Procalcitonin and C-reactive Protein as Diagnostic Biomarkers in COVID-19 and Non-COVID Sepsis Patients: A Comparative Study

doi:10.21203/rs.3.rs-3336975/v1

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Procalcitonin and C-reactive Protein as Diagnostic Biomarkers in COVID-19 and Non-COVID Sepsis Patients: A Comparative Study

https://doi.org/10.21203/rs.3.rs-3336975/v1

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published 04 Jan, 2024

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Background: to compare procalcitonin (PCT) and C-reactive protein (CRP) between COVID-19 sepsis patients and non-COVID control subjects (CNT), and to evaluate the diagnostic efficiency of PCT and CRP in distinguishing between Gram-positive (GP) and Gram-negative (GN) bacterial infections and their association with specific pathogens.

Methods: A total of 121 consecutive patients who underwent blood culture testing during the COVID-19 epidemic were included in the study. PCT and CRP were measured, and reverse transcriptase-polymerase chain reaction (RT-PCR) was used to detect COVID-19 nucleic acid. PCT and CRP were compared between two groups using the Mann-Whitney U-test. Receiver operating characteristic (ROC) curve comparing PCT and CRP levels in GN group versus GP group was performed to assess the diagnostic efficiency. Specific pathogen groups on PCT concentrations were evaluated by Kruskal-Wallis H test.

Results: A total of 121 bloodstream infection inpatients were categorized into a COVID-19 patient group (n=25) and CNT group (n=96). There were no significant differences in age and gender between the COVID-19 and CNT groups. The comparison of biomarkers between these groups showed no significant differences, except for CRP (p = 0.051). The optimal cut-off values for PCT and CRP in differentiating between GP and GN infections were 1.03 ng/mL and 34.02 mgL, respectively. The area under the ROC curve was 0.689 (95% confidence interval (CI) 0.591 - 0.786) for PCT and 0.611(95% CI 0.505 - 0.717) for CRP. The diagnostic accuracy was 74.05% for PCT and 58.69% for CRP. The study found a significant difference in PCT levels among specific groups of pathogens (p < 0.001), with the highest levels observed in Escherichia Coli. The frequency of Staphylococcus SPP. positive results was significantly higher (36.0%) in COVID-19 patients compared to the control group (p = 0.047).

Conclusion: Sepsis patients with COVID-19 had a significantly higher culture positivity of staphylococcus SPP. than the CNT group. PCT and CRP showed moderate diagnostic efficiency in differentiating between GP and GN bacterial infections. PCT may be more useful in identifying Escherichia coliinfections.

COVID-19

sepsis

procalcitonin

C-reactive protein

biomarkers

Gram-positive bacteria

Gram-negative bacteria

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection, resulting in high morbidity and mortality rates worldwide ^[1]. Early diagnosis and appropriate treatment of sepsis are crucial for improving patient outcomes ^[2]. The COVID-19 pandemic has posed additional challenges in the management of sepsis due to the similarities in clinical presentation and the potential for bacterial coinfections ^[3]. Biomarkers such as procalcitonin (PCT) and C-reactive protein (CRP) have been widely used to aid in the diagnosis and management of sepsis, as they are known to increase in response to bacterial infections ^[4–7].

Procalcitonin is a peptide precursor of calcitonin, which is produced by the C-cells of the thyroid gland. Its serum levels rise rapidly in response to bacterial infections and can help differentiate between bacterial and viral infections ^[6]. Although elevated PCT serum concentrations are not exclusive to infections, PCT is still considered to be one of the best biomarkers available to diagnose sepsis ^[8]. CRP is an acute-phase protein synthesized by the liver in response to inflammation and tissue damage. It is a nonspecific marker of inflammation, but its levels can be significantly elevated in bacterial infections compared to viral infections ^{[9, 10]}.

However, our knowledge about the specific PCT and CRP levels associated with distinct pathogens in COVID-19 sepsis patients remains limited. Notably, there has been no prior investigation into the comparative frequency and microbiological characteristics of pathogen distribution in COVID-19 sepsis concerning PCT and CRP levels, particularly in the context of China. In this study, we aimed to compare PCT and CRP between COVID-19 and non-COVID control subjects (CNT), to evaluate the diagnostic efficiency of PCT and CRP in distinguishing between Gram-positive (GP) and Gram-negative (GN) bacterial infections, and to analyze whether particular pathogens have a relevant impact on serum concentrations of PCT.

2.1. Study Design and Participants

This retrospective study included 25 COVID-19 sepsis patients and 96 non-COVID control subjects (CNT) admitted to our hospital between January and December 2022. COVID-19 sepsis patients were diagnosed based on a positive reverse transcriptase-polymerase chain reaction (RT-PCR) test for SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). The presence of sepsis was determined according to the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) ^[11]. Non-COVID control subjects were patients diagnosed with sepsis without COVID-19. The study protocol was approved by the institutional review board (the ethical approval number: Fuding Hospital 2023001), and the requirement for informed consent was waived due to the retrospective nature of the study.

2.2. Data Collection

The patient's blood was collected by peripheral venous puncture and injected into a Bactec vial prior to antibiotic treatment. The samples were then placed in a Bactec incubator (BD Diagnostics, Franklin Lakes, NJ, USA) and incubated until the results were ready. Only isolates meeting predefined criteria for pathogenicity were reported. Pathogens are divided into two major categories, Gram-positive ,and Gram-negative bacteria. Within each of these categories, pathogens are further divided into five distinct phylogenetic groups: Staphylococcus spp., Streptococcus spp., Enterococcus spp., Escherichia coli, and Klebsiella pneumonia. A new sixth group was created to classify the rarely detected pathogens that did not fall into any of the five pre-existing groups based on specific criteria.

Before the patient received anti-infective therapy upon admission, a nasopharyngeal swab was tested for coronavirus nucleic acid by RT-PCR (real-time polymerase chain reaction) technology as well. Xi'an Tianlong Gene Co., Ltd. provided the reagents, positive and negative controls, an automated nucleic acid extractor, and a fluorescent PCR instrument. Cycle threshold (Ct) values of N gene (virus nucleocapsid) and ORF1ab gene (open reading frame 1a and 1b) below 40 were judged as positive results, as this threshold aligns with the latest guideline in China (Trial 9th version) ^[12].

Clinical parameters, including age, gender, and laboratory data, were extracted from electronic medical records. Biomarkers, including PCT, CRP, white blood cell count (WBC), total protein (TP), albumin protein (ALB), platelet count (PLT), red blood cell distribution width (RDW), and neutrophil-lymphocyte count ratio (NLR), were measured within 24 hours of admission.

Plasma PCT levels were measured using the Cobas e411/E601 (Roche Diagnostics, Mannheim, Germany). The Dimension Vista 1500 Intelligent Lab system (Siemens Healthcare GmbH, Erlangen, Germany) was utilized to analyze CRP in accordance with the manufacturer's instructions. A fully automated clinical chemistry analyzer (Beckman Coulter AU 5800, USA) was utilized to measure TP and ALB. Additionally, the Sysmex XE-2100 hematology analyzer (Sysmex Corporation, Kobe, Japan) was employed to determine the complete blood cell count, WBC, PLT, RDW, and calculated NLR.

2.3. Statistical Analysis

Statistical analysis was carried out using SPSS 22.0 software package. The Kolmogorov-Smirnov test was used to assessed the distribution of PCT and CRP concentrations in both groups ,providing median values and interquartile ranges (IQR). The Chi-square test was used to compare PCT and CRP concentration levels between the COVID-19 and non-COVID control groups. The Mann-Whitney U-test, a nonparametric statistical test, was used to compare two independent samples when the data could not be assumed to be normally distributed. This test determines if there is a significant difference between the mean ranks of the two groups.The Kruskal-Wallis H test, suitable for comparing more than two subgroups when the data cannot be assumed to be normally distributed, was employed to assess differences among multiple subgroups. The receiver operating characteristic (ROC) curves, which correlated true-positive (sensitivity) and false-positive (1-specificity) rates, were constructed to analyze diagnosis value of CRP and PCT for predication of GN. In all tests, statistical significance was considered at p value less than 0.05.

3.1 Clinical Parameters of Study Population

The current study with 121 patients who had undergone blood culture tests at Fuding Hospital was conducted, including 25 patients in the COVID-19 group and 96 in the CNT group. The attributes of the patients included in the study were listed in Table 1. The median age of COVID-19 patients was 71.0 years (Interquartile Range (IQR): 51.0–79.5), while that of CNT was 66.0 years (IQR: 49.5–73.8). However, the difference was not significant (p = 0.158). The gender distribution was similar between the two groups (p = 0.601).

Table 1

Comparison of Age and Gender Distribution between COVID-19 group and CNT group [number (%)].
Items	COVID-19 (n = 25)	CNT (n = 96)	Mann-Whitney U-test /χ²	p - value
Age Median (IQR) (years)	71.0 (51.0–79.5)	66.0 (49.5–73.8)	-1.412	0.158
Gender, n (%)			0.273	0.601
Male	15 (60.0)	52 (54.2)
Female	10 (40.0)	44 (45.8)
IQR, interquartile range; CNT, non-COVID control subjects.

3.2 Kolmogorov-Sminov test

To analyze the statistical distribution of each marker tested in each group, a Kolmogorov-Smirnov test was conducted. The results showed that the ORF and N markers had a normal distribution, with a mean cycle threshold value (CT) of ORF gene (26.94 ± 5.49) and N gene (24.29 ± 4.95), respectively, and a Z score range of 0.138–0.140 and a p -value of 0.200. However, the results of the other groups indicated a non-normal distribution, with a Z score range of 0.081 to 0.368 and a p - value of less than 0.01. Therefore, non-parametric tests were used to conduct subsequent statistical analyses.

3.3 Laboratory Parameters

The results of the laboratory parameters were presented in Table 2. The biomarker levels of the COVID-19 group were compared with those of the CNT group, revealing no significant differences in PCT and CRP as well as TP, ALB, WBC, PLT, RDW, or NLR between the two groups.

Table 2

Comparison of biomarkers between COVID-19 group and CNT group (IQR).
Item	COVID-19 (n = 25)	CNT (n = 96)	Z	p -value
CRP (mg/L)	43.88 (7.76–148.80)	87.69 (43.21–140.05)	-1.947	0.051
PCT (ng/mL)	0.89 (0.10–74.21)	3.12 (0.27–17.45)	-0. 490	0.624
TP (g/L)	60.00 (53.05–64.20)	61.00 (55.43–67.30)	-1.402	0.161
ALB (g/L)	30.80 (28.50–33.95)	32.05 (28.88–36.95)	-0.615	0.539
WBC (10⁹/L)	9.23 (7.24–11.75)	8.51 (6.11–12.16)	-0.333	0.739
Neutrophiles (10⁹/L)	8.11 (4.17–11.11)	6.48 (3.67–10.88)	-0.653	0.514
Lymphocytes (10⁹/L)	0.62 (0.34–1.33)	0.89 (0.56–1.47)	-1.748	0.080
PLT (10⁹/L)	174.00 (103.50–273.00)	170.50 (98.50–249.00)	-0.285	0.776
RDW (%)	13.20 (12.70–15.00)	14.00 (13.00–15.00)	-0.663	0.507
NLR	12.18 (4.98–35.17)	7.67 (3.48–14.38)	-1.620	0.105
Mann-Whitney U-test was carried out to compare results between the two groups.
IQR, interquartile range; PCT, procalcitonin; CRP, C-reactive protein; WBC, white blood cell count; TP, total protein; ALB, albumin protein; PLT, platelet count; RDW, red blood cell distribution width; NLR, neutrophil-lymphocyte count ratio; CNT, non-COVID control subjects group.

3.4 Biomarkers in GP and GN Bacterial Bloodstream Infections

Table 3 compared biomarkers in GP and GN BSI. CRP and PCT levels were higher in GN infections (p = 0.038 and p < 0.001, respectively), with no significant differences in TP, ALB, WBC, PLT, or NLR.

Table 3

Comparison of biomarkers between GP group and GN one (IQR).
Item	GP (n = 49)	GN (n = 72)	Z	p - value
CRP (mg/L)	59.50 (10.41–125.26)	90.82 (48.25–149.72)	-2.070	0.038
PCT (ng/mL)	0.46 (0.15–6.80)	8.08 (0.95–24.80)	-3.514	< 0.001
TP (g/L)	60.60 (55.55–65.60)	60.65 (54.70–62.28)	-0.119	0.905
ALB (g/L)	30.70 (28.60–34.30)	32.25 (29.83–37.23)	-1.362	0.173
WBC (10⁹/L)	8.44 (5.45–11.92)	9.26 (6.54–12.39)	-0.908	0.364
Neutrophiles (10⁹/L)	6.12 (3.32–10.20)	7.89 (4.46–11.27)	-1.064	0.287
Lymphocytes (10⁹/L)	0.86 (0.51–1.34)	0.86 (0.52–1.44)	-0.011	0.992
PLT (10⁹/L)	181.00 (110.00–293.50)	167.00 (88.00–242.00)	-1.489	0.136
RDW (%)	14.00 (13.00–16.00)	14.00 (13.00–15.00)	-0.779	0.436
NLR	7.52 (3.34–14.53)	8.33 (3.69–16.85)	-0.808	0.419
Mann-Whitney U-test was carried out to compare results between GP and GN group.
IQR, interquartile range; GP, Gram-positive; GN, Gram-negative; PCT, procalcitonin; CRP, C-reactive protein; WBC, white blood cell count; TP, total protein; ALB, albumin protein; PLT, platelet count; RDW, red blood cell distribution width; NLR, neutrophil-lymphocyte count ratio; CNT, non-COVID control subjects group.

3.5 Prediction of Gram-negative bacteremia

Blood cultures positive were found in 121 patients, including 49 patients with GP and 72 with GN bloodstream infections.

The study constructed ROC to analyze PCT and CRP in the GN group compared to the GP group. The AUC of PCT and CRP were 0.689 (95% confidence interval (CI) 0.591–0.786) and 0.611 (95% CI 0.505–0.717), respectively. Sensitivity, specificity, Youden’s index (sensitivity + specificity − 1), and diagnostic accuracy were calculated for PCT and CRP in accordance with the ROC curves, as shown in Table 4 and Fig. 1.

Table 4

The diagnosis efficiency of PCT and CRP (GN group versus GP group).
Items	PCT (ng/mL)	CRP (mg/L)
Optimal Cut-off	1.03	34.02
Sensitivity (%)	75.00	81.90
Specificity (%)	66.79	42.90
Diagnostic accuracy (%)	74.05	58.69
Youden’s index (%)	36.20	24.80
SE	0.050	0.054
AUC (95% CI)	0.689 (0.591–0.786)	0.611 (0.505–0.717)
p -value	< 0.001	0.039
SE, standard error. AUC, area under the receiver operating characteristic. CI, confidence interval. PCT, procalcitonin. CRP, C-reactive protein.

3.6 Distribution of Microorganisms

In COVID-19 patients, E. coli (40.0%) and Staphylococcus SPP. (36.0%) were the primary bacterial findings, while the control group exhibited E. coli (37.5%) and K. pneumoniae (18.8%). Staphylococcus SPP. was significantly more common in COVID-19 patients (36.0%) than in the control group (p = 0.047) (Table 5).

Table 5

Frequency of bacterial pathogens isolated from blood cultures in COVID-19 sepsis patients and CNT subjects (%).
Microorganisms		CNT (n = 96)	COVID-19 (n = 25)	χ²	P - value
Gram-positive bacteria (%)		36 (37.5)	13 (52.0)	1.731	0.188
1, Staphylococcus SPP.		17 (17.7)	9 (36.0)	3.934	0.047
	Staphylococcus aureus	6	1
	Staphylococcus hominis	3	1
	Streptococcus mitis	4	1
	Staphylococcus epidermidis	4	6
2, Streptococcus SPP.		15 (15.6)	2 (8.0)	0.428	0.513
	Streptococcus pneumoniae	6
	Streptococcus sanguinis	1
	Streptococcus agalactiae	1
	Neisseria gonorrhoeae	1
	Streptococcus milleri	1
	Streptococcus pyogenes	2	1
	Streptococcus salivarius	1
	Streptococcus intermedius	2
	Streptococcus adjacens	0	1
3, Enterococcus SPP.		4 (4.2)	2 (8.0)	0.613	0.434
	enterococcus faecium	0	1
	Enterococcus faecalis	4	1
Gram-negative bacteria		60 (62.5)	12 (48.0)
4,	Escherichia coli	36 (37.5)	10 (40.0)	0.053	0.819
5,	Klebsiella pneumoniae	18 (18.8)	2 (8.0)	0.974	0.324
6,	Rare pathogens	6
Rare pathogens (each with only one reported case): Mycoplasma, Enterobacteriaceae, Campylobacter jejuni, Pseudomonas aeruginosa, Chlamydia trachomatis, Moraxella Osloensis. CNT, non-COVID control subjects.

PCT levels varied significantly among patients with different bacterial infections. Staphylococcus SPP. had the lowest median PCT levels (0.28 ng/mL), while Enterococcus SPP. had a median of 0.29 ng/mL. In contrast, E. coli had the highest median PCT levels (9.43 ng/mL), followed by K. pneumoniae (6.76 ng/mL). Patients with GN BSI from E. coli had significantly higher PCT values than those with BSI from Staphylococcus SPP. (9.43 vs. 0.28 ng/mL, p < 0.001). No significant PCT differences were observed among bacteremia caused by Staphylococcus SPP., Streptococcus SPP., Enterococcus SPP., K. pneumoniae, and rare pathogens (Table 6) (Fig. 2).

Table 6

Procalcitonin values associated with different pathogens.
Bacteria	Number	PCT (IQR) (ng/mL)	H	p - value
Staphylococcus SPP.	26	0.28 (0.14–2.61)	21.959	< 0.001
Streptococcus SPP.	17	2.85 (0.22–40.39)
Enterococcus SPP.	6	0.29 (0.05–1.49)
Escherichia coli	46	9.43 (1.50–27.72)
Klebsiella pneumoniae	20	6.76 (1.06–23.91)
Rare pathogens	6	0.58 (0.13–35.83)
Kruskal-Wallis H test were performing among the six subgroups. Only subgroup of Escherichia coli showed statistically significant deference. Then, Mann-Whitney U test was carried out between Staphylococcus SPP. and E. coli, with the result (Z = − 4.005, p < 0.001). IQR, interquartile range; PCT, procalcitonin.

Our study investigated how COVID-19 and other pathogens affected PCT and CRP in sepsis patients. CRP levels were slightly higher in CNT than in the COVID-19 group, with limited diagnostic accuracy. Staphylococcus SPP. was more common (36.0%) in COVID-19 patients, and PCT seemed better at identifying E. coli infections.

In the comparison of PCT and CRP between the GP and GN groups, we observed significant differences in PCT and CRP levels. The optimal cut-off values for PCT and CRP in distinguishing between these infections were 1.03 ng/mL and 34.02 mg/L, respectively. The diagnostic efficiency of PCT was higher than that of CRP. Results from Table 2 revealed that PCT and CRP concentrations were notably higher in the GN group than the GP group. The ROC curve presented in Fig. 1 demonstrated that PCT possessed a sensitivity of 75.00%, specificity of 66.79%, and diagnostic accuracy of 74.05% when distinguishing between GN and GP. These outcomes indicated that approximately 34% of the pathogens examined were GN. This was consistent with prior findings by Daniel O. Thomas-Rüddel et al. ^[13], who reported a PCT sensitivity of 69.0% and specificity of 65.0%. Several researchers have also suggested that extremely elevated PCT concentrations are linked to GN bacteremia, and thus to tailor antimicrobial therapy ^{[14, 15]}. Variations in PCT concentrations may be linked to pathogen-specific signaling, as inflammatory cytokines partially induce PCT expression ^[16]. Lipopolysaccharides (LPS) are cell wall components of GN bacteria and represent the prototypical class of pathogen-associated molecular patterns (PAMPs) ^[17]. They are recognized by cells of the innate immune system through toll-like receptor 4 (TLR4) ^[17]. Nevertheless, the TLR4-dominant activation by GN bacteria results in a very different induction of several inflammatory cytokines, potentially contributing to the observed differences in PCT response seen with GN.

The study highlighted the importance of simultaneously measuring PCT and CRP for accurately detecting GN bacteremia. Therefore, utilizing PCT alongside other diagnostic tools could enhance the accuracy of diagnosing and treating bacterial infections^[18].

The distribution of microorganisms in COVID-19 sepsis patients and non-COVID control subjects was not significantly different, except for Staphylococcus SPP. This finding implied that the etiology of sepsis in COVID-19 patients might be similar to that in non-COVID control subjects. However, the significantly higher PCT values observed in E.coli infections compared to Staphylococcus SPP. infections suggested that PCT levels might be useful in identifying the causative pathogen in sepsis patients, particularly when distinguishing between these two common bacterial infections. These findings were consistent with the results reported by Thomas-Rüddel DO, et al., who observed that PCT concentrations differed significantly between specific groups of pathogens (p < 0.001), with the highest concentrations in E. coli, Streptococcus species, and other Enterobacteriaceae ^[13].

Our study has some limitations. First, the sample size was relatively small, which might have limited the statistical power of our findings. Second, the retrospective nature of the study might have introduced some bias in data collection and analysis. Finally, our study population comprised patients from a single center, which might limit the generalizability of our findings to other settings.

In conclusion, our study found that CRP and PCT levels may have some utility in differentiating between COVID-19 sepsis patients and non-COVID control subjects, as well as between GP and GN bacterial infections in sepsis patients. The diagnostic efficiency of PCT was higher than that of CRP, suggesting that PCT may be a more reliable biomarker for differentiating between these infections. Further studies with larger sample sizes and multi-center designs are needed to confirm our findings and explore the potential clinical applications of these biomarkers in the management of sepsis patients.

Ethics approval

This study was approved by Fuding Hospital, Fujian University of Traditional Chinese Medicine (The ethnical approval number: Fuding 2023001).

Consent for publication

Not applicable.

Availability of data and materials

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

Competing interests

None

Funding

Financial support from Natural Science Foundation Joint Project of Ningde, Ningde, China (No. 2022J54).

Author contributions

J. S. Conceptualization (supporting); Writing - original draft (supporting); Writing -review and editing (equal); Software (lead). Y. Z. Conceptualization (supporting); Writing - original draft (supporting); Writing - review and editing (equal); Software (lead). T-Q. W.Software (equal); writing - revised the draft. C-E. L. Contributed reagents, materials, analysis tools or data; L-H.Y. analyzed and interpreted the data; S-Y. Z. Conceptualization writing - original draft (lead); formal analysis (lead);writing - review and editing (equal). Software (lead). All the authors read and approved the final manuscript.

Acknowledgements

None.

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Procalcitonin and C-reactive Protein as Diagnostic Biomarkers in COVID-19 and Non-COVID Sepsis Patients: A Comparative Study

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Abstract

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1. Introduction

2. Materials and Methods

2.1. Study Design and Participants

2.2. Data Collection

2.3. Statistical Analysis

3. Results

3.1 Clinical Parameters of Study Population

3.2 Kolmogorov-Sminov test

3.3 Laboratory Parameters

3.4 Biomarkers in GP and GN Bacterial Bloodstream Infections

3.5 Prediction of Gram-negative bacteremia

3.6 Distribution of Microorganisms

4. Discussion

Declarations

References

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