Abstract

Background:

To develop a new non-invasive screening method for sepsis by detecting urine samples.

Methods:

A prospective study was conducted to collect urine samples from a cohort of 22 individuals diagnosed with sepsis and admitted to the Intensive Care Unit (ICU) of a university-affiliated teaching hospital in China. Utilizing proteomic and bioinformatics analyses, we sought to identify potential biomarkers indicative of sepsis. These biomarkers were subsequently validated using serum and urine samples from 31 patients with septic shock, 83 patients with sepsis, and 50 healthy controls. Receiver operating characteristic (ROC) curves were employed to determine the optimal cutoff values for these biomarkers. Based on the diagnostic thresholds derived from ROC analysis, colloidal gold test strips were developed and applied to screen a cohort of 92 ICU patients. The diagnostic accuracy of these test strips was rigorously assessed by comparing their results with those from immunofluorescence assays.

Results:

Data-independent acquisition (DIA) proteomics analysis of urine samples identified 2,846 proteins, with stringent filtration criteria (fold change > 2 or < 0.5, P-value < 0.05) yielding 178 differentially expressed proteins (DEPs). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed significant enrichment of DEPs in pathways associated with “cell adhesion molecules,” “lysosomes,” and metabolic processes. The Boruta algorithm, integrating Random Forest and Support Vector Machine (SVM) analysis, identified urinary thrombomodulin (TM) as a key candidate molecule. Immunofluorescence analysis for validation trial showed rising trend in blood TM levels across disease severities: 7.55 (6.58-8.72) TU/mL in healthy controls, 10.08 (8.00-14.15) TU/mL in general sepsis, and 12.30 (7.54-18.68) TU/mL in septic shock. Conversely, urinary TM levels decreased: 23.65 (18.08-31.06) TU/mL, 17.70 (13.80-28.80) TU/mL, and 5.84 (4.00-11.59) TU/mL, respectively. At a urinary TM threshold of 15.46 TU/mL, the ROC AUC for sepsis diagnosis is 0.72, with 57% sensitivity and 88% specificity (P<0.05), showing no significant difference comparable to blood TM (P>0.05). For septic shock diagnosis and 28-day mortality prediction, a urinary TM threshold of 11.85 TU/mL yields an ROC AUC of 0.92, with 93% sensitivity and 81% specificity, outperforming blood TM (P<0.05). A urinary TM colloidal gold test strip, which turns red at TM levels above 15.46 TU/mL, was developed and validated on urine samples from 43 sepsis and 49 non-sepsis patients, achieving 86.1% sensitivity, 77.6% specificity and an overall accuracy of 81.5% for sepsis diagnosis. The Kappa test validated the concordance of the colloidal gold strip test with Sepsis 3.0 diagnostic criteria, while the McNemar test indicated no significant difference in sepsis diagnosis efficacy between the strip test and chemiluminescent immunofluorescence (p=0.228).

Conclusions:

The utilization of urine test strips for the detection of TM offers a precise, convenient, and practical method for the screening of sepsis.

1 Introduction

Sepsis, a critical illness characterized by life-threatening organ dysfunction due to a dysregulated immune response to infection, accounts for approximately 49 million annual cases and 11 million fatalities, representing 19.7% of all global deaths (Singer et al., 2016; Rudd et al., 2020). Septic shock, marked by persistent hypotension despite fluid resuscitation, is associated with severe circulatory compromise, tissue hypoxia, metabolic derangements, and multi-organ dysfunction, with mortality rates exceeding 60% (Carlos Sanchez et al., 2023). Timely recognition of sepsis and prompt therapeutic intervention are vital for reducing mortality (Martín-Fernández et al., 2021). Procalcitonin (PCT) and C-reactive protein (CRP) are widely accepted as sepsis biomarkers, yet inconsistencies limit their diagnostic utility (Mierzchała-Pasierb and Lipińska-Gediga, 2019; Tujula et al., 2020). Serial blood sampling and longitudinal assessment of plasma concentrations are crucial for diagnostic precision (Long and Gottlieb, 2025). However, in the context of scarce blood resources, the risk of iatrogenic anemia resulting from frequent venous blood sampling and the concomitant risk of bloodstream infections present an escalating challenge in the management of sepsis (Jandu et al., 2019; Holland et al., 2020). This underscores the urgent need for non-invasive, dynamic, and accurate diagnostic tools for sepsis.

Data-independent acquisition (DIA) proteomics, known for its comprehensive quantitative proteomic profiling with high quantitative accuracy and reproducibility, is instrumental in elucidating disease mechanisms, identifying early diagnostic biomarkers, and targeting therapeutic interventions (Demichev et al., 2022). Plasma, a complex biofluid with a vast concentration disparity among proteins, is dominated by high-abundance proteins like albumin and immunoglobulin G (IgG), which can obscure low-abundance proteins (Fliser et al., 2007). Urine-based proteomics offers advantages such as stable protein composition, ease of collection, non-invasiveness, and continuous sample availability (Issaq et al., 2007). Urinary biomarkers like interleukin-10 (IL-10), neutrophil gelatinase-associated lipocalin (NGAL), and TIMP-2 have shown diagnostic potential in sepsis, but their clinical application is often impeded by high detection costs, complex methodologies, and suboptimal accuracy (Amin et al., 2024; Palmowski et al., 2024; Zhao et al., 2024). This study aims to leverage urinary DIA proteomics to identify novel biomarkers and develop a simple, user-friendly, and accurate sepsis screening tool using colloidal gold detection technology.

2 Methods2.1 Study design and participants

From January to April 2024, we prospectively collected urine samples from 11 patients with sepsis and 11 with septic shock at the Intensive Care Unit (ICU) of Changcheng Hospital, affiliated with Nanchang University, to identify potential biomarkers through DIA-based proteomic and bioinformatics analyses. From May to October 2024, we obtained blood and urine samples from 31 patients with septic shock, 83 patients with sepsis, and 50 healthy controls to evaluate the diagnostic performance of blood and urine TM. From December 2024 to May 2025, we enrolled 43 patients with sepsis and 49 non-sepsis patients in the ICU to compare the diagnostic accuracy of urine TM assays with immunofluorescence and our novel colloidal gold assays for sepsis diagnosis.

Eligible patients met the following criteria: (1) age ≥18 years; (2) patients diagnosed with sepsis according to the 2016 Sepsis 3.0 criteria jointly published by the Society of Critical Care Medicine (SCCM) and the European Society of Intensive Care Medicine (ESICM), which define sepsis as a SOFA (Sequential Organ Failure Assessment) score ≥2 and evidence of infection (Singer et al., 2016). Exclude shock in which non-infectious factors are the primary cause of onset, and septic patients were diagnosed with septic shock if they required vasoactive agents to maintain a mean arterial pressure (MAP) ≥65 mmHg and had a lactate level >2 mmol/L, despite adequate fluid resuscitation (Singer et al., 2016). The exclusion criteria were: (1) anuric patients; (2) patients undergoing continuous renal replacement therapy; (3) chronic kidney disease; (4) patients who had undergone ureteral irrigation; (5) immunocompromised patients; (6) pregnant or lactating women; (7) individuals who refused to participate. The study was conducted in accordance with the ethical guidelines outlined in the Declaration of Helsinki and approved by the hospital’s ethics committee (approval number 908YYLL2024044). All family members of the patients have signed informed consent forms. The experimental protocol is depicted in Figure 1.

Experimental procedure.

2.2 Blood sample testing methods

Within two hours of intensive care unit (ICU) admission for septic patients, and concurrently for healthy controls, corresponding blood samples were procured for a spectrum of diagnostic assays. For complete blood count (CBC) analysis, 2 mL of blood was extracted from septic patients utilizing EDTA anticoagulant tubes. The CBC tests were executed using a BC-6900 automated hematology analyzer (Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, China), with parameters measured including white blood cell (WBC) count, absolute neutrophil count (ANC), absolute lymphocyte count (ALC), red blood cell (RBC) count, hemoglobin concentration, and platelet count.

For biochemical assays, 3 mL of peripheral venous blood was collected from both septic patients and healthy controls using serum separator tubes. These assays were performed with a BS-2000 automated biochemical analyzer (Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, China), with parameters evaluated encompassing CRP, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), total protein, albumin, and creatinine.

Routine coagulation tests were conducted on 2 mL of peripheral venous blood samples from both septic patients and healthy controls, collected using citrate anticoagulant tubes with a citrate-to-blood ratio of 1:9. The tests were executed with an ACL TOP700 automated coagulation analyzer (Werfen, USA), assessing parameters such as prothrombin time (PT), international normalized ratio (INR), activated partial thromboplastin time (APTT), thrombin time (TT), fibrinogen, D-dimer, and antithrombin.

Immunofluorescence assays were performed on 3 mL of peripheral venous blood samples from both septic patients and healthy controls, collected using dry tubes. These assays were conducted with a UPT-3A up-converting phosphor immunoassay analyzer (Beijing Hotgen Biotech Co., Ltd., Beijing, China), with biomarkers assessed including N-terminal pro-brain natriuretic peptide (NT-proBNP) and PCT.

For blood gas analysis, arterial blood gas analysis was performed on septic patients using an ABL90FLEX blood gas analyzer (Radiometer Medical ApS, Denmark), with documentation of blood lactate levels within two hours post-admission.

2.3 Disease severity assessment

Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) scores were calculated for septic patients. The incidence of Acute Kidney Injury (AKI) and the 28-day mortality rate were recorded. AKI was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines (Stevens et al., 2013), with criteria including: a serum creatinine (Cr) increase of ≥0.3 mg/dL (≥26.5 μmol/L) within 48 hours; a Cr elevation of ≥1.5-fold from baseline within the previous 7 days; and urine output <0.5 mL/kg/h for ≥6 hours.

2.4 Mass spectrometry experiments2.4.1 Protein extraction and peptide digestion

Urine samples were first centrifuged at 3,000 g for 10 min at 4 °C to remove cell debris. The resulting supernatant was concentrated and desalted using a 10 kDa molecular weight cut-off (MWCO) centrifugal filter (Millipore). Proteins were then extracted using an extraction buffer (4% SDS, 100 mM Tris-HCl, pH 7.6). Protein concentration was determined using the BCA Protein Assay Kit (Bio-Rad, USA). To evaluate protein integrity and ensure equal loading across samples, a quality control (QC) step was performed: 20 µg of protein from each sample was mixed with 5× loading buffer, boiled at 95 °C for 5 min, and resolved by SDS-PAGE (4%-20% precast gradient gel) at 180 V for 45 min. The gel was stained with Coomassie Brilliant Blue R-250 to visualize the protein profile (Supplementary Figure 1). The electrophoretic profiles revealed clear and well-resolved protein bands without significant degradation. The high degree of consistency in band distribution and intensity across all samples confirmed accurate protein quantification and uniform loading, ensuring that the concentration and total protein amount were sufficient for downstream mass spectrometry analysis.

Protein digestion was performed according to the Standard Operating Procedure (SOP) for urinary proteomics (Wiśniewski et al., 2009) using the Filter-aided Sample Preparation (FASP) method (Liu et al., 2025). Briefly, 20 µg of protein per sample was reduced with 100 mM DTT at 95 °C for 5 min and subsequently alkylated with 20 mM iodoacetamide (IAA) in the dark for 30 min. The protein mixture was then transferred into a 10 kDa MWCO filter unit (Microcon, Millipore) and washed three times with UA buffer (8 M urea, 0.1 M Tris-HCl, pH 8.5) to remove SDS and other small molecule contaminants. Digestion was carried out using trypsin (Promega) at a protein-to-enzyme ratio of 1:50 (wt/wt) at 37 °C for 16 h.A “Pool” sample was generated by combining equal protein amounts from all individual samples for spectral library construction. Peptides from the Pool sample were fractionated into 10 fractions using the High pH Reversed-Phase Peptide Fractionation Kit (Pierce, Thermo Scientific). All peptides were desalted using C18 cartridges (Empore), lyophilized, and reconstituted in 0.1% formic acid. Peptide concentrations were measured by UV absorbance at 280 nm. Indexed retention time (iRT) calibration peptides (Biognosys) were added to both Pool and individual sample peptides prior to mass spectrometry analysis.

2.4.2 Mass spectrometry assay

All fractionated and individual samples were analyzed using a timsTOF Pro mass spectrometer (Bruker, USA) interfaced with an Evosep One system (Evosep, Denmark).For spectral library generation, the mass spectrometer was operated in Data-Dependent Acquisition (DDA) mode with PASEF (Parallel Accumulation-Serial Fragmentation). The accumulation and ramp time were set to 100 ms each. Mass spectra were acquired in the range of m/z 100–1700 in positive electrospray mode. The ion mobility (1/K0) was scanned from 0.75 to 1.35 Vs/cm², followed by 10 PASEF MS/MS scans per cycle (with a total cycle time of 1.1 s). The dynamic exclusion was set to 24.0 s to prevent repeated sequencing of the same precursor. The ion source voltage was maintained at 1500 V, with a dry gas flow of 3 L/min at 180 °C.For individual sample analysis, the system was operated in Data-Independent Acquisition (DIA) mode. The mass spectrometer collected ion mobility MS spectra over a mass range of m/z 100-1700. Up to 4 windows were defined for each 100 ms TIMS scan based on the m/z-ion mobility plane. During MS/MS scanning, the collision energy was ramped linearly as a function of mobility, ranging from 20 eV at 1/K0 = 0.85 Vs/cm² to 59 eV at 1/K0 = 1.30 Vs/cm².

2.4.3 Mass spectrometry data analysis

DDA raw files were processed using Spectronaut™ (version 14.4.200727.47784, Biognosys, Switzerland) for spectral library construction. The MS/MS spectra were searched against the UniProtKB Homo sapiens database (Taxon ID: 9606, accessed in January 2024), which contained 204,318 sequences. To calibrate retention time, the iRT peptide sequences (iRT Kit, Biognosys) were incorporated into the FASTA database. The search parameters were configured as follows: enzyme, trypsin; maximum missed cleavages, 2; fixed modification, carbamidomethyl (C); and dynamic modifications, oxidation (M) and protein N-term acetylation. Protein identification was filtered using a false discovery rate (FDR) threshold of ≤ 1%.DIA data were analyzed using Spectronaut™ by searching against the previously constructed spectral library. The analysis parameters included: retention time prediction, dynamic iRT; interference correction at the MS2 level, enabled; and cross-run normalization, enabled. All results were filtered based on a Q-value cutoff of 0.01 (equivalent to FDR < 1%). The final list of identified proteins and peptides is provided in Appendix 1.

2.5 Detection of urinary TM and plasma TM

“Peripheral venous blood was collected from sepsis patients and healthy controls within 2 hours of admission into sodium citrate anticoagulant tubes (1:9 ratio), while midstream urine was collected in standard sterile tubes. All specimens were processed within 1 hour of collection. Plasma was isolated by centrifugation at 3000 rpm for 10 minutes at room temperature. TM concentrations in both plasma and urine were determined via chemiluminescent enzyme immunoassay (CLEIA) using the HISCL Thrombomodulin Assay Kit (Sysmex Corporation, Kobe, Japan; NMPA Registration No. 20152403877) on a HISCL-800 automated analyzer. The procedures were performed strictly in accordance with the manufacturer’s standardized protocol for the TM assay, including automated luminescence quantification and system calibration.”

2.6 Urinary TM colloidal gold test2.6.1 Synthesis of the colloidal gold-labeled mAb

Colloidal gold was prepared via the sodium citrate reduction method. Initially, 1 mL of 0.1% HAuCl4 solution was mixed with 99 mL of ultrapure water and brought to a boil with continuous stirring. Upon boiling, 2 mL of 1% sodium citrate was introduced, turning the solution wine-red, and the mixture was heated for an additional 6 minutes. The resulting colloidal gold solution was cooled and stored at 4 °C. For conjugation, 5 mL of colloidal gold solution was transferred to a 15 mL centrifuge tube, and the pH was adjusted to 8.2 using 0.2 M K2CO3. The anti-TM monoclonal antibody was diluted to 0.2 mg/mL in 0.02 M borate buffer. Subsequently, 10 μL of this diluted antibody was added to the 5 mL colloidal gold solution and incubated for 45 minutes. To block any unbound sites, 0.25 mL of a 10% BSA solution was added, and the mixture was further incubated for 2 hours. The conjugate was then centrifuged at 8000 rpm at 4 °C for 45 minutes. The supernatant was removed, and the precipitate was resuspended in 0.5 mL of gold conjugate suspension. The gold-labeled antibody was stored at 4 °C. This method has currently applied for a Chinese invention patent (No. CN118130788A, Supplementary File).

2.6.2 The method of utilizing urinary TM test strip

Dispense 40 μL of the resuspended colloidal gold-labeled solution into a well of a 96-well plate. Subsequently, introduce 10 μL of the colloidal gold-labeled antibody into the same well and ensure thorough mixing by pipetting up and down. Withdraw 10 μL of the supernatant from the urine sample that has been left to stand and add it to the well, followed by pipetting up and down to mix. Incubate the mixture for 5 minutes. Post incubation, add 80 μL of loading buffer and mix well by pipetting. Subsequently, transfer 120 μL of the reacted solution into the sample-application well of the test strip, initiate timing, and after 10 minutes, observe the results. It is imperative to interpret the results within 1 minute of the timing cessation; any interpretations made beyond this time frame are deemed invalid.

2.7 Statistical methods

The sample size calculation was performed using PASS 11 software, and clinical data analysis was conducted using SPSS 27.0 statistical software. Categorical variables were expressed as counts (percentages), and intergroup comparisons were conducted using the chi-square (χ²) test. The normality of continuous variables was assessed using the Shapiro-Wilk test. Continuous variables with normal distribution were presented as mean ± standard deviation (SD), whereas those with non-normal distribution were reported as median (interquartile range) [M (Q1, Q3)]. For parametric comparisons, two-group comparisons of normally distributed and homoscedastic data were performed using Student’s t-test. One-way ANOVA followed by Tukey’s post-hoc test was employed for multi-group comparisons to determine pairwise differences when a significant overall effect was observed. For non-parametric comparisons, the Mann-Whitney U test was used for two-group comparisons. The Kruskal-Wallis test was applied for multi-group comparisons, with significant results followed by Dunn’s test for pairwise comparisons.

Missing value imputation (k-nearest neighbors (KNN) algorithm), Principal Component Analysis (PCA), hierarchical clustering (Euclidean distance), identification of differentially expressed proteins (DEPs) (|log2 fold change (FC)| > 1, p < 0.05), and Gene Ontology (GO) enrichment were performed using Omicsolution (https://wkomics.omicsolution.com/wukong/NAguideR/), while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted via CNSknowall (https://cnsknowall.com/#/Home/HighAll). These platforms are integrated R-based web environments tailored for high-throughput proteomic data processing and statistical mining. The STRING website was used for protein-protein interaction (PPI) network analysis and visualization of relevant enrichment. Machine learning analyses were performed using the e1071 and Boruta packages in R version 4.4.2. GraphPad Prism statistical software was used to perform receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) value was utilized to assess the clinical utility of the biomarker and compare it with C-reactive protein (CRP) and procalcitonin (PCT). Statistical significance was defined as a two-tailed P-value < 0.05.

3 Result3.1 Urinary TM as a DEP identified through 4D proteomics

In this proteomics study, 22 patients were evenly divided into a sepsis group and a septic shock group, each with 11 participants. Comparative analysis revealed significant differences in critical biomarkers, including WBC, ANC, PT, creatinine, lactate, APACHE II score and SOFA score (Table 1). Mass spectrometry analysis of urine samples from 11 patients with sepsis and 11 with septic shock revealed the identification of 4554 distinct proteins. Upon stringent exclusion criteria of proteins with missing values exceeding 50%, a comparative Venn diagram analysis elucidated a core set of 2846 proteins that were common to both sepsis and septic shock cohorts (Figure 2A). PCA distinctly segregates the two groups in the reduced-dimensional space, exhibiting compact clustering within each group, which underscores the substantial and consistent differences in their protein expression profiles. (Figure 2B). A volcano plot graphically represented 178 DEPs, comprising 88 upregulated and 90 downregulated proteins.DEPs were identified using a threshold of |log2 fold change| > 1 and a p-value < 0.05 (Figure 2C). Hierarchical clustering analysis of DEPs delineated two distinct clusters, segregating the sepsis and septic shock groups. This finding underscores the substantial divergence in protein expression profiles between these two clinical entities (Figure 2D).

ParametersSepsis (N=11)Septic shock (N = 11)P valueAge, yr85.0 [58.0, 87.0]82.0 [72.0, 83.0]0.186Male, n (%)8(72.7%)4(36.4%)0.087WBC,×109/L9.3 (3.6)17.4 (4.9)<0.001ANC,×109/L7.7 (3.4)15.2 (4.3)<0.001ALC, ×109/L0.9 (0.5)0.9 (0.6)0.847RBC, ×1012/L3.4 (0.9)3.4 (1.1)0.967Platelet,×109/L110.0 [90.0, 171.0]34.0 [64.0, 92.0]0.009PT, s15.7 (1.6)18.8 (1.3)<0.001APTT, s35.6 [30.0, 43.9]35.8 [30.1, 68.3]0.450Fibrinogen, g/L2.9 (0.9)2.8 (0.9)0.879D-Dimer1.9 [1.4, 3.8]2.6 [1.3, 4.3]0.622ALT, U/L29.5 [25.3, 48.4]33.4 [26.7, 56.8]0.643AST, U/L43.9 [29.1, 63.2]45.7 [30.7, 70.3]0.533TBIL, mmol/L19.0 [10.2, 24.5]23.2 [11.3, 25.8]0.775Albumin, g/L29.8 (3.2)27.0 (3.7)0.074Creatinine, mol/L76.6 (21.9)168.7 (44.2)<0.001CRP, g/L67.9 (46.4)132.6 (100.2)0.042Lactate, mmol/L1.4 [1.1, 1.5]3.1 [2.6, 4.4]<0.001APACHE II score18.6 (2.4)25.8 (2.7)<0.001SOFA score6.0 [6.0, 7.0]11.0 [10.0, 13.0]<0.001

Baseline characteristics of patients in the proteomics study.

Comparative analysis of protein expression profiles. (A) Venn diagram: Overlapping proteins in sepsis and sepsis shock groups; (B) Principal component analysis; (C) Volcano plot: upregulated proteins in red and downregulated proteins in green; (D) DEPs clustering analysis.

GO functional annotation of 178 DEPs revealed predominant involvement in biological processes, notably in response to cellular stimuli and the regulation of cellular processes. In terms of cellular components, these DEPs were predominantly localized to the extracellular region. Concerning molecular functions, the DEPs were primarily associated with biomolecule interactions and catalytic activities (Figure 3A). KEGG enrichment analysis indicated significant enrichment of DEPs in pathways related to cell adhesion molecules, lysosomes, and metabolism-related pathways (Figure 3B). PPI network analysis was performed on the differentially expressed proteins, followed by enrichment analysis of the network. The results highlighted that the PPI network was predominantly enriched in pathways associated with immune response and coagulation (Figures 3C, D).

Biological function analysis of DEPs. (A) GO functional annotation; (B) KEGG enrichment; (C) PPI network analysis with a stringency threshold of 0.7; (D) Functional enrichment of local network clusters.

To identify clinically relevant diagnostic biomarkers, a preliminary screen of 178 DEPs was performed. Following the exclusion of immunoglobulins lacking gene names, the Boruta algorithm, leveraging Random Forest, was employed for feature selection (Figures 4A, B). This process culminated in the selection of 15 key variables by Boruta, which were then analyzed using SVM analysis. Among these, B3KVV1 (Thrombomodulin) emerged as the most significant (Figure 4C). The diagnostic accuracy for septic shock, as indicated by the area under the ROC curve, was 0.88 (Figure 4D). The concentration of TM in the urine of sepsis patients is notably elevated compared to that observed in patients with septic shock (Figure 4E).

Screening of biomarkers from DEPs using machine learning methods. (A) Dynamic change plot of feature importance based on 200 Boruta algorithm iterations; (B) Feature importance ranking plot (green denotes important variables, while red/blue/yellow indicate rejected variables); (C) Variable importance ranking in SVM; (D) ROC curve of TM for diagnosing septic shock. (E) The relative content of TM protein (Median with interquartile range).

3.2 Diagnostic value of urinary TM in patients with sepsis and septic shock

To ascertain the diagnostic utility of urinary thrombomodulin (TM) in sepsis, we recruited 83 individuals with sepsis and 31 with septic shock, using 50 healthy volunteers as a control group. As depicted in Table 2, in contrast to the healthy controls, sepsis patients demonstrated elevated white blood cell counts, ALT, AST, fibrinogen, and D-dimer levels, alongside reduced red blood cell count, platelet count, albumin levels, and antithrombin activity, with extended PT and APTT (P<0.001). Furthermore, when compared with sepsis patients, those experiencing septic shock presented with higher white blood cell counts, creatinine, lactate levels, SOFA and APACHE II scores, along with a greater incidence of AKI and a higher 28-day mortality rate (P<0.01).

ParametersHealthy controls
(N=50)Sepsis
(N = 83)Septic shock
(N = 31)P valueAge,yr71.0 [66.0, 76.0]72.0 [60.0, 83.0]82.0 [61.0, 86.0]NSa,b,cMale,n(%)29 (58.0%)54 (65.1%)19 (61.3%)NS*SOFA score-5 [4, 7]9 [7, 12]0.001cAPACHE II score-20 [15, 25]27 [21, 30]0.001cInflammation markersProcalcitonin,g/ml-0.4 [0.2, 1.8]1.4 [0.6, 5.3]0.010cCRP, g/L-54.8 [24.4, 103.3]89.3 [56.2, 130.4]0.032cComplete blood countWBC,×109/L6.0 [5.0, 7.1]8.4 [6.1, 13.0]11.7 [9.9, 15.1]0.001a,b 0.002cANC,×109/L3.5 [2.7, 4.3]6.6 [4.8, 11.3]10.0 [8.2, 14.0]0.001a,b 0.005cALC, ×109/L1.9 [1.6, 2.1]0.8 [0.6, 1.2]0.7 [0.4, 1.1]0.001a,b NScRBC, ×1012/L4.7 (0.6)3.4 (0.9)3.5 (1.1)0.001a,b NScHemoglobin, g/L134.0 (20.7)100.0 (23.0)102.0(31.9)0.001a,b NScPlatelet,×109/L232.5 (67.7)166.00 (80.5)151.00 (75.4)0.001a,b NScOrgan functionALT, U/L15.0 [12.0, 25.75]30.1 [16.9, 55.8]36.8 [16.3, 56.2]0.001a 0.005b NScAST, U/L19.0 [16.0, 23.0]39.5 [24.9, 66.2]41.4 [26.6, 74.3]0.001a,b NScTBIL, mmol/L12.3 [8.9, 16.0]12.6 [8.8, 16.7]12.9 [9.2, 17.8]NSa,b,cTotal protein, g/L66.3 [63.6, 71.7]56.9 [52.9, 62.1]56.8 [50.9, 63.7]0.001a,b NScAlbumin, g/L42.9 [41.6, 46.3]31.8 [29.0, 36.3]30.8 [28.9, 33.6]0.001a,b NScCreatinine,μmol/L67.0 [60.8, 84.0]71.6 [66.5, 108.0]100.2 [79.1, 166.8]NSa 0.002b,cNT-proBNP, pg/ml-991.3 [391.2, 3634.4]2389.6 [572.6, 8647.4]0.024cLactate, mmol/L-1.50 [1.0, 1.8]2.60 [2.3, 5.2]0.001cCoagulation testPT, s11.4 [11.0, 11.7]14.0 [12.6, 15.3]14.0 [12.9, 16.6]0.001a,b NScINR0.97 [0.95, 1.00]1.16 [1.05, 1.27]1.18 [1.08, 1.38]0.001a,b NScAPTT, s27.7 [26.3, 29.0]32.0 [27.8, 39.7]30.0 [28.0, 37.2]0.001a 0.005b NScFibrinogen, g/L2.74 [2.3;3.3]3.9 [3.0, 4.5]3.0 [2.0, 4.2]0.001a NSb 0.004cTT, s16.7 [15.9, 17.0]15.6 [14.5, 16.8]16.30 [15.7, 18.6]