Previous studies have established that patients with sickle cell disorders (SCDs), or conditions associated with a genetic mutation of the β‐globin gene, are at increased risk for atrial fibrillation (AF). 1 , 2 There is a growing body of research demonstrating rhythm control improves outcomes in patients with AF. However, many AF studies have not evaluated patients with SCDs, and it is unknown how frequently they are managed with rhythm control. Therefore, we sought to determine the frequency of AF ablation, antiarrhythmic drug (AAD) use, and direct current cardioversion (DCCV) in patients with SCD compared with a propensity score–matched (PSM) cohort without SCD.
The TriNetX Research Network can be accessed at https://live.trinetx.com. However, access to TriNetX deidentified data requires an approved user license and data sharing agreement and may incur cost.
A retrospective cohort study was performed using the TriNetX Analytics Research Network. TriNetX is a globally federated health research network using electronic health record data from >100 million patients. Patients aged ≥18 years with AF were identified by International Classification of Diseases, Tenth Revision (ICD‐10), codes from January 1, 2014 to August 1, 2022. The patient population was separated into 2 cohorts based on presence or absence of SCD. Patients were 1:1 PSM for age, self‐identified gender and race, hypertension, ischemic heart disease, heart failure, cardiomyopathy, cerebrovascular disease, atherosclerosis, diabetes, chronic kidney disease, neoplasms, left ventricular ejection fraction, body mass index, and cardiovascular medications. TriNetX uses a greedy nearest‐neighbor matching with a caliper of 0.1 pooled SDs. The primary end point was 3‐year rate of AF ablation within the 8‐year study period (identified by Current Procedural Terminology codes). Secondary end points included new or ongoing AAD use and DCCV. Statistical analysis was performed using the TriNetX platform, with significance set at P<0.05 (2‐sided). TriNetX calculates hazard ratios (HRs) and CIs using R survival package version 3.2‐3 with the proportional hazard assumption tested using Schoenfeld residuals. This study was exempt from Institutional Review Board review and does not require informed consent because it did not involve individually identifiable patient data.
A total of 1 568 951 patients with AF were identified, including 2925 diagnosed with SCD. After PSM, 2918 matched pairs were analyzed. A total of 78.2% of patients self‐identified as Black. Baseline characteristics were well balanced, with standardized mean difference <0.10 (Table). Patients with SCD were significantly less likely to undergo AF ablation (cumulative risk, 1.0% SCD versus 1.7% PSM cohort; HR, 0.56 [95% CI, 0.36–0.89]; P=0.01), initiate or continue AAD (cumulative risk, 22.1% SCD versus 25.8% PSM cohort; HR, 0.82 [95% CI, 0.74–0.91]; P<0.001), or undergo DCCV (cumulative risk, 5.2% SCD versus 6.8% PSM cohort; HR, 0.74 [95% CI, 0.60–0.92]; P=0.005).
Table 1.
Baseline Characteristics of Patients With AF With and Without SCDs Before and After PSM
| Before PSM | After PSM | |||||
|---|---|---|---|---|---|---|
| AF and no SCD (n=1 568 951) | AF and SCD (n=2925) | Standardized mean difference | AF and no SCD (n=2918) | AF and SCD (n=2918) | Standardized mean difference | |
| Demographics | ||||||
| Age, y | 68.5±12.2 | 56.3±16.4 | 0.845 | 56.8±15.9 | 56.4±16.3 | 0.025 |
| Male sex, % | 58.7 | 43.3 | 0.311 | 42.8 | 43.4 | 0.012 |
| Race and ethnicity, % | ||||||
| Asian | 1.5 | 0.7 | 0.076 | 0.5 | 0.7 | 0.031 |
| Black | 9.8 | 78.1 | 1.892 | 78.3 | 78.0 | 0.007 |
| Hispanic or Latino | 3.2 | 2.6 | 0.036 | 2.9 | 2.6 | 0.021 |
| Unknown race and ethnicity | 10.5 | 6.7 | 0.136 | 5.8 | 6.7 | 0.038 |
| White | 77.8 | 14.5 | 1.646 | 15.3 | 14.5 | 0.021 |
| Comorbidities, % | ||||||
| Hypertensive diseases | 45.7 | 70.4 | 0.517 | 71.8 | 70.4 | 0.033 |
| Ischemic heart disease | 24.6 | 38.2 | 0.297 | 37.6 | 38.2 | 0.011 |
| Heart failure | 16.4 | 39.4 | 0.529 | 41.2 | 39.3 | 0.038 |
| Cardiomyopathy | 6.8 | 18.4 | 0.353 | 18.7 | 18.3 | 0.011 |
| Cerebrovascular disease | 12.6 | 26.4 | 0.352 | 26.3 | 26.3 | 0.001 |
| Atherosclerosis | 5.8 | 13.1 | 0.251 | 12.6 | 13.2 | 0.016 |
| Diabetes | 20.5 | 39.3 | 0.421 | 40.3 | 39.4 | 0.018 |
| Chronic kidney disease | 12.1 | 35.6 | 0.573 | 36.6 | 35.6 | 0.021 |
| Neoplasms | 23.9 | 42.4 | 0.402 | 42.8 | 42.3 | 0.010 |
| Medications, % | ||||||
| β‐Blockers | 40.9 | 63.4 | 0.462 | 64.9 | 63.3 | 0.032 |
| Calcium channel blockers | 24.8 | 47.2 | 0.479 | 49.3 | 47.2 | 0.043 |
| Antiarrhythmics | 30.9 | 60.9 | 0.631 | 61.8 | 60.8 | 0.022 |
| Digoxin | 4.6 | 6.4 | 0.079 | 6.8 | 6.4 | 0.017 |
| Anticoagulants | 39.1 | 69.9 | 0.65 | 71.2 | 69.8 | 0.03 |
| Diuretics | 32.5 | 59.9 | 0.572 | 61.7 | 59.9 | 0.038 |
| Antilipemic agents | 35.8 | 45.1 | 0.19 | 46.0 | 45.2 | 0.017 |
| ACE inhibitor | 22.5 | 38.3 | 0.348 | 40.4 | 38.3 | 0.043 |
| Angiotensin II inhibitor | 14.8 | 22.5 | 0.205 | 22.5 | 22.5 | 0.002 |
| Antihypertensives | 14.8 | 37.2 | 0.528 | 37.8 | 37.1 | 0.014 |
| Miscellaneous | ||||||
| LV ejection fraction, % | 54.4±14.2 | 52.3±16.1 | 0.135 | 52.8±15.9 | 52.3±16.1 | 0.027 |
| Body mass index, kg/m2 | 29.9±6.9 | 29.8±7.7 | 0.014 | 29.8±7.8 | 29.8±7.7 | 0.004 |
Data are given as mean±SD unless otherwise indicated. ACE indicates angiotensin‐converting enzyme; AF, atrial fibrillation; LV, left ventricular; PSM, propensity score matching; and SCD, sickle cell disorder.
Subgroup analyses showed consistently lower AAD use and DCCV in Black patients (AAD: HR, 0.77 [95% CI, 0.59–0.99]; P=0.048; DCCV: HR, 0.42 [95% CI, 0.24–0.73]; P<0.01) and White patients with SCD (AAD: HR, 0.86 [95% CI, 0.76–0.98]; P=0.02; DCCV: HR, 0.72 [95% CI, 0.56–0.93]; P=0.01). Similarly, patients with heart failure and SCD had lower AAD use and DCCV compared with those without SCD (AAD: HR, 0.87 [95% CI, 0.77–0.98]; P=0.02; DCCV: HR, 0.74 [95% CI, 0.58–0.94]; P=0.01). However, no significant differences were observed in AF ablation by race or heart failure. Subcohorts of patients with SCD and paroxysmal AF were significantly less likely to receive AADs (HR, 0.87 [95% CI, 0.76–0.99]; P=0.046). Subcohorts of patients with SCD and persistent/chronic AF were significantly less likely to have ablation (HR, 0.49 [95% CI, 0.29–0.80]; P<0.010).
In our exploratory database study, patients with SCD were significantly less likely to have AF ablation, be prescribed AADs, or undergo DCCV compared with a PSM cohort. Differences in the use of AAD and DCCV were consistent across racial and heart failure/cardiomyopathy subgroups. The difference observed in AF ablation may be driven by variation in the treatment of patients with persistent/chronic AF. To our knowledge, this is the first study to specifically evaluate rhythm control strategies in patients with SCD.
Although an association between SCD and AF has been established, the mechanism underlying this association remains unclear. Mechanisms that have been proposed include the development of hemolytic anemia leading to iron overload, increased myocardial fibrosis, and damage to the conduction system secondary to repeated vaso‐occlusive crises. 3 , 4
We offer several hypotheses to explain the decreased use of rhythm control in patients with SCD. First, physicians may be hesitant to pursue rhythm control because of concerns about the need to discontinue anticoagulation given patients with SCD have lower baseline hemoglobin levels. Second, patients with SCD have significantly higher health care costs compared with adults without SCD. 5 Increased medical costs may prevent patients from pursuing procedures, such as AF ablation. Last, it is possible that underlying stigma or unconscious bias toward individuals with SCD may pose a potential barrier to rhythm control.
Our study suggests that patients with SCD and AF are less likely to be managed with rhythm control therapies compared with patients with PSM. In our efforts to provide equitable care for patients with SCD, prospective studies are needed to identify possible barriers to ablation/rhythm control in this population.
Sources of Funding
None.
Disclosures
None.
This article was sent to Kevin F. Kwaku, MD, PhD, Associate Editor, for review by expert referees, editorial decision, and final disposition.
For Sources of Funding and Disclosures, see page 3.
References
- 1. Douce DR, Soliman EZ, Naik R, Hyacinth HI, Cushman M, Winkler CA, Howard G, Lange EM, Lange LA, Irvin MR, et al. Association of sickle cell trait with atrial fibrillation: the REGARDS cohort. J Electrocardiol. 2019;55:1–5. doi: 10.1016/j.jelectrocard.2019.04.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Garadah T, Gabani S, Alawi MA, Abu‐Taleb A. Prevalence and predisposing factors of atrial fibrillation in a multi‐ethnic society: the impact of racial differences in Bahrain. Open J Cardiovasc Surg. 2011;4:9–16. doi: 10.4137/OJCS.S8032 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Sachdev V, Rosing DR, Thein SL. Cardiovascular complications of sickle cell disease. Trends Cardiovasc Med. 2021;31:187–193. doi: 10.1016/j.tcm.2020.02.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Patel U, Desai R, Hanna B, Patel D, Akbar S, Zubair M, Kumar G, Sachdeva R. Sickle cell disease‐associated arrhythmias and in‐hospital outcomes: insights from the National Inpatient Sample. J Arrhythmia. 2020;36:1068–1073. doi: 10.1002/joa3.12418 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Johnson KM, Jiao B, Ramsey SD, Bender MA, Devine B, Basu A. Lifetime medical costs attributable to sickle cell disease among nonelderly individuals with commercial insurance. Blood Adv. 2022;7:365–374. doi: 10.1182/bloodadvances.2021006281 [DOI] [PMC free article] [PubMed] [Google Scholar]