The Caprini Risk Score for Early Prediction of Mortality in Patients With Acute Coronary Syndrome : Journal of Cardiovascular Nursing

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The Caprini Risk Score for Early Prediction of Mortality in Patients With Acute Coronary Syndrome

Li, Wentao MBBS; Wang, Yujia MEd; Li, Dongze PhD, MBBS; Jia, Yu PhD, MBBS; Li, Fanghui PhD, MBBS; Chen, Tengda MBBS; Liu, Yi BS, RN; Zeng, Zhi MD; Wan, Zhi MD; Zeng, Rui MD; Wu, Hongying BS, RN

Author Information
The Journal of Cardiovascular Nursing: October 28, 2022 - Volume - Issue - 10.1097/JCN.0000000000000949
doi: 10.1097/JCN.0000000000000949
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Acute coronary syndrome (ACS) is a globally prevalent expression of cardiovascular disease associated with high complication and mortality rates.1,2 Early prognostic evaluation and management strategies for ACS have led to a 30% reduction in patient mortality3; however, the dramatic regional variations seen in mortality during hospitalization suggest the need for improved assessment instruments for patients with ACS.4,5 Previous research indicates that a higher thrombotic burden increases ACS incidence and infarct size.6 Furthermore, hypercoagulability is a strong risk factor for arterial and venous thrombosis, which also affects prognosis in patients with ACS.7 Consequently, there is an increased demand for prognostic assessment instruments that include thrombotic burden or coagulation problems to identify patients with ACS and poor outcomes.8

Traditionally, arterial and venous thromboses are associated with platelet activation and clotting system activation, respectively.9,10 However, an increasing number of researchers have noted the link between arterial and venous thromboses; basic and pathomorphological studies suggest that coagulation and platelet activation participate in both arterial and venous thrombogenesis.11 Thrombi are common in patients with coronary artery disease, and antithrombotic drugs are effective for the prevention and treatment of both venous thrombosis and arterial thrombosis of coronary artery disease.12,13 Furthermore, decreased blood flow rate, hypercoagulability, and destruction of the blood vessel wall as elements of venous thrombosis were found in patients with ACS.14 Therefore, the link between arterial and venous thrombosis may suggest that ACS patients with a higher risk of venous thrombosis were more likely to be hypercoagulable and have higher thrombotic burden.

The Caprini Risk Score (CRS) is widely used in routine nursing practice as a guideline-recommended instrument to predict the risk of venous thrombosis in surgical patients.15 Most components of the CRS are considered risk factors for both venous thrombosis and adverse cardiovascular events, such as stroke, obesity, older adults, chronic obstructive pulmonary disease, pneumonia, sepsis, congestive heart failure, malignancy.16–18 Previous investigators have highlighted that CRS predicts the risk of venous thrombosis and guides anticoagulation therapy in surgical patients by reflecting hypercoagulability status.19,20 D-dimer and fibrinogen are markers of hypercoagulability or a systemic prothrombotic state; therefore, reports of a correlation between these markers and CRS have validated the association of the CRS with hypercoagulability.21–23 In addition, previous investigators have found that a higher CRS was related to higher mortality in patients with pulmonary venous embolism.24 However, no investigators have reported whether CRS reflects hypercoagulability and mortality risk in patients with ACS. Therefore, we conducted this study to investigate the association between the CRS and patients with ACS.


Study Design

We conducted a secondary analysis of data from a clinical database (the Retrospective Acute Chest Pain study) to estimate the prognostic value of CRS in patients with ACS. The Retrospective Acute Chest Pain study was conducted at 7 chest pain centers of a class A tertiary hospital. This study was registered at This study was conducted in accordance with the principles of the Declaration of Helsinki and approved by the Institutional Ethics Committee of the Human Ethical Committee.

Study Sample

Our study included all patients 18 years or older, who received a primary diagnosis of ACS in the emergency department between January 2017 and December 2020, based on the sudden onset of severe chest discomfort, prompt electrocardiography, and high-sensitivity troponin measurements.25 In total, 2039 ACS patients with CRS assessed by an emergency nurse were enrolled in this study. Of these, 14 patients (0.7%) with thrombotic diseases and 38 patients (1.9%) who failed to follow-up were excluded. Finally, 1987 patients (97.4% retention rate) were included in this study.

Data Collection and Measures

Trained physicians collected clinical data through a standardized regulatory process at each medical center. Recorded baseline data included demographic data, vital signs, laboratory results, imaging examinations, electrocardiography, echocardiography, and coronary angiography. Blood samples were also collected when patients were admitted to the emergency department. The case report forms included admission diagnosis, readmission status, inpatient complications, in-hospital treatment, various adverse events during hospitalization, and discharge medications.

Clinical Risk Scores

The Global Registry of Acute Coronary Events and Gensini scores can predict mortality among patients with ACS and have been widely validated by earlier studies.26 These scores were used in this validation study. The Global Registry of Acute Coronary Events score is obtained based on the cardiovascular risk factors. The Gensini score was adopted to evaluate complex coronary lesions using quantitative coronary angiography; the lesion severity is calculated by assigning different weights for different degrees of lesions. The process of calculating the Global Registry of Acute Coronary Events and Gensini scores has been previously described.27,28

The Caprini Risk Score

The most widely used CRS in clinical practice is the 2010 version.29 According to the 2010 version, approximately 40 risk factors were assessed and weighted. The patients were categorized into 4 groups according to the cumulative score: low risk (CRS = 0 or 1), medium risk (CRS = 2), high risk (CRS = 3 or 4), and extremely high risk (CRS ≥ 5). No deaths occurred in the low-risk group. Therefore, the patients were categorized according to CRS as low (CRS ≤ 2), moderate (CRS = 3–4), and high (CRS ≥ 5) scores. The CRS were calculated by nurses trained in standardized administration of the CRS.

Follow-up and End Point

The primary outcome was mortality, and we used the hospital cause-of-death register to determine this outcome. The cause-of-death register is based on diagnosis from death certificates issued by the hospital during follow-up. The causes of cardiac death included coronary artery disease, cardiac arrest, heart failure death, cardiomyopathies, valve abnormalities, and major vessel ruptures.30 The follow-up duration was 12.7 ± 7.3 months.

Statistical Analysis

For a statistical power of 90% and a probability of a type I error of 0.05, taking into account a loss of 10%, the final projected sample size was 135. Continuous variables are presented as mean ± SD, if they follow a normal distribution; otherwise, they are presented as medians (25th and 75th percentiles). Normally distributed continuous variables were compared between groups using an independent sample t test; and nonnormally distributed samples, using the Mann-Whitney U test. Categorical variables were presented as percentages and compared using χ2 or exact P values. Kaplan-Meier curves were used to compare the cumulative survival rates of the 3 different groups of patients with ACS. Cox proportional hazards models were used to evaluate variables associated with time to mortality. The receiver operating characteristic curves for CRS, D-dimer, fibrinogen, Global Registry of Acute Coronary Events scores, and Gensini scores were used to compare differences in mortality with area under the curve (AUC) values. The Pearson correlation test was used to verify the relationships between the CRS and markers of activation of the coagulation system, including fibrinogen and D-dimer levels. Subgroup analysis was performed and used to evaluate the robustness of the relationship between the CRS and mortality. All statistical analyses were conducted using SPSS Statistics (version 21.0; IBM Corp, Armonk, New York), with a 2-tailed P value less than .05 being considered significant.


Baseline Patient Characteristics

A total of 2039 patients were enrolled for ACS, with complete data available for 1987 patients (97.4%) after excluding 52 patients who had thrombotic diseases or who failed to follow-up. Patients were divided into 3 groups: low-CRS (n = 392), moderate-CRS (n = 1027), and high-CRS (n = 568) groups. There were 254 patient (12.7%) deaths over the 12.7 ± 7.3 months of follow-up; 181 patients died (71.2% of all deaths) because of cardiovascular disease. Higher CRS was associated with older age, history of smoking or drinking, more comorbidities, lower hemoglobin levels, lower platelet levels, and higher creatinine, D-dimers, fibrinogen, Gensini scores, and Global Registry of Acute Coronary Events scores. Patient characteristics are presented in Table 1.

TABLE 1 - Baseline Clinical Characteristics in Patients With Acute Coronary Syndrome Based on Caprini Risk Score Groups
Characteristic CRS (≤2)
N = 392 (19.7%)
CRS (3–4)
N = 1027 (51.7%)
CRS (≥5)
N = 568 (28.6%)
Age, y 55.93 ± 11.14 65.65 ± 11.77 74.17 ± 11.40 <.001
Male, n (%) 332 (84.7) 764 (74.4) 382 (67.3) <.001
BMI, kg/cm2 24.80 ± 3.32 24.19 ± 3.32 23.54 ± 3.40 <.001
Han, n (%) 368 (93.8) 970 (94.4) 535 (94.1) .161
SBP, mm Hg 131.65 ± 22.85 128.35 ± 24.13 126.25 ± 26.36 .004
DBP, mm Hg 83.61 ± 15.20 78.63 ± 15.95 74.63 ± 16.59 <.001
Heart rate, per min 80.70 ± 16.41 81.41 ± 17.99 83.19 ± 21.66 .088
Killip class ≥ 2, % 141 (36.0) 436 (42.5) 342 (60.2) <.001
History, n (%)
 Smoking 144 (36.7) 456 (44.4) 295 (51.9) <.001
 Drinking 257 (65.6) 719 (70.0) 440 (77.5) <.001
 Hypertension 197 (50.3) 574 (55.9) 330 (58.1) .051
 Diabetes 100 (25.5) 307 (29.9) 171 (30.1) .219
 Hyperlipidemia 52 (13.3) 114 (11.1) 51 (9.0) .018
 COPD 7 (1.7) 31 (3.0) 33 (5.8) .002
Laboratory findings
 Hemoglobin, g/L 141.54 ± 19.54 133.62 ± 22.40 126.80 ± 22.54 <.001
 WBC, 109/L 9.48 ± 3.60 9.96 ± 5.23 9.94 ± 3.95 .194
 Platelet count, 109/L 202.34 ± 72.47 185.47 ± 70.26 182.41 ± 73.27 <.001
 Blood glucose, mmol/L 7.19 (5.78–9.61) 7.57 (6.17–10.00) 7.79 (6.40–10.44) .063
 Albumin, g/L 41.88 ± 4.28 40.53 ± 4.39 38.66 ± 4.82 .001
 Creatinine, mmol/L 76.3 (63.8–88.9) 95.2 (90.8–99.6) 113.9 (105.6–122.2) <.001
 Triglycerides, mmol/L 1.64 (1.05–2.44) 1.31 (0.91–1.98) 1.19 (0.84–1.65) <.001
 Total cholesterol, mmol/L 4.62 ± 1.45 4.43 ± 1.22 4.26 ± 1.15 .011
 LDL, mmol/L 2.93 ± 1.32 2.78 ± 1.07 2.63 ± 1.01 <.001
 HDL, mmol/L 1.09 ± 0.31 1.15 ± 0.35 1.16 ± 0.36 .003
 Fibrinogen, g/L 3.27 ± 1.29 3.49 ± 1.33 3.68 ± 1.40 <.001
 D-dimer, mg/L 0.27 (0.16–0.51) 0.41 (0.23–0.87) 0.75 (0.37–1.48) <.001
 Hs-cTnT, ng/L 486 (54–2467) 686 (105–2890) 1068 (156–4062) <.001
 NT-proBNP, pg/mL 436 (135–1272) 795 (210–2520) 1834 (397–5888) .001
Risk score
 Gensini score 35.0 (14.1–63.7) 45.8 (22.5–81.0) 107.8 (43.8–189.6) <.001
 GRACE score 123.64 ± 33.46 141.08 ± 35.96 165.71 ± 41.34 <.001
Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; CRS, Caprini Risk Score; DBP, diastolic blood pressure; GRACE score, Global Registry of Acute Coronary Events score; HDL, high-density lipoprotein; Hs-CTnT, high-sensitive cardiac troponin T; LDL, low-density lipoprotein; NT-proBNP, N-terminal pro-b-type natriuretic peptide; SBP, systolic blood pressure; WBC, white blood cell count.

Association Between the Caprini Risk Score and Mortality

As shown in Figure 1, patients with higher CRS had worse clinical outcomes. Kaplan-Meier analysis revealed that the cumulative survival rate was significantly higher in patients with higher CRS for all-cause and cardiac mortality (P < .001 for all; Figure 2). In multivariable analysis models for predicting the end point, model 1 included patient characteristics, vital signs, Killip class, smoking history, and drinking history. Model 2 retained the variables in model 1 and added the following laboratory predictors: platelet count, creatinine, triglycerides, total cholesterol, low-density lipoprotein, high-density lipoprotein, high-sensitivity cardiac troponin T, and N-terminal pro-b-type natriuretic peptide. After adjusting for potential confounders in multivariate Cox regression analysis, CRS was found to be an independent predictor of all-cause and cardiac mortality (Table 2).

Bar chart of all-cause mortality (A) and cardiac mortality (B) of patients with acute coronary syndrome among different CRS groups. CRS, Caprini Risk Score.
Kaplan-Meier survival curve of all-cause mortality (A) and cardiac mortality for acute coronary syndrome patients (B) by Caprini Risk Score (CRS).
TABLE 2 - Hazard Ratio by the Caprini Risk Score for All-Cause Mortality and Cardiac Mortality
Variables n/N Unadjusted Model 1 Model 2
HR (95% CI) P HR (95% CI) P HR (95% CI) P
All-cause mortality <.001 .001 .005
 CRS ≤ 2 12/392 1 (Ref.) 1 (Ref.) 1 (Ref.)
 CRS 3–4 106/1027 5.409 (2.637–11.096) <.001 3.134 (1.434–6.847) .004 3.268 (1.396–7.647) .006
 CRS ≥ 5 136/568 13.996 (6.862–28.546) <.001 4. 408 (1.975–9.839) <.001 4.099 (1.708–9.841) .002
 CRS (per 1 point) 1.476 (1.383–1.547) <.001 1.194 (1.089–1.308) .001 1.157 (1.045–1.281) .005
Cardiac mortality <.001 .002 .013
 CRS ≤ 2 5/392 1 (Ref.) 1 (Ref.) 1 (Ref.)
 CRS 3–4 77/1027 5.825 (2.357–14.393) <.001 3.536 (1.409–8.875) .007 3.327 (1.304–8.487) .012
 CRS ≥ 5 99/568 14.282 (5.815–35.078) <.001 4.881 (1.899–12.544) .001 4.169 (1.585–10.968) .004
 CRS (per 1 point) 1.452 (1.344–1.568) <.001 1.185 (1.067–1.308) <.001 1.169 (1.042–1.311) .008
Model 1 was adjusted by age, sex, body mass index, systolic blood pressure, diastolic blood pressure, Killip class, smoking history, and drinking history. Model 2 was adjusted by model 1 plus platelet count, creatinine, triglycerides, total cholesterol, low-density lipoprotein, high-density lipoprotein, high-sensitive cardiac troponin T, and N-terminal pro-b-type natriuretic peptide.
Abbreviations: CI, confidence interval; CRS, Caprini Risk Score; HR, hazard ratios; Ref., reference.

Subgroup Analysis

The patients were subgrouped according to age, sex, history of hypertension and hyperlipidemia, fibrinogen, D-dimer, Gensini scores, and Global Registry of Acute Coronary Events scores. Tables 3 and 4 show the all-cause and cardiac mortality for CRS = 3–4 versus CRS ≤ 2 and CRS ≥ 5 versus CRS ≤ 2, respectively. Cox regression analysis revealed that increased CRS was independently associated with mortality in patients with ACS.

TABLE 3 - Cox Regression Analysis of All-Cause Mortality in Different Subgroups According to Caprini Risk Score
Variables CRS 3–4 vs CRS ≤ 2 CRS ≥ 5 vs CRS ≤ 2
HR (95% CI) P HR (95% CI) P
Age, y
 ≤67 6.234 (2.184–17.793) .001 12.418 (4.130–37.341) <.001
 >67 1.591 (0.690–3.668) .276 2.550 (1.112–5.850) .027
 Male 2.297 (0.704–7.493) .168 3.832 (1.184–12.406) .025
 Female 4.385 (2.017–9.529) <.001 8.937 (4.117–19.399) <.001
 Yes 10.449 (2.556–42.718) .001 20.808 (5.108–84.761) <.001
 No 2.973 (1.266–6.978) .012 4.347 (1.813–10.423) .001
 Yes 3.897 (0.874–17.369) .074 7.352 (1.572–34.383) .011
 No 5.416 (2.371–12.371) <.001 10.392 (4.556–23.701) <.001
Fibrinogen, g/L
 ≤3.37 2.365 (1.087–5.149) .030 7.291 (3.117–17.051) <.001
 >3.37 5.479 (1.343–22.353) .018 7.916 (1.955–32.048) .004
D-dimer, mg/L
 ≤0.68 2.144 (1.023–4.494) .004 5.628 (2.544–12.453) <.001
 >0.68 13.674 (1.896–98.615) .009 20.958 (2.925–150.169) .002
Gensini score
 ≤51 4.891 (1.966–12.168) .001 10.554 (4.194–26.556) <.001
 >51 2.963 (1.175–7.469) .021 5.400 (2.174–13.413) <.001
GRACE score
 ≤142 4.996 (1.473–16.949) .010 9.370 (2.66–33.008) <.001
 >142 2.606 (1.204–5.640) .015 4.075 (1.897–8.756) <.001
 Prepandemic 8.037 (2.951–21.884) <.001 14.571 (5.359–39.617) .035
 Postpandemic 1.691 (1.068–5.031) .035 3.606 (1.216–10.693) .021
Model was adjusted for high-sensitive cardiac troponin T and N-terminal pro-b-type natriuretic peptide.
Abbreviations: CI, confidence interval; COVID-19, coronavirus disease 2019; CRS, Caprini Risk Score; HR, hazard ratio; GRACE score, Global Registry of Acute Coronary Events score.

TABLE 4 - Cox Regression Analysis of Cardiac Mortality in Different Subgroups According to Caprini Risk Score
Variables CRS 3–4 vs CRS ≤ 2 CRS ≥ 5 vs CRS ≤ 2
HR (95% CI) P HR (95% CI) P
Age, y
 ≤67 8.185 (1.953–34.305) .004 13.856 (3.100–61.933) .001
 >67 1.851 (0.667–5.133) .237 3.928 (1.441–10.706) .007
 Male 2.756 (0.651–11.662) .168 6.710 (1.618–27.833) .009
 Female 5.690 (2.059–15.721) .001 13.569 (4.933–37.326) <.001
 Yes 13.370 (1.842–97.07) .010 24.164 (3.334–175.115) .002
 No 2.905 (1.027–8.218) .044 4.152 (1.435–12.019) .009
 Yes 2.400 (0.507–11.347) .270 5.508 (1.116–27.198) .036
 No 7.007 (2.203–22.290) .001 12.332 (3.873–39.261) <.001
Fibrinogen, g/L
 ≤3.37 2.746 (1.044–7.225) .041 12.337 (4.562–33.359) <.001
 >3.37 6.221 (1.861–44.972) .030 9.912 (1.380–71.226) .023
D-dimer, mg/L
 ≤0.68 3.224 (1.236–8.414) .017 9.660 (3.610–25.847) <.001
 >0.68 7.333 (1.013–53.070) .048 14.358 (1.997–103.220) .008
Gensini score
 ≤51 8.938 (2.167–36.872) .002 23.791 (5.718–98.991) <.001
 >51 2.873 (1.018–8.111) .046 7.046 (2.565–19.354) <.001
GRACE score
 ≤142 10.543 (1.418–78.389) .021 15.020 (1.902–118.595) .010
 >142 2.432 (1.074–6.072) .037 4.732 (1.921–11.654) .001
 Prepandemic 8.699 (2.739–27.634) <.001 14.512 (4.574–46.038) <.001
 Postpandemic 2.053 (1.425–9.918) .031 6.014 (1.306–27.699) .002
Model was adjusted for high-sensitive cardiac troponin T and N-terminal pro-b-type natriuretic peptide.
Abbreviations: CI, confidence interval; COVID-19, coronavirus disease 2019; CRS, Caprini Risk Score; GRACE score, Global Registry of Acute Coronary Events Score; HR, hazard ratio.

Predictive Value of the Caprini Risk Score

According to receiver operating characteristic curve analysis, the AUCs of fibrinogen, D-dimer, and Gensini score were 0.641 (95% confidence interval, 0.594–0.686; P < .001), 0.703 (95% confidence interval, 0.661–0.746; P < .001), and 0.583 (95% confidence interval, 0.531–0.634; P < .001), respectively. These were inferior to those of CRS (AUC, 0.719; 95% confidence interval, 0.677–0.761; P < .001). The Global Registry of Acute Coronary Events score (AUC, 0.785; 95% confidence interval, 0.746–0.824; P < .001) was superior to that of CRS. The CRS combined with the Global Registry of Acute Coronary Events score achieved a significantly higher AUC than the Global Registry of Acute Coronary Events score alone (Figure 3).

Area under the receiver operating characteristic curve of the CRS and other risk factors and scores for all-cause mortality (A) and cardiac mortality (B) of acute coronary syndrome patients. *Compared GRACE score plus CRS with GRACE score of AUC is significant (P < .001). AUC, area under the curve; CI, confidence interval; CRS, Caprini Risk Score; GRACE score, Global Registry of Acute Coronary Events score.

Correlation Analysis of the Caprini Risk Score With Other Risk Factors

Pearson correlation analysis showed a moderate positive correlation between CRS and fibrinogen level (r = 0.486, R2 = 0.765, P < .001), D-dimer level (r = 0.480, R2 = 0.465, P < .001), Gensini score (r = 0.308, R2 = 0.390, P < .001), and Global Registry of Acute Coronary Events score (r = 0.398, R2 = 0.570, P < .001) (Figure 4).

Correlation analysis of CRS with fibrinogen (A), D-dimer (B), Gensini score (C), and GRACE score (D) in patients with acute coronary syndrome. CRS, Caprini Risk Score; GRACE score, the Global Registry of Acute Coronary Events score.


In this study, we demonstrated the usefulness of the CRS for predicting clinical outcomes in patients with ACS. A higher CRS was correlated with an increased risk of all-cause mortality. Subgroup analysis confirmed the stability of CRS as an outcome indicator in patients with ACS. The Global Registry of Acute Coronary Events scores supplemented the CRS for greater prognostic value. Thus, the CRS can predict venous thrombosis and may serve as a useful assessment instrument for patients with ACS.

In contrast to other venous thrombosis risk assessment models, the CRS is the most extensively validated and effective risk assessment instrument for venous thrombosis.31 The CRS consists of approximately 40 individual risk factors such as year; body mass index; history of myocardial infarction, congestive heart failure, or cancer; or recent previous surgery. Previous investigators have reported an association between CRS and patient outcomes. The CRS has been considered a potential predictor of all-cause mortality, with a reported statistically significant difference in hospital and 6-month mortality among patients at different levels of the CRS.24 This report was corroborated in another prospective observational study in which investigators reached the same conclusion.32 Patients with ACS in our study were followed for an average of 1 year, and mortality outcomes were recorded. To our knowledge, this is the first report to establish an association between the CRS and mortality risk in patients with ACS.

It is well established that microcirculatory obstruction aggravated by a high thrombotic burden leads to poor myocardial perfusion, poor cardiac function, and poor outcomes for patients with ACS.33 Hypercoagulability in patients with ACS is related to high rates of thromboembolic events, severity of coronary lesions, and restenosis of the coronary arteries, causing significant disability and mortality.34 Grant et al35 suggest pharmacologic prophylaxis for nonsurgical, medical patients with CRS ≥ 5 to prevent a hypercoagulative state. We identified potential associations of CRS with hypercoagulative state, as demonstrated by the association between patient CRS and D-dimer and fibrinogen levels. D-dimer is produced after the activation of coagulation and fibrinolysis, and fibrin formation is the last step of triggered coagulation activity,36,37 which is the underlying prothrombotic state that could link CRS and outcomes in patients with ACS. Moreover, the overlap of risk factors between venous thrombosis and coronary artery disease may also explain why CRS may predict the risk of mortality in patients with ACS.38

The CRS provides multidimensional indicators and highlights new approaches for clinical nursing on relevant information when making clinical decisions. Using the CRS for prognostic evaluation of patients with ACS can supplement traditional ACS prognostic scores such as the Global Registry of Acute Coronary Events score; this is a significant benefit of CRS, because it describes the hypercoagulability and thrombotic burden in patients with ACS. What is more, the CRS relies primarily on history for assessment rather than laboratory tests, which makes CRS more easily available than other commonly used risk assessment instruments for patients with ACS. Accordingly, early assessment of the risk level can be performed for patients with ACS based on the CRS, with a higher CRS indicating worse prognosis, providing guidance for appropriate interventions.


Our study has several limitations. First, this was a retrospective study, and a prospective study is necessary to verify the usefulness of CRS as a prognostic instrument. Second, there was no individual analysis of the variables in the CRS; further detailed assessments of individual variables may provide potential targets for interventional analysis. Third, we only collected CRS at admission instead of dynamic monitoring.


This multicenter retrospective study confirmed that the CRS can independently predict all-cause and cardiovascular mortality in patients with ACS. Risk stratification can be achieved using the CRS at admission, and routine recording of CRS in the emergency department may provide valuable information for clinical decision making in patients with ACS. Future research should focus on the development of interventions to improve clinical outcomes in CRS.

What’s New and Important

  • The study findings exemplify the predictive efficacy of the CRS for the prognosis of patients with ACS.
  • The CRS can be used to assess mortality among patients with ACS, partly because of its association with a high coagulation risk, and the overlap of risk factors between arterial and venous thrombosis.


The authors thank all the participants of this project and investigators for collecting the data. They are grateful for the support of the cooperating institutions participating in this research. They also sincerely thank the researchers for their contributions to this research.


1. Liu S, Li Y, Zeng X, et al. Burden of cardiovascular diseases in China, 1990–2016: findings from the 2016 Global Burden of Disease Study. JAMA Cardiol. 2019;4:342–352.
2. GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1736–1788.
3. Rogers WJ, Frederick PD, Stoehr E, et al. Trends in presenting characteristics and hospital mortality among patients with ST elevation and non-ST elevation myocardial infarction in the National Registry of Myocardial Infarction from 1990 to 2006. Am Heart J. 2008;156:1026–1034.
4. Feng L, Wu YF, Li M, et al. Status of the clopidogrel use in ACS patients and related factors among county hospitals in China. Zhonghua Xin Xue Guan Bing Za Zhi. 2019;47:985–992.
5. Ralapanawa U, Sivakanesan R. Epidemiology and the magnitude of coronary artery disease and acute coronary syndrome: a narrative review. J Epidemiol Glob Health. 2021;11:169–177.
6. Marti D, Salido L, Mestre JL, et al. Impact of thrombus burden on procedural and mid-term outcomes after primary percutaneous coronary intervention. Coron Artery Dis. 2016;27:169–175.
7. Siegerink B, Maino A, Algra A, et al. Hypercoagulability and the risk of myocardial infarction and ischemic stroke in young women. J Thromb Haemost. 2015;13:1568–1575.
8. Aradi D, Storey RF, Komocsi A, et al. Expert position paper on the role of platelet function testing in patients undergoing percutaneous coronary intervention. Eur Heart J. 2014;35:209–215.
9. Agnelli G, Becattini C. Venous thromboembolism and atherosclerosis: common denominators or different diseases?J Thromb Haemost. 2006;4:1886–1890.
10. Levi M, van der Poll T, Buller HR. Bidirectional relation between inflammation and coagulation. Circulation. 2004;109:2698–2704.
11. Poredos P. Interrelationship between venous and arterial thrombosis. Int Angiol. 2017;36:295–298.
12. You JJ, Singer DE, Howard PA, et al. Antithrombotic therapy for atrial fibrillation: antithrombotic therapy and prevention of thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines. Chest. 2012;141:e531S–e575S.
13. Vandvik PO, Lincoff AM, Gore JM, et al. Primary and secondary prevention of cardiovascular disease: antithrombotic therapy and prevention of thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines. Chest. 2012;141:e637S–e668S.
14. Gregson J, Kaptoge S, Bolton T, et al. Cardiovascular risk factors associated with venous thromboembolism. JAMA Cardiol. 2019;4:163–173.
15. Gould MK, Garcia DA, Wren SM, et al. Prevention of VTE in nonorthopedic surgical patients: antithrombotic therapy and prevention of thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines. Chest. 2012;141:e227S–e277S.
16. Jorgensen ME, Torp-Pedersen C, Gislason GH, et al. Time elapsed after ischemic stroke and risk of adverse cardiovascular events and mortality following elective noncardiac surgery. JAMA. 2014;312:269–277.
17. Aminian A, Zajichek A, Arterburn DE, et al. Association of metabolic surgery with major adverse cardiovascular outcomes in patients with type 2 diabetes and obesity. JAMA. 2019;322:1271–1282.
18. Milicic D, Samardzic J, Petricevic M. Antithrombotics in heart failure. Croat Med J. 2014;55:621–627.
19. Golemi I, Salazar Adum JP, Tafur A, et al. Venous thromboembolism prophylaxis using the Caprini score. Dis Mon. 2019;65:249–298.
20. Pannucci CJ, Swistun L, MacDonald JK, et al. Individualized venous thromboembolism risk stratification using the 2005 Caprini score to identify the benefits and harms of chemoprophylaxis in surgical patients: a meta-analysis. Ann Surg. 2017;265:1094–1103.
21. Shi J, Ye J, Zhuang X, et al. Application value of Caprini risk assessment model and elevated tumor-specific D-dimer level in predicting postoperative venous thromboembolism for patients undergoing surgery of gynecologic malignancies. J Obstet Gynaecol Res. 2019;45:657–664.
22. Chen R, Liu C, Zhou P, et al. Prognostic value of D-dimer in patients with acute coronary syndrome treated by percutaneous coronary intervention: a retrospective cohort study. Thromb J. 2021;19:30.
23. Fu Y, Liu Y, Chen S, et al. The combination of Caprini risk assessment scale and thrombotic biomarkers to evaluate the risk of venous thromboembolism in critically ill patients. Medicine (Baltimore). 2018;97:e13232.
24. Zhou H, Hu Y, Li X, et al. Assessment of the risk of venous thromboembolism in medical inpatients using the Padua prediction score and Caprini risk assessment model. J Atheroscler Thromb. 2018;25:1091–1104.
25. Bhatt DL, Lopes RD, Harrington RA. Diagnosis and treatment of acute coronary syndromes: a review. JAMA. 2022;327:662–675.
26. Cakar MA, Sahinkus S, Aydin E, et al. Relation between the GRACE score and severity of atherosclerosis in acute coronary syndrome. J Cardiol. 2014;63:24–28.
27. Granger CB, Goldberg RJ, Dabbous O, et al. Predictors of hospital mortality in the Global Registry of Acute Coronary Events. Arch Intern Med. 2003;163:2345–2353.
28. Gensini GG. A more meaningful scoring system for determining the severity of coronary heart disease. Am J Cardiol. 1983;51:606.
29. Caprini JA. Risk assessment as a guide for the prevention of the many faces of venous thromboembolism. Am J Surg. 2010;199:S3–S10.
30. Kumar A, Avishay DM, Jones CR, et al. Sudden cardiac death: epidemiology, pathogenesis and management. Rev Cardiovasc Med. 2021;22:147–158.
31. Maynard GA, Morris TA, Jenkins IH, et al. Optimizing prevention of hospital-acquired venous thromboembolism (VTE): prospective validation of a VTE risk assessment model. J Hosp Med. 2010;5:10–18.
32. Chamoun N, Matta S, Aderian SS, et al. A prospective observational cohort of clinical outcomes in medical inpatients prescribed pharmacological thromboprophylaxis using different clinical risk assessment models (COMPT RAMs). Sci Rep. 2019;9:18366.
33. Wang K, Zhang J, Zhang N, et al. Combined primary PCI with multiple thrombus burden reduction therapy improved cardiac function in patients with acute anterior myocardial infarction. Int Heart J. 2019;60:27–36.
34. Amin MM, Ibrahim AM, Fahmy EM, et al. Prognostic value of serum antiphospholipid antibodies in patients with ST-segment elevation myocardial infarction. Egypt J Immunol. 2018;25:143–151.
35. Grant PJ, Greene MT, Chopra V, et al. Assessing the Caprini score for risk assessment of venous thromboembolism in hospitalized medical patients. Am J Med. 2016;129:528–535.
36. Levi M, Toh CH, Thachil J, et al. Guidelines for the diagnosis and management of disseminated intravascular coagulation. British Committee for Standards in Haematology. Br J Haematol. 2009;145:24–33.
37. Zakai NA, McClure LA, Judd SE, et al. D-dimer and the risk of stroke and coronary heart disease. The REasons for Geographic and Racial Differences in Stroke (REGARDS) study. Thromb Haemost. 2017;117:618–624.
38. Ageno W, Becattini C, Brighton T, et al. Cardiovascular risk factors and venous thromboembolism: a meta-analysis. Circulation. 2008;117:93–102.

acute coronary syndrome; Caprini Risk Score; mortality; prognosis; risk stratification tool

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