Platelet Function in Patients with Disseminated Intravascular Coagulation: Potential Markers for Improving DIC Diagnosis?

Abstract

Disseminated intravascular coagulation (DIC) is a severe complication often associated with critical illness. DIC is characterized by an uncontrolled systemic activation of the hemostatic system, leading to substantial consumption of platelets and coagulation factors. The diagnosis of DIC relies on a combination of clinical findings and laboratory results, yet DIC remains challenging to confirm, especially in early stages. This systematic review investigates the reported associations between platelet function and DIC and evaluates the potential of using platelet function markers as a supplement for DIC diagnosis. PubMed and Embase were searched for relevant literature. Human studies, which included patients with DIC and assessed platelet function using dynamic platelet function assays or soluble markers, were included. In total, 24 studies met the inclusion criteria. We found that DIC patients generally exhibit increased platelet activation in vivo, indicated by elevated plasma levels of soluble markers, while ex vivo platelet aggregation was consistently reduced compared to non-DIC patients and healthy controls; however, not all studies adjusted their results for platelet count. Soluble P-selectin was the most frequently studied plasma marker and was consistently increased in DIC patients; this was most pronounced when adjusted for platelet count. However, there was considerable heterogeneity between studies regarding both study design, patient populations, platelet function assessment, and DIC diagnosis, which complicates the comparison of findings across studies. Future studies accounting for low platelet counts in dynamic function tests are necessary to assess the role of platelet aggregation in relation to DIC.

Keywords

disseminated intravascular coagulation - platelet function - biomarker

Disseminated intravascular coagulation (DIC) is a clinical pathological syndrome characterized by systemic, uncontrolled coagulation activation in the presence of a relevant underlying condition, such as sepsis, trauma, or malignancy[1] [2] [3] ([Fig. 1]). The pathophysiology of DIC includes an elevation in systemic tissue factor (TF) expression on circulating monocytes or tumor-derived microparticles (MPs),[4] [5] [6] endothelial activation,[2] and loss or consumption of natural anticoagulant activity.[2] [7] Furthermore, platelet consumption is a hallmark of DIC.

Fig. 1 Pathways of platelet activation in disseminated intravascular coagulation (DIC). Tissue factor (TF), present on subendothelial tissue, leukocytes, or tumor-derived microparticles, triggers factor VII activation, leading to thrombin generation and subsequent platelet activation. Negatively charged phospholipids on the platelet plug surface amplify coagulation via factor XI and IX. Neutrophil extracellular traps (NETs) released by activated neutrophils further induce platelet and coagulation activation and enhance thrombin generation. The combined effect of these mechanisms results in extensive platelet and coagulation activation, leading to fibrin formation and microthrombosis. ADP, adenosine diphosphate; DIC, disseminated intravascular coagulation; F, coagulation factor; GP, glycoprotein; NETs, neutrophil extracellular traps; PAR, protease-activated receptor; PC, protein C; PS, protein S; PSGL-1, P-selectin glycoprotein ligand-1; TF, tissue factor; TM, thrombomodulin; TXA2, thromboxane A2; vWF, von Willebrand factor. (Created in BioRender.com.)

Platelets are activated in DIC through multiple mechanisms, which include increased activation by thrombin through protease-activated receptor (PAR)-1 and -4, increased interaction with the endothelium through von Willebrand factor and collagen, and through interaction with immune cells and complement.[5] [7] Activated platelets express P-selectin on their surface, which stimulates neutrophils to release neutrophil extracellular traps (NETs) containing damage-associated molecular patterns. NETs reciprocally activate platelets and subsequently coagulation, hence amplifying an already unbalanced system, which may lead to microthrombosis and extensive tissue damage.[1] [5]

In summary, DIC encapsulates a condition with a comprehensive activation of platelets and the coagulation cascade, accompanied by uncontrolled consumption of coagulation factors and disturbances in the fibrinolytic system. Overall, DIC can be subdivided into a hemorrhagic and a thrombotic phenotype determined by the dominance of dysfunction within the hemostatic system, which varies according to the underlying etiology. A hemorrhagic phenotype, characterized by hypocoagulable and/or hyperfibrinolytic states, is often seen in trauma-related or hematological cancer-associated DIC, whereas a thrombotic phenotype, characterized by systemic prothrombotic and/or antifibrinolytic processes, is commonly observed in cases of septic DIC.[1] [2]

Diagnosing DIC is challenging due to a highly variable presentation of the syndrome, which can range from subclinical symptoms to predominant symptoms of thrombosis, hemorrhage, and multiorgan failure. Thus, the final diagnosis of DIC relies heavily on laboratory results.[8] One of the most frequently used scoring systems to support the diagnosis of DIC was developed by the International Society on Thrombosis and Haemostasis (ISTH) and assesses the patient's condition based on platelet count, prothrombin time, fibrinogen, and D-dimer.[8] [9] Besides the ISTH score, other scoring systems have been developed to evaluate the DIC patient. To complement laboratory results, the Japanese Ministry of Health and Welfare incorporates clinical symptoms and a relevant DIC etiology as a part of its DIC scoring system.[10] The Japanese Association for Acute Medicine includes the criteria of systemic inflammatory response syndrome (SIRS) and considers changes in platelet count as a supplement to the absolute platelet count.[11] Hence, platelet count is an important part in the assessment of the DIC patient, and a low platelet count induces a higher DIC score.[8] [10] [11] However, platelet count can be affected by multiple conditions or underlying diseases such as viral infection, antineoplastic treatment, liver cirrhosis, extracorporeal circulation, fluid resuscitation, or bleeding[7]; thus, platelet count is not a specific marker of DIC. Therefore, it is relevant to consider if platelet function parameters may be more sensitive and specific markers of DIC than platelet count.

Several studies have examined the association of platelet function markers and DIC. Nevertheless, these studies either included a small sample size[12] [13] [14] or only examined one relevant underlying etiology, primarily sepsis.[15] [16] [17] Further, many different methods have been utilized in the study of platelet function.[12] [15] [18] [19] The present systematic review summarizes current knowledge on platelet function markers in DIC of various etiologies and assesses the potential of platelet function markers as a supplement for DIC diagnosis.

Methods

Prior to data collection and analysis, a protocol based on the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) guidelines was prepared. A systematic search was conducted in two databases, namely, PubMed[20] and Embase,[21] on the 31st of January 2024. The final search strings were as follows:

PubMed: (“Disseminated Intravascular Coagulation”[MeSH Terms] OR “Disseminated Intravascular Coagulation”[All Fields] OR “Disseminated intravascular coagulopathy”[All Fields]) AND (“platelet function*”[All Fields] OR “platelet function tests”[MeSH Terms] OR “platelet activation”[MeSH Terms] OR “platelet activation*”[All Fields] OR “platelet aggregation”[MeSH Terms] OR “platelet aggregation*”[All Fields] OR “flow cytometr*”[All Fields] OR “flow cytometry”[MeSH Terms] OR “aggregometr*”[All Fields] OR “Platelet Membrane Glycoproteins”[MeSH Terms] OR “CD62p”[All Fields] OR “P-selectin”[All Fields] OR “platelet membrane glycoprotein*”[All Fields])

Embase: (“disseminated intravascular coagulation”/exp OR “disseminated intravascular coagulation” OR “disseminated intravascular coagulopathy”/exp OR “disseminated intravascular coagulopathy”) AND (“thrombocyte function”/exp OR “thrombocyte function” OR “thrombocyte activation”/exp OR “thrombocyte activation” OR “thrombocyte aggregation”/exp OR “thrombocyte aggregation” OR “flow cytometry”/exp OR “flow cytometr*” OR “padgem protein”/exp OR “padgem protein” OR “aggregometry”/exp OR “aggregometry” OR “fibrinogen receptor”/exp OR “fibrinogen receptor” OR “platelet function”/exp OR “platelet function*” OR “platelet function test”/exp OR “platelet activation*” OR “platelet aggregation*” OR “platelet membrane glycoprotein*” OR “p-selectin”/exp OR “p-selectin” OR “aggregometr*”)

The inclusion criteria were: (1) human studies, (2) investigation of an association between DIC and platelet function, (3) DIC diagnosis was based on one of the scoring systems from the following associations: ISTH, the Japanese Association for Acute Medicine or the Japanese Ministry of Health and Welfare, or, for studies predating these scoring systems, on similar diagnostic criteria based on platelet count and standard coagulation parameters, (4) the diagnostic criteria were used on a population with a relevant DIC etiology,[3] (5) platelet function and DIC were registered systematically and consistently across groups, (6) laboratory test estimates of platelet function were based on (a) whole blood or platelet-rich plasma for dynamic function tests or (b) plasma for soluble platelet activation markers. Exclusion criteria were: (1) letters to the editor, (2) reviews, (3) conference abstracts, (4) editorials and comments without original data, (5) guidelines, (6) case reports, (7) in vitro studies, and (8) studies with <5 DIC patients. Articles written in another language than English were excluded.

A total of 50 abstracts were randomly selected and screened by all three authors to ensure agreement on inclusion and exclusion criteria. Any disagreements were resolved through consensus. The remaining abstracts were then divided among the authors. When in doubt, the abstract proceeded to full text reading. All authors read full text on 10 randomly selected articles to validate criteria, while the remaining articles were read in full text by two of the three reviewers. In case of conflicts or doubt, studies were discussed until consensus was achieved.

The following information from the included studies were extracted: year of publication, main author, study design, study population, underlying condition, DIC diagnosis criteria, at what time point blood samples were collected, which platelet function parameters were collected, results, and clinical endpoints if reported.

Risk of bias was assessed using the Quality Assessment Tools for observational cohort, cross-sectional and case-control studies published by National Heart, Lung, and Blood Institute (NHLBI).[22] Based on the guidelines of the assessment tool, the studies were rated according to risk of bias: “low risk,” “moderate risk,” or “high risk.”

A high degree of heterogeneity among the included studies was found in terms of methodology, patient population, and platelet function analyses. Consequently, we did not perform a meta-analysis.

Results

The process of study inclusion is illustrated in [Fig. 2]. The systematic search resulted in 3,346 records after duplicates were removed, and a total of 24 studies were included in this review ([Table 1]). The most prevalent etiologies for DIC were sepsis or infection (n = 15)[12] [13] [14] [15] [16] [17] [18] [23] [24] [25] [26] [27] [28] [29] [30] and cancer (n = 4).[26] [31] [32] [33] One study focused on trauma patients.[34] Five studies included DIC patients with mixed etiologies.[19] [35] [36] [37] [38]

Table 1 Studies investigating the association between platelet function markers and disseminated intravascular coagulation (DIC), n = 24

Author, Year, Bias rating	Setting, Study population, DIC diagnostic criteria	Blood sampling, Platelet function marker(s)	Results – Platelet function parameters	Clinical endpoints
Boscolo et al (2019)[23] Low risk of bias	Case-control study Septic shock, n = 40, of which DIC n = 12 Healthy controls, n = 40 DIC diagnosis: JAAM	At ICU admission and on days 1, 3, and 7 CD61+ MP level × 103 platelets (PDMP/PLT ratio) (flow cytometry)	Microparticle analysis: ↑ PDMP/PLT at baseline DIC vs. Non-DIC (p  < 0.018) PDMP/PLT ratio at baseline significantly associated with DIC in septic shock patients: OR = 1.45 [1.08–1.96] (p  < 0.014)	Not reported
Brenner et al (2012)[18] Moderate risk of bias	Prospective cohort study Septic shock, n = 30, of which DIC n = 16 Healthy controls, n = 30 DIC diagnosis: ISTH	Healthy controls: once Septic shock: at diagnosis and on days 1, 4, 7, 14, and 28 Aggregation (MEA): AA, ADP, TRAP-6, COL	↓ platelet aggregation in DIC vs. non-DIC: AA test: Days 0–4: p  < 0.01; Days 7–28: p > 0.05 TRAP-6 test: Days 0–1: p  < 0.05; Days 4–28: p > 0.05 ADP test: Days 0–7: p  < 0.05, Days 14–28: p > 0.05 COL test: Days 0 + 28: p  < 0.05; Days 1–14: p > 0.05 Predictive value: TRAP-6 test: AUROC 0.853, sensitivity 0.98, 1-specificity 0.31; Cut-off 912.5AU*min AA test, ADP test, COL test: Not reported	Not reported
Chong et al (1994)[35] High risk of bias	Cross-sectional study DIC, n = 21 (sepsis, cancer, heatstroke, obstetric accidents) Healthy controls, n = 39 DIC diagnosis based on: ↓ plasma fibrinogen, ↑ D-dimer, thrombocytopenia, ↑ APTT and/or PT, and a predisposing cause for DIC	Within 3 days of DIC diagnosis sP-selectin, sβTG (ELISA)	sP-selectin: ↑ in DIC vs. age-matched healthy controls (<60 years: p  < 0.00001; >60 years: p <0.0002) sβTG: ↑ in DIC vs. age-matched healthy controls (<60 years: p  < 0.005; >60 years: p  < 0.0005) No significant difference between DIC and non-DIC patients	Not reported
Connolly-Andersen et al (2015)[14] Moderate risk of bias	Prospective cohort study Patients w. puumala virus and hemorrhagic fever with renal syndrome, n = 35, of which DIC n = 10 DIC diagnosis based on modified ISTH scoring system, fibrinogen/CRP ratio added	≥ 2 blood samples at varying timepoints sP-selectin and sGPVI (ELISA)	sP-selectin: ↑ on days 1–7 in DIC vs. non-DIC (p  < 0.05) ↔ on days 8–30 in DIC vs. non-DIC sGPVI: ↔ on days 1–14 in DIC vs. non-DIC ↑ on days 15–30 in DIC vs. non-DIC (p  < 0.01) sP-selectin positively associated with maximum DIC score (r = 0.36, p  = 0.04)	Thrombotic events, n = 2 (radiologically verified). sP-selectin (thrombosis vs. non-thrombosis) ↔ on days 1–7 ↑ on days 8–30 (p  < 0.01)
Delabranche et al (2013)[15] Low risk of bias	Prospective cohort study Septic shock, n = 92, of which DIC n = 40 DIC diagnosis: ISTH and JAAM	At diagnosis and on days 2, 3, and 7. sP-selectin, sGPV (ELISA) GPIb+ MPs (prothrombinase assay)	sP-selectin: ↔ sP-selectin in DIC vs. non-DIC sP-selectin/platelets: ↑ DIC vs. non-DIC (days 1–7, p  < 0.05) sGPV: ↑ sGPV/platelets in DIC vs. non-DIC on day 1: p  < 0.05 GPIb-MPs: ↓ sGPIb-MPs in DIC vs. non-DIC: p = 0.02 ↔ GPIb-MPs/Platelets in DIC vs. non-DIC	Not reported
Fogagnolo et al (2020)[13] Low risk of bias	Case control study Septic shock, n = 30, of which DIC n = 10 DIC diagnosis: ISTH	At diagnosis and once daily until day 5 Aggregation (LTA): ADP, AA, TRAP-6	↓ ADPmaxagg in DIC vs. non-DIC from day 3 ↓ AAmaxagg and TRAPmaxagg in DIC vs. non-DIC from day 4	90-day mortality: ↑ aggregation in survivors vs. non-survivors on days 3, 4, and 5 (p  < 0.05)
Folman et al (2000)[24] High risk of bias	Cross-sectional study DIC (sepsis), n = 15 ICU controls, n = 16 DIC diagnosis based on platelet count <100 × 109/L, prolongation of PTT and aPTT, elevated FDPs	1 sample, varying time points sGlycocalicin (ELISA)	↔ sGlycocalicin in DIC vs. non-DIC	Not reported
Gando et al (2005)[17] Moderate risk of bias	Prospective cohort study Sepsis/septic shock, n = 45, of which DIC n = 27 Healthy controls, n = 8 DIC diagnosis: JMHW and ISTH	At diagnosis and on days 1–4 sP-selectin (EIA)	sP-selectin: ↔ in DIC vs. non-DIC (all days) sP-selectin/platelets: ↔ DIC vs. non-DIC on day 0 ↑ DIC vs. non-DIC (days 1, 2, and 4: p  < 0.01; day 3: p  < 0.05)	Not reported
Gando et al (2002)[34] High risk of bias	Prospective cohort study Trauma patients, n = 58, of which DIC, n = 29 Healthy controls, n = 8 DIC diagnosis: JMHW and ISTH	At arrival and on days 1–4 sP-selectin (EIA)	sP-selectin: DIC vs. controls: ↑ on days 0, 1, 2, 3, and 4 (p  < 0.05) DIC vs. non-DIC: ↔ on days 0, 1, and 2, ↑ on day 3 (p  < 0.05) and 4 (p  < 0.01)	Not reported
Ishikura et al (2022)[16] Moderate risk of bias	Retrospective cohort study Sepsis, n = 70, of which DIC n = 53 DIC diagnosis: JAAM	At admission and on days 3 and 5 sCLEC-2 (ELISA), C2PAC index (sCLEC2/PLT ratio)	sCLEC-2 level: ↔ in DIC vs. non-DIC No significant change over time in both DIC and non-DIC group Predictive value: AUROC: 0.51 C2PAC index: ↑ in DIC vs. non-DIC (p  < 0.01) ↑ in C2PAC index as the JAAM DIC score increase Independent predictor of DIC (OR = 2.35 [1.11–6.67], p  = 0.0228) Predictive value: AUROC: 0.81	Not reported
Kander et al (2016)[36] High risk of bias	Prospective cohort study ICU patients w. relevant DIC etiology, n = 136, of which DIC = 38 DIC diagnosis: JAAM	Within 10 days after ICU admission Aggregation (MEA): ADP, TRAP-6, COL	↓ platelet aggregation in DIC vs. non-DIC in ADP-, COL- and TRAP6-test, p  < 0.01	MEA: ↓ platelet aggregation in non-survivors vs. survivors on days 4–10 (p  < 0.05) ↔ platelet aggregation, non-survivors vs. survivors at days 0–3.
Kunishima et al (1996)[31] High risk of bias	Cross-sectional study Cancer, n = 36, of which DIC = 18 DIC diagnosis: JMHW	Timing not reported sGlycocalicin (ELISA)	↑ sGlycocalicin in DIC vs. non-DIC (p  < 0.05)	Not reported
Laursen et al (2020)[12] Low risk of bias	Prospective cohort study Septic shock, n = 38, of which DIC = 12 DIC diagnosis: ISTH	On days 1–3 of ICU admission Aggregation (MEA): TRAP, ADP Surface P-selectin, bound fibrinogen, and CD63 expression (flow cytometry) sP-selectin (ELISA)	Platelet aggregation: ↓ in DIC vs. non-DIC ADP-test: day 1: p  < 0.05, day 2: p  < 0.01, day 3: p  < 0.01 TRAP-test: day 2: p  < 0.01; day 3: p  < 0.01 Not significant when adjusted for platelet count on days 1, 2, and 3 Flow cytometry: %positive gated platelets: bound fibrinogen (ADP agonist): ↓ in DIC vs. non-DIC (p  < 0.01) Rest of the markers and agonists: ↔ in DIC vs. non-DIC (p > 0.24) MFI: Bound fibrinogen, CD63, P-selectin (all agonists): ↔ in DIC vs. non-DIC (p > 0.05) sP-selectin: ↔ in DIC vs. non-DIC (p = 0.20)	No significant association between SOFA score and platelet aggregation (p = 0.16)
Lehner et al (2016)[25] Low risk of bias	Cross-sectional study Septic shock, n = 30, of which DIC n = 14. DIC diagnosis: ISTH	Within 48 h after onset of septic shock or ICU admission CD41+ microvesicles (flow cytometry)	Microvesicles (CD41 + ): ↓ CD41+ microvesicles in DIC vs. non-DIC (p  = 0.040)	SOFA: no significant association between SOFA-score and CD41+ microvesicles 48-h mortality: ↑ CD41+ microvesicles in non-survivors vs. survivors (p  < 0.05)
Mosad et al (2011)[30] High risk of bias	Cross-sectional study Children admitted to the ICU (infection), n = 176, of which DIC n = 32 DIC diagnosis: ISTH	Timing not reported sP-selectin (ELISA)	sP-selectin: GI-infection: ↑ sP-selectin in DIC vs. non-DIC (p  < 0.0001) RT infection: ↑ sP-selectin in DIC vs. non-DIC (p  < 0.0001)	SOFA-score: positively correlated with sP-selectin in DIC patients (p  = 0.03)
Ohuchi et al (2015)[38] Moderate risk of bias	Prospective cohort study ICU patients, n = 119, of which DIC n = 31 (mixed etiology) DIC diagnosis: JAAM	3 times a week during ICU admission GPIb +/GPIX+ MPs (ELISA)	GPIb +/GPIX+ MPs ↑ GPIb +/IX+ MP level in DIC vs. non-DIC (p  = 0.001) ↑ GPIb +/IX+ MP/PLT ratio in DIC vs. non-DIC (p  < 0.001)	Hospital mortality: ↔ GPIb + IX-MP level in survivors vs. non-survivors; OR 1.006 (95%CI: 0.997–1.016) (p = 0.17) ↑ GPIb + IX-MP/PLT ratio in non-survivors vs. survivors; OR 1.21 (95%CI: 1.01–1.44) ( p  = 0.04) Predictive value: AUROC 0.77
Shimizu et al (2018)[26] Moderate risk of bias	Prospective cohort study Sepsis, n = 25, of which DIC n = 18 Hematological cancer and DIC, n = 15 Healthy controls, n = 12 DIC diagnosis: JAAM and JMHW	On days 0, 7, 14, and 21 sP-selectin, GPIb +/GPIX+ MPs (ELISA)	sP-selectin: ↑ in sepsis w. DIC vs. healthy controls (p  < 0.05) ↔ in hematological cancer w. DIC vs. healthy controls GPIb +/GPIX+ MPs: ↑ in sepsis w. DIC vs. healthy controls (p  < 0.05) ↔ in hematological cancer w. DIC vs. healthy controls	Not reported
Shimura et al (1998)[32] High risk of bias	Cross-sectional study DIC, n = 59 (cancer) Healthy controls, n = 20 DIC diagnosis: JMHW	At admission or disease onset sP-selectin (EIA)	↑ sP-selectin in DIC vs. healthy controls (p  < 0.01)	Not reported
Wan et al (2014)[19] Moderate risk of bias	Cross-sectional study ICU patients w. relevant DIC etiology, n = 498, of which DIC n = 237 DIC diagnosis: ISTH	At ICU admission Clot retraction (Sonoclot)	↓ clot retraction in DIC vs. non-DIC (p  < 0.05) Predictive value: AUROC: 0.35 (95%CI: 0.239–0.461), ( p  = 0.01	Not reported
Washington et al (2009)[27] Moderate risk of bias	Prospective cohort study Sepsis, n = 46, of which DIC n = 18 DIC diagnosis: ISTH	Day 4 after sepsis diagnosis sTLT-1 (dot blot analysis, immunoblot)	↑ sTLT-1 in DIC vs. non-DIC (Rs = 0.75, p  < 0.0001) Predictive value: AUROC 0.87, sensitivity 76%, specificity 76%, Cut-off: 50 μg/mL	28-day mortality: At day 1: ↔ sTLT-1 in survivors vs. non-survivors On days 3–14: ↑ in sTLT-1 in non-survivors vs. survivors (p  < 0.03)
Wegrzyn et al (2021)[28] Moderate risk of bias	Cross-sectional study ICU patients w. sepsis, n = 103, of which DIC n = 24 Healthy controls, n = 50 DIC diagnosis: ISTH	Within 48 h of ICU admission sPF4, sCD40 ligand and MPs (ELISA)	PF4, CD40 ligand and MP levels: ↔ in DIC vs. non-DIC ↑ in DIC vs. healthy controls (p  < 0.05)	28-day mortality: ↓ PF4 in non-survivors vs. survivors (p  = 0.016) ↔ CD40L, MP in non-survivors vs. survivors. Predictive value: AUROC: PF4: 0.70 ( p  = 0.016) CD40L: 0.55 MP: 0.53
Yaguchi et al (2004)[29] Moderate risk of bias	Cross-sectional study Sepsis/septic shock, n = 47 of which DIC n = 20 Healthy controls, n = 15 DIC diagnosis: ISTH	At ICU admission or onset of sepsis in the ICU Aggregation (LTA): AA, COL, TXA2, TRAP6	↔ platelet aggregation in DIC vs. non-DIC	Not reported
Yahara et al (1983)[33] High risk of bias	Cross-sectional study Cancer, n = 75, of which DIC n = 6 Healthy controls, n = 109 DIC diagnosis based on: Platelet count < 150 × 103/μL, PT >1 sec., fibrinogen <250 mg/dL, FDP ≥ 20 μg/mL DIC score 0–4. 3 points: probable DIC, 4 points: clinical DIC	Timing not reported Aggregation (LTA): ADP, epinephrine, COL sβTG (RIA)	Platelet aggregation: ↓ in DIC vs. controls (p  < 0.01) (Epinephrine + COL) (ADP: ↔ in DIC vs. controls) sβTG: ↑ in DIC vs. controls (p < 0.01)	Not reported
Yamamoto et al (2023)[37] High risk of bias	Prospective cohort study ICU patients w. relevant DIC etiology, n = 299, of which DIC n = 38 DIC diagnosis: JMHW	Timing not reported sCLEC-2 (EIA)	sCLEC-2: ↑ in DIC vs. non-DIC (OR = 4.7, p  < 0.001) Predictive value: AUROC 0.801, sensitivity (specificity) 68.4%, cut-off 287 (ng/L) sCLEC-2/PLT: ↑ in DIC vs. non-DIC (OR = 100.9, p  < 0.001) Predictive value: AUROC 0.970, sensitivity (specificity) 89.6%, cut-off 2.07	Mortality: ↑ sCLEC-2 in non-survivors vs. survivors (OR = 3.2, p  < 0.001) Predictive value: AUROC 0.695, sensitivity (specificity) 64.3%, cut-off 286 (ng/L) ↑ sCLEC-2/PLT in non-survivors vs. survivors (OR = 6.3, p  < 0.001) Predictive value: AUROC 0.781, sensitivity (specificity) 71.4%, cut-off 1.67

Abbreviations: AA, arachidonic acid; ADP, adenosine diphosphate; APTT, activated partial thromboplastin time; AUC, area under the curve; AUROC, area under receiver operator characteristics curve; βTG, beta-thromboglobulin; CI, confidence interval; CLEC-2, C-type lectin-like receptor 2; COL, collagen; C2PAC index, sCLEC-2/platelet count ratio; DIC, disseminated intravascular coagulation; EIA, enzyme immunoassay; ELISA, enzyme-linked immunosorbent assay; FDP, fibrin degradation products; GI, gastrointestinal; GP, glycoprotein; h, hour; ICU, intensive care unit; ISTH, International Society on Thrombosis and Haemostasis; JAAM, Japanese Association for Acute Medicine; JMHW, Japanese Ministry of Health and Welfare; LTA, light transmission aggregometry; MEA, multiple electrode aggregometry (multiplate); MFI, median fluorescence intensity; MP, microparticle; OR, odds ratio; PDMP, platelet-derived microparticles; PF4, platelet factor 4; PLT, platelets; PT, prothrombin time; PTT, partial thromboplastin time; RIA, radioimmunoassay; RT, respiratory tract; s, soluble; SOFA, Sequential Organ Failure Assessment score; TLT-1, TREM-like transcript 1; TRAP, thrombin receptor activating peptide.

Note: ↔ = non-significant, ↑ = significant increase, ↓ = significant decrease.

Table 2 Summary of findings according to different markers of platelet function

Platelet function marker	Number of studies [reference]	Findings, DIC vs. non-DIC	Findings, DIC vs. healthy controls
Platelet aggregation	6; 3 LTA [13, 29, 33], 3 MEA [12, 18, 36]	↓/↔	↓/↔
Bound fibrinogen	1 [12]	↑/↔	−
Sonoclot	1 [19]	↓	−
sP-selectin	9 [12, 14, 15, 17, 26, 30, 32, 34, 35]	↑/↔	↑/↔
PDMP	6 [15, 23, 25, 26, 28, 38]	↑/↓/↔	↑/↔
GPVI	2 [14, 15]	↑	↑
sCLEC-2	2 [16, 37]	↑	−
βTG	2 [33, 35]	−	↑
Glycocalicin	2 [24, 31]	↑/↔	−
sTLT-1	1 [27]	↑	−
PF4	1 [28]	↔	↑
CD63	1 [12]	↔	−

Abbreviations: βTG, beta-thromboglobulin; CLEC-2, C-type lectin-like receptor 2; DIC, disseminated intravascular coagulation; GP, glycoprotein; LTA, light transmission aggregometry; MEA, multiple electrode aggregometry (multiplate); PDMP, platelet-derived microparticles; PF4, platelet factor 4; s, soluble; TLT-1, TREM-like transcript 1.

Note: ↔ = non-significant, ↑ = significant increase, ↓ = significant decrease.

Fig. 2 Flowchart illustrating the inclusion process of eligible studies. *The authors were contacted, but no response was received. DIC, disseminated intravascular coagulation.

Of 24 studies 19 investigated platelet activation using various plasma-based, platelet-associated biomarkers.[12] [14] [15] [16] [17] [23] [24] [25] [26] [27] [28] [30] [31] [32] [33] [34] [35] [37] [38] The most frequently studied biomarker was soluble P-selectin (sP-selectin) (n = 9).[12] [14] [15] [17] [26] [30] [32] [34] [35] Other biomarkers measured in multiple studies included: platelet-derived microparticles (MP) (n = 6),[15] [23] [25] [26] [28] [38] glycoproteins (GP) (n = 2),[14] [15] glycocalicin (n = 2),[24] [31] soluble C-type lectin-like receptor-2 (sCLEC-2) (n = 2),[16] [37] and β-thromboglobulin (βTG) (n = 2).[33] [35] [Table 2] outlines the overall findings on platelet function across all included studies.

Soluble Markers of Platelet Activation

Studies measuring sP-selectin found significantly higher levels in DIC patients than in non-DIC patients[14] [15] [26] [30] [34] and healthy controls.[17] [26] [32] [34] [35] Across studies, mean or median P-selectin levels in DIC patients were up to 1.5-fold higher compared to non-DIC patients[14] [26] [30] [34] and 1.6- to 3.0-fold higher compared to healthy controls,[26] [34] [35] as assessed from tables and figures presented in the studies, hence not included in [Table 1]. When accounting for platelet count, studies found mean or median sP-selectin levels to be 2.3- to 5.3-fold higher in DIC patients compared to non-DIC patients.[15] [17] Furthermore, Gando et al reported 21-fold higher mean sP-selectin in DIC patients than in healthy controls after adjusting for platelet count.[17] Only one study did not find a significant difference in sP-selectin levels between DIC and non-DIC patients.[12] The study did not appear to adjust for platelet count.

The majority of the included studies investigating platelet-derived MPs reported higher plasma levels of MPs in DIC patients versus non-DIC patients[23] [38] and healthy controls.[26] [28] Ohuchi et al found that levels of GPIb + GPIX+ MPs was significantly increased in patients with DIC versus patients without DIC.[38] Additionally, Shimizu et al reported significantly higher levels of GPIb + GPIX+ MPs in patients with sepsis-related DIC than in healthy controls, but no difference in GPIb + GPIX+ MP levels between cancer-related DIC patients and healthy controls.[26] Conversely, Delabranche et al reported significantly lower levels of GPIb+ MPs in DIC patients compared to non-DIC patients.[15] However, there was a variation in timing of blood samples between the studies. Lehner et al found lower levels of CD41+ MPs in DIC patients than in non-DIC patients.[25] One study showed no difference in platelet-derived MPs between DIC patients and non-DIC patients.[28]

sCLEC-2 was examined as a biomarker for platelet activation by two studies.[16] [37] Yamamoto et al found significantly higher levels in DIC patients compared to non-DIC patients.[37] Additionally, Ishikura et al found significantly higher levels of sCLEC-2 in DIC patients than in non-DIC patients, but only when adjusting for platelet count.[16] Both studies performed receiver operating characteristic (ROC) analysis. Ishikura et al reported an area under the ROC curve (AUROC) of 0.51 for sCLEC-2 levels and AUROC of 0.81 when adjusting for platelet count.[16] Yamamoto et al reported a AUROC of 0.80 for sCLEC-2 and a AUROC of 0.97 when adjusting for platelet count.[37]

Both studies investigating soluble GPs found significantly higher plasma levels in DIC patients compared to non-DIC patients.[14] [15]

Two studies investigated plasma levels of glycocalicin,[24] [31] but only Kunishima et al reported a significant difference as they found higher plasma levels of glycocalicin in patients with DIC than in those without DIC.[31]

The two studies measuring βTG found significantly higher levels in DIC patients compared to healthy controls.[33] [35]

Finally, a few biomarkers were examined only once among the 24 studies. Platelet factor-4 (PF4) and CD40 ligand[28] were found to be significantly elevated in DIC patients compared to healthy controls, although no difference was observed between patients with and without DIC. Soluble TREM-like transcript-1 was increased in DIC patients compared to non-DIC patients and the study reported a AUROC of 0.87 for this biomarker.[27] Laursen et al found no difference in CD63 levels between DIC patients and non-DIC patients.[12]

Dynamic Platelet Function Tests

Six studies investigated platelet aggregation in DIC patients[12] [13] [18] [29] [33] [36]: three utilized light aggregometry,[13] [29] [33] and three employed impedance aggregometry.[12] [18] [36] Overall, the studies observed significantly lower platelet aggregation in DIC patients, with median AUC estimated to be reduced by 27 to 59% compared to non-DIC patients[12] [13] [18] [36] and by 29 to 60% compared to healthy controls,[18] as interpreted from figures presented in the studies; therefore, these details are not displayed in [Table 1]. Additionally, Yahara et al reported approximately 60% reduction in mean platelet aggregation percentages in DIC patients relative to healthy controls.[33] Brenner et al reported an AUROC of 0.85 for TRAP-induced platelet aggregation.[18] Yaguchi et al reported no significant alteration in platelet aggregation between DIC and non-DIC patients.[29] Two out of six studies adjusted their results based on the patients' platelet counts.[12] [29]

Two studies employed other dynamic function tests.[12] [19] Laursen et al utilized flow cytometry and observed significantly lower percentage of positive gated platelets with bound fibrinogen in DIC patients compared to non-DIC patients when samples were stimulated with adenosine diphosphate (ADP).[12] Wan et al reported reduced Sonoclot clot retraction in DIC patients compared to those without DIC.[19]

An association between platelet function and clinical endpoints was reported in 10 studies included.[12] [13] [14] [25] [27] [28] [30] [36] [37] [38] Four studies reported an association between platelet activation and mortality, as they found significantly increased plasma levels of platelet activation biomarkers in non-survivors compared to survivors.[25] [27] [37] [38] In this context, Ohuchi et al presented an AUROC of 0.77 as an indicator of PDMP's predictive value for mortality.[38] Separately, Wegrzyn et al observed significantly lower levels of PF4 in non-survivors than in survivors and reported an AUROC of 0.70 for mortality.[28] Two studies showed significantly reduced platelet aggregation in non-survivors compared to survivors.[13] [36]

Regarding the association between platelet function and organ failure, Mosad et al observed a positive correlation between Sequential Organ Failure Assessment (SOFA) score, which is used to assess the degree of organ failure in critically ill patients, and levels of sP-selectin in DIC patients,[30] while Lehner et al reported no significant association between SOFA score and level of MPs.[25] Laursen et al found no correlation between platelet aggregation and SOFA score.[12]

Connolly-Andersen et al found that two patients with thrombosis had significantly higher levels of serum P-selectin on days 8 to 30 than patients without thrombosis.[14]

When evaluating bias, five of the included studies were assessed as having a low risk of bias,[12] [13] [15] [23] [25] while 10 studies were categorized as having a moderate risk of bias.[14] [16] [17] [18] [19] [26] [27] [28] [29] [38] The remaining nine studies were assessed to be at high risk of bias.[24] [30] [31] [32] [33] [34] [35] [36] [37]

Discussion

The main finding of this review was that DIC patients consistently appear to have elevated plasma levels of soluble platelet function markers. Soluble P-selectin was the most studied biomarker and was consistently increased in DIC, which was even more pronounced when adjusting for platelet count. Contrary to this, platelet aggregation was reduced in DIC patients compared to non-DIC patients and healthy controls.

Upon activation, platelets alter their surface expression of adhesion molecules, secrete platelet granules, and change shape in favor of promoting aggregation and a hemostatic response.[39] Yet, dynamic function tests in the reviewed studies revealed lower aggregation in DIC patients,[12] [13] [18] [36] but the extent of the reduction varied considerably both across and within studies depending on the agonist used and the timing of blood sampling. Only one of the studies investigating platelet aggregation calculated the predictive value of platelet aggregation through ROC analysis,[18] finding that TRAP-induced aggregation had a good ability to identify DIC.

Nevertheless, the consensus on reduced platelet aggregation in DIC patients was surprising. However, the reduced aggregation in cases of DIC could be due to in vivo activation of circulating platelets, causing them to appear exhausted in ex vivo analysis.[40] [41] This consideration is supported by findings from the included studies that demonstrated elevated biomarkers of platelet activation in vivo in DIC patients. Another major cause could be that platelet count affects aggregation analysis, potentially leading to an underestimation of platelet function if platelet count is not considered. This limits the utility of aggregation tests in DIC patients, who often have thrombocytopenia. One approach to this could be to adjust the aggregation results for platelet counts, calculating an aggregation index; however, this would require establishment of reference intervals which might be difficult to standardize between laboratories. Only two studies tried to account for the patients' platelet counts.[12] [29] Another approach is the use of flow cytometry, which has the ability to examine platelet function independently of the platelet count.[42] Laursen et al conducted this dynamic analysis and found no significant differences in platelet activation in DIC compared to non-DIC patients, except a reduction in the percentage of positive gated platelets with bound fibrinogen in patients with DIC versus those without DIC after stimulation with ADP.[12] This could also be explained by exhausted platelets, or possibly by lower plasma fibrinogen, as the DIC patients included in the study by Laursen et al had significantly lower plasma levels of fibrinogen than non-DIC patients.[12] However, flow cytometric analysis of platelet activation is currently not widely available in the routine diagnostic laboratory as it is work-heavy and requires skilled personnel.

Besides dynamic function analyses, platelet activation can be reflected in plasma levels of circulating biomarkers. These include surface molecules cleaved off the platelet upon activation, molecules stored in granules or MPs released from activated platelets, and metabolites produced during platelet activation.[43]

sP-selectin is a well-established marker of platelet activation. Upon activation, P-selectin translocate to the platelet surface, facilitating platelet–leukocyte interactions, particularly with neutrophils.[5] [44] In cases of infection and trauma-induced DIC, this review found that sP-selectin levels were significantly elevated, indicating increased platelet function.[14] [30] [34] However, the studies focusing on septic patients found no difference in sP-selectin levels between patients with and without DIC, when no adjustments for platelet count were implemented.[12] [15] [17] sP-selectin is not exclusively derived from activated platelets but can also arise from activated endothelial cells or as a released intracellular component during cell death.[4] In septic patients, increased sP-selectin levels may result from the massive inflammatory response, affecting all septic patients and not just those with DIC. Consequently, sP-selectin's utility as a DIC marker in sepsis could be limited. However, platelet count could also influence total circulating P-selectin (and other soluble markers) since a lower platelet count would lead to less total released P-selectin, potentially masking a difference between groups. The studies by Delabranche et al and Gando et al underline the relevance of considering platelet count, as they presented an up to fivefold higher mean sP-selectin expressed per platelet in septic DIC patients compared to septic non-DIC patients.[15] [17] Thus, sP-selectin adjusted for platelet count appears promising, and adjustment for platelet count should be implemented in future studies. Since the studies assessing sP-selectin levels primarily utilized bar charts and tables to present their results, it was not possible for the authors of this review to extract data on the predictive value of sP-selectin in DIC. One included study examined P-selectin expression on the platelet surface using flow cytometry and found no significant difference in P-selectin between DIC and non-DIC patients.[12] The discrepancy in results between the study utilizing flow cytometry and those employing immunoassay to assess P-selectin may stem from the methodological differences, as flow cytometry is an ex vivo technique, whereas immunoassay measures P-selectin released in vivo.

Based on two included studies, sCLEC-2/platelet count ratio also appears to have potential for diagnostic use in DIC.[16] [37] However, like sP-selectin, the marker is not specific to platelet activation, and plasma levels can be elevated in several conditions such as cancer, atherosclerosis, and inflammation.[45] [46] The two studies, which investigated a sepsis population[16] and a population with mixed DIC etiologies,[37] showed a strong predictive value of sCLEC-2/platelet count with AUROCs of 0.81 and 0.97. However, the evidence is limited, and further studies are needed to confirm this potential.

Glycoprotein (GP) V and VI are exclusively located on the surface of platelets and megakaryocytes making them particularly interesting markers for platelet activation.[47] [48] All studies examining sGPV and sGPVI in this review agree that soluble levels of the GPs are significantly higher in patients with DIC than in those without.[14] [15] Connolly-Andersen et al and Delabranche et al[14] [15] studied DIC patients with infectious conditions. The latter observed a significant increase in sGPV only on day 1,[15] while the former found elevated sGPVI levels between days 15 and 30 post disease onset.[14] This may limit the use of sGPVI in an acute setting.

Two studies measured glycocalicin.[24] [31] Yet, glycocalicin, a fragment of GPIb, is released during platelet destruction and can be elevated in various conditions including cardiovascular diseases, thrombotic conditions, and infections.[48] Since these conditions are common among DIC patients, elevated glycocalicin levels may not solely indicate DIC-induced platelet activation but may also be due to concurrent conditions.

Several studies in this review have measured platelet-derived MPs to assess platelet activation.[15] [23] [25] [26] [28] [38] Generally, the studies found elevated levels of MPs, but the varied methods of measurement across the studies make it difficult to draw a firm conclusion. Theoretically, it is unclear whether the MPs indicate platelet activation or originate from degraded platelets. Furthermore, the measured markers on the MPs are not necessarily specific to platelets, e.g., CD41 + ,[25] [49] and one study did not specify which markers they used to identify the MPs.[28]

The investigation of a wide variety of biomarkers for platelet activation and the use of different methodologies, both for biochemical analyses of platelet function and for the assessment of the DIC diagnosis, result in a large heterogeneity among the included studies. This makes cross-studies comparison difficult, and it eliminates the possibility of conducting a meta-analysis.

The strengths of this review include a comprehensive systematic literature search conducted across multiple databases to ensure the inclusion of relevant published studies. The review also included a thorough data analysis, incorporating a wide range of platelet function analyses. The validity of the DIC diagnosis is enhanced by including studies that diagnosed DIC based on verified scoring systems or relevant biochemical parameters. Finally, only studies that applied the diagnostic criteria to a study population with a relevant underlying etiology were included.

However, some limitations must be considered. The quality of the included studies is a constraint of this review, as several were assessed to have a high risk of bias, which is threatening the internal validity of the studies. Many of the studies use a cross-sectional design, which makes it difficult to establish causal associations between platelet function and DIC progression, and a single measurement of platelet function at one time point cannot accurately reflect the complex changes occurring over the course of DIC. Further, many of the included studies did not report timing of blood sampling. This introduces a risk of selection bias, as it poses the possibility that only DIC patients who survived long enough to participate are included in the studies. Consequently, the included patients may not represent all stages of DIC, resulting in a distorted depiction of platelet function in the patient population.

Finally, it is worth mentioning that there are various global definitions of DIC. Despite strict standards for the DIC diagnosis established in the inclusion criteria of this review, the studies use different scoring systems, which presents a challenge in conducting cross-study comparisons, as they have different sensitivities and specificities for DIC. Furthermore, since the DIC score was used to define DIC diagnosis in the included studies, i.e., as gold standard, the evaluation of platelet function markers to improve the diagnostic utility of the DIC score could by definition not be directly investigated.

Future studies aiming to generate new knowledge about platelet function in DIC patients should employ a prospective study design with continuous blood sample collections and utilization of dynamic function tests. This approach will provide a transparent picture of platelet function and its evolution over the course of DIC. Moreover, since DIC can occur in various clinical settings (sepsis, trauma, cancer, etc.), future studies should strive to include a large study population to ensure that multiple underlying etiologies are represented, making the study's findings more relevant in clinical practice.

Although some studies were excluded from this review due to unspecified criteria for DIC diagnosis or lack of information on the patients' underlying etiology, their findings align with those of the included studies. However, one excluded study investigated patients with acute renal failure (ARF) and identified a significantly elevated βTG /creatinine ratio in ARF patients with DIC compared to those without DIC.[50] This finding offers an intriguing perspective for future studies aiming to explore platelet function.

Conclusion

This review found that soluble markers of platelet activation are consistently increased in DIC patients when compared to non-DIC patients and healthy controls, indicating a potential to supplement the laboratory diagnosis of DIC. Reduced platelet aggregation is described in patients with DIC compared to those without; however, these results may be influenced by thrombocytopenia. To better understand the function of platelets in DIC patients and the diagnostic potential of platelet function markers, more prospective studies are needed. These studies should employ dynamic function tests, adjust for platelet count, and conduct analyses at multiple time points during DIC.

Investigating platelet function in DIC is of great interest, as this knowledge can contribute to an improved understanding of the disease burden and severity, and enhance diagnostic and monitoring strategies, potentially leading to better management and prognosis for these critically ill patients.

Conflict of Interest

J.D.P. and C.L.H. have no conflicts of interest. J.B.L. has no conflicts of interest pertaining to the present paper but has the following general conflicts of interest: Has received speaker's fees from Bristol-Myers Squibb and Merck (paid to her institution) and has received travel support from Bayer.

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Publication History

Article published online:
17 June 2025