This cohort study investigates if there is an increased risk of symptomatic intracerebral hemorrhage associated with prestroke antiplatelet therapy after treatment in intravenous alteplase.
Key Points
Question
Is prestroke antiplatelet therapy associated with an increased risk of symptomatic intracerebral hemorrhage (sICH) after treatment with intravenous alteplase?
Findings
In this cohort study of 321 819 patients from the Get With The Guidelines–Stroke registry, prestroke single and dual antiplatelet exposure was associated with an increased risk of sICH. There were no differences in rates of sICH or functional outcomes in aspirin-clopidogrel vs aspirin-ticagrelor groups.
Meaning
Prestroke dual antiplatelet therapy was associated with a significantly elevated risk of sICH, but the absolute increase in risk was small.
Abstract
Importance
Intravenous alteplase (IV-tPA) can be administered to patients with acute ischemic stroke but is associated with symptomatic intracerebral hemorrhage (sICH). It is unclear if patients taking prestroke dual antiplatelet therapy (DAPT) are at higher risk of sICH.
Objective
To determine the associated risk of sICH in patients taking prestroke dual antiplatelet therapy receiving alteplase for acute ischemic stroke using propensity score matching analysis.
Design, Setting, and Participants
This cohort study used data from the American Heart Association and American Stroke Association Get With The Guidelines–Stroke (GWTG-Stroke) registry between 2013 and 2021. Data were obtained from hospitals in the GWTG-Stroke registry. This study included patients hospitalized with acute ischemic stroke and treated with IV-tPA. Data were analyzed from January 2013 to December 2021.
Exposures
Prestroke DAPT before treatment with IV-tPA for acute ischemic stroke.
Main Outcome Measures
sICH, In-hospital death, discharge modified Rankin scale score, and other life-threatening systemic hemorrhages.
Results
Of 409 673 participants, 321 819 patients (mean [SD] age, 68.6 [15.1] years; 164 587 female [51.1%]) who were hospitalized with acute ischemic stroke and treated with IV-tPA were included in the analysis. The rate of sICH was 2.9% (5200 of 182 344), 3.8% (4457 of 117 670), and 4.1% (893 of 21 805) among patients treated with no antiplatelet therapy, single antiplatelet therapy (SAPT), and DAPT, respectively (P < .001). In adjusted analyses after propensity score subclassification, both SAPT (odds ratio [OR], 1.13; 95% CI, 1.07-1.19) and DAPT (OR, 1.28; 95% CI, 1.14-1.42) were associated with increased risks of sICH. Prestroke antiplatelet medications were associated with lower odds of discharge mRS score of 2 or less compared with no medication (SAPT OR, 0.92; 95% CI, 0.90-0.95; DAPT OR, 0.94; 95% CI, 0.88-0.98). Results of a subgroup analysis of patients taking DAPT exposed to aspirin-clopidogrel vs aspirin-ticagrelor combination therapy were not significant (OR, 1.35; 95% CI, 0.84-1.86).
Conclusions and Relevance
Prestroke DAPT was associated with a significantly elevated risk of sICH among patients with ischemic stroke who were treated with thrombolysis; however, the absolute increase in risk was small. Patients exposed to antiplatelet medications did not have excess sICH compared with landmark trials, which demonstrated overall clinical benefit of thrombolysis therapy for acute ischemic stroke.
Introduction
Stroke is a leading cause of mortality and morbidity, with ischemic strokes accounting for 87% of all strokes in the US.1 Intravenous tissue plasminogen activator (IV-tPA) is recommended for patients presenting with acute ischemic stroke (AIS) within 4.5 hours of last known well (LKW) to reduce stroke-related morbidity.2 However, IV-tPA also carries a risk of symptomatic intracranial hemorrhage (sICH).3 In patients treated with IV-tPA, early administration of aspirin was shown to increase the risk of sICH in a clinical trial,4 but it is unclear to what extent prestroke antiplatelet treatment is associated with the risk of sICH and functional outcomes.
A Get With The Guidelines (GWTG)–Stroke study5 and a meta-analysis6 demonstrated that among patients treated with IV-tPA, prestroke antiplatelet therapy was associated with an increased risk of sICH, but a paradoxically higher likelihood of better functional outcomes. Since their publication in 2016, newer antiplatelet medications have been increasingly used. Clinical trials have established dual antiplatelet therapy (DAPT) superiority for patients with high-risk coronary artery disease, acute coronary syndrome, percutaneous coronary intervention, and those with a recent history of high-risk transient ischemic attack (TIA) or minor stroke.7,8,9 The most common DAPT is a combination of aspirin and clopidogrel, but newer and more effective antiplatelet combinations have been adopted for patients with coronary artery disease with distinct risk-benefit profiles.10 Evaluating newer antiplatelet agents is important because the combination of aspirin and ticagrelor has been associated with a higher risk of hemorrhage.11 To date, studies exploring the association between prestroke antiplatelet use and thrombolysis safety were limited due to a focus on single antiplatelet therapy, a paucity of patients taking modern and dual antiplatelet therapies, and not addressing potential confounding by indication.5,6,12,13,14,15,16 Stroke guidelines17 do not prohibit IV-tPA administration to patients receiving antiplatelet therapy, but further evidence is needed about the safety of prethrombolysis use of contemporaneous antiplatelet therapies to clarify their associated risk.
We evaluated the associations between prestroke antiplatelet therapies and the risk of sICH and functional outcomes among patients with AIS treated with IV-tPA using propensity-score analysis. We also specifically compared the safety of thrombolysis among patients receiving aspirin-clopidogrel and aspirin-ticagrelor combination therapy. We hypothesized that patients taking prestroke DAPT are at a higher associated risk of sICH compared with patients who are taking single antiplatelet therapy (SAPT) or no antiplatelet therapy after IV-tPA therapy.
Methods
Study Design and Participants
The GWTG-Stroke registry is an ongoing national, online reporting system sponsored by the American Heart Association (AHA) and American Stroke Association. Details on the registry have been previously described.18 This study was reviewed and approved by the AHA GWTG-Stroke Systems of Care Advisory Group and the Duke University institutional review board. The Duke Clinical Research institute is the data analytics center for GWTG and has an agreement to analyze the aggregate deidentified data for research purposes. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.
A total of 409 673 patients in the GWTG-Stroke registry had a final diagnosis of ischemic stroke and received IV-tPA between January 1, 2013, and December 31, 2021. Race and ethnicities were self-reported by each patient in the GWTG-Stroke registry and recorded by trained hospital personnel. Patients self-identified the following races and ethnicities: Asian, Black, Hispanic, White, and other, which included American Indian or Alaska Native, Pacific Islander, or race not able to be determined. Race and ethnicity information was included to account for patient-level demographic factors. We excluded patients who received IV-tPA more than 4.5 hours after LKW, patients with missing IV-tPA complications or preadmission antiplatelet therapy information, patients who were taking anticoagulation therapy or had an admission international normalized ratio greater than 1.7, patients transferred in or out of the hospital, and patients who received IV-tPA in a mobile stroke unit. We also excluded patients with antiplatelet monotherapy or combination therapy not evaluated in this study due to low utilization. Frequencies of specific antiplatelet therapies recorded before IV-tPA administration from 2013 to 2021 are provided in eTable 1 in Supplement 1.
Exposures
Antithrombotic medication exposure was deemed present if the following response was selected in the registry: “Yes: there is documentation that the patient has taken any antiplatelet or anticoagulant medication(s) within the past week and prior to hospital arrival.” Among records demonstrating exposure to antithrombotic medications within the past week and before hospital arrival, SAPT exposure was defined as aspirin, clopidogrel, prasugrel, or ticagrelor monotherapy. Similarly, DAPT exposure was defined as aspirin-clopidogrel, aspirin-ticagrelor, or aspirin-prasugrel combination therapy.
Outcomes
The primary outcome of our study was sICH within 36 hours of IV-tPA administration. The GWTG-Stroke definition of sICH is the presence of sICH documented by computed tomography or magnetic resonance imaging and a physician’s note indicating clinical deterioration due to the hemorrhage. This outcome is defined based on sICH criteria established in the National Institute of Neurological Disorders and Stroke tPA trials.19 Secondary outcomes include other life-threatening systemic hemorrhages occurring within 36 hours of IV tPA, in-hospital all-cause mortality, and discharge modified Rankin Scale (mRS) score. GWTG-Stroke defines life-threatening, serious systemic hemorrhage as bleeding within 36 hours of IV-tPA administration that requires 3 or more transfused units of blood within 7 days or before discharge (whichever is earlier) and a physician’s note attributing the bleeding as the reason for the transfusion. The mRS score measures functional outcome from 0 (no symptoms) to 6 (death).20 We dichotomized mRS as 0 to 2 representing functional independence and greater than 2 for functional dependence.
We also performed a separate exploratory outcome analysis evaluating the association of prestroke antiplatelet therapy and 90-day mortality and functional outcomes (eAppendix in Supplement 1). These outcomes are available in the GWTG-Stroke database but are missing in many patient records.
Statistical Analysis
Baseline characteristics of the study population were analyzed according to the exposure to no antiplatelet therapy, SAPT, and DAPT. Categorical variables were compared using χ2 tests for proportions. Continuous variables, described as means with SD or medians with IQR, were compared using independent-samples t tests for normal data or Mann-Whitney U tests for nonnormal data. Temporal trends in antiplatelet medication use from 2013 to 2021 were evaluated using logistic regression analysis with antiplatelet medication type set as the dependent variable and calendar year of presentation as a continuous independent variable. To evaluate outcome differences between prestroke antiplatelet groups, we used propensity score matching to reduce imbalances between baseline patient characteristics followed by sequential logistic regression models to adjust for stroke severity, treatment, and hospital variability.
Propensity Score Subclassification
To reduce imbalances in the distribution of confounders across prestroke antiplatelet treatment groups that may confer indication biases, we used a propensity score subclassification methodology.21,22 We performed separate analyses of 3 prestroke antiplatelet comparator groups: (1) no antiplatelet therapy vs SAPT, (2) no antiplatelet therapy vs DAPT, and (3) SAPT vs DAPT. First, to account for missing values, a multiple imputation algorithm was used to create 5 separate imputed data sets for each comparison group. Next, patients in each exposure comparator group were grouped into subclasses based on propensity scores or the predicted probability of prestroke treatment with each of the prestroke antiplatelet regimens. The propensity scores were derived from the following potentially confounding variables: prestroke characteristics such as age, sex, race, body mass index (BMI), medical history (hypertension, dyslipidemia, diabetes, atrial fibrillation, prior stroke or TIA, heart failure, heart valve disease, carotid stenosis, myocardial infarction or coronary artery disease, peripheral vascular disease, dementia, and current smoking), and current use of blood pressure, cholesterol, or glucose-lowering medication. Propensity scores were averaged within each imputed data set using covariate values. To ensure that the distribution of potential confounders is similar across exposure groups after propensity score subclassification, the balance of variables in each comparator group was evaluated by computing the average standardized mean difference and Kolmogorov-Smirnov statistics for each covariate within and across all imputed data sets. A threshold of average standardized mean differences defined as less than 0.1 a priori indicated that the covariates were well balanced between treatment groups on propensity score subclassification.22
Primary Analysis
Logistic regression analyses were used to investigate the association between prestroke antiplatelet exposure and outcomes. The associations between prestroke antiplatelet exposure and outcomes of interest in each propensity score subclass were estimated. These associations within propensity score subclasses were then pooled by weighting by the proportion of patients in each subclass to calculate the overall association for each outcome.23 These models were sequentially adjusted for (1) acute illness and stroke severity markers (platelet count, creatinine level, and National Institutes of Health Stroke Scale, a measure of neurological disability ranging from 0-42 where higher scores indicate more disability, on arrival to the hospital), (2) thrombolysis delivery metrics (time from symptom onset to IV-tPA administration, door to needle time) and endovascular thrombectomy, and (3) hospital characteristics (number of hospital beds, annual hospital IV-tPA administration, teaching hospital status, Comprehensive Stroke Center certification status, and rural hospital status). For the primary outcome of sICH, interaction analyses were performed to evaluate heterogeneity in risk as a function of baseline characteristics that may be associated with increased sICH risk. These covariates were sex, age older than 80 years, BMI greater than 25 (calculated as weight in kilograms divided by height in meters squared), creatinine level greater than 1 mg/dL (to convert to micromoles per liter, multiply by 88.4), IV-tPA administered between 3 and 4.5 hours of LKW, endovascular thrombectomy, and history of hypertension, diabetes, coronary artery disease or myocardial infarction, and stroke or TIA.
Subgroup Analyses by DAPT Type
To evaluate the differences between aspirin-clopidogrel vs aspirin-ticagrelor combination therapy, we isolated patients in these exposure groups and used the multiple imputation and propensity score subclassification procedure described previously. Logistic regression analysis was used to compare exposure groups. The aspirin-prasugrel group was not evaluated due to its low frequency in the sample.
Exploratory Mediation Analysis
We sought to understand whether an association between prestroke antiplatelet use and discharge mRS score was mediated by an interval sICH event. We performed a mediation analysis using a structural equation model to determine both the direct effects of prestroke antiplatelet use on the outcome of discharge mRS score and the indirect effects of sICH on discharge mRS score as a mediator.24 This was a complete analysis of cases with available discharge mRS score and the result of bootstrapping the structural equation model 500 times.
All data analyses were performed using open-source software R, version 4.2.0 (R Foundation for Statistical Computing) through the AHA Precision Medicine Platform using mice, version 3.15.0, matchThem, version 1.0.1, cobalt, version 4.5.1, and mediation, version 4.5.0, packages.25 A 2-sided P < .05 was considered statistically significant, and no adjustments were performed for multiple testing. Missingness rates can be found in eTable 2 in Supplement 1. A sensitivity analysis comparing baseline characteristics of the analytic cohort vs patients excluded due to missing prestroke antiplatelet use and postthrombolytic complications can be found in eTable 3 in Supplement 2 (all Jupyter Notebook code26 [Jupyter] that executes the described analysis on the Precision Medicine Platform will be made available on reasonable request). Data were analyzed from January 2013 to December 2021.
Results
Of 409 673 participants, 321 819 patients (mean [SD] age, 68.6 [15.1] years; 164 587 female [51.1%]; 157 120 male [48.8%]) who were hospitalized with acute ischemic stroke and treated with IV-tPA were included in the analysis (Figure). Patients self-identified with the following races and ethnicities: 9720 Asian (3.0%), 50 857 Black (15.8%), 26 467 Hispanic (8.2%), 220 459 White (68.5%), and 14 174 other (4.4%). A total of 321 819 patients with a diagnosis of ischemic stroke received IV-tPA from 2013 to 2021, of whom 182 344 (56.7%) did not use antiplatelet therapy, 117 670 (36.5%) used SAPT, and 21 805 (6.8%) used DAPT before IV-tPA (Figure). Between 2013 and 2021, we saw an increasing trend of no antiplatelet use (10 697 of 19 870 patients [53.8%] in 2013 to 27 204 of 44 874 patients [60.6%] in 2021; P = .03), a decreasing trend of SAPT use (7901 of 19 870 patients [39.7%] in 2013 to 14 618 of 44 874 patients [32.6%] in 2021; P = .04), and an increasing trend of DAPT use (1272 of 19 870 patients [6.4%] in 2013 to 3052 of 44 874 patients [6.8%] in 2021; P = .009) (eFigure 1 and eTable 1 in Supplement 1). The increase in DAPT use was due to increased patients taking aspirin and clopidogrel (1072 patients in 2013 and 2758 patients in 2021) despite a decrease in patients taking aspirin and dipyridamole (197 patients in 2013 and 77 patients in 2021). Ticagrelor monotherapy and aspirin-ticagrelor dual therapy increased numerically, although the trends were not significant. Demographic, clinical, and hospital characteristics are presented in Table 1 according to antiplatelet exposure groups of no therapy, SAPT, and DAPT. Rates of missingness are presented in eTable 2 in Supplement 1. A sensitivity analysis evaluating baseline characteristics of patients missing documentation on prestroke antiplatelet use and complications after thrombolysis can be found in eTable 3 in Supplement 1. There were no statistically significant differences in baseline patient-level characteristics between this cohort including patients with these missing data points and the analytic cohort. On propensity score subclassification, the adjusted average standardized mean differences of covariates stratified by antiplatelet exposure were less than 0.1, indicating adequate balance of these measured covariates by this methodology (eFigure 2 in Supplement 1).
Figure. Study Population.
GWTG, Get With The Guidelines registry; IV-tPA, intravenous tissue plasminogen activator; LKW, last known well.
aOther antiplatelet therapy: ticlopidine only (n = 4), other monotherapy (n = 120), aspirin and ticlopidine (n = 7), clopidogrel and ticagrelor (n = 9), clopidogrel/aspirin/dipyridamole triple therapy (n = 79), and other antiplatelet combinations (n = 449).
bSingle antiplatelet therapy: aspirin, clopidogrel, prasugrel, or ticagrelor monotherapy.
cDual antiplatelet therapy: aspirin and dipyridamole, aspirin and clopidogrel, aspirin and ticagrelor, or aspirin and prasugrel.
Table 1. Patient- and Hospital-Level Characteristics Stratified by Prestroke Antiplatelet Treatment Statusa.
| Variable | Total (N = 321 819) | No antiplatelet (n = 182 344) | Single antiplatelet (n = 117 670) | Dual antiplatelet (n = 21 805) | P value |
|---|---|---|---|---|---|
| Demographics | |||||
| Age, mean (SD), y | 68.6 (15.1) | 65.7 (15.6) | 72.8 (13.4) | 71.2 (12.9) | <.001 |
| Sex, No. (%) | |||||
| Female | 164 587 (51.1) | 91 861 (50.4) | 60 067 (51.0) | 12 659 (58.1) | <.001 |
| Male | 157 120 (48.8) | 90 406 (49.6) | 57 568 (48.9) | 9146 (41.9) | |
| Race and ethnicity, No. (%) | |||||
| Asian | 9720 (3.0) | 6172 (3.4) | 3087 (2.6) | 461 (2.1) | <.001 |
| Black | 50 857 (15.8) | 30 920 (17.0) | 16 683 (14.2) | 3254 (14.9) | |
| Hispanic | 26 467 (8.2) | 16 582 (9.1) | 8262 (7.0) | 1623 (7.4) | |
| White | 220 459 (68.5) | 119 638 (65.6) | 85 172 (72.4) | 15 649 (71.8) | |
| Otherb | 14 174 (4.4) | 8945 (4.9) | 4417 (3.8) | 812 (3.7) | |
| Body mass index, mean (SD)c | 29.0 (7.0) | 29.1 (7.1) | 28.9 (6.9) | 29.3 (6.8) | .008 |
| Medical history, No. (%) | |||||
| Hypertension | 228 820 (71.1) | 114 294 (62.7) | 95 822 (81.4) | 18 704 (85.8) | <.001 |
| Dyslipidemia | 141 687 (44.0) | 59 661 (32.7) | 67 377 (57.3) | 14 649 (67.2) | <.001 |
| Diabetes | 91 902 (28.6) | 41 271 (22.6) | 40 850 (34.7) | 9781 (44.9) | <.001 |
| Atrial fibrillation | 45 471 (14.1) | 19 171 (10.5) | 23 400 (19.9) | 2900 (13.3) | <.001 |
| Previous stroke and/or transient ischemic attack | 80 029 (24.9) | 26 221 (14.4) | 41 413 (35.2) | 12 395 (56.8) | <.001 |
| Heart failure | 26 495 (8.2) | 9333 (5.1) | 13 839 (11.8) | 3323 (15.2) | <.001 |
| Prosthetic heart valve | 2433 (0.8) | 792 (0.4) | 1366 (1.2) | 275 (1.3) | <.001 |
| Carotid stenosis | 8254 (2.6) | 2097 (1.2) | 4342 (3.7) | 1815 (8.5) | <.001 |
| Coronary artery disease and/or myocardial infarction | 67 459 (21.0) | 18 165 (10.0) | 36 588 (31.1) | 12 706 (58.3) | <.001 |
| Peripheral vascular disease | 9617 (3.0) | 2637 (1.4) | 5017 (4.3) | 1963 (9.0) | <.001 |
| Obesity | 85 033 (26.4) | 46 791 (25.7) | 31 688 (26.9) | 6554 (30.1) | <.001 |
| Dementia | 3256 (1.0) | 1575 (0.9) | 1423 (1.2) | 258 (1.2) | <.001 |
| Current smoker | 56 414 (17.5) | 35 888 (19.7) | 16 784 (14.3) | 3742 (17.2) | <.001 |
| Medications prior to admission, No. (%) | |||||
| Antihypertensive | 164 876 (51.2) | 70 141 (38.5) | 78 652 (66.8) | 16 083 (73.8) | <.001 |
| Cholesterol reducer | 133 902 (41.6) | 42 735 (23.4) | 73 420 (62.4) | 17 747 (81.4) | <.001 |
| Glucose reducer | 58 595 (18.2) | 23 046 (12.6) | 28 380 (24.1) | 7169 (32.9) | <.001 |
| Laboratory values on admission | |||||
| Creatinine level, mean (SD), mg/dL | 1.35 (4.32) | 1.30 (4.27) | 1.41 (4.40) | 1.48 (4.24) | <.001 |
| Platelet count, mean (SD), ×103/μL | 235 (78.5) | 239 (79.0) | 229 (77.3) | 233 (77.9) | <.001 |
| Initial NIHSS | |||||
| Mean (SD) | 9.85 (7.55) | 9.77 (7.51) | 10.0 (7.66) | 9.62 (7.29) | <.001 |
| Median (IQR) | 7.00 (4.00-14.0) | 7.00 (4.00-14.0) | 8.00 (4.00-15.0) | 7.00 (4.00-14.0) | |
| Last known well to IV-tPA treatment time, min | |||||
| Mean (SD) | 135 (54.2) | 134 (54.8) | 135 (53.3) | 136 (53.1) | <.001 |
| Median (IQR) | 125 (92.0-170) | 125 (91.0-170) | 126 (93.0-169) | 128 (95.0-171) | |
| Door-to-needle time, min | |||||
| Mean (SD) | 56.8 (29.6) | 56.5 (30.1) | 57.0 (29.0) | 58.1 (29.1) | <.001 |
| Median (IQR) | 51.0 (37.0-69.0) | 50.0 (36.0-69.0) | 51.0 (37.0-70.0) | 52.0 (38.0-71.0) | |
| Endovascular therapy attempted, No. (%) | 25 847 (8.0) | 16405 (9.0) | 8178 (7.0) | 1264 (5.8) | <.001 |
| TICI 2b or greater, No. (%) | 25 059 (7.8) | 15849 (8.7) | 7981 (6.8) | 1229 (5.6) | .403 |
| No. of beds in hospital | |||||
| Mean (SD) | 436 (321) | 436 (325) | 434 (315) | 440 (315) | .27 |
| Median (IQR) | 359 (233-546) | 357 (230-548) | 360 (234-545) | 365 (238-554) | |
| Hospital characteristics | |||||
| Annual number of IV thrombolysis cases, median (IQR) | 32.9 (20.0-48.6) | 32.6 (19.9-48.5) | 32.9 (20.1-48.5) | 33.6 (20.3-49.1) | .08 |
| Teaching hospital, No. (%) | 235 202 (73.1) | 132 887 (72.9) | 86514 (73.5) | 15 801 (72.5) | .09 |
| Comprehensive stroke center, No. (%) | 81639 (25.4) | 46 657 (25.6) | 29 660 (25.2) | 5322 (24.4) | .05 |
| Rural hospital, No. (%) | 13 840 (4.3) | 7756 (4.3) | 5088 (4.3) | 996 (4.6) | .61 |
Abbreviations: IV-tPA, intravenous tissue plasminogen activator; NIHSS, National Institutes of Health Stroke Scale; TICI, thrombolysis in cerebral infarction.
SI conversion factor: To convert creatinine to micromoles per liter, multiply by 88.4; to convert platelet count to ×109/L, multiply by 1.
Continuous variables were analyzed using Kruskal-Wallis test and categorical variables using χ2 test.
Other included American Indian or Alaska Native, Pacific Islander, or not able to be determined.
Body mass index is calculated as weight in kilograms divided by height in meters squared.
sICH
A total of 5200 of 182 344 patients (2.9%) in the group with no antiplatelet exposure developed sICH compared with 4457 of 117 670 patients (3.8%) in the SAPT exposure group and 893 of 21 805 patients (4.1%) in the DAPT exposure group (P < .001) (Table 2). After propensity score subclassification and adjusting for the aforementioned characteristics, prestroke SAPT exposure was associated with a 13% increased odds of sICH (odds ratio [OR], 1.13; 95% CI, 1.07-1.19; P < .001) and prestroke DAPT exposure with a 28% increased odds of sICH (OR, 1.28; 95% CI, 1.14-1.42; P < .001) when compared with no antiplatelet exposure (Table 3) among patients with AIS treated with IV-tPA. DAPT was independently associated with an increased risk of sICH compared with single antiplatelet exposure (OR, 1.18; 95% CI, 1.08-1.27; P < .001). Compared with patients taking no prestroke antiplatelet therapy, there was an absolute risk increase of sICH of 0.9% in the SAPT group and 1.2% in the DAPT group.
Table 2. Outcomes by Prestroke Antiplatelet Treatment Statusa.
| Variable | Total (N = 321 819) | No antiplatelet (n = 182 344) | Single antiplatelet (n = 117 670) | Dual antiplatelet (n = 21 805) | P value |
|---|---|---|---|---|---|
| Symptomatic ICH, No. (%) | 10 550 (3.3) | 5200 (2.9) | 4457 (3.8) | 893 (4.1) | <.001 |
| In-hospital death, No. (%) | 14 188 (4.4) | 7220 (4.0) | 5868 (5.0) | 1100 (5.0) | <.001 |
| mRS of 0-2 at discharge, No. (%) | 80 701 (25.1) | 48 067 (26.4) | 27 566 (23.4) | 5068 (23.2) | <.001 |
| Life-threatening systemic hemorrhage. No. (%) | 1788 (0.6) | 868 (0.5) | 761 (0.6) | 159 (0.7) | <.001 |
| Death at 90 d, No. (%) | 19 816 (6.2)b | 9948 (5.5) | 8327 (7.1) | 1541 (7.1) | <.001 |
| mRS of 0-2 at 90 d, No. (%) | 34 750 (10.8)c | 20 676 (11.3) | 11 961 (10.2) | 2113 (9.7) | <.011 |
Abbreviations: ICH, intracerebral hemorrhage; IV-tPA, intravenous tissue plasminogen activator; mRS, modified Rankin Scale.
Groups were compared using χ2 test.
Patients missing 90-day death values: 251 655.
Patients missing 90-day mRS values: 262 232.
Table 3. Odds Ratio (OR) of Outcomes of Interest by Antiplatelet Treatment Regimens With Sequential Adjustments.
| Outcomes | Single vs no antiplatelet therapy (n = 300 014) |
Dual vs no antiplatelet therapy (n = 204 149) | Dual vs single antiplatelet therapy (n = 139 475) | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | |
| sICH | ||||||
| Unadjusted | 1.09 (1.03-1.15) | .002 | 1.21 (1.08-1.34) | <.001 | 1.14 (1.05-1.23) | <.001 |
| Model 1a | 1.12 (1.06-1.18) | <.001 | 1.27 (1.13-1.40) | <.001 | 1.17 (1.08-1.26) | <.001 |
| Model 2b | 1.13 (1.07-1.19) | <.001 | 1.29 (1.15-1.43) | <.001 | 1.17 (1.09-1.27) | <.001 |
| Model 3c | 1.13 (1.07-1.19) | <.001 | 1.28 (1.14-1.42) | <.001 | 1.18 (1.08-1.27) | <.001 |
| In-hospital mortality | ||||||
| Unadjusted | 0.91 (0.87-0.95) | .001 | 0.99 (0.99-1.00) | .81 | 1.04 (0.97-1.11) | .26 |
| Model 1a | 0.98 (0.93-1.02) | .31 | 1.00 (1.00-1.01) | .04 | 1.09 (1.01-1.17) | .02 |
| Model 2b | 0.98 (0.93-1.03) | .42 | 1.02 (1.00-1.03) | .02 | 1.09 (1.02-1.18) | .01 |
| Model 3c | 0.98 (0.93-1.02) | .35 | 1.10 (1.00-1.21) | .05 | 1.10 (1.02-1.19) | .007 |
| mRS≤2 at discharge | ||||||
| Unadjusted | 0.88 (0.86-0.90) | <.001 | 0.89 (0.85-0.94) | <.001 | 0.99 (0.96-1.06) | .61 |
| Model 1a | 0.92 (0.90-0.95) | <.001 | 0.96 (0.90-1.01) | .13 | 0.99 (0.94-1.04) | .77 |
| Model 2b | 0.92 (0.90-0.95) | <.001 | 0.96 (0.91-1.02) | .15 | 0.99 (0.95-1.04) | .82 |
| Model 3c | 0.92 (0.90-0.95) | <.001 | 0.94 (0.88-0.98) | .01 | 0.98 (0.96-1.03) | .57 |
| Life-threatening systemic hemorrhage | ||||||
| Unadjusted | 1.11 (0.93-1.20) | .36 | 1.12 (0.82-1.41) | .40 | 1.08 (0.89-1.28) | .38 |
| Model 1a | 1.11 (0.96-1.25) | .12 | 1.20 (0.88-1.51) | .18 | 1.12 (0.91-1.32) | .24 |
| Model 2b | 1.11 (0.97-1.26) | .10 | 1.21 (0.89-1.53) | .16 | 1.12 (0.92-1.33) | .21 |
| Model 3c | 1.10 (0.96-1.24) | .13 | 1.19 (0.87-1.52) | .19 | 1.14 (0.93-1.35) | .16 |
Abbreviations: mRS, modified Rankin Scale; sICH, symptomatic intracerebral hemorrhage.
Model 1 = acute illness and stroke severity marker (platelet count, creatinine level, and National Institutes of Health Stroke Scale).
Model 2 = thrombolysis delivery metrics (time from symptom onset to intravenous tissue plasminogen activator administration, door to needle time) and endovascular thrombectomy.
Model 3 = hospital characteristics (number of hospital beds, annual hospital intravenous tissue plasminogen activator administration, teaching hospital status, Comprehensive Stroke Center certification status, and rural hospital status).
Interaction analyses revealed that IV-tPA administration 3 to 4.5 hours was associated with slightly higher odds of sICH (OR, 1.05; 95% CI, 1.01-1.08; P = .02) in the DAPT exposure groups compared with the SAPT exposure group (eTable 4 in Supplement 1). There was no significant interaction between exposure groups and sex, BMI, history of hypertension, coronary artery disease and/or myocardial infarction, history of diabetes, serum creatinine level, and endovascular thrombectomy treatment.
Secondary Outcomes
Rates of in-hospital mortality and life-threatening systemic hemorrhage were higher in the SAPT (5868 of 117 670 [5%]) and DAPT (1100 of 21 805 [5%]) groups compared with the no antiplatelet group (7220 of 182 344 [4%]; P < .001) (Table 2). Similarly, rates of functional independence defined by mRS score of 0 to 2 at discharge were significantly higher in the no antiplatelet group (48 067 of 182 344 [26.4%]) compared with the SAPT (27 566 of 117 670 [23.4%]) and DAPT (5068 of 21 805 [23.2%]) groups (P value < .001). After propensity score subclassification and adjusting for covariates, patients with SAPT and DAPT exposure were less likely to have functional independence at discharge compared with no antiplatelet exposure (OR, 0.92; 95% CI, 0.90-0.95; P < .001 and OR, 0.94; 95% CI, 0.88-0.98; P = .01, respectively). There was a slight associated increase in in-hospital mortality in patients with DAPT compared with SAPT (OR, 1.10; 95% CI, 1.02-1.19; P = .007) and no antiplatelet therapy (OR, 1.10; 95% CI, 1.00-1.21; P = .045) but no significant associations between SAPT and no antiplatelet therapy groups (Table 3).
Subgroup Analysis Results by DAPT Type
Subgroup analysis of patients exposed to aspirin-clopidogrel (n = 19 570) vs aspirin-ticagrelor (n = 689) revealed a nonsignificant result (OR 1.26; 95% CI, 0.80-1.72; P = .21 for unadjusted analysis and OR, 1.35; 95% CI, 0.84-1.86; P = .12 for adjusted analysis) (Table 4). We found no difference in secondary outcomes between aspirin-clopidogrel vs aspirin-ticagrelor subgroups.
Table 4. Subgroup Analysis by Dual Antiplatelet Therapy Type of Aspirin and Clopidogrel (n = 19 570) vs Aspirin and Ticagrelor (n = 689)a.
| Outcomes | Unadjusted OR (95% CI) | P value | Adjusted OR (95% CI) | P value |
|---|---|---|---|---|
| sICH | 1.26 (0.80-1.72) | .21 | 1.35 (0.84-1.86) | .12 |
| In-hospital mortality | 1.09 (0.70-1.48) | .63 | 1.00 (0.81-1.77) | .18 |
| mRS of 0-2 at discharge | 0.98 (0.82-1.18) | .87 | 1.04 (0.84-1.24) | .67 |
| Life-threatening systemic hemorrhage | 1.54 (0.35-2.74) | .27 | 1.77 (0.38-3.16) | .16 |
Abbreviations: mRS, modified Rankin Scale; sICH, symptomatic intracerebral hemorrhage.
Groups were evaluated using logistic regression analysis and adjusted for in-hospital and stroke characteristics (platelet count, creatinine level, National Institutes of Health Stroke Scale on admission, symptom onset to intravenous tissue plasminogen activator, door to needle time, endovascular thrombectomy, number of hospital beds, annual hospital intravenous tissue plasminogen activator administration, teaching hospital status, comprehensive stroke center status, rural hospital status, year of admission).
Mediation Analysis of sICH on Discharge mRS
The structural equation model mediation analysis demonstrated a significant mediation effect of sICH on discharge mRS (eFigure 3 in the Supplement). In this model, prestroke antiplatelet therapy was associated with discharge mRS score (β = 0.04; P < .001) and with discharge mRS score through sICH (β = 0.04; P < .001). The mediation effect was β = 0.003 (P < .001), suggesting that sICH partially but significantly mediated the association between prestroke antiplatelet status and discharge mRS.
Discussion
In this cohort study of patients in the GWTG-Stroke registry who were treated with IV-tPA from 2013 to 2021, prestroke antiplatelet exposure was independently associated with increased odds of sICH. Patients taking prestroke DAPT had higher odds of sICH and in-hospital mortality compared with patients exposed to SAPT and to no antiplatelet therapy (Table 3). Patients taking DAPT and SAPT had, respectively, 1.2 and 0.9 additional cases of sICH per 100 people compared with patients taking no antiplatelet therapy. On interaction analyses, there was an increase in risk of sICH in patients treated with IV-tPA in the late 3- to 4.5-hour window compared with those treated in the less than 3-hour window, particularly in patients taking DAPT. The odds of functional independence (mRS score, 0-2) at hospital discharge in the SAPT and DAPT exposure groups were lower than in the no antiplatelet exposure group; however, absolute differences in rates of functional independence between groups were small. When comparing contemporary DAPT exposures of aspirin-clopidogrel with aspirin-ticagrelor combination therapy, there were no differences in rates of sICH, mortality, or functional outcome.
The association between antiplatelet therapy exposure and the risk of sICH in patients with AIS treated with IV-tPA has been previously studied. Subgroup analyses of the European Cooperative Acute Stroke Study III (ECASS III)27 and the National Institute of Neurological Disorder and Stroke tPA Stroke28 trials showed no association between antiplatelet therapy and sICH rates. Conversely, the Stroke-Acute ischemic NXY treatment trial (SAINT I and II),15 a subanalysis of the Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST) study, and a meta-analysis of 19 individual trials6 found that prestroke antiplatelet therapy exposure was associated with an increased risk of sICH but had no effect on clinical outcome. In a prior GWTG-Stroke study, Xian et al5 found an increase in risk of sICH between aspirin monotherapy (OR, 1.19; 95% CI, 1.06-1.34) and aspirin-clopidogrel DAPT (OR, 1.47; 95% CI, 1.16-1.86) similar to our findings. However, Xian et al5 found that patients in the aspirin monotherapy group had higher odds of favorable functional outcomes at discharge. In contrast, we found that SAPT use and DAPT use were associated with lower odds of favorable outcome at discharge and at 90 days, which may be a more biologically congruent natural history given the increased odds of sICH we observed in this population. We performed a mediation analysis that showed a significant mediation association of sICH with discharge mRS. The divergent findings by Xian et al5 may be due to methodological differences. Limitations of that study were the homogeneity of antiplatelet therapies and lack of adequate matching of patient characteristics, which may have introduced an indication bias. We accounted for prestroke risk factors by propensity score subclassification before our outcomes analysis, a method that is commonly adopted in pharmacoepidemiologic studies to address confounding by indication.29
To our knowledge, this was the first study to compare the outcomes of prestroke contemporary DAPT regimens (aspirin-clopidogrel vs aspirin-ticagrelor) in a cohort of patients receiving IV-tPA. In our study, we found no difference between aspirin-clopidogrel and aspirin-ticagrelor concerning the risk of sICH, other life-threatening systemic hemorrhages, in-hospital mortality, and mRS score at discharge and at 90 days. However, our study was not powered to exclude a clinically important difference. The overall rate of sICH in the DAPT sample is 4.1%, which is mostly accounted for by the aspirin and clopidogrel group, if we assume an estimated 5% rate of sICH for the aspirin and ticagrelor group based on the Platelet Inhibition and Patient Outcomes (PLATO) study, we would need at least 8415 patients per group to have sufficient statistical power.30 Despite these shortcomings, our finding in the context of acute thrombolysis therapy was clinically reassuring considering that prior trials with aspirin-ticagrelor such as the Acute Stroke or Transient Ischemic Attack Treated With Ticagrelor and Aspirin for Prevention of Stroke and Death (THALES) study demonstrated higher rates of sICH.31,32
Our study may have clinical implications for patients with ischemic stroke receiving IV-tPA. Although we found that prestroke antiplatelet use was associated with an increased risk of sICH, absolute rate differences of sICH in SAPT and DAPT groups compared with no antiplatelet therapy were small (0.9% and 1.2%, respectively). Considering differences in sICH definitions, sICH rates in our study are comparable with those of thrombolysis landmark studies that established the overall benefit of alteplase.19,33,34,35 In our study, SAPT and DAPT were associated with less favorable outcomes at hospital discharge; however, only SAPT was marginally associated with less favorable outcomes at 90 days in our exploratory analysis (eTables 5 and 6 in Supplement 1). We demonstrated that prestroke DAPT use was not associated with higher risk of unfavorable outcomes longitudinally compared with SAPT use when treated with IV-tPA, reflecting the variability in the causal link between thrombolysis-associated sICH and the ultimate functional outcome that has been previously described.36 The results of our study should not discourage treatment with IV-tPA in patients receiving antiplatelet therapy who present with AIS, as they likely derive benefit from thrombolysis despite the short-term increased risk of bleeding.27,37 Nevertheless, given the results of our subgroup analyses, our findings may inform risk-benefit considerations among candidates for thrombolysis who are taking prestroke dual antiplatelet therapy and have mild or rapidly improving symptoms in the 3- to 4.5-hour treatment window.
Despite the perceived increased risk of sICH in patients with aspirin-ticagrelor compared with other DAPT treatments, this finding was not supported in our study, although we did not have adequate power to rule out a potentially clinically important association. Further studies will be needed to evaluate prasugrel and other emerging antiplatelet agents to understand their contribution to thrombolysis-associated hemorrhage. Additional studies will also be needed to assess the effects of antiplatelet therapy in patients treated with tenecteplase.
Limitations
This study has several limitations. First, this was a retrospective, observational study subject to selection bias, which might be substantial given the large size of the analytic sample. Due to the large size of the GWTG-Stroke database and the numerous patients in each antiplatelet exposure group, we could account for many prestroke differences by leveraging a propensity score subclassification algorithm. However, we could not account for prestroke functional status (such as mRS score) because this was not a measurement collected universally by participating sites in the GWTG-Stroke data set. Second, patients in the antiplatelet exposure groups used antiplatelet therapy within 7 days before the stroke encounter, but we do not know the precise time when patients last took their antiplatelet medication. Although the effects of antiplatelet medications may linger for up to 10 days or the duration of the life of the platelet,38 bleeding risk may be underestimated for patients who did not adhere to antiplatelet therapy or whose antiplatelet therapy may have been stopped before the IV-tPA administration. Alternatively, bleeding risk may also be overestimated if patients take extra antiplatelet medication before presentation to the emergency department. Regardless, these data present an estimate of the real-world risk in clinical practice, although our findings may not generalize to populations outside the US. Third, the comparison between aspirin-clopidogrel and aspirin-ticagrelor was not powered to rule out a clinically important difference. Fourth, the diagnosis of sICH was not centrally adjudicated; therefore, imaging interpretation and definition of clinical deterioration may vary in clinical practice. Finally, given the retrospective nature of this study, our findings should be interpreted as correlations and not as causation.
Conclusions
In this cohort study of the GWTG-Stroke registry, prestroke SAPT and DAPT were associated with a modestly increased risk of sICH among patients with ischemic stroke who were treated with IV-tPA that did not exceed sICH rates seen in the seminal alteplase trials. Absolute sICH rate differences were relatively small. The results of our study do not detract from the treatment benefit conferred by IV-tPA in patients taking antiplatelet therapy who present with AIS. Further studies are needed to elucidate the sICH risk associated with use of prestroke antiplatelet medications and thrombolysis in the era of increasing tenecteplase usage.
eFigure 1. Numbers and Proportions of Patients Taking Prestroke Antiplatelet Therapies Each Year From 2013-2021 in the Analytic Sample
eFigure 2. Mean Standardized Differences of Covariates Between Prestroke Antiplatelet Use Treatment Groups
eFigure 3. Structural Equation Model Showing the Direct Effects of Prestroke Antiplatelet Use on sICH and Discharge mRS and of sICH on Discharge mRS
eTable 1. Frequency of Prestroke Antiplatelet Medication Use Over Time From 2013-2021
eTable 2. Missing Variables From Variables on Table 1 and Table 2
eTable 3. Sensitivity Analysis of Analysis Cohort vs Patients With Missing Data on Thrombolytic Complications and Prestroke Antiplatelet Therapy
eTable 4. Interaction Analysis for Risk of Symptomatic ICH
eAppendix. Exploratory 90-Day Outcomes
eTable 5. Exploratory Analysis of 90-Day Mortality and Functional Outcomes Between Propensity-Matched Treatment Groups
eTable 6. Exploratory 90-Day Analysis of DAPT Type Subgroups of Aspirin and Clopidogrel vs Aspirin and Ticagrelor
Data Sharing Statement.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eFigure 1. Numbers and Proportions of Patients Taking Prestroke Antiplatelet Therapies Each Year From 2013-2021 in the Analytic Sample
eFigure 2. Mean Standardized Differences of Covariates Between Prestroke Antiplatelet Use Treatment Groups
eFigure 3. Structural Equation Model Showing the Direct Effects of Prestroke Antiplatelet Use on sICH and Discharge mRS and of sICH on Discharge mRS
eTable 1. Frequency of Prestroke Antiplatelet Medication Use Over Time From 2013-2021
eTable 2. Missing Variables From Variables on Table 1 and Table 2
eTable 3. Sensitivity Analysis of Analysis Cohort vs Patients With Missing Data on Thrombolytic Complications and Prestroke Antiplatelet Therapy
eTable 4. Interaction Analysis for Risk of Symptomatic ICH
eAppendix. Exploratory 90-Day Outcomes
eTable 5. Exploratory Analysis of 90-Day Mortality and Functional Outcomes Between Propensity-Matched Treatment Groups
eTable 6. Exploratory 90-Day Analysis of DAPT Type Subgroups of Aspirin and Clopidogrel vs Aspirin and Ticagrelor
Data Sharing Statement.
