1 ):R6. https://doi.org/10.1186/cc13183 PMID: 24393424 .
16. BeňovskaÂ M , BučkovaÂ D , PetřÂõkovaÂ D , StasÏek J , GottwaldovaÂ J . Presepsin as a diagnostic and prognostic tool for sepsis . Klinicka Biochemie a Metabolismus . 2015 ; 23 ( 3 ): 89 ± 94 .
17. Carpio R , Zapata J , Spanuth E , Hess G . Utility of presepsin (sCD14-ST) as a diagnostic and prognostic marker of sepsis in the emergency department . Clin Chim Acta . 2015 ; 450 : 169 ± 75 . https://doi.org/10. 1016/j.cca. 2015 . 08 .013 PMID: 26296897 .
18. Ali FT , Ali MAM , Elnakeeb MM , Bendary HNM . Presepsin is an early monitoring biomarker for predicting clinical outcome in patients with sepsis . Clin Chim Acta . 2016 ; 460 : 93 ± 101 . https://doi.org/10.1016/j. cca. 2016 . 06 .030 PMID: 27353646 .
19. Klouche K , Cristol JP , Devin J , Gilles V , Kuster N , Larcher R , et al. Diagnostic and prognostic value of soluble CD14 subtype (Presepsin) for sepsis and community-acquired pneumonia in ICU patients . Ann Intensive Care. 2016 ; 6 ( 1 ):59 https://doi.org/10.1186/s13613-016-0160-6 PMID: 27389015 .
20. El-Shafie MES , Taema KM , El-Hallag MM , Kandeel AMA . Role of presepsin compared to C-reactive protein in sepsis diagnosis and prognostication . Egyptian Journal of Critical Care Medicine . 2017 ; 5 ( 1 ):1± 12 . https://doi.org/10.1016/j.ejccm. 2017 . 02 .001
21. Kim H , Hur M , Moon HW , Yun YM , Di Somma S. Multi-marker approach using procalcitonin, presepsin, galectin-3, and soluble suppression of tumorigenicity 2 for the prediction of mortality in sepsis . Ann Intensive Care. 2017 ; 7 ( 1 ): 27 . https://doi.org/10.1186/s13613-017-0252-y PMID: 28271449 .
22. Yu H , Qi Z , Hang C , Fang Y , Shao R , Li C . Evaluating the value of dynamic procalcitonin and presepsin measurements for patients with severe sepsis . Am J Emerg Med . 2017 ; 35 ( 6 ): 835 ± 41 . https://doi.org/ 10.1016/j.ajem. 2017 . 01 .037 PMID: 28153679 .
23. Kweon OJ , Choi JH , Park SK , Park AJ . Usefulness of presepsin (sCD14 subtype) measurements as a new marker for the diagnosis and prediction of disease severity of sepsis in the Korean population . J Crit Care . 2014 ; 29 ( 6 ): 965 ± 70 . https://doi.org/10.1016/j.jcrc. 2014 . 06 .014 PMID: 25042676 .
24. Rhodes A , Evans LE , Alhazzani W , Levy MM , Antonelli M , Ferrer R , et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016 . Crit Care Med . 2017 ; 45 ( 3 ): 486 ± 552 . https://doi.org/10.1097/CCM.0000000000002255 PMID: 28098591 .
25. Levy MM , Fink MP , Marshall JC , Abraham E , Angus D , Cook D , et al. 2001 SCCM/ESICM/ACCP/ATS/ SIS International Sepsis Definitions Conference. Intensive Care Med . 2003 ; 29 ( 4 ): 530 ±8. https://doi. org/10.1007/s00134-003-1662 -x PMID : 12664219 .
26. Dellinger RP , Levy MM , Rhodes A , Annane D , Gerlach H , Opal SM , et al. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012 . Crit Care Med . 2013 ; 41 ( 2 ): 580 ± 637 . https://doi.org/10.1097/CCM.0b013e31827e83af PMID: 23353941 .
27. American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis . Crit Care Med . 1992 ; 20 ( 6 ): 864 ± 74 . PMID: 1597042 .
28. Hozo SP , Djulbegovic B , Hozo I. Estimating the mean and variance from the median, range, and the size of a sample . BMC Med Res Methodol . 2005 ; 5 : 13 . https://doi.org/10.1186/ 1471 -2288-5-13 PMID: 15840177 .
29. Higgins JPT G S. Cochrane Handbook for Systematic Reviews of Interventions . 2008 .
30. Whiting PF , Rutjes AW , Westwood ME , Mallett S , Deeks JJ , Reitsma JB , et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies . Ann of Intern Med . 2011 ; 155 ( 8 ): 529 ± 36 . https://doi.org/10.7326/ 0003 -4819-155-8- 201110180 -00009 PMID: 22007046 .
31. Zhang Z , Ni H . C-reactive protein as a predictor of mortality in critically ill patients: a meta-analysis and systematic review . Anaesth Intensive Care . 2011 ; 39 ( 5 ): 854 ± 61 . PMID: 21970129 .
32. Mitaka C. Clinical laboratory differentiation of infectious versus non-infectious systemic inflammatory response syndrome . Clin Chim Acta . 2005 ; 351 ( 1 ±2): 17 ± 29 . https://doi.org/10.1016/j.cccn. 2004 . 08 . 018 PMID: 15563869 .
33. Arora S , Singh P , Singh PM , Trikha A . Procalcitonin Levels in Survivors and Nonsurvivors of Sepsis: Systematic Review and Meta-Analysis . Shock . 2015 ; 43 ( 3 ): 212 ± 21 . https://doi.org/10.1097/SHK. 0000000000000305 PMID: 25423128 .
34. Liu D , Su L , Han G , Yan P , Xie L . Prognostic Value of Procalcitonin in Adult Patients with Sepsis: A Systematic Review and Meta-Analysis . PLoS One . 2015 ; 10 ( 6 ):e0129450. https://doi.org/10.1371/journal. pone.0129450 PMID: 26076027 .
35. Bomberg H , Klingele M , Wagenpfeil S , Spanuth E , Volk T , Sessler DI , et al. Presepsin (sCD14-ST) Is a Novel Marker for Risk Stratification in Cardiac Surgery Patients . Anesthesiology. 2017 ; 126 ( 4 ): 631 ± 42 . https://doi.org/10.1097/ALN.0000000000001522 PMID: 28099244 .
36. Nanno S , Koh H , Katayama T , Hashiba M , Sato A , Makuuchi Y , et al. Plasma Levels of Presepsin (Soluble CD14-subtype) as a Novel Prognostic Marker for Hemophagocytic Syndrome in Hematological Malignancies . Intern Med . 2016 ; 55 ( 16 ): 2173 ± 84 . https://doi.org/10.2169/internalmedicine.55.6524 PMID: 27522992 .
37. Nagata T , Yasuda Y , Ando M , Abe T , Katsuno T , Kato S , et al. Clinical impact of kidney function on presepsin levels . PLoS One . 2015 ; 10 ( 6 ):e0129159. https://doi.org/10.1371/journal.pone.0129159 PMID: 26030716 .
38. Nakamura Y , Ishikura H , Nishida T , Kawano Y , Yuge R , Ichiki R , et al. Usefulness of presepsin in the diagnosis of sepsis in patients with or without acute kidney injury . BMC Anesthesiol . 2014 ; 14 : 88 . https://doi.org/10.1186/ 1471 -2253-14-88 PMID: 25309126 .