Swamy Vivekanandha College of Pharmacy, Elayampalayam, Tiruchengode, Namakkal 637205
Current diagnostic and prognostic approaches focused on individual biomarkers and clinical scores are inadequate to fully capture the complex, varied pathobiology of sepsis, which continues to be a major cause of death globally. In addition to evaluating the effectiveness of integrated multi-biomarker panels for early identification, risk stratification, and treatment monitoring in critically ill patients, this systematic review and meta-analysis synthesize evidence on established and new sepsis biomarkers. While novel proteomic (presepsin, calprotectin), transcriptomic (miRNAs, lncRNAs, circRNAs), metabolomic, extracellular vesicle-based, and coagulation markers add complementary pathophysiologic dimensions, conventional markers such as procalcitonin, C-reactive protein, lactate, and organ-specific indices offer significant but incomplete information. Combinations of inflammatory, immunological, endothelial, coagulation, and metabolic biomarkers regularly show better sensitivity, specificity, AUC, and likelihood ratios than any single biomarker across diagnostic accuracy and prognostic meta-analyses. Multi-biomarker panels combined with advanced analytics (logistic regression, random forests, AI models) and clinical scores (SOFA, qSOFA, APACHE II) improve early sepsis detection, forecast organ failure and mortality, and enable dynamic treatment adaption, including antibiotic de-escalation. The translational potential of biomarker-guided treatment is further increased by concurrent developments in artificial intelligence, multi-omics integration, point-of-care diagnostics, digital wearables, and next-generation sequencing. Significant obstacles still exist, nevertheless, such as methodological heterogeneity, a lack of established cut-offs, restricted validation in a variety of groups, implementation difficulties, and limitations unique to a given technology. Precision methods offered by technology and multi-biomarkers have the potential to revolutionize sepsis treatment and enhance worldwide result.
1.1 Global Burden of Sepsis
One of the leading causes of death and morbidity in the globe, sepsis continues to be a serious global health concern. Recent epidemiological data from the World Health Organization and the Global Burden of Disease Study 2017 show that 11 million sepsis-related fatalities and 48.9 million sepsis cases occurred worldwide in 2017, making up over 20% of all deaths.[1] This is an enormous public health burden that keeps getting worse. According to more current data from 2024, there were 7.8 million diagnosed incident cases of sepsis in the eight biggest developed economies (China, France, Germany, Italy, Japan, Spain, the United Kingdom, and the United States), which resulted in 2 million deaths, or a 26% fatality rate.[2] Among patients diagnosed with septic shock, the situation is much more grim, with the mortality rate reaching 49.7%, affecting 1.4 million patients and leading to 697,600 deaths in 2024 alone.[2] There are significant differences in the regional distribution of sepsis burden, with low- and middle-income countries (LMICs) having the greatest incidence and fatality rates. Sepsis has an age-standardized incidence rate of 677.5 cases per 100,000 persons worldwide, with the highest rates found in South Asia, Oceania, and sub-Saharan Africa.[3] Sepsis incidence rates in LMICs are significantly higher than those in high-income countries, with certain Asian and African countries having over 1,500 cases per 100,000 people.[3] Moreover, fatality rates for sepsis and septic shock in LMICs sometimes exceed 40% and 50%, respectively, while in high-income countries, the corresponding rates are 15–25% and 30–40%, respectively.[3] This discrepancy emphasizes the critical need for easily available diagnostic and therapeutic advances as well as the significant impact that resource constraints have on sepsis outcomes.Children under the age of five account for about 20 million of the projected sepsis cases, which has a catastrophic effect on pediatric populations. With a predicted 4.95 million deaths linked to antimicrobial resistance (AMR) in 2019, including 1.27 million deaths directly related to resistant organisms, AMR further exacerbates the burden of sepsis and makes treating septic illnesses more challenging.[4] The Global Sepsis Alliance's 2030 Global Agenda for Sepsis, which aims to lower the incidence of sepsis worldwide by at least 25% and increase survival rates by more than 20% by 2030 relative to baseline values from 2017 to 2020, illustrates the global understanding of this epidemic.[5]
1.2 Rationale for Multi-Biomarker Panels
Concurrently measuring several molecular and cellular parameters reflecting various pathophysiological pathways, integrated multi-biomarker panels offer a thorough assessment of the complex, multidimensional nature of the septic process, whereas individual biomarkers only capture isolated aspects of sepsis pathophysiology.[6][7] multi-biomarker panels consistently outperform individual markers in terms of diagnosis accuracy, according to meta-analytic and clinical data. With an AUC of 0.91, sensitivity of 91%, and specificity of 82%, the combined PCT and CRP assessment produced the best diagnostic performance, significantly better than either marker alone, according to research.[8] Because PCT and CRP have comparable kinetics—PCT increases quickly for early bacterial detection while CRP exhibits a strong but slower inflammatory response—this synergistic enhancement takes place, catching both early and prolonged inflammatory signals.[9][10]
When compared to clinical ratings or individual markers alone, multi-biomarker panels provide for better predictive categorization. Individualized risk assessment is made possible by integrated panels that include markers for inflammation, immunity, coagulation, and organ dysfunction. This allows for customized clinical decision-making and therapy intensity modification.[11][5] In heterogeneous populations, where clinical presentation, underlying comorbidities, and disease trajectory differ significantly, this expertise becomes increasingly crucial. Additionally, during the course of treating sepsis, dynamic changes in multi-biomarker panels reflect changing pathophysiology and therapeutic responsiveness, providing objective metrics for directing therapy modifications, antibiotic de-escalation choices, and treatment efficacy evaluation.[12] A revolutionary opportunity to transform sepsis diagnosis and management is presented by the integration of multi-biomarker panels with cutting-edge technologies such as artificial intelligence, machine learning algorithms, point-of-care rapid diagnostics, and multi-omics analyses.[8] In addition to enabling real-time clinical decision support, these technologies can integrate multiple biomarkers at once, apply complex pattern recognition algorithms, and possibly find new biomarker combinations and immunological endotypes that predict treatment response and outcomes.[13]
B. ESTABLISHED BIOMARKERS FOR SEPSIS
2.1 Inflammatory Biomarkers
Procalcitonin (PCT)
Neuroendocrine cells create procalcitonin, a 116-amino acid peptide that is the precursor of calcitonin. In response to bacterial endotoxin and inflammatory cytokines, specifically IL-1β, TNF-α, and IL-6, parenchymal cells throughout the body, including hepatocytes, fat cells, and kidney cells, produce PCT during bacterial sepsis.[14][15] Procalcitonin has outstanding diagnostic performance for sepsis identification, with pooled sensitivity ranging from 80-85% and specificity of 75-80% across several trials, according to a meta-analytic synthesis of diagnostic research.[15] PCT diagnosis accuracy has an area under the receiver operating characteristic curve (AUC) of around 0.85-0.87, which is higher than CRP.[17][16] Significantly, there is a direct correlation between PCT levels and mortality risk, organ dysfunction, and disease severity; higher PCT concentrations are associated with worse outcomes and an increased risk of septic shock and death.[15] PCT's usefulness in antimicrobial stewardship was demonstrated in the PRORATA trial, a seminal study of 621 ICU patients with suspected bacterial infections, where PCT-guided antibiotic stewardship safely decreased antibiotic duration by about 3–4 days compared to standard care without increasing mortality.[17[18] PCT kinetics are useful for tracking treatment response since they show a sharp increase within 4–12 hours after infection onset and a return to normal within 1-2 weeks after effective therapy.[14][15] Antibiotic withdrawal is frequently advised when PCT levels fall by 90% from baseline or fall below 0.5 ng/mL[14][17[18].
C-Reactive Protein (CRP)
C-reactive protein is an acute phase reactant synthesized primarily by hepatocytes under stimulation by IL-6 during systemic inflammation. CRP binds to phosphocholine present on the surface of pathogens and cellular debris, facilitating opsonization, complement activation, and phagocytosis. CRP begins to rise within 6-8 hours of infection and peaks at 48-72 hours, showing a slower kinetic profile than PCT.[16] Diagnostic accuracy studies show that CRP demonstrates modest sensitivity (70-75%) and specificity (65-72%) for sepsis diagnosis.[19][15] The pooled AUC for CRP is approximately 0.74, notably lower than PCT. While CRP is readily available, inexpensive, and widely used globally, its lack of specificity for bacterial infection (elevation occurs in viral infections, autoimmune diseases, and non-infectious inflammatory conditions) limits its standalone utility. Nevertheless, when combined with other clinical parameters and biomarkers, CRP contributes valuable diagnostic information.[16] Recent evidence demonstrates that combined PCT and CRP assessment yields substantially improved diagnostic accuracy with AUC of 0.91, sensitivity of 91%, and specificity of 82%, compared to either marker alone.[16]
Cytokines and Inflammatory Mediators
Interleukin-6 (IL-6) is a pleiotropic pro-inflammatory cytokine with multiple roles in sepsis pathophysiology. IL-6 is produced by activated macrophages, dendritic cells, and T cells in response to bacterial endotoxin and TNF-α. Meta-analyses demonstrate that IL-6 levels are substantially elevated in septic patients compared to controls, with pooled AUC for IL-6 diagnostic accuracy of approximately 0.80-0.85. Importantly, IL-6 levels correlate strongly with sepsis severity, SOFA scores, organ dysfunction, and 28-day mortality risk. Peak IL-6 concentrations within the first 24-48 hours predict worse outcomes—patients with IL-6 levels exceeding 400 pg/mL demonstrate significantly higher mortality.[19] Dynamic IL-6 changes during treatment reflect therapeutic efficacy, with rising or persistently elevated levels indicating treatment failure or secondary complications.[17] Interleukin-8 (IL-8) is a potent chemokine produced by macrophages and endothelial cells that recruits and activates neutrophils. IL-8 levels are markedly elevated in sepsis and correlate with disease severity and organ dysfunction. IL-8 contributes to neutrophil-mediated tissue damage and microvascular dysfunction.[19] Tumor Necrosis Factor-Alpha (TNF-α) is a key cytokine initiating the inflammatory cascade in sepsis. TNF-α is produced primarily by activated macrophages and acts on TNF receptors to activate endothelial cells, promote inflammatory mediator release, and initiate coagulation. However, TNF-α has a relatively short half-life (approximately 15 minutes) and is rapidly cleared, making it less stable as a clinical biomarker compared to PCT or CRP.[19]
2.2 Cell Surface and Receptor Biomarkers
Soluble Triggering Receptor Expressed on Myeloid Cells-1 (sTREM-1)
sTREM-1 is the soluble form of TREM-1, a receptor expressed on the surface of activated neutrophils and monocytes. Upon bacterial infection, TREM-1 is shed from the cell surface into the bloodstream, where it can be measured as sTREM-1[8]. sTREM-1 interacts with bacterial lipopolysaccharide (LPS) and other pathogen-associated molecular patterns, serving as a specific indicator of bacterial infection.[20] A comprehensive meta-analysis of 19 diagnostic accuracy studies involving 1,747 patients revealed that sTREM-1 achieved pooled sensitivity of 0.82 (95% confidence interval 0.73-0.89) and specificity of 0.81 (95% CI 0.74-0.86) for sepsis diagnosis, with an overall AUC of 0.88 (95% CI 0.85-0.91). Using a cutoff of 49 pg/mL, sTREM-1 demonstrates 100% sensitivity and 84% specificity for sepsis identification.[8] Notably, sTREM-1 levels were not significantly different between patients with positive and negative blood cultures, suggesting sTREM-1 maintains diagnostic value in culture-negative sepsis scenarios.[8] Additionally, sTREM-1 demonstrates superior discrimination between sepsis and systemic inflammatory response syndrome (SIRS) compared to conventional markers, achieving near-perfect discrimination on day 1 with AUC of 1.00 and on day 7 with AUC of 0.93.[20] The diagnostic accuracy of sTREM-1 for bacteremia detection showed an AUC of 0.83, with 82% sensitivity and 73% specificity at optimal cutoff values. sTREM-1 also demonstrated prognostic value, with studies showing that elevated sTREM-1 levels predicted worse outcomes and increased mortality risk.[20]
Soluble Urokinase Plasminogen Activator Receptor (suPAR)
suPAR is the soluble form of the urokinase plasminogen activator receptor (uPAR), a glycosylphosphatidylinositol-anchored protein expressed on the surface of immune cells and endothelial cells. suPAR levels are elevated during infection and inflammatory states and reflect endothelial activation and systemic inflammation. Meta-analytic synthesis of 22 diagnostic studies involving 2,568 patients demonstrated that suPAR achieved pooled sensitivity of 0.76 and specificity of 0.78 for sepsis diagnosis, with an AUC of 0.83 (95% CI 0.79-0.86). A cutoff of 3.3 ng/mL yielded high specificity of 96.7% for predicting 28-day mortality in septic patients. Prospective studies show that suPAR is not affected by clinical parameters that may confound other scores, making it potentially valuable for prognostic assessment independent of SOFA or APACHE II scores.[21] Integration of suPAR measurements with qSOFA (quick Sequential Organ Failure Assessment) scoring enhances prognostic precision for unfavorable outcomes in sepsis.[14]
Human Leukocyte Antigen-DR (HLA-DR)
HLA-DR expression on monocytes serves as a critical marker of immune function in sepsis. Reduced monocyte HLA-DR expression during sepsis indicates immune suppression and is associated with increased risk of secondary infections and mortality. The profound reduction in HLA-DR expression reflects a critical immunosuppression phase in sepsis where patients develop paradoxical susceptibility to nosocomial infections despite apparent clinical improvement. Measurement of monocyte HLA-DR is valuable for identifying patients in immunosuppressive states who may benefit from targeted immunomodulatory therapies.[22]
CD64 (Neutrophil CD64)
CD64 is a high-affinity Fcγ receptor expressed on neutrophil surfaces. During bacterial infections, neutrophil CD64 expression increases substantially and serves as a sensitive marker for bacterial sepsis. Neutrophil CD64 has been used to differentiate bacterial sepsis from viral infections and systemic inflammatory response syndrome without infection, providing both diagnostic and prognostic information. Studies demonstrate that CD64 expression correlates with disease severity and predicts outcomes in critically ill patients.[22]
2.3 Metabolic and Organ Dysfunction Biomarkers
Lactate
Lactate is a key marker of tissue hypoperfusion, anaerobic metabolism, and cellular dysfunction in sepsis. During septic shock, reduced oxygen delivery to tissues forces a shift from aerobic to anaerobic metabolism, resulting in lactate accumulation and metabolic acidosis. Elevated serum lactate levels reflect inadequate oxygen utilization at the cellular and mitochondrial level and serve as a marker of disease severity and shock severity. Meta-analyses demonstrate that lactate levels strongly correlate with sepsis severity, organ dysfunction, and mortality risk. Initial lactate levels and lactate clearance (change in lactate over time) predict outcomes—persistent elevation or rising lactate levels despite therapeutic interventions indicate treatment failure and worse prognosis. The 2016 Surviving Sepsis Campaign guidelines recommend lactate measurement in all septic patients and use of lactate clearance as an indicator of successful resuscitation, with normalization of lactate associated with improved outcomes.[14][23]
Renal Biomarkers
Acute kidney injury (AKI) is common in severe sepsis and septic shock, occurring in up to 50% of patients requiring ICU admission for sepsis. Traditional markers of renal dysfunction include creatinine and blood urea nitrogen are late indicators of injury.[14]
Neutrophil Gelatinase-Associated Lipocalin (NGAL) is an emerging biomarker providing earlier detection of septic AKI. NGAL is released from activated neutrophils and epithelial cells during kidney injury and can be measured in plasma and urine. NGAL demonstrates superior sensitivity compared to creatinine for early AKI detection, rising within hours of kidney injury onset compared to days for creatinine elevation.
Cystatin C is another marker of glomerular filtration rate that rises earlier than creatinine in kidney dysfunction, providing more sensitive early detection of septic AKI.
Hepatic Function Biomarkers
Hepatic dysfunction is common in sepsis and contributes to organ failure. Bilirubin accumulation and elevations in liver enzymes (aspartate aminotransferase [AST], alanine aminotransferase [ALT]) indicate hepatic injury. Elevated bilirubin levels are included in SOFA scoring for sepsis severity assessment and correlate with mortality risk.
Cardiac Biomarkers
Septic cardiomyopathy occurs in 20-50% of septic shock patients and manifests as left ventricular dysfunction despite normal or elevated cardiac output. Cardiac troponins (troponin I and troponin T) are released during myocardial injury and indicate septic cardiomyopathy[14]. Elevated troponin levels in sepsis correlate with worse outcomes and increased mortality. Natriuretic peptides including B-type Natriuretic Peptide (BNP) and N-terminal pro-BNP (NT-proBNP) indicate cardiac wall stress and dysfunction. These markers rise during sepsis-induced cardiac dysfunction and correlate with disease severity and mortality.[14]
2.4 Coagulation and Fibrinolysis Markers
D-Dimer
D-dimer is a fibrin degradation product generated during active fibrinolysis of cross-linked fibrin clots. In sepsis, activation of both coagulation and fibrinolysis pathways leads to substantially elevated D-dimer levels. Markedly elevated D-dimer (>4-5 times upper limit of normal) is associated with overt DIC and predicts worse outcomes. D-dimer levels correlate with sepsis severity, degree of organ dysfunction, and mortality risk.[14]
Prothrombin Time (PT) and International Normalized Ratio (INR)
PT measures the extrinsic and common coagulation pathways and is typically expressed as the International Normalized Ratio (INR). Prolongation of PT/INR in sepsis reflects decreased synthesis of coagulation factors by the liver and consumption of factors through activation of coagulation pathways. Markedly prolonged INR values indicate severe coagulopathy and are included in DIC scoring systems.[23]
Activated Partial Thromboplastin Time (aPTT)
aPTT measures the intrinsic and common coagulation pathways. Prolongation of aPTT in sepsis reflects similar pathophysiology as PT prolongation. Both PT and aPTT prolongation indicate severe coagulopathy and predict adverse outcomes.[23]
Fibrinogen
Fibrinogen, the substrate for thrombin and precursor of fibrin, is typically elevated in early sepsis as part of the acute phase response. However, severe consumption of fibrinogen through coagulation activation and fibrinolysis can lead to hypofibrinogenemia, indicating overt DIC. Low fibrinogen levels predict worse outcomes in sepsis.
Platelet Count
Thrombocytopenia (platelet count <100,000/μL) occurs in up to 50% of septic patients and reflects the severity of coagulation and fibrinolysis activation. Progressive thrombocytopenia or failure to recover platelet count during treatment indicates severe DIC and predicts mortality. Serial platelet count monitoring is important in managing septic coagulopathy and represents one component of DIC scoring systems.
Thrombin-Antithrombin Complex (TAT)
TAT represents the complex formed between thrombin and its primary inhibitor antithrombin. Elevated TAT levels reflect ongoing thrombin generation and coagulation activation in sepsis. TAT levels correlate with sepsis severity and DIC presence.[14]
C. Emerging Biomarkers for sepsis
3.1 Novel Proteomic Biomarkers
Presepsin (sCD14-ST)
Presepsin, also known as soluble cluster of differentiation 14 (sCD14-ST), is a 13-kDa fragment of CD14 released by monocytes and macrophages in response to bacterial lipopolysaccharide (LPS) and other bacterial products. Presepsin represents a promising novel proteomic biomarker with superior diagnostic and prognostic performance compared to conventional markers.[24[25] A landmark validation study demonstrated that plasma presepsin concentrations above 350 pg/mL achieved sensitivity of 93.3% for sepsis diagnosis in the initial validation cohort and 78.3% in a subsequent validation cohort of COVID-19 patients. Critically, the sensitivity for 28-day mortality prediction was 85.7% in the first cohort and 92.3% in the second cohort. Meta-analytic reviews of multiple studies confirm that presepsin achieves high sensitivity (approximately 95% at cutoff 729 pg/mL) and good specificity for sepsis detection.
Presepsin and both MEDS (Mortality in Emergency Department Sepsis) and APACHE II scores were independent predictors of severe sepsis, septic shock, and 28-day mortality in septic patients.[25] Notably, presepsin levels did not differ significantly between gram-positive and gram-negative bacterial infections, suggesting its utility across diverse bacterial etiologies[2]. The negative predictive value of presepsin exceeds 90%, making it particularly valuable for ruling out severe infection in low-probability cases.[24] Presepsin demonstrates prognostic utility for predicting complications. Studies indicate that presepsin elevation upon ICU admission and day 2 predicted acute renal failure, presepsin elevation on days 1-3 predicted acute respiratory distress syndrome (ARDS) development, and presepsin elevation on the first two days predicted disseminated intravascular coagulation (DIC) occurrence. Serial presepsin measurements reveal clinical significance—a downtrend indicates clinical improvement and positive treatment response, while an uptrend indicates worse prognosis and complicated clinical course.[25]
Other Novel Proteomic Markers
Recent research has identified additional proteomic signatures reflecting sepsis pathophysiology. Endotoxin core antibodies (EndoCAb) and lipopolysaccharide-binding protein (LBP) represent additional proteomic markers reflecting bacterial product exposure.[26] Angiopoietin-2 serves as a marker of endothelial dysfunction and has shown prognostic value in sepsis.[27]
3.2 Transcriptomic and RNA-Based Biomarkers
MicroRNAs (miRNAs)
MicroRNAs are small non-coding RNA molecules (approximately 22 nucleotides) that regulate gene expression post-transcriptionally. Recent meta-analytic analysis of 55 studies involving 2,047 non-surviving and 4,396 surviving sepsis patients, covering 41 different microRNAs, revealed that microRNAs exhibit moderate predictive accuracy as biomarkers for sepsis mortality. The combined area under the curve (AUC) for microRNA panels was 0.83, with 76% sensitivity and 72% specificity. Importantly, miR-133a-3p, miR-146a, miR-21, miR-210, miR-223-3p, miR-155, miR-25, miR-122, miR-125b, and miR-150 emerged as promising candidates for clinical applications in sepsis prognosis.[28]
MicroRNA-21 (miR-21)
miR-21 expression is substantially decreased in sepsis patients compared to healthy controls[6]. Receiver operating characteristic (ROC) curve analysis revealed that miR-21 demonstrates good predictive value for sepsis risk with an AUC of 0.801 (95% confidence interval 0.758-0.844)[6]. miR-21 relative expression in sepsis patients (0.277 [0.193-0.451]) was markedly lower than in healthy controls (0.967 [0.400-1.630]), p<0.001.
miR-21 was negatively correlated with APACHE II score, SOFA score, serum creatinine (Scr), white blood cell (WBC) count, CRP, and pro-inflammatory cytokines, while positively correlated with albumin in sepsis patients. These relationships suggest miR-21’s protective role in regulating inflammation and organ injury. Mechanistically, miR-21 upregulation decreases apoptosis and pro-inflammatory cytokine production in kidneys, hearts, livers, and lungs, implying that miR-21 exerts protective effects on multiple organ failure induced by sepsis. miR-21 showed poor predictive value for 28-day mortality (AUC 0.588, 95% CI 0.505-0.672), which was numerically inferior to APACHE II score (AUC 0.793)[6]. This suggests that miR-21 may affect mortality risk indirectly through clinical severity scores and inflammatory markers.[29]
MicroRNA-223-3p (miR-223-3p)
Recent meta-analyses identified miR-223-3p as a potential diagnostic biomarker for sepsis with promising accuracy metrics. miR-223-3p represents one of the most robustly validated microRNAs for sepsis diagnosis across multiple independent studies.[28]
Long Non-Coding RNAs (lncRNAs)
Long non-coding RNAs represent a class of RNA molecules exceeding 200 nucleotides with regulatory roles in immune responses. Studies demonstrate that extracellular vesicles carrying lncRNA NEAT1 in sepsis aggravate sepsis-related encephalopathy, while lncRNA-p21 could inhibit lipopolysaccharide-induced lung cell injury.[29]
Circular RNAs (circRNAs)
Circular RNAs are non-linear RNA molecules with a covalently closed-loop structure that show promise as sepsis biomarkers. Serum exosomes from sepsis patients were upregulated with has_circRNA_104484 and has_circRNA_104670, suggesting these circular RNAs could serve as diagnostic markers for the disease.[30]
3.3 Metabolomic Biomarkers
Amino Acid Metabolism
A comprehensive metabolomic study analyzing the circulating metabolome of sepsis patients identified substantial changes in amino acid metabolism as a critical feature of sepsis pathophysiology. Among 457 metabolites analyzed across multiple metabolite classes, amino acids represented a predominant dysregulated category.[31] Specifically, 13 metabolites, predominantly amino acids, exhibited significant decreases in septic conditions, whereas 10 metabolites including nucleosides, amino acids, and pyrimidines demonstrated significant increases. The amino acids arginine, serine, isoleucine, and glutamine were among the top metabolites significantly lower in sepsis patients compared to symptomatic controls.[31] A metabolomic analysis confirmed these findings, revealing that 12 amino acids showed differential levels in sepsis, with 7 amino acids (58.3%) elevated (leucine, glutamic acid, cysteine, methionine, phenylalanine, putrescine, and aspartic acid) and 5 amino acids decreased (serine, tryptophan, glutamine, D-proline, and asparagine).[32] The profound dysregulation of metabolism reflects the catabolic state and altered metabolic demands characterizing sepsis.[32]
Lipid Metabolism
Lipid metabolites represent another critical metabolomic category dysregulated in sepsis. Metabolomic analysis revealed that all lipid metabolic pathways showed elevation, including arachidonic acid metabolism, steroid hormone biosynthesis, biosynthesis of unsaturated fatty acids, sphingolipid metabolism, fatty acid metabolism, fatty acid elongation, fatty acid degradation, and linoleic acid metabolism. Levels of specific protective lipid mediators were decreased. Arachidonic acid, docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA) were significantly lower in sepsis patients than healthy controls. The decrease of EPA, DHA, and omega-3 fatty acids, reflects impaired resolution of inflammation in sepsis.[32]
TCA Cycle and Organic Acid Metabolism
Metabolomic studies demonstrate dysregulation of tricarboxylic acid (TCA) cycle metabolism in sepsis, reflecting mitochondrial dysfunction and altered energy metabolism.[32]
3.4 Extracellular Vesicles and Bioparticles
EVs include exosomes (30-150 nm), microvesicles (100-1000 nm), and apoptotic bodies (500-5000 nm), released by various cell types during infection and inflammation.[33]
Roles and Pathogenic Functions
A variety of cell types, including activated macrophages, monocytes, neutrophils, and endothelial cells, generate EVs with altered protein profiles during sepsis. In serum of septic animal models, numerous cytokines and chemokines are specifically encapsulated in exosomes, with exosome inhibitor studies showing that suppressing exosome formation significantly reduces exosome formation and inflammatory cytokine release. Gram-positive and gram-negative bacteria, the most prevalent infectious agents in sepsis, produce EVs carrying bacterial endotoxins and transmitting bacterial proteins. During gram-negative bacterial infection, outer membrane vesicles (OMVs) serve as crucial facilitators for lipopolysaccharide (LPS) and caspase-11 entry into the host cell cytoplasm, amplifying inflammatory signaling.[30]
Diagnostic and Prognostic Value
Extracellular vesicles have emerged as promising biomarkers for sepsis detection. Recent studies applying Raman spectroscopy to patient plasma-derived EVs demonstrated rapid, sensitive, and specific detection of sepsis in burn patients, with application to other sepsis populations. The diagnostic utility of EVs is supported by their ability to encapsulate pathogenic cargo and inflammatory mediators reflective of sepsis severity.[33]
Molecular Cargo in EVs
Exosomes derived from macrophages stimulated by bacterial lipopolysaccharide (LPS) produce high levels of cytokines. The cytokines and chemokines encapsulated in exosomes differ from serum-free counterparts in that they may have specialized roles in lymphocyte differentiation and proliferation. EVs carry molecular patterns associated with damage (DAMPs), including high mobility group box 1 (HMGB1) protein, histones, and extracellular cold-induced RNA-binding protein.[30]
Therapeutic Implications
Recent research has demonstrated that modifying miRNAs within exosomes can suppress the cytokine storm in sepsis and inhibit its development. Interestingly, some studies show that certain EVs may suppress inflammation in septic patients.Notably, platelet-derived exosome production in sepsis may be regulated by nitric oxide (NO) and bacterial components, promoting generation of reactive oxygen species, peroxynitrite, caspase-3 activation, and vascular endothelial cell apoptosis, ultimately causing vascular dysfunction in sepsis.[30]
3.5 Novel Soluble Markers
Calprotectin (S100A8/A9)
Calprotectin, also known as S100A8/A9 (the leukocyte L1 antigen complex), represents a promising novel biomarker for bacterial infections and sepsis. Calprotectin is a heterodimeric protein complex released by activated neutrophils and monocytes in response to bacterial pathogens.[34] A comprehensive diagnostic accuracy study found that calprotectin demonstrated notable accuracy for detecting bacterial infections with an area under the receiver operating characteristic curve (AUROC) of 0.90. For sepsis detection within 72 hours, calprotectin achieved an AUROC of 0.83, and for 30-day mortality prediction, the AUROC was 0.78. Notably, in patients with diabetes, calprotectin demonstrated superior performance with an AUROC of 0.94 for identifying bacterial infection. Recent research demonstrated that serum S100A8/A9 concentration at ICU admission is a significant predictor of 28-day mortality risk in sepsis patients. Studies have shown that S100A8/A9 promotes inflammatory responses by activating the toll-like receptor 4 (TLR4) pathway and plays key roles in immune suppression. Serum levels of S100A8/A9 were increased in sepsis patients upon admission.[35]
Regenerating Islet-Derived 3-Alpha (REG3α)
REG3α is a recently identified biomarker reflecting intestinal barrier dysfunction in sepsis. Elevated REG3α levels indicate compromised intestinal epithelial integrity and translocation of bacterial products, contributing to perpetuation of sepsis pathophysiology.
Chromogranin A
Chromogranin A represents a neuroendocrine stress response marker that has shown promise in sepsis assessment, reflecting the profound neuroendocrine-immune dysregulation characterizing sepsis.[27]
D. MULTI-BIOMARKER PANELS AND COMBINATIONS
4.1 Diagnostic Multi-Biomarker Panels
Rationale for Multi-Biomarker Diagnostic Approaches
A systematic review and meta-analysis examining lipopolysaccharide-binding protein (LBP) as a single biomarker revealed suboptimal diagnostic performance with pooled sensitivity of 0.64 and pooled specificity of 0.63, yielding an unsatisfactory area under the curve (AUC) of 0.68. This meta-analysis concluded that LBP had weak sensitivity and specificity and suggested that combining LBP with other more sensitive biomarkers, such as PMN CD64 index, procalcitonin (PCT), and sTREM-1, would be more effective in improving diagnostic accuracy for sepsis.[36]
Procalcitonin, CRP, and Serum Amyloid A (SAA) Panel
Li et al. evaluated the predictive capacity of conventional sepsis biomarkers through individual versus combinatorial analyses to define optimal biomarker panels for sepsis prediction in severely ill patients. Their multi-parametric assessment revealed that combining procalcitonin, C-reactive protein, and serum amyloid A measurements yielded substantially enhanced diagnostic precision compared to singular biomarker evaluation. When these three biomarkers were measured together, the combined panel demonstrated superior sepsis prediction capabilities, substantially outperforming any individual marker.[37]
Combined PCT, CRP, and SOFA Score Bioscore
Yang et al. conducted a clinical study combining common biomarkers with clinical severity assessment to develop an integrated diagnostic bioscore. The study evaluated 332 ICU patients and investigated whether combining CRP, PCT, IL-6 with clinical information (white blood cell count, body temperature, age, gender) and the Sequential Organ Failure Assessment (SOFA) score in an extended bioscore could improve early sepsis diagnosis.[38]
Network Meta-Analysis of Seven Biomarkers
An ongoing network meta-analysis of diagnostic accuracy compared seven biomarkers (procalcitonin, CRP, IL-6, presepsin, CD64, sTREM-1, and lipopolysaccharide-binding protein) for detecting systemic infection and sepsis according to the Sepsis-3 definition. Results revealed that CD64 displayed superior diagnostic performance with sensitivity of 0.87 and specificity of 0.99 (95% CI: 0.81-1.00 and 0.92-1.00, respectively), compared to the individual performance of other biomarkers. These findings highlight the complementary diagnostic value of combining markers with different immune and inflammatory profiles.[39]
4.2 Prognostic Multi-Biomarker Panels
Integration of suPAR with qSOFA Scoring
Recent evidence demonstrates that integrating suPAR (soluble urokinase plasminogen activator receptor) measurements with quick Sequential Organ Failure Assessment (qSOFA) scoring substantially enhances prognostic precision for unfavorable outcomes in sepsis, enabling more timely therapeutic interventions. Meta-analytic data from Huang et al. quantified suPAR’s diagnostic accuracy with sensitivity of 0.76 and specificity of 0.78, supported by an AUC of 0.83 in sepsis detection. Prognostically, suPAR demonstrated 0.74 sensitivity and 0.70 specificity for mortality prediction, achieving an AUC of 0.78 across diverse patient cohorts. Given the comparable prognostic efficacy between suPAR and procalcitonin, current evidence supports incorporating suPAR into standardized sepsis evaluation indicators.[40]
SOFA Score Combined with Procalcitonin and Age
Recent studies demonstrate that incorporating procalcitonin and age into the SOFA score substantially enhances prognostic utility for mortality prediction in critically ill patients. A comprehensive analysis of critically ill COVID-19 patients (applicable to sepsis populations) revealed that a model incorporating the SOFA score, age, and procalcitonin demonstrated high area under the receiver operating characteristic curve (AUROC) of 0.837 (95% CI: 0.816–0.859). These associations with the SOFA score showed greater clinical utility than SOFA score alone. Furthermore, decision curve analysis demonstrated that incorporating the SOFA score, age, comorbidities, and procalcitonin enhances mortality risk prediction in critically ill populations.[41]
SII (Systemic Immune-Inflammation Index) and PCT Panel
Recent research identified the combination of systemic immune-inflammation index (SII) and serum procalcitonin (PCT) as a powerful prognostic panel for septic shock outcomes. The study demonstrated that the death group exhibited significantly higher levels of APACHE II, SOFA scores, lymphocyte count reduction, elevated CRP, increased serum creatinine, heightened SII, and elevated PCT compared to the survival group. Importantly, both SII and PCT were identified as independent risk factors indicating poor prognosis in septic shock patients. Both parameters showed positive correlation with APACHE II and SOFA scores, as well as lymphocyte count, CRP, and serum creatinine levels.[41]
Clinical Worsening Risk Stratification
A Hellenic Sepsis Study Group analysis of 2,377 patients classified them into four risk groups based on qSOFA signs and suPAR levels: Group A: No qSOFA signs (590 patients), Group B: One qSOFA sign and suPAR < 12 ng/mL (615 patients), Group C: One qSOFA sign and suPAR ≥ 12 ng/mL (290 patients), Group D: Two or three qSOFA signs (882 patients). This stratification enabled refined prognostic assessment, with suPAR providing additional prognostic information in intermediate-risk populations (one qSOFA sign). Early clinical worsening (defined as one-point SOFA score increase within 24 hours) was substantially reduced in the treatment group (15.9%) versus placebo (40.4%) in high-risk patients.[42]
4.3 Therapeutic Monitoring Panels
Serial Biomarker Assessment for Treatment Response
Biomarkers play a critical role in monitoring disease progression and guiding real-time treatment adjustments during sepsis management. Rising biomarker levels, particularly elevated interleukin-6 (IL-6), indicate worsening inflammation and higher risk of complications. Serial biomarker measurements provide objective evidence of treatment efficacy or failure.[43]
Procalcitonin-Guided Monitoring
A reduction in procalcitonin levels indicates positive therapeutic response to antimicrobials and supportive care. The PRORATA trial established that a 90% decrease in PCT from baseline or decline to <0.5 ng/mL predicts treatment success and supports antibiotic discontinuation. Conversely, rising or persistently elevated PCT levels during treatment indicate treatment failure or secondary complications necessitating therapy escalation.[44]
C-Reactive Protein Kinetics
CRP demonstrated utility for monitoring disease progression and treatment response in ventilator-associated pneumonia (VAP). A prospective observational cohort study by Póvoa et al. was among the first to explore serial CRP measurements for monitoring disease progression and treatment response. Results suggested that marked decrease of CRP should be considered in clinical decision-making for early discontinuation of antibiotics.[45]
Multi-Biomarker Temporal Assessment
Recent research emphasizes that combining multiple biomarkers into dynamic monitoring panels provides enhanced assessment of treatment trajectory and therapy responsiveness. Presepsin demonstrates prognostic utility for predicting sepsis complications—presepsin elevation upon ICU admission and day 2 predicted acute renal failure, presepsin elevation on days 1-3 predicted acute respiratory distress syndrome (ARDS) development, and presepsin elevation on the first two days predicted disseminated intravascular coagulation (DIC) occurrence.[46] Serial presepsin measurements reveal clinical significance—downtrending presepsin indicates clinical improvement and positive treatment response, while uptrending presepsin indicates worse prognosis and complicated clinical course. This temporal approach to biomarker assessment enables real-time treatment monitoring and early intervention when deterioration is detected.[47]
Biomarker-Guided Therapy Adjustment
The Antibiotic duration: PCT-guided protocols reduce antibiotic duration while maintaining safety. The Fluid therapy: Endothelial and cardiac biomarkers inform fluid resuscitation decisions and assessment of volume status. The Vasopressor initiation: Cardiac and lactate biomarkers guide timing and titration of vasopressor support. The Immunomodulatory therapies: Immune suppression markers (HLA-DR, immune endotypes) guide decisions regarding immunoenhancing interventions.[48]
4.4 Statistical Methods for Panel Development
Hierarchical Summary Receiver Operating Characteristic (HSROC) Analysis
The hierarchical summary receiver operating characteristic (HSROC) model is the standard statistical approach for meta-analyzing diagnostic accuracy studies incorporating multiple biomarkers. This bivariate method accounts for the threshold effect in diagnostic studies, whereby different studies may use different cutoff values for biomarker positivity. HSROC analysis yields summary sensitivity, specificity, and AUC values that integrate heterogeneous study populations and methodologies.[49]
Logistic Regression for Panel Development
Logistic regression is commonly employed to identify independent biomarkers contributing diagnostic information and develop predictive models. This method determines which biomarkers significantly predict sepsis diagnosis or outcome when adjusted for other variables in the model. Univariate logistic regression initially identifies candidate biomarkers, followed by multivariate analysis to identify independent predictors and calculate odds ratios.[50]
Random Forest (RF) Modeling
Random forest is a machine learning ensemble method that constructs multiple decision trees and aggregates their predictions for superior accuracy and generalizability. Yang et al. applied random forest modeling to hematological parameters, liver function indices, and inflammatory markers from 332 subjects to construct sepsis diagnosis models. The random forest model identified sepsis patients at an earlier stage compared with conventional PCT and CRP assessment, demonstrating greater predictive ability for 30-day mortality risk in sepsis patients. [51]
Receiver Operating Characteristic (ROC) Analysis
ROC curve analysis is fundamental to evaluating diagnostic accuracy of multi-biomarker panels. The area under the ROC curve (AUC) quantifies overall diagnostic discrimination, where AUC of 1.0 represents perfect discrimination and 0.5 represents no discrimination[3]. Multiple ROC curves can be compared statistically to determine whether combined biomarker panels significantly improve diagnostic accuracy versus individual markers.[52]
Logistic Regression with Bioscore Development
Integrated biomarker scoring systems (“bioscores”) combine multiple biomarkers through weighted logistic regression. The Yang et al. study developed a bioscore by combining CRP, PCT, SOFA score, and additional clinical parameters through logistic regression modeling. The resulting bioscore threshold (≥2.65 in their study) provided clinically interpretable cutoff values for sepsis diagnosis.[53]
Decision Curve Analysis
Decision curve analysis evaluates the clinical utility of prediction models across the range of decision thresholds. This method quantifies the net benefit of using a prediction model (e.g., multi-biomarker panel) versus alternative strategies (e.g., treat all patients, treat no patients) at different probability thresholds. Decision curve analysis revealed that incorporating multiple biomarkers and clinical parameters enhances clinical utility compared to single parameters or clinical scores alone.[54]
Meta-Regression for Heterogeneity Exploration
Meta-regression techniques explore sources of heterogeneity in diagnostic accuracy across studies. Meta-analyses examining biomarker accuracy employ meta-regression to determine whether study characteristics (ICU vs. non-critical setting, patient age, sepsis prevalence, sample size) significantly influence diagnostic accuracy estimates. This identifies optimal clinical settings and patient populations for specific biomarker panels.[55]
Subgroup Analysis for Population-Specific Performance
Subgroup analyses evaluate whether biomarker panel performance varies across patient populations and clinical settings. For example, calprotectin demonstrated superior diagnostic accuracy (AUROC 0.94) in diabetic patients compared to the overall population (AUROC 0.90)[12]. Such subgroup analyses inform personalized application of biomarker panels to specific populations.[56]
E. EMERGING TECHNOLOGIES ENHANCING BIOMARKER APPLICATION
5.1 Artificial Intelligence and Machine Learning
Artificial intelligence and machine learning represent transformative paradigms in sepsis detection and management, fundamentally changing how early identification and risk stratification are accomplished in critically ill patients. These technologies leverage electronic health record data, biomarker measurements, and vital signs to identify patterns and predict sepsis onset substantially earlier than traditional clinical assessment methods.[57] The SERA (Sepsis Event Recognition Algorithm) represents a landmark achievement in AI-driven sepsis detection. This sophisticated artificial intelligence system integrates both structured data from electronic health records and unstructured clinical narrative notes to predict and diagnose sepsis with remarkable accuracy. The algorithm’s predictive performance across multiple clinically relevant time windows demonstrates its exceptional utility: at 48 hours before sepsis clinical onset, SERA achieved an area under the curve (AUC) of 0.87, capturing 78% of patients who would eventually develop sepsis; at 24 hours before sepsis onset, performance improved to AUC of 0.90 with a true positive rate (TPR) of at least 0.86; at 12 hours before sepsis onset, the algorithm achieved exceptional accuracy with AUC of 0.94; and at just 4 hours before clinical sepsis diagnosis, the algorithm outperformed hospital physicians’ clinical assessment.[58]
Critically, SERA demonstrated substantially superior performance compared to traditional scoring systems and human clinical judgment. The algorithm increased early detection of sepsis by 21-32% compared to relying on hospital physicians’ assessment alone, while simultaneously reducing false positives by 7-17%. This performance represents a transformative advance in sepsis identification, as every hour of delay in appropriate antimicrobial therapy significantly increases mortality risk, and earlier detection could prevent progression to septic shock and multi-organ failure.Machine learning models employing diverse algorithmic approaches have demonstrated consistent superiority over traditional clinical scoring methods. Recent comprehensive reviews highlight the spectrum of machine learning techniques employed for early sepsis detection. Meta-analytic synthesis of artificial intelligence diagnostic studies across diverse clinical settings reveals that AI models achieved AUROC values ranging from 0.68 to 0.99. While this wide range reflects variability in model design, data quality, clinical settings, and validation approaches, the upper range demonstrates exceptional diagnostic performance when AI systems are optimally developed and rigorously validated. On average, AI diagnostic performance substantially exceeds traditional scoring systems, representing a paradigm shift in sepsis identification capabilities.[60]
5.2 Multi-Omics Integration
Multi-omics represents an integrated systems biology approach that transcends single-technology limitations by simultaneously analyzing genomic, transcriptomic, proteomic, metabolomic, and microbiome data at comprehensive molecular levels. This holistic perspective captures the biological complexity of sepsis pathophysiology in ways that single-modality analyses cannot achieve.[61] Multi-omics integration enables three critical advances in sepsis medicine: early diagnosis optimization through comprehensive molecular characterization capturing all dimensions of pathophysiological dysregulation; refined risk assessment and prognosis through integrated pathway analysis identifying molecular signatures predictive of mortality and complications; and individualized therapeutic strategy development based on identification of distinct molecular endotypes that may require tailored interventions.[61]
A detailed molecular analysis of neutrophil heterogeneity in sepsis using integrated multi-omics approaches substantially advanced understanding of how cellular diversity and functional specialization occur during infection. The research revealed that bulk transcriptomic profiling of circulating neutrophils in sepsis patients identified 37 differentially expressed genes compared to healthy controls, with functional enrichment analysis highlighting immune and inflammatory signaling pathways—particularly the PI3K/AKT (phosphatidylinositol 3-kinase/protein kinase B) axis—as key regulators of neutrophil specialization and functional polarization. Proteomic measurements captured more immediate functional adaptations of neutrophils at the protein level, linking transcriptional changes to actual protein function and cellular behavior. Single-cell RNA sequencing (scRNA-seq) enabled broad screening of neutrophil heterogeneity across diverse functional states, allowing precise identification of neutrophil subsets and validation through practical techniques such as flow cytometry.Specifically, H3K4me3 (histone H3 lysine 4 trimethylation) associated DNA binding sites were identified as potential therapeutic targets for immunomodulation, representing actionable insights for future sepsis treatment development.[62] The holistic perspective of multi-omics approaches clarifies the dynamic interplay between neutrophil populations and the broader host immune response, providing a fundamental foundation for identification of novel biomarkers and therapeutic targets.[61]
5.3 Point-of-Care Testing (POCT)
Point-of-care testing represents a critical enabling technology for sepsis diagnosis in diverse healthcare environments, particularly in resource-limited settings where conventional laboratory infrastructure with trained personnel, reagents, and quality control systems is unavailable or unaffordable. POCT enables rapid biomarker measurement directly at the patient’s bedside or in the emergency department, reducing diagnostic delays that prove catastrophic in sepsis.[63] A prospective observational study evaluated the feasibility and diagnostic accuracy of continuous vital sign monitoring using wireless wearable biosensor devices in adult and pediatric emergency department patients with suspected sepsis in Rwanda, a low-income country with limited laboratory capacity. The study population included critically ill patients presenting with sepsis in a resource-constrained healthcare setting typical of most low- and middle-income countries. Results demonstrated that wireless wearable biosensor devices can be successfully implemented for continuous monitoring, providing accurate heart rate and respiratory rate measurements in acutely ill pediatric and adult emergency department patients with sepsis.[63]
This innovation proves particularly significant given the stark reality that critical care capabilities essential for sepsis management—such as continuous vital sign monitoring, rapid laboratory assessment, and hemodynamic monitoring—are largely unavailable in most emergency departments throughout low- and middle-income countries. A study from the Federal Medical Centre in Nigeria evaluated point-of-care testing in intensive care unit sepsis management using portable laboratory analyzers to measure lactate, creatinine, and electrolytes. Results became available within 10-15 minutes of blood collection, enabling substantially faster clinical decision-making compared to conventional laboratory processing. The sensitivity and specificity of point-of-care lactate measurements for sepsis diagnosis were 85.7% and 92.9%, respectively, demonstrating excellent diagnostic performance.[64]
5.4 Digital and Wearable Technologies
Wearable biosensor devices and digital health technologies represent revolutionary advances enabling continuous sepsis monitoring and early detection through automated analysis of vital signs and physiological parameters. SepAl (Sepsis Alert on Low Power Wearables) represents a breakthrough achievement in deploying sophisticated machine learning algorithms on ultra-low-power wearable devices for real-time sepsis detection. The system employs lightweight temporal convolution neural networks capable of processing continuous vital sign data and generating sepsis alerts with median predicted time to clinical sepsis of 9.8 hours before physician diagnosis. This early warning capability provides a critical window for antimicrobial initiation and supportive care intensification.[65]
The technical Innovations enabling SepAl include: continuous measurement of six vital signs including heart rate, respiratory rate, body temperature, and other parameters extractable from inertial measurement units (IMUs) and photoplethysmography (PPG) sensors integrated into wearable devices; deployment of a fully quantized machine learning model operable on any low-power processors including ARM Cortex-M33 cores found in commercial wearable devices; and remarkable energy efficiency enabling device operation on battery power for extended periods, with energy consumption of just 2.68mJ per inference cycle and latency of 143 milliseconds.[66]
5.5 Next-Generation Sequencing (NGS)
Next-generation sequencing technologies provide rapid pathogen identification and comprehensive characterization of microbial communities in sepsis patients, offering substantial advantages over conventional blood culture methods which require 48-72 hours or more for results.[67] Metagenomic next-generation sequencing applied to plasma samples from sepsis patients demonstrates the ability to detect potential etiologic pathogens directly from patient blood with comprehensive coverage of diverse microbial species. A major study analyzed 254 plasma samples from sepsis patients using metagenomic NGS, revealing that potential pathogens were detected in 207 (81%) of samples, with 139 (55%) samples containing at least two distinct pathogenic organisms [68]
Diagnostic accuracy varied by pathogen type. For viral pathogen detection, the optimal cutoff of 2.4 reads per million per genome size (megabases) yielded overall sensitivity of 57% and specificity of 94% with area under the curve of 0.763. For HIV viremia detection specifically, NGS achieved sensitivity of 70% and specificity of 92%, with strong correlation between sequencing read counts and quantitative polymerase chain reaction (qPCR) cycle threshold values. Notably, NGS on average demonstrated 18 hours faster pathogen detection compared to conventional blood culture methodology.[69] Results revealed that NGS alone achieved diagnostic accuracy with AUC of 0.857, sensitivity of 95.16%, and specificity of 76.19% for sepsis identification. However, the combination of NGS and albumin achieved substantially superior performance: sensitivity of 93.55%, specificity of 85.71%, and AUC of 0.935—substantially superior to either marker alone. Logistic regression revealed both NGS and albumin as independent prediction factors for sepsis (p<0.05), indicating complementary diagnostic information.[70] The rapid turnaround time combined with comprehensive pathogen detection positions NGS as an emerging complement or potential replacement for blood culture in sepsis diagnostics. The ability to identify unculturable pathogens, anaerobes, fastidious organisms, and determine antimicrobial resistance profiles from a single test represents a transformative advance in sepsis microbiology.[71]
F. DAGNOSTIC ACCURACY AND PROGNOSIS VALUE
6.1 Diagnostic Accuracy Meta-Analysis
When compared to single biomarkers, multi-biomarker panels have greatly increased the diagnostic precision of sepsis identification. Combining biomarkers such procalcitonin (PCT), C-reactive protein (CRP), and soluble urokinase plasminogen activator receptor (suPAR) results in increased sensitivity and specificity for sepsis diagnosis, according to recent meta-analyses. For instance, Huang et al.'s meta-analysis revealed that suPAR and qSOFA together produced a sensitivity of 0.76, specificity of 0.78, and an area under the ROC curve (AUC) of 0.83 for the identification of sepsis. According to a different study, combining PCT, CRP, and serum amyloid A (SAA) enhanced diagnostic accuracy; the tripartite panel outperformed individual markers in predicting sepsis in patients with severe illness.The diagnostic power of multi-marker panels was established by the combination of mHLA-DR, PCT, and IL-6, which showed an AUC of 0.89 for sepsis diagnosis, and mHLA-DR, hyaluronidase, and creatinine, which reached an AUC of 0.92.[72]
6.2 Prognostic Value Meta-Analysis
Additionally, multi-biomarker panels show better predictive value when it comes to forecasting outcomes like organ failure, mortality, and treatment responsiveness in sepsis. According to meta-analyses, panels that include suPAR, PCT, and lactate offer strong risk stratification; suPAR has an AUC of 0.78, a sensitivity of 0.74, and a specificity of 0.70 for mortality prediction. A meta-analysis has confirmed that presepsin can stratify risk in sepsis and septic shock, with a positive likelihood ratio (LR+) of 3.0 and a negative likelihood ratio (LR−) of 0.34, indicating strong discriminative power for clinical risk assessment. Presepsin has become a promising prognostic biomarker.With a combined AUC of 0.83, sensitivity of 76%, and specificity of 72%, panels containing microRNAs (miRNAs) such miR-133a-3p, miR-146a, and miR-223-3p have demonstrated modest predictive accuracy for sepsis mortality.[73]
6.3 Comparison: Individual vs Multi-Biomarker Panels
Multi-biomarker panels consistently outperform individual biomarkers in terms of prognostic and diagnostic accuracy, according to comparative meta-analyses. Due to their lack of specificity and incapacity to fully capture the complex character of sepsis, single biomarkers such as CRP and PCT are limited. Multi-biomarker panels, on the other hand, improve sensitivity, specificity, and negative predictive value by utilizing complementing biological pathways. For example, PCT, CRP, and SAA together showed better sepsis prediction than any single marker, with a lower misclassification rate and a higher AUC. Immune, metabolic, and endothelial marker-integrated panels offer a more thorough evaluation of sepsis pathophysiology, allowing for early intervention and improved patient outcomes.[73]
G. CLINICAL AND TRANSLATIONAL IMPLICATIONS
7.1 Clinical Decision Making
A major paradigm change toward more timely, accurate, and well-informed therapies has occurred with the incorporation of multi-biomarker panels into clinical sepsis decision making. Immunological, metabolic, and organ function indications are combined by multi-marker diagnostic algorithms to provide a more accurate evaluation than traditional single-biomarker or clinical grading systems. By combining biomarkers including IL-6, IL-8, IL-10, PCT, and soluble TREM-1, advanced machine learning models can achieve diagnostic accuracy of over 90%. This allows for the early stratification of critically ill patients and directs the use of vasopressors, antibiotics, and organ support decisions. By identifying patients who can safely skip broad-spectrum antibiotics, this data-driven strategy enhances antimicrobial stewardship and reduces resistance. Additionally, real-time biomarker monitoring enables physicians to assess therapeutic responsiveness, dynamically adjust therapies, and foresee problems such as secondary infections or acute renal injury Combining CRP, PCT, and serum amyloid A significantly enhanced early sepsis detection, directing treatment choices and enhancing results, as Li et al. showed.[74]
7.2 Precision Medicine Application
By categorizing sepsis into various endotypes with diverse pathophysiology and therapeutic requirements, biomarker-guided precision medicine provides a new frontier in sepsis care that tackles patient heterogeneity. Combining biomarker data with multi-omics integration, which includes proteomics, transcriptomics, and metabolomics, allows for the discovery of molecular phenotypes and customized immunomodulatory treatments. Biomarkers such as IL-6, ferritin, IL-7, sTREM-1, and HLA-DR are currently used in a number of clinical studies for patient stratification and precision-guided immunotherapies. By matching therapies to each patient's immunological state and pathobiology, such as giving immunostimulatory drugs to immunosuppressive phenotypes or anti-inflammatory agents to hyperinflammatory subtypes, this precision medicine model seeks to maximize therapy success. The ability to dynamically personalize sepsis care based on ongoing biomarker monitoring and clinical data is further improved by the incorporation of AI-powered prognostic models.[75]
7.3 Guideline and Protocol Development
To improve clinical outcomes and standardize sepsis management, it is essential to translate the multi-biomarker panel evidence into workable guidelines and practices. Based on subgroup risk classification, biomarker-driven procedures provide individualized decision routes, including immunomodulation tactics, hydration management, and antibiotic stewardship. In order to improve early detection and maximize the use of antibiotics, the Surviving Sepsis Campaign and other worldwide organizations are pushing for the integration of validated biomarkers, such as procalcitonin, into diagnostic and treatment algorithms. In order to minimize overtreatment and reduce adverse effects, adaptive procedures that modify treatment intensity in response to biomarker trends are made possible by the dynamic monitoring of biomarkers. In order to facilitate the smooth implementation of protocols in a variety of healthcare contexts, standardization initiatives focus on harmonizing biomarker assay platforms, cut-off values, and point-of-care testing integration.[76]
7.4 Health Economic Outcome
In terms of cost reductions and value optimization in healthcare systems, the use of multi-biomarker panels and related technologies in sepsis management shows promise. Biomarker-guided techniques for early and accurate sepsis detection result in significant cost savings by reducing hospital length of stay, antibiotic consumption, and intensive care unit (ICU) stays. Procalcitonin-guided antibiotic stewardship practices are predicted to save hospitals millions of dollars a year by avoiding needless antibiotic usage and improving clinical outcomes, according to economic studies. Even though they need a lot of resources at first, molecular diagnostic tests and AI-assisted prediction algorithms reduce mortality, lower readmission rates, and stop the progression and complications of sepsis. Additionally, early triage in emergency and critical care situations is made easier by point-of-care biomarker testing, which improves patient outcomes and the efficient use of limited ICU resources.[77]
H. limitation and challenges:
8.1 Methodology Challenges
Sepsis biomarker meta-analysis continues to encounter methodological challenges. The reliability of meta-analytic synthesis is hampered by the lack of a widely recognized gold standard for sepsis diagnosis, which results in inherent bias and heterogeneity among research. Sample size restrictions cause inflated performance measures that are frequently not repeatable in bigger, multicenter trials, especially in the early assessment of novel biomarkers. Statistical and clinical heterogeneity are caused by significant inconsistencies in patient populations, inclusion criteria, reference standards, and outcome definitions. When data quality varies and study designs are inconsistent, even sophisticated statistical techniques like latent class analysis and summary ROC modeling are unable to adequately address these issues.Additionally, the formulation of guidelines is impacted by the lack of strong external validation and the inadequate enrichment of clinical trial groups with complicated situations (e.g., elderly, surgical patients, and immunocompromised individuals).[78]
8.2 Clinical Implementation Barriers
There are also major challenges in the clinical application of multi-biomarker panels. Adoption is hampered by assay performance variability, financial limitations, and variations in biomarker availability among healthcare settings. The interpretability and reproducibility of biomarker assessment can be impacted by comorbidities, concurrent medications, or patient characteristics (age, immunological status). Protocol-driven sepsis management is made more difficult by the unresolved issues of platform harmonization and threshold value standardization. Furthermore, clinical procedures must be integrated with electronic health records and decision support systems for real-time utility because they are frequently not built for quick, high-throughput biomarker testing outside of research settings. Lack of external validation in typical clinical settings, opposition to protocol changes, and low clinician awareness all exacerbate implementation difficulties.[79]
8.3 Technology-Specific Challenges
Despite their potential, emerging biomarker detection systems have translational and technical constraints that affect research and clinical application. Microfluidic platforms and multiplexed sensors may have poor repeatability, non-specific binding, and subpar sensitivity in practical applications. Many emerging biomarkers, such as transcriptome or proteomic signatures, are difficult to measure, unstable, and lack adequate clinical validation, which limits their use in standardized diagnostic panels. True point-of-care deployment requires device portability and miniaturization, yet these developments call for strong regulatory monitoring and economical production. Additional difficulties include the requirement for sophisticated data processing, preventing cross-reactivity-related false positives, and modifying assay topologies to suit a variety of sample kinds and patient demographics. The transition of novel biomarkers from the research bench to the therapeutic bedside is slowed by these technological obstacles.[80]
I. FUTURE DIRACTIONS AND RESEARCH PRIORITIES:
9.1 Research Gaps
Even though more than 200 potential sepsis indicators have been found, there are still a lot of obstacles to overcome before these findings can be effectively used in clinical settings. One of the main obstacles is the dearth of high-quality, standardized prospective studies that combine biomarker testing with clinical outcomes in a variety of patient populations. Large-scale multicenter studies that gather biospecimens over time are clearly needed in order to describe sepsis heterogeneity and verify biomarkers predictive of therapy responsiveness. Standardization is necessary to increase reliability and clinical usability of statistical techniques for identifying subphenotypes and heterogeneity of treatment effects. Additionally, the majority of biomarker research has been on diagnoses, with little information available to support therapeutic choices or track the effectiveness of treatment. Pediatric and immunocompromised patient populations, who are underrepresented in the majority of research, continue to have significant gaps.[81]
9.2 Technology Developments
AI-driven biomarker discovery platforms, microfluidic point-of-care devices, and high-throughput multi-omics are examples of emerging technologies that have the potential to improve sepsis diagnosis and treatment. To improve multiplex biomarker panels that provide quick, accurate, and affordable bedside diagnostics, more research is required. It is anticipated that advancements in lab-on-a-chip technology and portable biosensors would significantly shorten the time it takes to diagnose a condition and enable dynamic monitoring of its course and response to treatment. The identification of minimal biomarker sets that preserve good prediction accuracy while lowering complexity and expense will also be made possible by the incorporation of machine learning and deep learning into biomarker validation. Another top goal to improve discovery and execution is the creation of standardized, interoperable data platforms that combine clinical, molecular, and outcome data.[82]
9.3 Implementation Science
Barriers to the clinical deployment of multi-biomarker panels, such as assay standardization, clinician education, workflow integration, and clinical usefulness demonstration in practical practice, must be addressed by effective implementation science. Designing and testing interventions that incorporate biomarker-guided algorithms into electronic health records with decision assistance and guaranteeing fair access to modern diagnostics in all healthcare settings are among the top research priorities. Along with therapeutic results, implementation studies should assess the social, ethical, and economic ramifications, with an emphasis on scalability and sustainability in a range of demographics. To expedite translation and produce consensus about biomarker use in clinical procedures, collaborative networks and data-sharing activities will be crucial.[83]
9.4 Personalized and Precision Medicine
Precision medicine techniques that use multi-biomarker data to customize treatments are the key to the future of sepsis therapy. Creating and validating biomarker-driven endotypes that guide customized immunomodulatory and supportive therapies is one of the top research goals. Real-time therapeutic adaption and dynamic risk classification will be made possible by integrating genomes, transcriptomics, proteomics, and metabolomics with clinical biomarkers and AI models. To assess targeted medicines and enhance results, clinical studies utilizing biomarker-defined subgroups are essential. In order to guarantee inclusion and equity in care improvements, personalized medicine initiatives must also concentrate on immunocompromised, elderly, and pediatric populations.[84]
J. CONCLUSION:
10.1 Summary of meta-analysis evidence
For the diagnosis of sepsis, risk assessment, and treatment monitoring in a variety of critically ill populations, integrated multi-biomarker panels consistently perform better than single markers, according to this systematic meta-analysis. PCT, CRP, sTREM-1, suPAR, mHLA-DR, presepsin, lactate, and certain miRNAs are examples of inflammatory, immune, coagulation, metabolic, and organ-dysfunction biomarkers. These panels achieve higher pooled sensitivity, specificity, and AUC values than any single analyte, including commonly used markers like PCT or CRP alone. Additionally, meta-analytic data show that composite signatures (such as presepsin-based panels, suPAR–PCT–lactate combinations, and miRNA panels) have strong prognostic value for predicting treatment response, organ failure, and mortality, especially when combined with clinical scores like SOFA or qSOFA.
10.2 Clinical implications
In practice, prognostic and monitoring panels enable dynamic tailoring of antibiotics, fluids, vasopressors, and immunomodulation, thereby improving antimicrobial stewardship and reducing ICU length of stay and costs; assay harmonization, validated cut-offs, simple bioscores, and scalable point-of-care or near-patient platforms that are feasible in both high-resource and LMIC settings. The evidence supports a shift from reliance on single biomarker panels embedded within sepsis bundles and electronic decision-support pathways.
10.3 Future outlook
Large, prospective multicenter cohorts to validate minimal biomarker sets, endotype-specific panels, and AI-enhanced multi-omics signatures that can be translated into bedside tools are among the future research priorities. It is anticipated that developments in lab-on-a-chip microfluidics, wearable biosensors, metagenomic NGS, and interoperable data platforms will converge with machine learning to deliver real-time, low-cost, multi-parametric sepsis diagnostics at the point of care. Ultimately, biomarker-guided precision sepsis medicine—linking molecular endotypes to targeted immunomodulatory and supportive strategies, including in immunocompromised populations.
REFERENCES
Dr. Subashini R, Grace S, Janani B S, Jayashree T, Gayathri M, Integrating Multi-Biomarker Panels and Emerging Technologies in A Systematic Meta Analysis of Biomarkers for Early Sepsis Detection, Prognosis and Therapeutic Monitoring in Critically Ill Patients, Int. J. of Pharm. Sci., 2025, Vol 3, Issue 12, 785-812. https://doi.org/10.5281/zenodo.17816585
10.5281/zenodo.17816585
