Tissue Factor, a Membrane-associated Marker of Platelet Activation, Predicts 5-year Cardiovascular Mortality in Coronary Artery Disease Patients

Abstract

Background

Thrombotic risk stratification in coronary artery disease (CAD) patients is an unmet need. CAD patients show increased platelet activation, but its prognostic relevance remains unexplored. We aimed to assess the prognostic value of platelet-activation markers on mortality in CAD patients.

Methods

Surface expression of platelet-associated activated GPIIbIIIa, P-selectin, tissue factor (TF), and platelet–leukocyte aggregate was analyzed in 527 CAD patients (acute coronary syndromes [ACS, n = 149] and chronic coronary syndrome [CCS, n = 378]) by whole-blood flow-cytometry and plasma F1 + 2 by ELISA. With COX regression model 5-year survival analysis from all-cause (AC) and cardiovascular (CV) mortality was performed. Cross-validated cut-off of TF^pos platelets was calculated by Euclidean distance method.

Results

AC and CV mortality rates were 9.7 and 6.5%, respectively. Among the biomarkers evaluated, only TF independently predicted AC mortality (hazard ratio [HR] =2.02, p = 0.042) also after adjustment for CAD presentation. ACS and CCS patients with TF^pos platelets >4% (the best cut-off value for all-cause mortality prediction) had the highest levels of F1 + 2 and a worse prognosis for AC and CV mortality (HR = 1.91; p = 0.018 and HR = 2.51; p = 0.005; respectively) than those with <4% TF^pos platelets. Interestingly, patients on dual antiplatelet therapy (n = 246, 46.8%) responder to P2Y₁₂ inhibitors with TF^pos platelets >4% had the highest risk for AC mortality (HR = 4.11; p = 0.0215) and CV mortality (HR = 6.88; p = 0.0408). In these patients, TF^pos platelet levels outperformed a clinical model in CV mortality prediction (net reclassification improvement = 0.436, p < 0.001). Platelet TF predicted AC (HR = 3.03; p = 0.012) and CV mortality (HR = 3.56; p = 0.008) in aspirin-only treated patients also (n = 239, 45.3%).

Conclusion

The percentage of circulating TF^pos platelets may serve as an independent predictor of AC and CV mortality in CAD patients on antiplatelet therapy.

Keywords

tissue factor - platelets - cardiovascular risk stratification - all-cause mortality - cardiovascular death

Introduction

Despite significant diagnostic and therapeutic advancements in cardiovascular (CV) disease over the past years, patients with acute coronary syndromes (ACS) and chronic coronary syndromes (CCS) still face a substantial ongoing risk of recurrent events, morbidity, and mortality.[1] This risk is particularly pronounced in patients who have experienced a previous CV event, with recurrence rates as high as 5 to 7% per year, even with the best available antiplatelet therapy.[2] This scenario underscores the need for improving thrombotic risk stratification. Notably, current risk scores do not incorporate platelet activation markers, even though thrombus formation is driven by platelet activation and activated platelets are commonly found in patients with both ACS and CCS.[3] [4] Indeed, current risk scores are based on clinical variables such as arterial hypertension, chronic kidney disease, diabetes mellitus, and smoking habits to describe the complexity of CV risk in coronary artery disease (CAD) patients.[5] [6] [7] [8] So far, none of these clinical variables have shown sufficient strength to reliably risk-stratify CAD patients and accurately predict CV death and major adverse cardiovascular events (MACE) in them.[9] [10] The challenge with using platelet activation markers lies in the absence of a definitive gold standard biomarker for evaluating platelet activation status in patients with ACS and CCS.[11] [12] [13] Soluble mediators like P-selectin, CD40 ligand, and platelet factor 4 lack absolute specificity for platelets since they can also be expressed by other cells.[14] [15] [16] Therefore, focusing on proteins expressed on the platelet membrane during activation would be of help to refine risk prediction.

Among the surface-associated platelet activation markers, such as activated GPIIbIIIa (aGPIIbIIIa) and P-selectin, tissue factor (TF) stands out as being the key protein that initiates the coagulation cascade and thrombus formation[17] and, together with phosphatidylserine, regulates the procoagulant/prothrombotic properties of platelets.[18] [19] [20] [21] Elevated levels of TF^pos platelets have been observed in several pathological conditions,[22] [23] [24] [25] including CV disease.[26] [27] TF is currently regarded as the protein that connects prothrombotic and proinflammatory mechanisms in the progression of atherosclerosis.[28] As the thrombogenicity of atherosclerotic plaques is directly proportional to their TF content,[29] the thrombogenic potential of platelets is also similarly determined by the amount of TF expressed on their surface.[26] [27]

Based on this rationale, we evaluated the prognostic value of platelet-associated activation markers in predicting 5-year all-cause (AC) mortality and CV mortality in a cohort of ACS and CCS patients.

Materials and Methods

Study Population

This is a prospective, observational study (clinicaltrial.gov identification number: NCT06919731) that enrolled patients with ACS and CCS hospitalized at the Centro Cardiologico Monzino, Milan, Italy between 2013 and 2018 ([Fig. 1]). Enrollment was consecutive and included ACS patients with non-ST-segment elevation myocardial infarction (NSTEMI) who presented within 24 hours of the onset of symptoms, and CCS patients with stable angina and a planned coronary angiography. Non-ST-elevation-ACS (NSTE-ACS) was defined as chest pain at rest in the last 48 hours preceding the admission associated with evidence of transient ST-segment depression on 12-lead electrocardiogram and normal (unstable angina) or elevated (non-ST-elevation myocardial infarction) high sensitivity troponin I levels and found to have significant coronary artery stenosis at coronary angiography. Stable angina was defined as angina on effort with a stable pattern of symptoms for at least the last 6 months prior to admission. All patients with CCS and without angiographically documented significant coronary artery stenosis and indication to percutaneous coronary intervention (PCI) were excluded. Additional exclusion criteria were: severe renal dysfunction (eGFR <30 mL/min/1.73 m²), malignancies, inflammatory disorders, recent major surgery, and hemodynamic instability.

Fig. 1 Study flow. Study population grouped according to coronary artery disease (CAD) clinical presentation and pharmacological treatment. All-cause and cardiovascular mortality were assessed on patients stratified according to the percentage of TF^pos platelets. ACS, acute coronary syndromes; CCS, chronic coronary syndromes; DAPT, dual antiplatelet therapy; HTPR, high on-treatment platelet reactivity; TF, tissue factor.

Healthy subjects (HS, n = 251), with a negative CV history and free from any CV risk factor (but smoke), were analyzed as control group for platelet activation assessment.

Consent

The study was conducted according to the Declaration of Helsinki, it was approved by the institutional Ethics Committee (n. R1436/21-CCM 1508) and written informed consent was obtained from all participants.

Study Protocol

Demographical, clinical, biochemical, and echocardiographic data of the enrolled patients were obtained. The estimated glomerular filtration rate (eGFR) was calculated according to the Modification of Diet in Renal Disease (MDRD) formula. The left ventricular ejection fraction (LVEF; echocardiogram) was measured in all patients within 24 hours from hospital admission. During hospitalization, venous blood samples for determination of platelet activation markers were obtained before administration of any anticoagulant therapy and before coronary angiography. Therapeutic and interventional strategies for management of ACS and CCS was left to the discretion of the attending physician, on the basis of the current standards of care recommended by published guidelines. At the time of blood withdrawal 41.9% of CCS patients were on dual antiplatelet therapy (DAPT) (aspirin 100 mg once daily and clopidogrel 75 mg once daily) due to prior coronary stent implantation or pre-treatment with clopidogrel 300 mg loading dose the day before coronary angiography. In NSTEMI patients, clopidogrel (300–600 mg loading dose) was administered after diagnosis and at least 24 hours before coronary angiography according to the clinical protocol. All patients who underwent PCI were treated with DAPT for 3 to 12 months as per guidelines recommendations.[30] [31] After this period, DAPT was discontinued, and patients switched to lifelong single antiplatelet therapy (SAPT) with ASA alone.

Systematic follow-up included regular outpatient visits at Centro Cardiologico Monzino IRCCS and structured telephone interviews conducted by trained personnel. Due to privacy restrictions preventing access to public health databases and the limited accuracy of telephone-based CV outcome assessments, we opted to collect only the cause of death in these cases, without capturing non-fatal CV events. For the assessment of CV death, in most cases, the cardiologist study team (G.M. and N.C.) was able to review supporting documentation, such as discharge summaries and death certificates, that was provided by relatives or healthcare professionals during the phone contact. Confirmation of mortality status also relied on hospital records and national death registries, consistent with established methodologies. CV death was defined according to international guidelines, encompassing mortality due to myocardial infarction, heart failure, sudden cardiac death, stroke, or other vascular causes.

The primary endpoint of the study was 5-year AC mortality, and 5-year CV mortality was considered as a secondary endpoint.

Blood Collection, F1 + 2 Measurement and Platelet Activation Assessment

Venous blood samples were obtained before administration of any anticoagulant therapy and the morning of coronary angiography or just before the access to the angiography room for CCS and NSTEMI patients, respectively. Whole blood (WB) was drawn with a 19-gauge needle without venous stasis into sodium citrate (0.129 M, 1/10 volume/volume) containing tubes (Vacutainer, Becton Dickinson), discarding the first 4 mL. To monitor changes in thrombin generation, plasma levels of prothrombin fragment 1 + 2 (F₁₊₂) were measured by commercial enzyme-linked immunosorbent assay (Enzygnost F1 + 2, Siemens Healthcare Diagnostics, Marburg, Germany). Surface expression of platelet activation markers was analyzed by flow cytometry as previously described[32] and modified as reported in the [Supplementary Material]. The gating strategy for flow cytometry analysis is reported in the [Supplementary Fig. S1] (available in the online version only).

Statistical Analysis

Sample Size

A sample of 527 subjects, assuming an AC mortality rate of 10%, was required to detect a statistically significant hazard ratio (HR) of 2.1 (alpha = 0.05) with 80% statistical power.

Statistical Analysis

Continuous variables were presented as mean ± standard deviation (SD) if normally distributed and as median and interquartile range (IQR) otherwise. Categorical variables were expressed as frequencies and percentages. Comparisons of continuous variables were performed using Student's T test or Wilcoxon-Mann-Whitney test as appropriate, and comparisons of categorical variables were made using χ² test or Fisher's exact test. Variables with right skewed distribution were log-transformed for the analysis. A multivariate model was implemented to investigate the possible predictor/s of the AC and CV mortality endpoints. Euclidean distance method was used to calculate the best cut-off value of platelet-associated TF expression. A cross-validation procedure was employed to calculate the best cut-off of TF to identify AC mortality (primary endpoint). The study sample was randomly divided in half 200 times; the best cut-off by Euclidean distance was estimated in the first arm (training set), and its sensitivity and specificity were subsequently tested in the second half (testing set). The mean value of each cut-off was considered for the final value. Cox regression model was used to perform 5-year survival analysis. Survival in the groups was reported using the Kaplan-Meier method, and the log-rank test was used for comparison. Analyses were also performed with adjustment for age, CAD clinical presentation (ACS vs. CCS), antiplatelet therapy (single vs. DAPT), angiotensin II receptor blocker (ARB) and antiarrhythmic use, and renal function (eGFR). Analysis was also performed by stratifying based on ACS/CCS classification. A sub-analysis was performed stratifying the population according to antiplatelet therapy. In patients on DAPT, responder to P2Y₁₂ inhibitors, the ability of TF to improve the prediction of CV mortality compared with a clinical model, including well-established predictors such as sex, age, eGFR, LVEF, and diabetes mellitus, was calculated by net reclassification improvement (NRI). A p-value <0.05 was considered statistically significant. Missing mortality data were minimal and did not significantly affect statistical power. As a result, multiple imputations were not performed. Statistical analyses have been performed with SAS software, version 9.4 (SAS Institute, Cary, NC, USA).

Results

Baseline Characteristics of the Enrolled Study Population

A total of 527 CAD patients (mean age 66.5 ± 9.6 years, males 84%) were included in the study. The baseline characteristics of the overall cohort and of the study population grouped according to CAD clinical presentation (ACS vs. CCS) are listed in [Table 1]. At the time of blood collection, 72% (n = 378) of the patients had CCS, whereas 28% (n = 149) had ACS. Many of ACS patients were on DAPT (59.1%), with aspirin plus clopidogrel mainly (79.5%), whereas 54% of CCS patients were on SAPT (95.6% on aspirin; [Table 1]), while 24 patients were not on any antiplatelet therapy. None of the healthy subjects (HS) was on antiplatelet therapy or on antihypertensive, lipid-lowering or chronic anti-inflammatory treatment ([Supplementary Table S1], available in the online version only). All patients were discharged on DAPT according to current guidelines.

Table 1 Baseline characteristics of patients grouped according to CAD presentation

Variable	All	ACS	CCS	P-value
	n = 527	n = 149 (28.3%)	n = 378 (71.7%)
Age, years	66.5 ± 9.6	67.3 ± 11.2	66.2 ± 8.8	0.26
Male sex, n (%)	444 (84.2)	115 (77.2)	329 (87)	0.005
BMI (kg/m2)	27.1 ± 4.3	27.5 ± 5.1	26.9 ± 3.9	0.18
Left ventricular ejection fraction, %	56.5 ± 10.5	54.4 ± 11.3	57.5 ± 9.9	0.003
Serum creatinine, mg/dL	0.95 (0.83; 1.12)	0.93 (0.79; 1.1)	0.96 (0.84; 1.13)	0.15
eGFR, mL/min/1.73 m2	76.3 ± 20.3	75.7 ± 24.8	76.5 ± 18.1	0.69
hs-CRP, mg/dL	2 (1; 4.4)	3.4 (1.4; 8.4)	1.6 (0.6; 3.1)	<0.001
White blood cells, x103/mcL	7.2 ± 2.05	7.9 ± 2.6	6.9 ± 1.7	<0.001
Hemoglobin, g/dL	13.7 ± 1.64	12.9 ± 1.7	14.0 ± 1.5	<0.001
Platelets, x109/mcL	215.5 ± 57.4	216.4 ± 58.3	215.2 ± 57.1	0.84
Cardiovascular risk factors, n (%)
Current smoker	86 (16.5)	33 (22.3)	53 (14.2)	0.06
Prior smoker	213 (40.8)	52 (35.1)	161 (43)
Dyslipidemia	428 (82.5)	126 (85.7)	302 (81.2)	0.22
Hypercholesterolemia	270 (67.7)	84 (62.2)	186 (70.5)	0.10
Hypertension	416 (79.8)	115 (77.7)	301 (80.7)	0.44
Diabetes mellitus	181 (34.6)	49 (33.1)	132 (35.2)	0.65
On insulin	128 (24.7)	28 (18.9)	100 (27)	0.06
Family history of CHD	192 (39.7)	48 (38.7)	144 (40)	0.80
Cardiovascular history, n (%)
Prior CABG	77 (17.3)	33 (24.3)	44 (14.2)	0.010
Prior AMI	164 (31.4)	46 (31.1)	118 (31.5)	0.93
Prior PCI	171 (38.4)	36 (26.7)	135 (43.5)	0.001
Prior stable angina	169 (50)	4 (10)	165 (55.4)	<0.001
Prior unstable angina	23 (6.8)	5 (12.5)	18 (6)	0.13
Permanent atrial fibrillation	7 (2.1)	2 (2.3)	5 (2)	1.00
Prior stroke	27 (5.2)	5 (3.4)	22 (5.9)	0.24
Aortic aneurism	21 (4.0)	10 (6.8)	11 (2.9)	0.046
Therapy [a] , n (%)
ACE-I	184 (35.5)	60 (40.5)	124 (33.5)	0.13
ARB	112 (21.7)	27 (18.2)	85 (23)	0.23
Beta-blockers	322 (62.2)	97 (65.5)	225 (60.8)	0.32
Alpha-blockers	17 (3.3)	5 (3.4)	12 (3.3)	0.94
Calcium antagonist	160 (31.0)	31 (20.9)	129 (34.9)	0.002
Statins	338 (65)	90 (60.8)	248 (66.7)	0.21
Fibrates	11 (2.1)	1 (0.7)	10 (2.7)	0.19
Antiarrhythmics	40 (7.7)	13 (8.8)	27 (7.3)	0.57
NSADs	14 (2.7)	11 (7.4)	3 (0.8)	<0.001
Antiplatelet therapy [a] , n (%)
No antiplatelet therapy	24 (4.4)	8 (5.4)	16 (4)	<0.001
SAPT	257 (48.8)	53 (35.6)	204 (54.1)
DAPT	246 (46.8)	88 (59.1)	158 (41.9)
SAPT with ASA	239 (93.0[b])	44 (83[b])	195 (95.6[b])	0.001
SAPT with Clopidogrel/Ticagrelor	9 (3.5[b])	5 (9.4[b])	4 (2[b])	0.020
SAPT with ticlopidine	9 (3.5[b])	4 (7.5[b])	5 (2.5[b])	0.09
DAPT with clopidogrel + ASA	203 (83.9[c])	70 (79.5[c])	133 (86.4[c])	0.16
DAPT with prasugrel + ASA	13 (5.4[c])	12 (13.6[c])	1 (0.6[c])	<0.001
DAPT with ticagrelor + ASA	5 (2.1[c])	4 (4.5[c])	1 (0.6[c])	0.06
DAPT with ticlopidine + ASA	22 (9.1[c])	3 (3.4[c])	19 (12.3[c])	0.020
Platelet activation markers, %
TFpos platelets	1.84 (0.51; 6.38)	1.57 (0.4; 5.54)	1.87 (0.54; 6.64)	0.56
aGPIIbIIIapos platelets	0.20 (0.10; 0.47)	0.15 (0.07; 0.34)	0.22 (0.11; 0.51)	<0.001
P-selectinpos platelets	0.46 (0.25; 0.93)	0.48 (0.25; 1.03)	0.44 (0.25; 0.88)	0.65
Platelet–monocyte aggregates	12.6 (8.3; 19.0)	12.72 (9.2; 19.66)	12.61 (8.24; 18.93)	0.48

Abbreviations: ACE-I, angiotensin-converting enzyme inhibitors; ACS, acute coronary syndrome; aGPIIbIIIa, activated glycoprotein IIbIIIa; AMI, acute myocardial infarction; ARB, angiotensin II receptor blockers; ASA, aspirin; BMI, body mass index; CABG, coronary artery by-pass graft; CAD, coronary artery disease; CCS, chronic coronary syndromes; CHD, congenital heart disease; CRP, C reactive protein; DAPT, dual antiplatelet therapy; eGFR, estimated glomerular filtration rate; NSADs, non-steroid anti-inflammatory drugs; PCI, percutaneous coronary intervention; SAPT, single antiplatelet therapy; TF, tissue factor.

Note: Data are reported as mean ± standard deviation (SD) or median [IQR] unless otherwise specified.

^a Therapy at time of blood withdrawal.

^b Percentage over patients on SAPT.

^c Percentage over patients on DAPT.

During the 5-year follow-up, there were 51 (9.7%) AC deaths: 34 (6.5%) due to CV disease (33 CV thrombotic events and 1 acute pulmonary edema), whereas 17 attributable to other causes, including cancer (2 cases), pneumonia (1 case), small intestinal infarction (1 case), acute pulmonary edema (1 case), iatrogenic pulmonary fibrosis (1 case), end-stage renal disease (1 case), severe valvular aortic stenosis (1 case), and various unknown causes. In ACS patients, AC and CV mortality were 16.8 and 12.8%, respectively, while in CCS patients, they occurred in 6.9 and 4.0%, respectively.

Flow cytometry analysis of platelet activation markers revealed that the levels of aGPIIbIIIa, P-selectin-positive platelets, and platelet–monocyte aggregates were lower in CAD patients compared to HS (p < 0.0001 for all comparisons) due to the ongoing antiplatelet therapy. Despite this, levels of TF^pos platelets had a clear trend toward much higher values in patients with CAD than HS ([Supplementary Fig. S2], available in the online version only).

The Percentage of TF^pos Platelet Predicts All-cause and CV Mortality

Multivariable COX regression analyses showed that, among the platelet activation markers analyzed, only TF was an independent predictor of AC mortality (HR = 2.02, 95% CI 1.03–3.98 for each logarithmic unit increase; [Table 2]). Interestingly, AC mortality risk increases by 1.7-fold for every 4% increment in the TF^pos platelet value. In the ROC curve analysis, the cut-off value for TF^pos platelets to predict AC mortality was 3.93% (approximated to 4%); this value was validated through cross-validation analysis. Patients stratified above and below this threshold comprised 34 and 66% of the whole CAD cohort, respectively ([Table 3]). This distribution was similar among those with ACS and CCS, as shown in [Supplementary Tables S3] and [S4] (available in the online version only).

Table 2 Multivariable analysis for predictors of 5-year all-cause mortality

	Harzard ratio	CI 95%		P-value
TFpos platelets (%)	2.02	1.03	3.98	0.042
aGPIIbIIIapos platelets (%)	2.46	0.32	19.03	0.39
P-selectinpos platelets (%)	0.81	0.15	4.32	0.81
Platelet–monocyte aggregates (%)	1.50	0.44	5.07	0.51

Abbreviations: aGPIIbIIIa, activated glycoprotein IIbIIIa; TF, tissue factor.

Note: Analysis was performed on log-transformed variables.

Table 3 Characteristics of CAD patients grouped according to TFpos platelets' levels

Variable	<4% TFpos platelets	>4% TFpos platelets	p-value
	n = 346 (66%)	n = 181 (34%)
Age	65.9 ± 10.1	67.8 ± 8.4	0.030
Male sex, n (%)	295 (85.3)	149 (82.3)	0.38
BMI	27.1 ± 4.2	27.1 ± 4.6	0.92
CCS	243 (70.2)	135 (74.6)	0.29
ACS	103 (29.8)	46 (25.4)
Left ventricular ejection fraction	57.2 ± 9.5	55.3 ± 12.1	0.08
Serum creatinine, mg/dL	0.94 (0.82; 1.13)	0.96 (0.85; 1.12)	0.26
eGFR, mL/min/1.73 m2	77.45 ± 20.22	73.92 ± 20.23	0.07
hs-CRP, mg/dL	2 (1; 4.3)	1.9 (1.07; 4.8)	0.84
White blood cells, x103/mcL	7.18 ± 1.91	7.25 ± 2.3	0.71
Hemoglobin, g/dL	13.72 ± 1.59	13.67 ± 1.73	0.76
Platelets, x109/mcL	216.8 ± 54.1	213.1 ± 63.3	0.48
Cardiovascular risk factors, n (%)
Current smoker	55 (16.0)	31 (17.3)	0.71
Prior smoker	143 (41.9)	69 (38.5)
Dyslipidemia	290 (84.8)	138 (77.9)	0.052
Hypercholesterolemia	194 (67.6)	76 (67.9)	0.96
Hypertension	277 (80.5)	139 (78.5)	0.59
Diabetes mellitus	115 (33.4)	66 (36.9)	0.43
On insulin	77 (22.6)	51 (28.6)	0.13
Family history of CHD	131 (40.4)	61 (38.1)	0.62
Cardiovascular history, n (%)
Prior CABG	50 (16.7)	27 (18.5)	0.64
Prior AMI	110 (32.0)	54 (30.2)	0.67
Prior PCI	121 (40.3)	149 (82.3)	0.23
Prior unstable angina	16 (7.0)	7 (6.3)	0.81
Permanent atrial fibrillation	6 (2.5)	1 (1.1)	0.68
Prior stroke	15 (4.4)	12 (6.7)	0.25
Aortic aneurism	10 (2.9)	11 (6.2)	0.07
Therapy, n (%)
ACE-I	120 (35.3)	64 (36.0)	0.88
ARB	64 (18.9)	48 (26.9)	0.034
Beta-blockers	205 (60.3)	117 (65.7)	0.22
Alpha-blockers	10 (2.9)	7 (3.9)	0.55
Calcium antagonist	109 (32.1)	51 (28.6)	0.42
Statins	219 (64.0)	119 (66.8)	0.52
Fibrates	9 (2.6)	2 (1.1)	0.35
Antiarrhythmics	20 (5.9)	20 (11.2)	0.030
NSADs	7 (2.1)	7 (3.9)	0.21
Antiplatelet therapy, n (%)
No antiplatelet therapy	20 (5.8)	4 (2.2)	0.17
SAPT	170 (49.1)	87 (48.1)
DAPT	156 (45.1)	90 (49.7)
SAPT with ASA	156 (91.8[a])	83 (95.4[a])	0.28
SAPT with Clopidogrel/Ticagrelor	7 (4.1[a])	2 (2.3[a])	0.72
SAPT with ticlopidine	7 (4.1[a])	2 (2.3[a])	0.72
DAPT with clopidogrel + ASA	133 (86.9[b])	70 (78.7[b])	0.09
DAPT with prasugrel + ASA	7 (4.6[b])	6 (6.7[b])	0.56
DAPT with ticagrelor + ASA	3 (2.0[b])	2 (2.3[b])	1.00
DAPT with ticlopidine + ASA	11 (7.2[b])	11 (12.4[b])	0.18

Abbreviations: ACE-I, angiotensin-converting enzyme inhibitors; AMI, acute myocardial infarction; ARB, angiotensin II receptor blockers; ASA, aspirin; BMI, body mass index; CABG, coronary artery by-pass graft; CAD, coronary artery disease; CHD, congenital heart disease; CRP, C reactive protein; DAPT, dual antiplatelet therapy; eGFR, estimated glomerular filtration rate; NSADs, non-steroid anti-inflammatory drugs; PCI, percutaneous coronary intervention; SAPT, single antiplatelet therapy.

Note: Data are reported as mean ± standard deviation (SD) or median [IQR] unless otherwise specified.

^a Percentage over patients on SAPT.

^b Percentage over patients on DAPT.

The median level of TF^pos platelets was 8.97% (6.33–15.12) for patients above the 4% threshold, and 0.81% (0.27–1.80) for those below it (p < 0.001), the latter being comparable to the levels measured in HS (1.92% [1.13; 3.01]). A statistically significant trend toward a higher percentage of platelets expressing the classic platelet activation markers—although remaining below the levels measured in HS—was observed in patients with TF^pos platelets above 4% ([Supplementary Fig. S3], available in the online version only). Of note, however, when patients were stratified according to their clinical presentation, this trend was observed in CCS only ([Supplementary Fig. S4], available in the online version only). Indeed, in ACS patients the percentage of platelets expressing aGPIIbIIIa, P-selectin, and the number of PMA was comparable in patients with TF^pos platelets above or below 4% ([Supplementary Fig. S5], available in the online version only). Interestingly, patients with levels of TF^pos platelets greater than 4% had the highest plasma levels of prothrombin fragment 1 + 2 (F1 + 2; [Supplementary Fig. S6], available in the online version only), a well-known specific marker of in vivo thrombin generation.

The percentage of patients treated with SAPT only and those on DAPT in the >4 and <4% TF^pos platelets groups was comparable (49.1% vs. 48.1% for aspirin only; 45.2% vs. 49.7% for DAPT) both in the whole CAD cohort and when ACS and CCS patients were analyzed separately. There were no statistically significant differences between the two groups for all the other demographic and clinical parameters evaluated ([Table 3] and [Supplementary Tables S2] and [S3], available in the online version only).

The incidence of AC mortality in patients above and below the TF^pos platelets cut-off was 13.8 and 7.5%, respectively (p = 0.020) with an HR of 1.9 (95% CI: 1.10–3.31). The annual CV mortality incidence was significantly higher in patients with TF^pos platelets values above 4% compared to those with values below 4% (10.5 and 4.3%, respectively, p = 0.006), with an HR of 2.51 (95% CI: 1.28–4.95). Associations between the percentage of TF^pos platelets and the rate of AC and CV mortality remained significant after adjustment for age, eGFR, ARB, antiarrhythmics, DAPT use, and ACS versus CCS presentation (HR = 1.77; 95% CI: 1.01–3.13 and HR = 2.33, 95% CI: 1.16–4.70, respectively). Adjusted Kaplan-Meier curves are shown in [Fig. 2]. Interestingly, TF^pos platelet levels above 4% were predictor of CV mortality in both ACS and CCS patients ([Fig. 2C, D], respectively). Notably, the level of TF^pos monocytes was not an independent predictor of either AC mortality (p = 0.34) or CV mortality (p = 0.71).

Fig. 2 Kaplan-Meier survival probability of coronary artery disease (CAD) patients according to TF^pos platelet values. Patients were stratified according to TF^pos platelet values below (blue curve) or above 4% (red curve). Kaplan-Meier (KM) survival curves for all-cause (A) and cardiovascular (CV) (B) mortality in the whole patient cohort (n = 527) are shown. KM curves are adjusted for age, CAD clinical presentation, dual antiplatelet therapy (DAPT), angiotensin II receptor blocker (ARB) and antiarrhythmic use, and renal function. Unadjusted KM survival curves for CV mortality in acute coronary syndromes (ACS) (n = 149; C) and chronic coronary syndrome (CCS) (n = 378; D) are reported. Log-rank test was used for comparison.

Subgroup Analysis According to Antiplatelet Drug Treatment

The association of TF^pos platelet levels with AC mortality and CV mortality according to antiplatelet drug treatment was examined in subgroup analyses.

In patients on DAPT, who were grouped according to their response to P2Y₁₂ inhibitors (136 responders and 110 with high-on-treatment platelet reactivity [HTPR], to the vasodilator stimulated phosphoprotein [VASP] assay), we found a trend toward a higher risk of AC and CV mortality in HTPR compared to responder (13.08% vs. 8.82% and 8.41% vs. 4.41%, respectively).

When patients were stratified according to the TF^pos platelet cut-off value, those with TF^pos platelet levels >4% had, among the responders, the highest increased risk for both AC mortality (HR 4.11; 95% CI 1.11–15.17) and CV mortality (HR 6.88; 95% CI 1.0–58.89) ([Fig. 3A, B]). The CV mortality risk in DAPT patients who responded to P2Y₁₂ antagonists was 1.06 (95% CI = 1.01–1.11, for each percentage unit) when TF^pos platelets were analyzed as a continuous variable. Interestingly, in DAPT patients who were responders to P2Y₁₂ antagonists, the TF^pos platelet levels had better predictive value for CV mortality than a robust clinical model incorporating sex, age, eGFR, LVEF, and diabetes mellitus (NRI = 0.44; 95% CI: 0.30–0.57, p < 0.001). Notably, the sensitivity and specificity of the 4% TF^pos platelet cut-off value for predicting CV mortality in DAPT-treated patients were 83 and 58%, respectively ([Fig. 3C]). Unfortunately, in the HTPR group the statistical power of 80% necessary to assess the predictive value of the biomarker was not achieved.

Fig. 3 Kaplan-Meier (KM) survival curves of patients responding to P2Y₁₂ inhibitors. Unadjusted KM curves for (A) all-cause mortality and (B) cardiovascular (CV) mortality in patients responding to P2Y₁₂ inhibitors (n = 136) stratified by TF^pos platelet values below (blue curve) or above 4% (red curve). Sensibility and specificity of 4% TF^pos platelet cut-off value for CV mortality are reported in C. Log-rank test was used for comparison.

TF^pos platelets were also predictors of AC mortality (HR 2.67; 95% CI 1.12–76.34; [Fig. 4A]) and CV mortality (HR 3.12; 95% CI 1.21–8.05; [Fig. 4B]) in patients receiving aspirin only, with a sensitivity and specificity for CV mortality of 67 and 68%, respectively.

Fig. 4 Kaplan-Meier (KM) survival curves of patients treated with aspirin only. Unadjusted KM curves for (A) all-cause mortality and (B) cardiovascular (CV) mortality in aspirin-treated patients (n = 257) stratified according to TF^pos platelet values below (blue curve) or above 4% (red curve). Log-rank test was used for comparison.

In brief, these data indicate that in CAD patients responding to P2Y₁₂ antagonists, as well as in those treated with aspirin alone, the CV mortality rate increases by 7-fold and 4-fold, respectively, when circulating levels of TF^pos platelets exceed a 4% value.

Discussion

This is the first and largest prospective study investigating the relationship between cell-associated markers of platelet activation and the risk of AC and CV mortality in patients with CAD. There are five major findings. First, among the platelet activation markers considered—activated GPIIbIIIa, P-selectin, TF, and platelet–leukocyte aggregates—only TF, the key activator of the coagulation cascade and thrombus formation, is an independent predictor of AC and CV mortality, irrespective of treatment with DAPT or aspirin alone. Second, the number of TF^pos monocytes, a potential additional source of TF, was not an independent predictor of AC and CV mortality. Third, we have identified a cut-off level of TF^pos platelets (4%) with good discriminative power for predicting clinical outcomes. Fourth, in patients on DAPT, circulating levels of TF^pos platelets greater than 4%—despite a good response to P2Y₁₂ inhibitors—are associated with an HR of 6.88 for CV death, with a 83% sensitivity and 58% specificity for CV mortality. In these patients, the TF^pos platelets outperformed a robust clinical model in predicting CV mortality. Fifth, levels of TF^pos platelets greater than 4% are predictors of AC and CV death in patients on aspirin alone also.

The identification of biomarkers to improve thrombotic risk stratification in patients with atherosclerotic CV disease, enabling the detection of those at higher risk of recurrence, remains a major unmet clinical need. Recently, Neumann et al using the largest multinational individual-level dataset analysis provided compelling evidence that adding CV biomarkers to established risk factors results in only minimal improvement in risk prediction for atherosclerotic CV disease, with limited clinical value.[33] This is because new biomarkers can only enhance prediction if they provide information not already captured by existing models.[34] Therefore, improving risk prediction and patient outcomes requires novel insights and new clues about the mechanisms underlying the substantial residual CV risk that remains unexplained by known risk factors and biomarkers.[35] It is important to note, however, that not all current knowledge about the pathogenesis of atherosclerotic coronary artery disease is fully integrated into risk scores. For instance, platelet activation plays a crucial role not only in thrombus formation during plaque rupture but also in the initiation and progression of atherothrombotic disease.[36] Additionally, it is well established that platelets in patients with coronary artery disease exhibit a more activated phenotype.[11] Despite this solid understanding, current risk scores are based on known risk factor patterns and might be implemented by new biomarker/s reflecting the contribution of platelets to coagulation.[37] The reason for this omission is the absence of a gold-standard marker for in vivo platelet activation. Over the last 20 years, research has focused on analyzing soluble markers of platelet activation. However, the data remain inconclusive, as none of these markers have demonstrated diagnostic robustness to warrant inclusion into routine clinical practice.[38] [39] A further limitation of soluble markers is their lack of absolute platelet specificity, as they may also be expressed by other cell types.[14] [15] [16] Consequently, we chose to investigate the predictive value of platelet-specific markers, considering membrane-associated aGPIIbIIIa, P-selectin, TF, and platelet–leukocyte aggregates—another recognized marker of platelet activation. Among these, we highlighted the prognostic power of TF^pos platelets. Although TF^pos platelets are not yet ready for routine clinical use, the observed improvement over established risk models supports their potential relevance as a novel biomarker, especially in patients who are good responders to P2Y₁₂ inhibitors.

TF^pos platelets represent a subset (20–30%) of the whole platelet population generated by bone marrow megakaryocytes and released into circulation. This platelet subset carries not only ready-to-use TF protein but also pre-mRNA/mRNA, allowing for de novo protein synthesis.[19] [20] TF is localized in the open canalicular system (OCS), stored far from circulating FVII to prevent activation of blood coagulation.[21] Thus, under physiological/resting conditions, only a negligible percentage of platelets expose TF on the cell surface (1.9% [1–3%]).[18] [20] [21] [40] Upon stimulation with classical agonists platelets undergo shape change and a concentration-dependent increase in surface-exposed, functionally active TF, which can be detected by flow cytometry and thrombin generation assays.[25] [27]

Increased levels of TF^pos platelets have been reported in several pathological conditions associated with a prothrombotic phenotype, including thrombocythemia,[22] cancer,[23] antiphospholipid syndrome,[24] [41] viral infections,[25] [42] and CV diseases.[26] There is also evidence that CV risk factors can directly upregulate circulating TF^pos platelet levels.[43] The clinical relevance of these findings lies in the significant correlation between platelet-associated TF expression and its functional activity, measured as thrombin generation capacity.[25] [27]

In this study patients with TF^pos platelets levels above 4% have a worse prognosis compared to those with levels below 4%, with an HR of 1.77 (p = 0.048) for AC mortality and 2.33 (p = 0.017) for CV mortality. The significantly greater levels of F1 + 2 found in patients with TF^pos platelets levels above 4% reflect an increased tendency to in vivo thrombin generation. Although data on the association of F1 + 2 and CAD events are limited, thus far Hansen et al showed that F1 + 2 was strongly associated with a composite of AC mortality, reinfarction, stroke, unscheduled revascularization, or rehospitalization for heart failure and total mortality in STEMI patients.[44]

Subgroup analysis based on drug treatment revealed that the predictive value of platelet-associated TF was even stronger in patients on aspirin only, who had a more than three-fold higher risk of CV mortality if their TF^pos platelet levels exceeded the 4% cut-off value. Similarly, patients on DAPT, despite a good response to P2Y₁₂ antagonists, had the highest risk of AC mortality and CV mortality if their TF^pos platelet levels were above 4%. Interestingly, in these patients, the TF^pos platelet levels outperformed a clinical model in predicting CV mortality. It is also noteworthy that in HTPR patients the risk of events remained unchanged regardless of TF^pos platelet levels.

We have recently provided evidence that the expression of TF on the platelet surface is regulated solely by P2Y₁₂ receptor activation, and not by P2Y₁.[21] In patients with CAD, P2Y₁₂ antagonists inhibit the expression of the classical platelet activation markers—aGPIIbIIIa and P-selectin—while also reducing ADP-induced cell surface exposure of TF. Interestingly, however, the inhibition of ADP-induced platelet-associated TF exposure requires a significantly higher degree of P2Y₁₂ inhibition compared to what is needed to reduce aGPIIbIIIa, P-selectin, and the VASP (vasodilator-stimulated phosphoprotein) platelet reactivity index (PRI), which is considered the gold standard for assessing responses to P2Y₁₂ receptor inhibitors. For instance, clopidogrel significantly inhibited P-selectin expression to a similar extent in all CAD patients, regardless of whether they were classified as clopidogrel responders or HTPR patients.[21]

Thus, based on these findings, it is possible that despite a good response to clopidogrel (as determined by the VASP assay), some patients still exhibit residual platelet reactivity in terms of prothrombotic potential due to insufficient inhibition of TF. Indeed, in this study we have found that although levels of classical platelet activation markers were significantly lower in CAD patients compared to HS, TF levels were not, thus providing a novel element to implement stratification of thrombotic risk.

The association of HTPR in clopidogrel-treated patients with increased thrombotic risk is well known.[45] [46] The variability in response to antiplatelet therapy has been recently highlighted in a large single-center study including almost 6,000 patients undergoing platelet aggregation assessment using a standardized methodology.[47] Thus, in the era of precision medicine, a guided therapy would optimize its safety and efficacy.[48] The superior performance of a guided approach compared with potent P2Y₁₂ inhibitor treatments, in terms of reduction in ischemic events without a significant increase in the incidence of bleeding, has been indeed recently highlighted in a network meta-analyses that included data from more than 60,000 patients.[49] Furthermore, recently published expert consensus statements on the role of platelet function tests and genetic tests provide updates on the latest evidence in the field as well as recommendations for clinical practice.[50]

Altogether, the present data support the predictive value of TF over other markers of platelet activation. They also provide a rationale for the inability of the VASP assay, as well as platelet function tests, like platelet aggregometry, to predict major adverse CV events[51] or to alter clinical outcome.[52] [53] [54] Among the markers of platelet activation, the immature platelet fraction (IPF), also known as reticulated platelets (RP), has gained significant attention in recent years. These newly released platelets are typically larger than mature platelets and possess greater reactivity and capacity to initiate blood clotting. Although IPF primarily serves as a marker of platelet production and turnover, several studies have demonstrated its association with the effectiveness of DAPT[55] [56] and its correlation with CV events.[57] Interestingly, TF^pos platelets are also among the largest within the platelet population.[20]

Unfortunately, in the present study, IPF data were only available for 91 patients, limiting our ability to explore the association between IPF and CV events. However, in patients with TF^pos platelets >4%, IPF was significantly higher compared to those with TF^pos platelets <4% (5.3 × 10³/μL [3.3; 10.2] vs. 4.0 × 10³/μL [2.4; 5.9], p = 0.033), despite no difference in total platelet count. This suggests that TF^pos platelets may share some features with IPF. However, it should be noted that TF^pos platelets account for a much larger proportion of the platelet population (approximately 25%) compared to IPF (approximately 3%), making them an independent subset of platelets. The key distinction between the two platelet types lies in their clinical significance. Although IPF is a marker of residual platelet reactivity due to drug resistance, TF^pos platelets provide an opportunity to identify patients who, despite responding well to antiplatelet therapy and passing standard therapy control tests, are still at the highest risk for CV events, based on the findings of this study. Therefore, TF^pos platelets represent a unique biomarker for stratifying thrombotic risk in patients with CAD.

Study Limitations

Our findings should be interpreted within the context of certain limitations. First, the data presented are from a single-center study only. In the effort to design a future multicenter study, it is worth mentioning that the authors recently coordinated an international project promoted and funded by the Scientific and Standardization Committee of the International Society of Thrombosis and Haemostasis (SSC-ISTH) to standardize the measurement of platelet-associated TF. Over 20 laboratories worldwide participated in this study, which is now being completed and results will be published shortly. Second, the assessment of CV death was conducted by phone for about half of the patients. Although independent adjudication would have further strengthened the reliability of the endpoint assessment, the use of official documents such as death certificates and discharge letters provided a reasonable basis for classifying deaths as CV when appropriate. The few cases of uncertain cause of death were considered as AC mortality. Third, we reported only CV and AC mortality. This decision was driven by the fact that 47% of the included patients did not attend an outpatient visit after enrolment mainly due to a residency far from the enrolling hospital, and mortality outcomes were the only data collected via telephone for these patients. The accuracy of telephone-based assessment of CV outcomes besides mortality such as MACE was deemed insufficient; furthermore, privacy restrictions prevented access to public health databases for outcome adjudication. Consequently, MACE data were available for 53% of the cohort only, resulting in limited statistical power to assess the association between TF and MACE. As a result, this analysis was not pursued. Fourth, there is a lack of information regarding therapeutic changes and compliance after discharge. During the first 12 months of follow-up, the study population underwent two treatment strategies (prolonged vs. shorter length of DAPT), which could be a confounding factor. This was taken into account by adjusting and stratifying the analysis according to the two treatment strategies. Notably, the mortality curves diverge and reach statistical significance after 12 months of follow-up, corresponding with a period when most participants received ASA monotherapy. This suggests that differences in medical therapy did not significantly affect the prognosis of our population. A landmark analysis would have enabled us to understand how the predictive effect of TF^pos platelets may vary over time. However, the sample size was insufficient to produce robust and meaningful results. This is an issue that will be addressed in ongoing studies. Fifth, different cut-off values for determining the appropriate response to treatment with P2Y₁₂ inhibitors have been proposed.[46] [58] A cut-off of 50% is suggested for patients on treatment with prasugrel and ticagrelor, whereas a cut-off of 60% is deemed more appropriate for patients treated with clopidogrel. Given that 83% of the subjects in the study cohort were on clopidogrel, we defined non-responders to P2Y₁₂ inhibitors as those with VASP PRI levels above 60%. Sixth, data on the stability of the TF^pos platelet levels over time are lacking. Studies to assess this issue are currently underway. Finally, we did not assess treatment response in patients on aspirin monotherapy.

Conclusion

Our findings highlight for the first time that the percentage of circulating TF^pos platelets may serve as an independent predictor of AC and CV disease mortality in CAD patients on antiplatelet therapy. Confirmation in larger, well-characterized external cohorts is necessary to establish its validity and clinical utility.

Contributors' Statement

M.Ca., E.T., and N.C. conceived the study and wrote the manuscript; M.B. designed the flow cytometry experiments and analyzed data; A.B., M.Co., and K.N. performed flow cytometry experiments; P.D.V. performed the ELISA experiments; A.G., A.B., and F.V. performed statistical analysis; D.T., F.F., F.B., and V.M. enrolled the patients; P.P., M.G., and G.M. revised the manuscript. All authors have read, commented, and approved the final version of the manuscript.

Conflict of Interest

The authors declare that they have no conflict of Interest.

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Publication History

Received: 25 August 2025

Accepted after revision: 11 December 2025

Accepted Manuscript online:
15 December 2025

Article published online:
26 December 2025