Complementary use of Presepsin with the Sepsis-3 Criteria Improved Identification of High-Risk Patients with Suspected Sepsis

doi:10.21203/rs.3.rs-659486/v1

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Complementary use of Presepsin with the Sepsis-3 Criteria Improved Identification of High-Risk Patients with Suspected Sepsis

https://doi.org/10.21203/rs.3.rs-659486/v1

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Background: We aimed to evaluate the prognostic accuracy of presepsin levels and additional value for identifying high-risk patients when taken together with the current sepsis criteria.

Methods: This was a single-center, prospective, observational study of patients with suspected sepsis. The primary outcome was 28-day mortality. The prognostic performance of presepsin was evaluated by the area under the receiver operating characteristic curve (AUC), according to the sepsis definition using the Sequential Organ Failure Assessment (SOFA) score change (delta SOFA ≥ 2) and lactate level ≥ 2 mmol/L.

Results: A total of 775 patients were included. The AUC of presepsin for predicting 28-day mortality was 0.747 (95% confidence interval [CI]: 0.701-0.792). Presepsin showed adequate prognostic accuracy regardless of the delta SOFA score or lactate level. High presepsin levels (>755 pg/ml) showed an independent association with 28-day mortality (adjusted odds ratio: 5.17, 95% CI: 2.92–9.16), and significant differences in mortality were observed, even in patients with non-sepsis (13.3% in the high presepsin group vs. 3.6% in the low presepsin group) or low lactate level (14.4% vs. 2.7%). Compared with a single criterion of the delta SOFA score (AUC: 0.670) or lactate (AUC: 0.682), addition of the high presepsin criterion significantly increased discrimination (presepsin + delta SOFA score, AUC: 0.750; presepsin + lactate, AUC: 0.772; presepsin + delta SOFA score+ lactate, AUC: 0.795).

Conclusion: Presepsin showed fair prognostic performance regardless of the clinical sepsis criteria. Complementary use of presepsin with the Sepsis-3 criteria may identify more high-risk septic patients and provide useful prognostic information.

Critical Care & Emergency Medicine

Presepsin

Sepsis

Septic shock

Mortality

The worldwide incidence of sepsis-related morbidity and mortality remains high [1]. To improve clinical outcomes, early identification of septic patients at high risk of mortality is critical, but accurate, timely diagnosis of sepsis is challenging. In terms of pathophysiology, sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection [2]. A change in the Sequential Organ Failure Assessment (SOFA) score of 2 or more is the clinical surrogate criteria of sepsis and is associated with mortality of approximately 10% [2].

Along with clinical scoring, biomarker measurement can help physicians predict outcomes of patients with suspected infection and detecting sepsis syndrome. Various circulating biomarkers have shown prognostic value in many studies, but few markers have been used in clinical practice due to the controversial issues in clinical value [3]. Among them, procalcitonin (PCT), lactate, and C-reactive protein (CRP) have been widely used for risk assessments of critically ill septic patients [4]. However, these biomarkers also have limitations, including the nonspecific nature of sepsis and the fact that no single biomarker is sufficient to represent all aspects of sepsis [5].

Presespin, also known as soluble CD14 subtype (sCD14-ST), is a fragment of CD14 that is released from activated monocytes following recognition of lipopolysaccharide (LPS) [6, 7]. It has been suggested as an early indicator for diagnosis and prognosis in sepsis [6, 8]. Compared to CRP and PCT, presepsin may be secreted earlier and can reach its peak faster [6, 8, 9]. Therefore, presepsin is expected to be a promising biomarker that could identify patients with sepsis who are at high risk for poor prognosis more quickly and sensitively. However, there have been conflicting results regarding the clinical value of presepsin [4, 10]

In this study, the primary aim is to evaluate the prognostic accuracy of presepsin in patients with suspected sepsis. We also investigated whether presepsin could be used complementarily with the current sepsis definition and lactate to identify high-risk patients.

Study design and population

This was a single-center, prospective, observational study of patients with suspected sepsis who visited Samsung Medical Center, a 1,989-bed, university-affiliated, tertiary care referral hospital located in Seoul, Korea. The study period was July 2020 through September 2020. We prospectively included consecutive patients according to the following inclusion criteria: age ≥ 18 years old; suspected sepsis with procalcitonin measurement and blood culture test; and visited the emergency department (ED) or admitted to either the hematology-oncology department or the intensive care unit (ICU). We tested presepsin from 778 patients using residual plasma after procalcitonin measurement. Among the collected samples tested for presepsin, samples that fulfilled either of the following conditions were excluded: presepsin measured more than two hours after measuring procalcitonin, or duplicate samples during a hospitalization period lasting less than one week. If there were multiple results for a patient, only the first test performed was incorporated in the analysis. The patients without blood cultures at the same time were also excluded. The Institutional Review Board of Samsung Medical Center approved this study. Informed consent was waived because this study was anonymous using residual samples.

Measurement of presepsin

Samples were collected with a plasma separating tube (PST) containing sodium heparin anticoagulant (Vacutainer PST Tube 8.0 mL, #367964; Becton Dickinson [BD], Sunnyvale, CA, USA). Procalcitonin (PCT) was tested with Elecsys BRAHMS PCT assay (Roche, Basel, Switzerland) on Cobas e801 (Roche). After testing PCT, the residual samples were subjected to presepsin measurement with the PATHFAST™ assay kit (LSI Medience Corporation, Tokyo, Japan) and PATHFAST™ analyzer (LSI Medience Corporation) within two hours. The PATHFAST presepsin assay is a cartridge-type, fully-automated enzyme immunoassay system that consists of magnetic particles coated with mouse monoclonal antibodies and alkaline phosphatase (ALP)-conjugated rabbit polyclonal antibodies [11]. The immunocomplex resulting from presepsin binding with the two antibodies generates luminescence after incubation with the chemiluminescent substrate [11]. The test requires 100 µL as the minimum sample volume and takes about 17 minutes [11].

Prior to measuring the subjects’ presepsin levels, laboratory performance validation of the PATHFAST™ assay kit and analyzer was conducted. Imprecision was assessed with two different levels of quality control (QC) materials measured in duplicate runs, twice a day, for five days. The results showed within-run coefficient of variation (CV) and within-laboratory CV below 5%. Linearity was evaluated with five different levels of verification control measured in four replicates. The coefficient of determination (R²) was 0.998, and the 95% confidence interval (CI) for the slope of the linear regression model ranged from 0.904 to 1.073.

Stability over time at room temperature was also evaluated in order to determine the exclusion criterion. A total of 45 samples evenly distributed along the measuring range (low, < 300 pg/mL; medium, ≥ 300 pg/mL and < 1000 pg/mL; and high, ≥ 1000 pg/mL) were tested 0, 1, 2, and 3 hours after testing PCT. The mean value for each sample was calculated, and the difference between each test and the mean value was determined. While the difference was less than 20% of the mean value until 2 hours after testing PCT, the difference between the two tests exceeded 20% of the mean value after 3 hours. In our institute, PCT is a stat test which should be reported within 60 min after sample collection; thus, it was determined that presepsin tests performed more than 3 hours after sample collection should be excluded.

Data collection and definitions

Along with procalcitonin and presepsin, clinical data including demographic data, comorbidities, suspected infection focus, vital signs, laboratory tests, and blood cultures were obtained. The nearest values from the time of procalcitonin measurement were used, and the SOFA score was calculated with them. Missing values were not imputed. According to the Sepsis-3 definition, sepsis was defined as an acute change in total SOFA score (delta SOFA) ≥ 2 points consequent to the infection [2]. The baseline SOFA score was assumed to be 0 if it was unknown. Septic shock was defined as sepsis with persisting hypotension requiring vasopressors to maintain mean arterial pressure (MAP) ≥ 65 mmHg and a serum lactate level > 2 mmol/L despite adequate volume resuscitation. The primary outcome was 28-day mortality.

Statistical analysis

Standard descriptive statistics were used for all variables including baseline demographics and outcomes. Categorical variables were presented as numbers with percentages and compared using a chi-square test. Continuous variables were presented as medians and interquartile ranges (IQRs) and compared using the Wilcoxon rank-sum test. The prognostic performance of presepsin for 28-day mortality was assessed as the area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We used a nonparametric approach for the comparison of two AUCs [12]. The Youden index was used to determine the optimal cut-off value for presepsin. According to the cut-off level, patients were categorized into the high presepsin group or the low presepsin group.

A multivariable logistic regression model was developed to assess variables related to 28-day mortality. Adjusting variables were predefined and included age, sex, infection focus, SOFA score, lactate level, procalcitonin level, and comorbidities with a significant difference between survivors and non-survivors. The goodness-of-fit of the final logistic regression model was assessed using the Hosmer–Lemeshow test. The results were described as adjusted odds ratio (aOR) with a 95% confidence interval (CI). A p-value less than 0.05 was considered to indicate a statistically significant difference. STATA 15.0 (STATA Corporation, College Station, TX, USA) was used for statistical analysis.

Baseline characteristics

A total of 755 patients with suspected sepsis were included (Fig. 1). There was no confirmed case of coronavirus disease 2019 (COVID-19) in this study. By hospital location, the proportions of patients were 72.5% (n = 547) in the ED, 20.3% (n = 153) in the general ward, and 7.3% (n = 55) in the ICU. The 28-day mortality was 13.5% (n = 102). The baseline characteristics of all patients and a comparison between survivors and non-survivors are shown in Table 1. The median age was 63.2 years (IQR 51.9–72.1), and 56.3% (n = 425) were men. The most common focus of infection was respiratory infection (24.5%), followed by urinary tract infection (15.5%) and gastrointestinal infection (14.2%). Characteristics such as older age, male sex, malignancy, respiratory infection focus, and number of patients with sepsis were more frequently observed among non-survivors than in survivors. The SOFA score, lactate, CRP, and procalcitonin at enrollment were significantly higher in non-survivors than survivors.

Table 1

Baseline characteristics
	Overall (n = 755)	Survivors (n = 653, 86.5%)	Non-survivors (n = 102, 13.5%)	p-value
Age, years	63.2 (51.9–72.1)	62.3 (49.8–72.1)	65.8 (59.8–71.7)	0.004
Sex, male	425 (56.3)	352 (54.0)	73 (71.6)	0.001
Comorbidities
Diabetes	153 (20.3)	131 (20.1)	22 (21.6)	0.725
Cerebrovascular disease	63 (8.4)	57 (8.7)	6 (5.9)	0.334
Chronic cardiac disease	101 (13.4)	83 (13.5)	13 (12.8)	0.840
Chronic lung disease	41 (5.4)	34 (5.2)	7 (6.9)	0.492
Chronic liver disease	101 (13.4)	83 (12.7)	18 (17.7)	0.173
Chronic renal disease	74 (9.8)	66 (10.1)	8 (7.8)	0.472
Malignancy	497 (65.8)	409 (62.7)	88 (86.3)	< 0.001
Infection focus
Respiratory	185 (24.5)	138 (21.1)	47 (46.1)	< 0.001
Urinary tract	117 (15.5)	107 (16.4)	10 (9.8)	0.087
Gastrointestinal	107 (14.2)	94 (14.4)	13 (12.8)	0.657
Hepatobiliary and pancreatic	101 (13.4)	88 (13.5)	13 (12.8)	0.840
Bone and soft tissues	66 (8.7)	62 (9.5)	4 (3.9)	0.063
Others	82 (10.9)	74 (11.3)	8 (7.8)	0.292
SOFA score	2 (1.0–5.0)	2 (1.0–4.0)	5 (3.0–9.0)	< 0.001
Patients with sepsis	318 (42.1)	245 (37.5)	73 (71.6)	< 0.001
Patients with septic shock	44 (5.8)	26 (4.0)	18 (17.7)	< 0.001
Laboratory tests
WBC (10³/µL)	7.8 (3.9–12.4)	7.7 (4.0–12.0)	9.3 (3.1–17.4)	0.030
Hemoglobin (g/dL)	10.4 (9.0-12.3)	10.5 (9.1–12.5)	9.5 (8.5–10.8)	< 0.001
Platelet (10³/µL)	152 (75.0-242.0)	159 (84.0-245.0)	101 (36.0-226.0)	0.001
Total bilirubin (mg/dL)	0.8 (0.5–1.5)	0.7 (0.4–1.3)	1.3 (0.6–3.8)	< 0.001
Creatinine (mg/dl)	0.8 (0.6–1.2)	0.8 (0.6–1.1)	1.1 (0.6–1.9)	0.001
Lactate (mmol/L)	1.5 (1.1–2.3)	1.4 (1.1–2.1)	2.4 (1.7–4.4)	< 0.001
CRP (mg/dL)	6.8 (2.9–14.4)	6.4 (2.6–13.2)	11.2 (6.1–18.9)	< 0.001
Procalcitonin (ng/mL)	0.4 (0.1–1.6)	0.3 (0.1–1.5)	1.0 (0.4–4.3)	< 0.001
Monocytopenia	348 (46.1)	298 (45.6)	50 (49.0)	0.524
The data are presented as median (IQRs) for continuous variables or as numbers (%) for categorical variables.
IQR interquartile range, SOFA Sequential Organ Failure Assessment, WBC white blood cell count, CRP C-reactive protein.

The number of non-sepsis, sepsis, and septic shock patients was 437 (57.9%), 274 (36.3%), and 44 (5.8%), respectively, and the 28-day mortality in each of the three groups was 6.6%, 20.1%, and 40.9% respectively. Baseline characteristics of the patients with non-sepsis, sepsis, and septic shock are shown in Supplementary Table S1.

Presepsin levels

The median presepsin level was significantly higher in non-survivors than in survivors (1283 pg/ml [IQR, 765–3208] vs. 558 pg/ml [IQR, 315–1164], p < 0.001) (Fig. 2). The level of presepsin was highest in septic shock (1179 pg/ml [IQR, 643–3225]), followed by sepsis (933 pg/ml [IQR, 472–1861]) and non-sepsis (466 pg/ml [IQR, 284–938]), and there was statistically significant difference in each group. Among sepsis patients, the presepsin level of septic shock patients was also significantly higher than in patients without shock (p = 0.003). The presepsin levels were still significantly different according to survival and the sepsis criteria, regardless of the presence of monocytopenia (< 500x10³/uL). In patients with monocytopenia, the median presepsin levels were 570 pg/ml (IQR, 295–1189) in survivors vs. 1438.5 pg/ml (IQR, 889–4311) in non-survivors (p < 0.001), and 441.5 pg/ml (IQR, 266–937) in non-sepsis patients vs. 1054 pg/ml (IQR, 505–1906) in sepsis patients vs. 1246.5 (IQR 732–3181) in patients with septic shock (p = 0.028). According to the Youden index, the optimal presepsin cut-off point for predicting 28-day mortality was 755 pg/ml.

Multivariable logistic regression analysis for 28-day mortality

The results from univariable and multivariable logistic regression analysis for 28-day mortality are presented in Table 2. A high presepsin level, over 755 pg/ml, was significantly associated with 28-day mortality in both the univariable analysis (Odds ratio; OR 5.61, 95% CI 3.44–9.16, p < 0.001) and the multivariable model that adjusted for confounding factors (aOR 5.17, 95% CI 2.92–9.16, p < 0.001). The presence of malignancy, respiratory infection, delta SOFA score ≥ 2, and lactate ≥ 2 mmol/L were also significantly associated with 28-day mortality.

Table 2

Univariate and multivariable logistic regression analysis for 28-day mortality
	Univariable			Multivariable
	OR	95% CI	p-value	OR	95% CI	p-value
Presepsin > 755 (pg/ml)	5.61	3.44–9.16	< 0.001	5.17	2.92–9.16	< 0.001
Age, years	1.03	1.01–1.04	< 0.001	1.02	1.01–1.04	0.016
Sex, male	2.15	1.36–3.39	< 0.001	1.14	0.67–1.95	0.626
Malignancy*	3.75	2.09–6.74	< 0.001	5.34	2.71–10.53	< 0.001
Respiratory infection	3.19	2.07–4.91	< 0.001	3.34	1.98–5.65	< 0.001
Delta SOFA score ≥ 2	4.19	2.65–6.63	< 0.001	2.83	1.65–4.83	< 0.001
Lactate ≥ 2 (mmol/L)	4.60	2.95–7.17	< 0.001	3.37	2.04–5.57	< 0.001
Procalcitonin > 0.5 (ng/mL)	2.49	1.61–3.84	< 0.001	0.99	0.57–1.71	0.967
^*Malignancy refers to metastatic solid cancer or hematologic malignancy.

p-value of goodness-of-fit with the Hosmer–Lemeshow test = 0.7595

OR odds ratio, CI confidence interval, SOFA Sequential Organ Failure Assessment

Discriminating ability of presepsin for 28-day mortality based on delta SOFA score and lactate level.

The 28-day mortality was 5.4% (n = 23/428) in the low presepsin group (≤ 755 pg/ml) and 24.2% (n = 79/327) in the high presepsin group (> 755 pg/ml) (p < 0.001). The 28-day mortality was significantly different between the low presepsin group and the high presepsin group, even in subgroups including non-sepsis patients with delta SOFA < 2 (3.6% vs. 13.3%, p < 0.001) and patients with low lactate levels < 2 mmol/L (2.7% vs. 14.4%, p < 0.001) (Fig. 3).

The AUC of presepsin for predicting 28-day mortality was 0.747 (95% CI: 0.701–0.792). (Fig. 4), which was significantly higher than that of PCT (AUC: 0.678, 95% CI: 0.630–0.725) (Supplementary Fig. S1). The AUC was similar between non-sepsis patients with delta SOFA score < 2 (0.755, 95% CI: 0.701–0.792) and sepsis patients with delta SOFA ≥ 2 (0.709, 95% CI: 0.635–0.783). This trend was also consistent when the patients were divided according to the lactate level (< 2 mmol/L vs. ≥ 2 mmol/L).

Combined interpretation of the high presepsin cut-off, delta SOFA score ≥ 2 points, and lactate ≥ 2 mmol/L

The AUC of presepsin with a cut-off value of 755 pg/mL in predicting 28-day mortality was 0.697 (95% CI: 0.653–0.742), which was the higher than that of delta SOFA score ≥ 2 (0.670 [95% CI: 0.623–0.718]) and lactate ≥ 2 (0.682 [95% CI: 0.633–0.731]). When presepsin was combined with delta SOFA score and lactate individually, the AUC in predicting 28-day mortality was 0.750 (95% CI: 0.703–0.797) and 0.772 (95% CI: 0.727–0.818), respectively, showing a greater AUC than with exclusive use of each criterion (Fig. 5). Combining the three criteria exhibited the greatest AUC of 0.795 (95% CI: 0.749–0.841).

The sensitivity and specificity of presepsin with a cut-off value of 755 pg/mL were 77.5% (95% CI: 68.1–85.1) and 62% (95% CI: 58.2–65.8), respectively (Table 3). If criteria for either presepsin or delta SOFA was fulfilled, greater sensitivity (89.2%, 95% CI: 81.5–94.5) was obtained compared to using the delta SOFA score alone. Positive criteria for both values increased specificity (79.9%, 95% CI: 76.7–82.9). Likewise, adding the lactate criterion or combining all three criteria showed similar trends. The highest sensitivity (94.1%, 95% CI: 87.6–97.8) was achieved when the three criteria were combined in an “or” manner; highest specificity (93.7%, 95% CI: 91.6–95.5) and PPV (51.8%, 95% CI: 40.7–62.7) were achieved with an “and” combination.

Table 3

Prognostic accuracy of the presepsin cut-off value, delta SOFA score ≥ 2 points, and lactate ≥ 2 mmol/L for predicting 28-day mortality
	Sensitivity (95% CI)	Specificity (95% CI)	PPV (95% CI)	NPV (95% CI)
Presepsin > 755 pg/ml	77.5 (68.1–85.1)	62 (58.2–65.8)	24.2 (19.6–29.2)	94.6 (92-96.6)
Delta SOFA ≥ 2	71.6 (61.8–80.1)	62.5 (58.6–66.2)	23 (18.4–28)	93.4 (90.6–95.5)
Lactate ≥ 2 mmol/L	66.7 (56.6–75.7)	69.7 (66-73.2)	25.6 (20.4–31.2)	93.0 (90.4–95.1)
Presepsin > 755 pg/ml or delta SOFA ≥ 2	89.2 (81.5–94.5)	44.6 (40.7–48.5)	20.1 (16.5–24.1)	96.4 (93.6–98.2)
Presepsin > 755 pg/ml or lactate ≥ 2 mmol/L	92.2 (85.1–96.6)	44.6 (40.7–48.5)	20.6 (17-24.6)	97.3 (94.8–98.8)
Presepsin > 755 pg/ml or delta SOFA ≥ 2 or lactate ≥ 2 mmol/L	94.1 (87.6–97.8)	34.3 (30.7–38.1)	18.3 (15.1–21.9)	97.4 (94.4–99)
Presepsin > 755 pg/ml and delta SOFA ≥ 2	59.8 (49.6–69.4)	79.9 (76.7–82.9)	31.8 (25.3–38.9)	92.7 (90.3–94.7)
Presepsin > 755 pg/ml and lactate ≥ 2 mmol/L	52.0 (41.8–62.0)	87.1 (84.3–89.6)	38.7 (30.5–47.4)	92.1 (89.7–94.1)
Presepsin > 755 pg/ml and delta SOFA ≥ 2 and lactate ≥ 2 mmol/L	43.1 (33.4–53.2)	93.7 (91.6–95.5)	51.8 (40.7–62.7)	91.3 (89.0-93.4)
SOFA Sequential Organ Failure Assessment, CI confidence interval, PPV positive predictive value, NPV negative predictive value.

Since presepsin was first discovered as a novel biomarker for sepsis in 2002 [13], a number of studies have demonstrated the significance of presepsin for identifying sepsis or predicting mortality. Although the diagnostic and prognostic values of presepsin for sepsis have been demonstrated in various studies [14–17], the complementary utility and applicability of presepsin in clinical practice still need further research.

Sepsis, a life-threatening organ dysfunction caused by infection, continues to be a major cause of death globally [18]. Multiple studies have established the advantage of prompt intervention, such as rapid antibiotics administration, in reducing mortality [19, 20]. Early recognition of high-risk patients is crucial to improving patient outcomes. Nonetheless, it is often challenging to sort out high-risk patients in real-life clinical practice.

In this study, our data showed a significant difference in presepsin levels among the non-sepsis, sepsis, and septic shock patients, when categorized by the Sepsis-3 criteria. In addition, presepsin levels demonstrated a statistically significant difference between survivors and non-survivors in both sepsis and non-sepsis groups. It is noteworthy that presepsin was able to predict mortality even in the non-sepsis group. Moreover, combining presepsin with other well-known markers such as the Sepsis-3 definition by SOFA score and lactate provided greater predictive power. When presepsin was combined with the delta SOFA score, the predictive power for mortality was greater than using SOFA score alone, advocating the benefit of ancillary use of presepsin along with other markers. The results are in line with previous notions addressing the prognostic value of presepsin. Thus, with the help of presepsin, the chance of identifying high-risk patients could be improved.

Although the biological function of presepsin itself has yet to be elucidated, the primary source of presepsin secretion is CD14-positive monocyte/macrophage lineages which are triggered by bacterial phagocytosis [21]. Presepsin is currently known to rise faster than PCT and CRP [6, 7, 10]. It is also speculated that presepsin rises faster than lactate, considering the fact that increases in lactate level result from tissue hypoperfusion. Besides, presepsin shows excellent correlation between sample types including whole blood, and the result can be obtained within 17 minutes [11]. Hence, presepsin is a feasible marker for predicting survival in sepsis, especially at an early stage before progression of organ failure. Utilization of presepsin may assist in identifying patients with potentially unfavorable outcomes and could improve mortality rates by facilitating prompt treatment.

Although other markers such as CRP and PCT have been widely used, those markers have their own limitations in terms of predicting prognosis [22]. Lactate levels could be also increased by hepatic dysfunction, renal dysfunction, or catecholamine, as well as sepsis-induced tissue hypoxia [23]. Compared with those biomarkers, presepsin might have some advantages in that it could be more specific for sepsis and could have an earlier increase. On the other hand, presepsin may be elevated in renal or hepatic dysfunction without infection [3, 24] so further research would be needed for clinical interpretation.

There are several limitations to our study that deserve to be acknowledged. First, while the subjects were enrolled in a prospective manner, the selection criteria included procalcitonin and blood culture, which could cause selection bias. However, since procalcitonin and blood culture are generally ordered for almost all suspected sepsis patients, the chance of selection bias is relatively low. Besides, to the best of our knowledge, the number of reviewed subjects in this study is the largest among studies evaluating presepsin under the Sepsis-3 criteria, to date. Second, this study was conducted in a single institution, and approximately two-thirds of the enrolled subjects had a malignancy. Third, we were unable to obtain follow-up presepsin level changes over time. Since we only retrieved the presepsin level from a single time point at presentation, the actual onset of sepsis and whether the measured presepsin level represents the peak level are uncertain. Nonetheless, the characteristics of our data may in actuality better reflect the real-world clinical setting. Further research regarding the underlying mechanism of presepsin secretion, the biological role of presepsin, and the serial kinetics of presepsin according to the patient status are warranted.

Presepsin measurement had prognostic value for predicting 28-day mortality both in non-sepsis patients and sepsis patients. Complementary use of presepsin with the SOFA score of the Sepsis-3 clinical criteria or with the lactate level may identify more high-risk septic patients and provide useful prognostic information.

ROC

Receiver operating characteristic

AUC

Area under the curve

IQR

Interquartile range

SOFA

Sequential organ failure assessment

PCT

Procalcitonin

CRP

C-reactive protein

LPS

lipopolysaccharide

emergency department

ICU

Intensive Care Unit

PST

plasma separating tube

ALP

Alkaline phosphatase

Quality control

Coefficient of variation

Confidence interval

MAP

Mean arterial pressure

PPV

Positive predictive value

NPV

Negative predictive value

COVID-19

Coronavirus disease 2019

Odds ratio

aOR

Adjusted odds ratio

Ethics approval and consent to participate

The Institutional Review Board of Samsung Medical Center approved this study. Informed consent was waived because this study was anonymous using residual samples.

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors have no competing interests to declare.

Funding

Not applicable

Authors’ Contributions

Study concepts and design: ESK, TGS, CMP, CWJ. Literature search: JEP, BL, SJY. Database development: ESK, JEP, TGS, SJY. Data collection: All authors. Technical support: ESK, HDP. Statistical analysis: JEP, TGS. Manuscript preparation: JEP, BL, TGS, ESK. Figures: JEP, SYH. Final manuscript review: All authors.

Acknowledgements

We would like to thank HJ Shon and MJ Jung for their technical support.

Author information

Jong Eun Park and Beomki Lee contributed equally to this work.

Corresponding authors

Tae Gun Shinand Eun-Suk Kang contributed equally to this work.

Affiliations

¹Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea ²Department of Emergency Medicine, College of Medicine, Kangwon National University, Chuncheon, Gangwon-do, Korea ³Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea ⁴Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea ⁵Department of Critical Care Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea ⁶Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea ⁷Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

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Complementary use of Presepsin with the Sepsis-3 Criteria Improved Identification of High-Risk Patients with Suspected Sepsis

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Abstract

Figures

Background

Methods

Study design and population

Measurement of presepsin

Data collection and definitions

Statistical analysis

Results

Baseline characteristics

Presepsin levels

Multivariable logistic regression analysis for 28-day mortality

Discussion

Conclusions

Abbreviations

Declarations

References

Supplementary Files

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