Abstract
Background
Dual antiplatelet therapy after percutaneous coronary intervention reduces myocardial infarctions but increases bleeding. The risk of bleeding may be higher among Black patients for unknown reasons. Bleeding risk scores have not been validated among Black patients. We assessed the difference in bleeding risk between Black and White patients along with the performance of the Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Anti Platelet Therapy, Patterns of Nonadherence to Antiplatelet Regimens in Stented Patients, and Academic Research Consortium for High Bleeding Risk scores among both groups.
Methods and Results
This was a single‐center prospective study of patients who underwent percutaneous coronary intervention (2014–2019) and were followed for 1 year. The outcome was postdischarge Bleeding Academic Research Consortium 2 to 5 bleeding. Incidence rates were reported. Cox proportional hazards models measured the effect of self‐reported Black race on bleeding and determined the predictors of bleeding among 19 a priori variables. The 3 risk scores were assessed among Black and White patients separately using the Harrell concordance index. Of 1529 included patients, 342 (22.4%) self‐reported as being Black race. Unadjusted bleeding rates were 22.7 per 100 person‐years among Black patients versus 16.3 among White patients (hazard ratio, 1.41 [95% CI, 1.00–2.00], P=0.052). Predictors of bleeding were age, glomerular filtration rate <30 mL/min per 1.73 m2, prior bleeding, ticagrelor or prasugrel use, and anticoagulant use. Among Black and White patients, respectively, the C‐indexes were the following: 0.644 versus 0.600 for Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Anti Platelet Therapy (P<0.001 for both), 0.620 versus 0.612 for Patterns of Nonadherence to Antiplatelet Regimens in Stented Patients (P=0.003 and P<0.001, respectively), and 0.600 versus 0.598 for Academic Research Consortium for High Bleeding Risk (P=0.006 and P<0.001, respectively).
Conclusions
The risk of dual antiplatelet therapy–associated postdischarge Bleeding Academic Research Consortium 2 to 5 bleeding was not significantly different between self‐reported Black and White patients. Bleeding risk scores performed similarly among both groups.
Keywords: incidence, percutaneous coronary intervention, platelet aggregation inhibitors, prasugrel hydrochloride, proportional hazards models, prospective studies, ticagrelor
Subject Categories: Complications, Disparities, Health Equity, Quality and Outcomes
Nonstandard Abbreviations and Acronyms
- ARC‐HBR
Academic Research Consortium for High Bleeding Risk
- BARC
Bleeding Academic Research Consortium
- DAPT
dual antiplatelet therapy
- PARIS
Patterns of Nonadherence to Antiplatelet Regimens in Stented Patients
- PRECISE‐DAPT
Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Anti Platelet Therapy
Clinical Perspective
What Is New?
The risk of dual antiplatelet therapy–associated postdischarge bleeding was not statistically higher for Black patients compared with White patients.
A nonsignificant numerical difference was present, and this difference was primarily explained by a higher proportion of Black patients having severe kidney disease, defined by a glomerular filtration rate <30 mL/kg per 1.73 m2 or end‐stage renal disease.
The Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Anti Platelet Therapy, Academic Research Consortium for High Bleeding Risk, and Patterns of Nonadherence to Antiplatelet Regimens in Stented Patients scores had moderate predictive abilities among both Black and White patients.
What Are the Clinical Implications?
Race should not be considered by clinicians when assessing bleeding risk while on dual antiplatelet therapy.
Clinicians should consider the following 5 predominant factors when assessing bleeding risk: severe kidney disease, age, prasugrel or ticagrelor use, anticoagulant use, and prior bleeding.
The Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Anti Platelet Therapy, Academic Research Consortium for High Bleeding Risk, and Patterns of Nonadherence to Antiplatelet Regimens in Stented Patients scores can be confidently applied to both Black and White patients in clinical practice, with the Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Anti Platelet Therapy score better measuring gradations in age and kidney disease.
Dual antiplatelet therapy (DAPT) is recommended after an acute myocardial infarction or percutaneous coronary intervention (PCI). 1 Although DAPT reduces the incidence of subsequent myocardial infarction, it causes increased bleeding and, through unclear mechanisms, is associated with excess noncardiac mortality 2 and a lower quality of life. 3 Patients at high bleeding risk experience worse outcomes from DAPT, 4 and US guidelines recommend shorter durations of DAPT for patients at high bleeding risk. 1
Risk factors associated with bleeding while on DAPT have been combined into risk scores to determine high bleeding risk. Three scores (Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Anti Platelet Therapy [PRECISE‐DAPT], 5 Patterns of Nonadherence to Antiplatelet Regimens in Stented Patients [PARIS], 6 and Academic Research Consortium for High Bleeding Risk [ARC‐HBR 7 ]) were designed to predict bleeding after hospital discharge and have been validated in external cohorts. They have been referenced in European antiplatelet guidelines, 8 but there is no consensus on which score should be used in clinical practice. Current US guidelines have not referenced these risk scores.
Prior studies have demonstrated increased bleeding from DAPT among Black adults compared with White adults. 9 , 10 Factors contributing to this difference are not known and may not be measured by the PRECISE‐DAPT, PARIS, or ARC‐HBR risk scores. In addition, the cohorts used to validate these 3 scores have been predominantly from European or Asian countries. 11 , 12 They have not been validated in self‐reported Black adults—a subgroup underrepresented in PCI trials. 13 In the present study, we aim to (1) compare postdischarge bleeding between self‐reported Black and White patients; (2) identify clinical factors that contribute to this difference; and (3) assess the ability of the PRECISE‐DAPT, PARIS, and ARC‐HBR risk scores to predict postdischarge bleeding among Black and White patients separately.
METHODS
The data that support the findings of this study are available from the authors on reasonable request. The PRiME‐GGAT (Pharmacogenomic Resource to Improve Medication Effectiveness‐Genotype‐Guided Antiplatelet Therapy) prospective cohort study enrolled patients aged ≥18 years who underwent PCI at the University of Alabama at Birmingham Hospital. The study was approved by the University of Alabama at Birmingham Hospital institutional review board. Consent was obtained at enrollment, which occurred during the index PCI hospitalization (June 2014–November 2019).
A structured form was used to record age, self‐reported race, sex, and smoking status. Height, weight, and laboratory values were recorded from the medical record, measured on the day of PCI. Laboratory values included serum creatinine, white cell count, platelet count, and hemoglobin. The Chronic Kidney Disease Epidemiology Collaboration equation was used to derive an estimated glomerular filtration rate (GFR; mL/min per 1.73 m2), which does not consider self‐reported race as a variable. 14 The following variables were obtained from the medical record: history of diabetes (or the use of glucose‐lowering medications), hypertension (or the use of antihypertensive medications), stroke (or transient ischemic attack), prior bleeding, and liver cirrhosis (with portal hypertension). Home and discharge medications (antiplatelets, anticoagulants, proton pump inhibitors, nonsteroidal anti‐inflammatory drugs, and corticosteroids) were recorded from the medical record.
Patients were followed for 1 year. All University of Alabama at Birmingham medical records were reviewed, and records from facilities outside of the University of Alabama at Birmingham system were requested and reviewed. Postdischarge bleeding events were documented by study personnel and adjudicated by 2 physicians. Patients were right‐censored from the analysis for the following 4 reasons: (1) they had any Bleeding Academic Research Consortium (BARC) 2 to 5 bleeding event, (2) they were no longer taking DAPT therapy, (3) they died, or (4) they were lost to follow‐up. For patients lost to follow‐up, the last known clinic visit or hospitalization was the point of censor.
Postdischarge bleeding events were categorized based on the BARC statement (Table S1). 15 The outcome for each of our analyses was postdischarge BARC 2 to 5 bleeding. The following summarizes the BARC schema: type 1 bleeding does not cause the patient to seek unscheduled care, type 2 bleeding prompts evaluation and care but does not meet types 3 to 5 criteria, type 3 bleeding is major (hemoglobin drop >3 g/dL, cardiac tamponade, intracranial, intraocular, required transfusion, surgical intervention, or vasoactive agents), type 4 bleeding is a coronary artery bypass graft related, and type 5 bleeding is fatal.
We compared baseline variables between Black and White patients using t tests for continuous variables and χ2 tests for categorical variables. We chose these 19 variables because they were either included in the PRECISE‐DAPT, PARIS, or ARC‐HBR risk scores or they were determined a priori to be associated with bleeding.
We reported the number of bleeding events, person‐years of follow‐up, and the anatomical location of each bleeding event among all patients and then Black and White patients separately. Incidence rates of bleeding were calculated and reported as per 100 person‐years.
We assessed the influence of each baseline variable on bleeding with a time‐to‐event analyses by using the fit proportional hazards function to develop Cox proportional hazards models. An unadjusted analysis was first performed and then a multivariable analysis using only those variables associated with bleeding with an unadjusted P<0.20. Hazard ratios with 95% CIs were reported. A sensitivity analysis was performed using the same methodology but accounting for the competing risk of death using the PHREG procedure (PROC PHREG, SAS software, version 9.4).
Cox proportional hazards models were then used to measure the mediating effect of each predictor variable (those with P<0.05 after multivariable adjustment) on the association between self‐reported Black race and bleeding. Each model was adjusted for age and sex and then for each predictor variable independently. A sensitivity analysis was performed to determine the mediating effect of 3 forms of the GFR variable (GFR <30 mL/min per 1.73 m2, GFR <45 mL/min per 1.73 m2, and continuous) on the association between self‐reported Black race and bleeding. The significance of each mediating effect was measured by the Sobel test.
We then assessed the performance of 3 commonly used risk scores among Black and White patients separately.
The PRECISE‐DAPT score 5 is composed of 5 variables: age, GFR, hemoglobin, white cell count, and previous clinically significant bleeding. Each component is assigned point values as per Table S2. A score ≥25 denotes high bleeding risk, 18 to 24 denotes moderate risk, 11 to 17 denotes low bleeding risk, and ≤10 denotes very low bleeding risk. For this study, PRECISE‐DAPT was condensed into high (≥25), moderate (18–24), and low (≤17) categories.
The PARIS score 6 is composed of 6 variables: age, current smoking, body mass index, GFR, hemoglobin, and oral anticoagulant use. Each is assigned a point value as per Table S3. A score ≥8 denotes high bleeding risk, 4 to 7 denotes moderate bleeding risk, and ≤3 denotes low bleeding risk.
The ARC‐HBR score 7 is composed of 15 variables, classified as either major or minor criteria. For the present study, 7 variables were modified or excluded to fit our data set (Table S4). Having either 1 major or 2 minor criteria denote high bleeding risk.
We distributed patients into categories of risk for each score. For the PRECISE‐DAPT and PARIS scores, we distributed patients into quartiles of risk because these scores were intended to be continuous. For the ARC‐HBR score, we distributed patients into 2 categories of risk (low or moderate versus high risk) as this score was intended to be binary. Incident rates of bleeding were reported for each risk category, stratified by race. Proportional hazard models were used to compare each category with the lowest category of risk, also stratified by race. Adjustment was made for sex and the predictor variables determined from the aforementioned analyses (those with P<0.05 after multivariable adjustment), unless the predictor variable was included in any 1 of the 3 risk scores.
We quantified the discriminative abilities of the 3 risk scores by measuring the Harrell concordance index (C‐index). 16 To calculate this, we used the PHREG procedure (PROC PHREG, SAS software, version 9.4) to produce Cox proportional hazards models and selected the option to compute a Harrell C‐index. The scores were included as single continuous variables for PRECISE‐DAPT and PARIS and as a nominal variable for ARC‐HBR. C‐indexes were calculated for all patients and then Black and White patients separately.
All statistical tests were 2‐sided, with main effects tested at an α level of 0.05 unless otherwise specified. Incidence rates were calculated by using the OpenEpi online software platform. 17 The other analyses were performed by using JMP software, version 16.2, and SAS software, version 9.4.
RESULTS
Of the 1558 patients enrolled in the study, 29 were excluded from the analysis because their self‐reported race or ethnicity was other than Black race or White race, leaving 1529 patients included in the final analysis, of which 22.4% were Black patients. The analysis included 1027.1 person‐years of follow‐up. The mean follow‐up was 0.62 years per person for Black patients and 0.69 years for White patients. Among all patients, 908 (59.3%) were censored for any reason (64.3% Black patients versus 57.9% White patients), and 39 patients (2.5%) were censored because of death (1.5% Black patients versus 2.8% White patients). Of the included patients, <1% had missing data elements.
Black patients were younger than White patients, and a smaller proportion of Black patients were aged ≥75 years (Table 1). A greater proportion of Black patients were women and were current smokers. body mass index was higher among Black patients, and a larger proportion of Black patients had a body mass index ≥35 kg/m2. Mean GFR was lower among Black patients, and a greater proportion of Black patients had a GFR <30 mL/min per 1.73 m2. Black patients had a lower mean white cell count and a higher mean platelet count. Black patients had a lower mean hemoglobin concentration, and a greater proportion of Black patients had a hemoglobin <12 g/dL. A greater proportion of Black patients had diabetes and hypertension. Other baseline variables, including antithrombotic medication use, were not different between groups.
Table 1.
Baseline Characteristics of the Study Population
| All patients, N=1529 | Black patients, n=342 | White patients, n=1187 | P value | |
|---|---|---|---|---|
| Age, y | 62.2±11.9 | 58.9±11.4 | 63.1±11.8 | <0.001 |
| Age ≥75 y | 224 (14.7) | 22 (6.4) | 202 (17.0) | <0.001 |
| Female sex | 463 (30.3) | 146 (42.7) | 317 (26.7) | <0.001 |
| Smoking status | ||||
| Current smoker | 384 (25.8) | 108 (32.6) | 276 (23.9) | 0.005 |
| Former smoker | 552 (37.1) | 107 (32.3) | 436 (37.7) | |
| Never smoker | 552 (37.1) | 116 (35.1) | 445 (38.5) | |
| Body mass index, kg/m2 | 30.3±6.1 | 31.1±7.1 | 30.0±5.8 | 0.010 |
| <25 kg/m2 | 282 (18.5) | 66 (19.4) | 216 (18.2) | 0.007 |
| 25–34.9 kg/m2 | 953 (62.5) | 191 (56.0) | 762 (64.4) | |
| ≥35 kg/m2 | 290 (19.0) | 84 (24.6) | 206 (17.4) | |
| GFR, mL/min per 1.73 m2 | 74.8±25.7 | 67.8±30.4 | 76.8±23.8 | <0.001 |
| GFR ≥60 mL/min per 1.73 m2 | 1130 (74.2) | 227 (66.8) | 903 (76.3) | <0.001 |
| GFR 45–59 mL/min per 1.73 m2 | 202 (13.2) | 38 (11.2) | 164 (13.9) | |
| GFR 30–44 mL/min per 1.73 m2 | 90 (5.9) | 23 (6.8) | 67 (5.7) | |
| GFR <30 mL/min per 1.73 m2 or requiring dialysis | 101 (6.6) | 52 (15.3) | 49 (4.1) | |
| White cell count, ×103/μL | 8.4 (6.5–10.8) | 7.6 (3.7–10.3) | 8.5 (6.7–10.8) | <0.001 |
| Platelet count, ×109 per L | 222.9±70.2 | 238.5±73.4 | 218.5±68.7 | <0.001 |
| Platelet count, <100×109 per L | 27 (1.8) | 3 (0.9) | 24 (2.0) | 0.240 |
| Hemoglobin, g/dL | 13.6±1.9 | 13.0±1.9 | 13.8±1.9 | <0.001 |
| Hemoglobin, <12 g/dL | 291 (19.1) | 98 (28.9) | 193 (16.3) | <0.001 |
| Diabetes | 653 (42.7) | 165 (48.3) | 488 (41.1) | 0.019 |
| Hypertension | 1308 (85.5) | 308 (90.1) | 1000 (84.2) | 0.007 |
| Prior ischemic stroke or TIA | 210 (13.7) | 54 (15.8) | 156 (13.1) | 0.217 |
| Prior hemorrhage | 21 (1.4) | 2 (0.6) | 19 (1.6) | 0.194 |
| Previous bleeding requiring medical attention* | 105 (6.9) | 27 (7.9) | 78 (6.6) | 0.397 |
| ARC‐HBR major bleeding history† | 27 (1.8) | 8 (2.3) | 19 (1.6) | 0.366 |
| ARC‐HBR minor bleeding history† | 8 (0.5) | 3 (0.9) | 5 (0.4) | 0.388 |
| Liver cirrhosis with portal hypertension | 24 (1.6) | 5 (1.5) | 19 (1.6) | 1.000 |
| P2Y12 inhibitor use (in combination with aspirin) | ||||
| Clopidogrel | 1001 (65.8) | 228 (66.9) | 773 (65.5) | 0.182 |
| Prasugrel | 36 (2.4) | 4 (1.2) | 32 (2.7) | |
| Ticagrelor | 475 (31.2) | 104 (30.5) | 371 (31.4) | |
| Anticoagulant use | 218 (14.3) | 44 (12.9) | 174 (14.8) | 0.430 |
| Proton pump inhibitor use | 538 (35.4) | 115 (33.7) | 423 (35.9) | 0.480 |
| Long‐term NSAID use‡ | 63 (4.1) | 14 (4.1) | 49 (4.1) | 1.000 |
| Long‐term corticosteroid use‡ | 68 (4.4) | 13 (3.8) | 55 (4.6) | 0.655 |
Continuous variables are displayed as mean±SD or median (interquartile range) if the distribution was skewed, whereas categorical variables are displayed as number (percentage). ARC‐HBR indicates Academic Research Consortium for High Bleeding Risk; GFR, glomerular filtration rate; PRECISE‐DAPT, Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Anti Platelet Therapy; and TIA, transient ischemic attack.
Definition used for the PRECISE‐DAPT score.
Defined in Table S4.
Both a home medication at the time of index percutaneous coronary intervention and continued at discharge.
Overall, 159 patients (10.2%) experienced a postdischarge BARC 2 to 5 bleeding event, of which 44 were Black patients (12.9%) and 115 were White patients (9.7%). The number of events per BARC category, and the anatomical location of each event, are reported in Table S5. The largest proportion of BARC 2 to 5 events were from gastrointestinal bleeding (37.1%), followed by nonprocedural hematomas (21.4%). The incidence of bleeding by each BARC category is presented in Table 2. Among all patients, the incidence of postdischarge BARC 2 to 5 bleeding was 15.5 per 100 person‐years (Table 2).
Table 2.
Incidence Rates for Postdischarge Bleeding Events by BARC Category
| All patients, N=1529 | Black patients, n=342 | White patients, n=1187 | |
|---|---|---|---|
| Person‐years of follow‐up | 1027.1 | 213.2 | 813.9 |
| Category of bleeding | Incidence rate per 100 person‐years (95% CIs) | ||
| BARC 2 | 11.1 (9.3–13.4) | 12.7 (8.5–18.8) | 10.8 (8.7–13.3) |
| BARC 3 | 4.0 (2.9–5.4) | 7.5 (4.4–11.9) | 3.1 (2.0–4.5) |
| BARC 4 | 0 | 0 | 0 |
| BARC 5 | 0.3 (0.1–0.8) | 0.5 (0.02–2.3) | 0.2 (0.04–0.8) |
| Combined BARC 2 to 5 | 15.5 (13.2–18.0) | 20.6 (15.2–27.5) | 14.1 (11.7–16.9) |
BARC indicates Bleeding Academic Research Consortium.
For the time‐to‐event analysis, the unadjusted predictors of postdischarge BARC 2 to 5 bleeding were age, GFR <30 mL/min per 1.73 m2, previous bleeding (requiring medical attention), ticagrelor or prasugrel use, anticoagulant use, hemoglobin, and prior ischemic stroke or transient ischemic attack (Table 3). Sex, liver cirrhosis, and proton pump inhibitor use each had a P value between 0.05 and 0.20 and were also included in the adjusted analyses. After adjustment for these 11 variables, only age, GFR <30 mL/min per 1.73 m2, previous bleeding, ticagrelor or prasugrel use, and anticoagulant use remained predictors.
Table 3.
Unadjusted and Adjusted Associations of Bleeding Risk Factors With Bleeding Academic Research Consortium 2 to 5 Bleeding
| Unadjusted HR (95% CI) | P value | Adjusted HR (95% CI) | P value | |
|---|---|---|---|---|
| Self‐reported Black race | 1.41 (1.00–2.00) | 0.052 | 1.37 (0.94–1.99) | 0.098 |
| Age, per y | 1.01 (1.00–1.03) | 0.039 | 1.01 (1.00–1.03) | 0.044 |
| GFR <30 mL/min per 1.73 m2 or end‐stage renal disease | 2.65 (1.72–4.09) | <0.001 | 1.90 (1.15–3.11) | 0.011 |
| Previous bleeding requiring medical attention | 2.45 (1.57–3.81) | <0.001 | 1.80 (1.13–2.89) | 0.014 |
| Ticagrelor or prasugrel use vs clopidogrel use | 1.80 (1.32–2.46) | <0.001 | 2.15 (1.56–2.97) | <0.001 |
| Anticoagulant use | 2.41 (1.67–3.46) | <0.001 | 2.38 (1.64–3.46) | <0.001 |
| Hemoglobin, g/dL | 0.86 (0.80–0.93) | <0.001 | 0.94 (0.86–1.03) | 0.189 |
| Female sex | 1.26 (0.91–1.75) | 0.170 | 1.07 (0.77–1.52) | 0.693 |
| Prior ischemic stroke or TIA | 1.53 (1.03–2.28) | 0.034 | 1.24 (0.81–1.88) | 0.319 |
| Liver cirrhosis with portal hypertension | 2.10 (0.86–5.11) | 0.144 | 1.70 (0.67–4.35) | 0.265 |
| Proton pump inhibitor use | 1.25 (0.91–1.71) | 0.168 | 1.02 (0.74–1.42) | 0.905 |
| Current smoking, yes | 0.81 (0.55–1.19) | 0.269 | … | … |
| Body mass index ≥35 kg/m2 | 1.05 (0.71–1.55) | 0.783 | … | … |
| White cell count per 103/μL | 1.02 (0.97–1.05) | 0.974 | … | … |
| Platelet count per 109/L | 1.39 (0.39–4.68) | 0.597 | … | … |
| Diabetes | 1.11 (0.81–1.51) | 0.525 | … | … |
| Hypertension | 1.03 (0.40–2.65) | 0.95 | … | … |
| Long‐term NSAID use | 0.54 (0.20–1.45) | 0.219 | … | … |
| Long‐term corticosteroid use | 1.52 (0.80–2.88) | 0.200 | … | … |
GFR indicates glomerular filtration rate; HR, hazard ratio; and TIA, transient ischemic attack.
The variables included in the adjusted model were those with an unadjusted P value <0.20.
Self‐reported Black race was not a significant predictor of bleeding (unadjusted hazard ratio, 1.41 [95% CI, 1.00–2.00]; P=0.052). Unadjusted and adjusted time‐to‐event curves for Black and White patients separately are displayed in Figures S1 and S2, respectively. The results of a sensitivity analysis incorporating the competing risk of death were similar and are presented in Table S6.
The mediating effects of each predictor variable individually on the relationship between self‐reported Black race and BARC 2 to 5 bleeding are presented in Table S7. Only GFR<30 mL/min per 1.73 m2 was a significant mediator (34.8% reduction in effect; P<0.001). Alternative forms of the GFR variable (<45 mL/min per 1.73 m2 and continuous) did not have a mediating effect on the association between Black race and bleeding (Table S8).
There were differences in the proportions of patients classified as high risk, compared with low–moderate risk, between Black and White patients, for the PRECISE‐DAPT and PARIS scores. The PRECISE‐DAPT score classified 30.4% of all patients as high bleeding risk (35.4% of Black patients compared with 29.9% of White patients; P=0.023), the PARIS score classified 12.3% of all patients as high risk (15.8% of Black patients compared with 11.3% of White patients; P=0.031). There were no differences in the proportions classified as high risk, compared with low–moderate risk, between Black and White patients, for the ARC‐HBR criteria: 46.4% overall, with 48.3% of Black patients classified as high risk compared with 45.8% of White patients (P=0.46). The number of patients, events, and person‐years of follow‐up in each category of risk are provided in Table S9, along with unadjusted hazard ratios comparing risk categories. The incidence rates of BARC 2 to 5 bleeding among risk categories and adjusted hazard ratios comparing categories are presented in Figure.
Figure 1. Incidence of BARC 2 to 5 bleeding among categories of risk, stratified by race.
Models were adjusted for sex, ticagrelor or prasugrel use, and proton pump inhibitor use. ARC‐HBR indicates Academic Research Consortium Criteria for High Bleeding Risk; BARC, Bleeding Academic Research Consortium; PARIS, Patterns of Nonadherence to Antiplatelet Regimens in Stented Patients; and PRECISE‐DAPT, Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Anti Platelet Therapy.
For the PRECISE‐DAPT score, the Harrell C‐index was 0.614 for all patients (P<0.001), 0.644 for Black patients (P<0.001), and 0.600 for White patients (P<0.001). For the PARIS score, the C‐index was 0.617 for all patients (P<0.001), 0.620 for Black patients (P=0.003), and 0.612 for White patients (P<0.001). For the ARC‐HBR score, the C‐index was 0.600 for all patients (P<0.001), 0.600 for Black patients (P=0.006), and 0.598 for White patients (P<0.001).
DISCUSSION
In the present study of 1529 patients who underwent PCI and were placed on DAPT, the risk of postdischarge bleeding was not significantly higher among Black patients compared with White patients. The only predictor of bleeding that contributed to a numerical difference was severe chronic kidney disease (CKD), defined as a GFR <30 mL/min per 1.73 m2 or end‐stage renal disease. The proportions deemed high bleeding risk by the PRECISE‐DAPT and PARIS scores were higher among Black patients, with the PRECISE‐DAPT score classifying more patients as high risk than the PARIS score (30.4% versus 12.3%, respectively). Each score had a moderate predictive ability among both groups. Overall, our study suggests that differences in severe renal failure are the primary contributor to any differences in bleeding risk among self‐reported Black individuals and race should not be considered when deciding DAPT duration. The PRECISE‐DAPT score categorized GFR with greater granularity and better stratified patients at higher risk.
The unadjusted risk of bleeding was 41% higher among Black patients, but this difference did not meet statistical significance (P=0.052). A larger proportion of Black patients were censored from the analysis, for reasons other than death, and some events that occurred may not have been observed. Other studies have reported a higher unadjusted risk of DAPT‐associated bleeding among self‐reported Black adults compared with other racial or ethnic groups. 9 , 10 , 18 , 19 , 20 However, these studies were different than ours in multiple ways. The most prominent difference was that prior studies used definitions of bleeding other than the BARC criteria, such as transfusion requirements, International Classification of Diseases (ICD) codes, and non‐BARC definitions of major bleeding. To our knowledge, our study is the only to apply the BARC criteria to examine bleeding among self‐reported Black patients.
The only variable that reduced the effect of self‐reported Black race on postdischarge bleeding was the presence of severe CKD (GFR <30 mL/min per 1.73 m2, including end‐stage renal disease). It has been well demonstrated that CKD increases the risk of bleeding while taking antiplatelet medications. 21 Multiple mechanisms have been reported by which uremic toxins and increased fibrinogen levels reduce platelet adhesion and aggregation. 22 Also well documented is that self‐reported US Black adults have a higher prevalence of severe CKD, 23 compared with White adults, partially because of a higher prevalence of diabetes, 24 lower blood pressure control, 25 and more frequent homozygosity for variants of the Apolipoprotein L1 gene. 26 We also observed a higher prevalence of diabetes and hypertension among Black patients in our cohort.
In the present study, severe CKD alone did not entirely explain the numerical difference in postdischarge bleeding between Black and White adults, and other unmeasured variables must have contributed. We only included clinical variables that have been consistently and repeatedly associated with an increased risk of bleeding. Socioeconomic and structural differences between these groups almost certainly contribute to higher rates of bleeding as well as the development of diabetes, hypertension, and subsequently CKD among Black patients. An analysis of the National Cardiovascular Data Acute Coronary Treatment and Intervention Outcomes Network Registry found zip code, as a surrogate for socioeconomic status, to be associated with major bleeding events after multivariable adjustment (odds ratio, 1.10 [95% CI, 1.05–1.16]). 27 We did not report differences in variables that demonstrate this structural bias because no single socioeconomic variable has been repeatedly associated with DAPT‐associated bleeding, and socioeconomic variables were not included in widely cited risk scores. Because we felt that we could not adequately measure such differences, we chose to focus on clinical variables.
Among all patients, our analysis demonstrated a C‐index of 0.614 for the PRECISE‐DAPT score, 0.617 for the PARIS score, and 0.600 for the ARC‐HBR score. C‐indexes were higher among Black patients for all 3 scores compared with White patients. However, the C‐index values we reported for these scores are lower than those described in other studies. For example, the PRECISE‐DAPT derivation study reported a C‐index in the derivation cohort of 0.73 and 0.70 and 0.66 in the 2 validation cohorts. 5 Studies of the PRECISE‐DAPT score by Choi et al reported C‐statistics between 0.75 and 0.81. 28 , 29 , 30 The reasons for the different statistics reported by these studies, compared with our own, are likely methodological. Costa et al 5 used the end point of TIMI (Thrombolysis in Myocardial Infarction) major and minor bleeding as the outcome rather than BARC 2 to 5 bleeding. Choi et al stratified the PRECISE‐DAPT score into 2 to 3 categories of risk rather than evaluating it as a continuous score. In each referenced study, patients requiring oral anticoagulation were excluded, whereas in our study we included these patients. Systematic reviews of commonly used risk scores have reported similar wide variation in risk score performances because of methodological and study population differences. 31
Although the C‐indexes were similar between the PRECISE‐DAPT, PARIS, and ARC‐HBR scores, there are differences between these scores that should be considered. Apart from ticagrelor, prasugrel, and anticoagulant use, age and GFR <30 mL/min per 1.73 m2 were the strongest contributors to bleeding risk, and the PRECISE‐DAPT score contains 25 categories for GFR and 19 for age, compared with between 2 and 5, respectively, for PARIS and 3 and 2, respectively, for ARC‐HBR. This contributed to large differences in the proportions of patients classified as high risk between scores: 35.4% for the PRECISE‐DAPT score, 15.8% for the PARIS score, and 46.7% for the ARC‐HBR score. Because so few patients were deemed high risk by the PARIS score, the incidence of bleeding among patients in the quartile below the high‐risk quartile was 20.6 per 100 person‐years compared with 10.4 per 100 person‐years for the PRECISE‐DAPT score. The ARC‐HBR score simply classified so many patients as being high risk that it is not clinically useful. Finally, the PARIS and ARC‐HBR scores both include oral anticoagulation, whereas the PRECISE‐DAPT score does not. Oral anticoagulation is already included in guideline‐recommended algorithms for choosing the duration of DAPT, and therefore its inclusion in a risk score is not beneficial. For these reasons, we believe that the PRECISE‐DAPT score should be used for risk‐stratifying patients taking DAPT.
Limitations were present in our study. First, self‐reported race is a social construct, and we did not report the social determinants of health that contribute to disparate outcomes. The definitions for socioeconomic variables remain heterogenous and without clear standards, and we felt that including these variables would not produce clinical value. Second, past medical history items were obtained by screening the patient's medical record, yet some data elements may have not been recorded in the medical record. Third, 4 of the ARC‐HBR criteria were excluded because the data required for these variables were incomplete. However, the prevalence of these variables in the general population is low, and their inclusion would likely not have affected the performance of the score. Finally, this was primarily a descriptive and exploratory analysis with substantial probability of type I error greater than nominal (α=0.05 for some of the hypothesis tests) because of multiple comparisons.
In conclusion, self‐reported Black patients did not have a statistically higher risk of postdischarge bleeding while on DAPT compared with White patients. The PRECISE‐DAPT, PARIS, and ARC‐HBR risk scores performed similarly among both Black and White patients, although more Black patients were classified as high risk by the PRECISE‐DAPT and PARIS scores. Clinicians should not consider self‐reported Black race in their assessment of bleeding risk, although they should consider age, GFR <30 mL/min per 1.73 m2, anticoagulant use, ticagrelor or prasugrel use, and prior bleeding, as these were the strongest predictors. The PRECISE‐DAPT score better characterized differences in GFR and thus may be the most appropriate score for this indication among the US population.
Sources of Funding
This work was supported by the National Institutes of Health (T32HL129948, RO1HL092173, and K24HL133373), Clinical and Translational Science Award UL1TR000165, University of Alabama Birmingham's Health Service Foundations' General Endowment Fund, and the Hugh Kaul Personalized Medicine Institute. Hillegass is supported by Institutional Development Award (IDeA) Networks for Clinical and Translational Research (IDeA‐CTR) (U54GM115428).
Disclosures
None.
Supporting information
Tables S1–S9
Figures S1–S2
Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.121.024412
For Sources of Funding and Disclosures, see page 9.
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Associated Data
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Supplementary Materials
Tables S1–S9
Figures S1–S2