Association of Ticagrelor vs Clopidogrel With Net Adverse Clinical Events in Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

IMPORTANCE

Current guidelines recommend ticagrelor as the preferred P2Y12 platelet inhibitor for patients with acute coronary syndrome (ACS), primarily based on a single large randomized clinical trial. The benefits and risks associated with ticagrelor vs clopidogrel in routine practice merits attention.

OBJECTIVE

To determine the association of ticagrelor vs clopidogrel with ischemic and hemorrhagic events in patients undergoing percutaneous coronary intervention (PCI) for ACS in clinical practice.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective cohort study of patients with ACS who underwent PCI and received ticagrelor or clopidogrel was conducted using 2 United States electronic health record-based databases and 1 nationwide South Korean database from November 2011 to March 2019. Patients were matched using a large-scale propensity score algorithm, and the date of final follow-up was March 2019.

EXPOSURES

Ticagrelor vs clopidogrel.

MAIN OUTCOMES AND MEASURES

The primary end point was net adverse clinical events (NACE) at 12 months, composed of ischemic events (recurrent myocardial infarction, revascularization, or ischemic stroke) and hemorrhagic events (hemorrhagic stroke or gastrointestinal bleeding). Secondary outcomes included NACE or mortality, all-cause mortality, ischemic events, hemorrhagic events, individual components of the primary outcome, and dyspnea at 12 months. The database-level hazard ratios (HRs) were pooled to calculate summary HRs by random-effects meta-analysis.

RESULTS

After propensity score matching among 31 290 propensity-matched pairs (median age group, 60-64 years; 29.3% women), 95.5% of patients took aspirin together with ticagrelor or clopidogrel. The 1-year risk of NACE was not significantly different between ticagrelor and clopidogrel (15.1% [3484/23 116 person-years] vs 14.6% [3290/22 587 person-years]; summary HR, 1.05 [95% CI, 1.00-1.10]; P = .06). There was also no significant difference in the risk of all-cause mortality (2.0% for ticagrelor vs 2.1% for clopidogrel; summary HR, 0.97 [95% CI, 0.81-1.16]; P = .74) or ischemic events (13.5% for ticagrelor vs 13.4% for clopidogrel; summary HR, 1.03 [95% CI, 0.98-1.08]; P = .32). The risks of hemorrhagic events (2.1% for ticagrelor vs 1.6% for clopidogrel; summary HR, 1.35 [95% CI, 1.13-1.61]; P = .001) and dyspnea (27.3% for ticagrelor vs 22.6% for clopidogrel; summary HR, 1.21 [95% CI, 1.17-1.26]; P < .001) were significantly higher in the ticagrelor group.

CONCLUSIONS AND RELEVANCE

Among patients with ACS who underwent PCI in routine clinical practice, ticagrelor, compared with clopidogrel, was not associated with significant difference in the risk of NACE at 12 months. Because the possibility of unmeasured confounders cannot be excluded, further research is needed to determine whether ticagrelor is more effective than clopidogrel in this setting.

Abstract 27: Getting granular: A structured database of doses and schedules in hematology/oncology

Introduction: Granular cancer patient treatment data collection, and subsequent mapping to standard regimen definitions, are vital next steps in advancement of observational studies in oncology. However, the identification of regimen details, including dose and schedule, is a prerequisite for both collection and mapping. At the patient level, claims databases are a useful but limited resource. Most cancer registries, such as the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) program and the Commission on Cancer National Cancer Database, capture only a simplification of actual treatments, such as binary exposure to chemotherapy (yes/no) or a list of individual chemotherapeutic agent names. Moreover, even with optimal patient data collection, a “gold standard” database of expected chemotherapy doses and schedules as part of standard-of-care (SOC) does not currently exist.

Methods: We extracted and normalized the semistructured dosing and timing data from HemOnc.org, the largest publicly available website of SOC chemotherapy drugs and regimens. To that end, we undertook two broad and parallel approaches: 1) standardization of prescribing instructions within a given cycle (SIGs) or pertaining to cycle timing (“cycleSIGs”); and 2) parsing of resultant content into structured variables. This effort was carried out iteratively with the goal of creating standard “canonical” forms for intermittent intravenous (IV), continuous IV (CIV), other routes (e.g., oral), and radiation SIGs. All SIGs were bound to regimen and treatment context (e.g., cyclophosphamide dosing differs in R-CHOP versus R-CVP, and number of cycles often differs between adjuvant and metastatic contexts).

Results: There are currently 14,569 regimen-context-SIG-cycleSIG quartets in the database (October 2019). Parsing of SIGs into structured variables resulted in 7,792 canonical IV, 675 canonical CIV, 3,762 canonical other, 510 canonical radiation, and 2,249 noncanonical results. Some SIGs are multipart and were broken into steps. For example, “6 mg/m2 IV once on day 1, then 3 mg/m2 IV once on day 8” constitutes two separate steps. There were 948 unique cycleSIGs (e.g., “21-day cycle for 4 cycles”), which were also parsed into components.

Discussion: This effort has produced a large dataset of granular drug and cycle SIG information that reflects SOC dosing parameters in hematology/oncology. This dataset can be used to understand discrepancies between real-world outcomes and clinical trial results, e.g., by elucidating the effect of dose reductions and treatment delays on treatment outcomes. The Observational Health Data Sciences and Informatics (OHDSI) oncology workgroup is arranging to add this new information to the portion of the HemOnc vocabulary that is available through the OHDSI terminology management tool. Ongoing efforts also include translating the maximum possible number of noncanonical sigs into canonical forms, which can further enhance simplicity and usability of the dataset.

Citation Format: Zachary H. Moldwin, Andrew Malty, Donna R. Rivera, Anastasios Siapos, Christian G. Reich, Michael J. Gurley, Rimma Belenkaya, Dmitry Dymshyts, Jeremy L. Warner. Getting granular: A structured database of doses and schedules in hematology/oncology [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 27.