Introduction

In the United States (US), 2.7–6.1 million people were affected by atrial fibrillation (AFib) annually and it is projected to reach 12 million by 2050 [1]. AFib is associated with more than 454,000 hospitalizations and 158,000 deaths each year [2,3,4,−4]. Among patients with cancer, AFib was also associated with higher burden of adverse outcomes, such as ischemic stroke, venous thromboembolism (VTE), bleeding, and death compared with AFib patients without cancer [5,6,7,−8].

Although the benefit of oral anticoagulants (OACs) in patients with AFib has been well established [9], the current management of patients with AFib and cancer regarding OAC treatments remains suboptimal due to insufficient evidence [10]. Among patients with AFib and cancer, OAC initiation was associated with a slightly reduced risk of adverse event (ischemic stroke and intracranial bleeding) compared with non-users [11]. However, recent studies found only half of patients with AFib and cancer initiated OAC, much less than those without cancer [11,12,13,14]. One of the major challenges is to determine the appropriate time when patients with AFib and cancer should start OACs to maximize the benefit of stroke prevention while minimizing the risk of bleeding. In general, OAC initiation is recommended for AFib patients with a CHA2DS2-VASc score ≥ 2, a composite stroke risk score of congestive heart failure, hypertension, age, diabetes mellitus, prior stroke, transient ischemic attack, thromboembolism, vascular disease and sex category [9, 15]. However, such threshold has not been explored in patients with AFib and cancer. For example, when patient with existing cancer is newly diagnosed with AFib with low risk of ischemic stroke (i.e., CHA2DS2-VASc < 2), whether this patient should start the treatment immediately or wait until they reach a higher risk of ischemic stroke (i.e., CHA2DS2-VASc ≥ 4 or CHA2DS2-VASc ≥ 6). In some patient groups, anticoagulation is withheld because of a perceived unfavorable risk-benefit ratio [16]. Since patients with AFib and cancer are at higher risk of stroke and bleeding [5, 6], initiating OAC at low risk may be beneficial in stroke prevention, but may result in increased risk of bleeding. On the other hand, late OAC initiation may prevent risk of bleeding but increase risk of stroke in these patients. Although recent studies found that patients with AFib and cancer who had CHA2DS2-VASc ≥ 4 were more likely to receive OACs compared to patients with lower risk of stroke [17], the benefit of this treatment strategy has never been explored. Determining the benefit of initiating OACs at different levels of risk of stroke is critically important to optimize the management of patients with AFib and cancer.

In this study, we assessed and compared benefits of multiple OAC initiation treatment strategies at different thresholds of risk of stroke among newly diagnosed AFib patients with cancer using the target trial framework. The target trial framework is the application of design principles from randomized controlled trials (RCTs) to the analysis of observational data to improve the quality of observational epidemiology when a comparator trial is not yet available or feasible [18].

Materials and methods

Study design and data source

We used the target trial framework and STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) checklist to conduct and report a retrospective cohort study using the SEER registry linked to the Medicare database (cancer sites: breast, prostate, and lung) from 2011–2019 [19, 20]. The SEER registry contains patient demographics, primary tumor site, tumor characteristics, and cancer stage at diagnosis, treatment, and follow-up of cancer patients across the US [21]. The Medicare data add to SEER data health care services utilization (medical claims, procedures, and prescriptions) [22]. Table 1 summarizes the protocol for target trial and emulation procedure. The study design and study timeline are illustrated by Figure S1.

Table 1 Protocol for a target trial and emulation procedure using the SEER-Medicare database
Full size table

Study sample and eligibility criteria

Study sample

We included individuals aged ≥ 66, newly diagnosed non-valvular atrial fibrillation (NVAF) between January 1, 2012 and December 31, 2019, defined as any International Classification of Disease-9th Revision-Clinical Modification (ICD-9-CM) codes 427.31 or 427.32 or any International Classification of Disease-10th Revision-Clinical Modification (ICD-10-CM) codes I48.xx in any position on one Medicare inpatient claim or on two outpatient claims at least 7 days but < 1 year apart [23]. We retained patients with breast, lung, or prostate cancer—the most common cancer types with AFib—from the SEER file at any time before the initial AFib diagnosis (ICD-O-3 codes C50.0-C50.9 for breast; C34.0, C34.1, C34.2, C34.3, C34.8, C34.9, C33.9 for lung; C61.9 for prostate cancer). Patients were required to continuously enroll in Medicare part A, B, D, and without Medicare Advantage or Health Maintenance Organization (HMO) for at least 12 months before initial NVAF diagnosis.

Exclusion criteria

We adapted exclusion criteria from clinical trials [24, 25]. In addition, patients were excluded if they had any other indication than NVAF, contraindication to OACs or had the event of interest shortly before cohort entry: (1) any OAC use during the 12 months baseline period, (2) presence of valvular diseases, repair, or replacement, venous thromboembolism, or joint replacement during the 12 months baseline period, (3) any stroke within 14 days before first NVAF diagnosis, (4) major surgery (i.e., hip fracture, cardiac surgery) or critical bleeding within 30 days before first NVAF diagnosis, (5) renal impairment stage 5 or end-stage renal diseases during the 12 months baseline period. All ICD codes for identification of these conditions can be found in Table S1, Supplementary materials.

Treatment strategies and assignments

In the hypothetical target trial, eligible individuals were randomly assigned to one of the following 5 treatment strategies: (Regimen 1) initiated OAC when CHA2DS2-VASc ≥ 1, (Regimen 2) initiated OAC when CHA2DS2-VASc ≥ 2, (Regimen 3) initiated OAC when CHA2DS2-VASc ≥ 4, (Regimen 4) initiated OAC when CHA2DS2-VASc ≥ 6, and (Regimen 5) never initiated OAC (reference group). In the emulation of target trial, cloning, censoring, and weighting approach were used to mimic the randomization [26]. OAC prescriptions (including warfarin and dabigatran, apixaban, rivaroxaban, edoxaban) were identified from Medicare Part D Prescription Drug Event (PDE) files using national drug code (NDC)) [27]. CHA2DS2-VASc scores were computed from Medicare claims during12 months before AFib diagnosis and monthly during follow-up, based on a composite of conditions including congestive heart failure (1 point), hypertension (1), age ≥ 75 (2 point), diabetes mellitus (1 point), prior stroke, TIA, or thromboembolism (2 point), vascular disease (e.g. peripheral artery disease, myocardial infarction, aortic plaque) (1 point), age 65–74 years (1 point), and sex category (1 point) [15].

Follow-up

The follow-up started at the initial NVAF diagnosis (index date) and ended at the occurrence of a study outcome, the end of administrative censoring (12 months after baseline), death (all-cause deaths from the SEER and Medicare files via the variables of “Date of Death Flag”), loss to follow-up (the earliest of 30 days after the end of continuous Medicare Part A, B, or D enrollment or enrollment in an HMO), or December 31, 2019, whichever came first.

Outcomes

The outcomes of interest were ischemic stroke and major bleeding. We defined major bleeding and ischemic stroke using validated algorithms defined by ICD-9-CM and ICD-10-CM codes in the primary diagnosis from Medicare medical claims files [28, 29, 30].

Covariates

Covariates selected from prior literature were adjusted in the analysis [24, 28, 31]. Time-fixed baseline covariates were extracted within 12-month period prior to first AFib diagnosis, including: demographics (index age, sex, race/ethnicity, calendar year, geographical region, urbanicity), socioeconomic factors (household median income, percentage of household with education level below high school, and Medicaid eligibility), comorbidity risk scores (CHA2DS2-VASc, HAS-BLED, and Comorbidity Scores SEER-Medicare version 2021 by NCI) [32], individual comorbidities (asthma/chronic obstructive pulmonary disease, hematological disorders, dementia, depression, thrombocytopenia, acute kidney disease (AKD), peptic ulcer disease), cancer characteristics (time from cancer diagnosis to the onset of AFib, cancer type, cancer stage, tumor grade, active cancer status [28, 31]), cancer treatment (radiation, and cancer-directed surgery, and potentially interacting antineoplastic agents), and medication history (angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers, calcium channel blockers, beta blockers, antiarrhythmic medications, diuretics, statin, pump proton inhibitors, and serotonin reuptake inhibitors). Socioeconomic factors such as household income and education level are available at the aggregate area level. If patients had more than one type of cancer before AFib diagnosis, we retained the most recent cancer diagnosis. Cancer treatments were obtained from diagnosis codes or procedures codes within 30 days before AFib diagnosis [33]. Due to high proportion of missing values, other cancer characteristics such as number of regional nodes examined, tumor size, TMN classification, and other cancer-type specific characteristics (i.e., hormone receptor status (HR) and human epidermal growth factor receptor 2 (HER2) for breast cancer or histologic type for lung cancer) were used for descriptive purpose but not adjusted in the models [34]. The following time-varying covariates were extracted monthly after AFib onset, including CHA2DS2-VASc score, HAS-BLED score, thrombocytopenia, AKD, radiation, cancer-directed surgery, and use of potentially interacting treatment with OACs. These variables may change over time and has an impact on outcomes and the OAC prescription in each month [35, 36]. All diagnosis codes, drug codes, and procedure codes for covariate ascertainment were described in Table S1, Supplementary materials. We used multiple imputation algorithms (fully conditional specification with logistic regression for categorical variables and predictive mean matching for continuous variables) to impute missing values (urbanicity, cancer summary stage, percentage of residents living below poverty, and percentage of non-high school graduates—Table S2, Supplementary Materials) [37].

Causal contrast

We computed the observational analog of per-protocol (PP) effects because cloning-censoring-weighting approach was used [38]. Those who were not compliant to their assigned treatment regimes were censored during follow-up.

Statistical analysis

Descriptive statistics such as mean and standard deviation (SD) for continuous variables, frequency count and percentage for categorical variables were used to describe the study sample. We quantified the incidence rates of ischemic stroke and major bleeding for each treatment strategy. In the main analysis, cloning-censoring-weighting procedure was used to estimate the treatment effect of 5 treatment strategies [26, 38]. Briefly, we created 5 copies for each individual’s person-time data, then assigned each copy to 5 treatment strategies. At baseline, replicates with baseline CHA2DS2-VASc score that did not comply with their assigned strategy were removed from the dataset. Next, replicates whose data were no longer consistent with their assigned strategy during follow-up were censored. To adjust for potential confounding during follow-up, unstabilized time-varying censoring weights were used. Cumulative weights at each time points due to protocol violation are the product of inverse probability of weights for treatment initiation (IPTWs) and inverse probability of censoring weights (IPCWs) due to loss to follow-up (See Technical Appendix). Total weights were truncated at 99th percentile to avoid extreme weights. To estimate the treatment effects for 5 strategies, we fitted a weighted pooled logistic regression estimated by generalized estimating equations (GEEs) with robust variance estimators. We obtained summary hazard ratios (HRs) with 95% confidence interval (95% CIs) and created weighted survival curves comparing four active treatment strategies with the reference strategy. Statistical analyses were conducted using SAS 9.4 (SAS Institute, Inc., Cary, NC, USA).

Subgroup analyses and sensitivity analyses

We conducted the following subgroup analyses: cancer type (breast, lung, prostate), cancer status at baseline (active, history), cancer stage (in situ, local, regional, and distant), and tumor grade (I, II, and III). In addition, a series of sensitivity analyses were conducted. First, we extended follow-up time to 36 months to explore long-term outcomes for each treatment strategies. Second, since metastatic cancer patients were removed from randomized control trials due to their short live expectancy, we excluded them in this sensitivity analysis [24, 25]. Third, we removed individuals with thrombocytopenia at baseline, since these patients are at elevated risk of bleeding and may not eligible for OAC initiation [39, 40]. Fourth, we further truncated weights at 95th percentile to test the robustness of the treatment effects to the presence of extreme weights [59]. Using stabilized variance may reduce the variance and avoid extreme weights, but the stabilization procedures might not valid for cloning-censoring-weighting approach like our study [60]. However, the results remain robust in the sensitivity analysis after we truncated the weights to 95th percentile. Sixth, our findings may not be generalizable beyond the target population in this study (i.e., patients with newly diagnosed cancer on existing AFib, other cancer types, or non-Medicare populations). Future studies are warranted to investigate the benefits and risks of OACs among patients with other advanced cancer such as hematological cancers due to higher risk of stroke and bleeding in this population [61, 62].

Although our study emulated a hypothetical target trial and adopted components (i.e., inclusion/exclusion criteria, outcomes, and follow-up) from prior RCTs [24, 25], several components were not perfectly mimicked. Specifically, RCTs removed patients platelet count < 90,000/µL, systolic blood pressure ≥ 180 mmHg or diastolic blood pressure ≥ 100 mmHg, or creatinine clearance less than 30 mL/min at the screening visit [24, 25]. However, these lab values were not available in SEER-Medicare data. We therefore replaced these conditions with the presence of thrombocytopenia or severe renal impairments. In addition, several components were defined by clinicians’ assessment in RCTs, such as AFib definition by an electrocardiogram (ECG) document or congestive heart left failure with ventricular ejection fraction ≤ 35% [24, 25]. Moreover, therapeutic responses and adverse events were monitored with international normalized ratio (INR) and liver-function tests, which are not available in our emulation [24, 25]. Although non-randomization component has been criticized as the main source of bias in observational studies, it was not proven as the primary cause of inconsistency between observational and RCTs. Successful emulation without randomization has been conducted to benchmark the estimates from observational studies to RCTs and vice versa, especially during the COVID pandemic when the need of RCTs could not be met due to time constraint [63,64,65]. In this study, randomization was assumed using a cloning-censoring-weighting approach and the adjustment of measured time-varying confounding during follow-up [26]. It is also necessary to highlight that misspecification of time zero has been found as the major source of failure in obtaining valid causal effects in observational studies [19, 20]. In our study, we specified time zero by aligning the time when all inclusion and exclusion criteria met, start of treatment strategies, and follow-up. Such practice removed immortal time bias and prevalent user bias from our analysis [19, 20].

Our study has many strengths. Using the target trial emulation framework to design the study and the cloning–censoring–weighting approach, we explicitly designed a trial to answer a causal question. We included patients with newly AFib diagnosis and followed them after AFib diagnosis to remove survival bias. In addition, we further adjusted for important confounders such as cancer characteristics by the linkage between Medicare administrative claims data and the SEER registry. We pre-specified a wide range of subgroup analyses and sensitivity analyses to confirm the robustness of the main analysis. Our findings are expected to help clinicians’ decision making in optimizing OAC initiation and individualizing their decisions based on patient’s cancer characteristics.

Conclusion

Among cancer patients with new AFib diagnosis, OAC initiation at higher risk of stroke (CHA2DS2-VASc score ≥ 6) may be more beneficial in preventing ischemic stroke and bleeding. Patients with advanced cancer status or low life-expectancy may initiate OACs when CHA2DS2-VASc score ≥ 6.