Citations

Introduction: Medicines don’t work in those who don’t take them. Although novel oral anticoagulants (NOACs) are more convenient than warfarin due to fewer drug-drug interactions and less need for laboratory monitoring, their considerable costs and lack of regular interactions with the healthcare system may also contribute to poor adherence. Little is known about the persistence and adherence in the era of NOACs, nor how such behaviors are associated with the risk of ischemic stroke and transient ischemic attack (TIA) among patients of varying baseline risk. Methods: We conducted a retrospective analysis using a large U.S. commercial insurance database, and identified 61,646 patients with atrial fibrillation who initiated one of OACs (warfarin, dabigatran, rivaroxaban and apixaban) between October 2010 and September 2014. We defined discontinuation as being off OACs for at least 3 months and adherence as the proportion of days covered of at least 80%. Cox proportional hazard models was used to examine whether the time that patients were off OACs, including both the gaps between medication fills and the time of discontinuation, increased the risk of ischemic stroke and TIA. Results: Approximately 1 in 3 patients discontinued OACs within one year after initiation and 1 in 10 switched to another OAC. The estimated likelihood of adherence was 0.77 during the time when patients were on their first-line medications. Apixaban had the highest continuation rate (65% at 1 year), while rivaroxaban had the best adherence (predicted probability of adherence 0.83). The event rate of stroke was 1.6 per 100 person years. Patients with high baseline risk (CHA2DS2-VASc ≥2) were more likely to have a stroke when they were off OACs for 1-3 months (HR=1.47, p<0.01), 3-6 months (HR=1.78, p<0.001) and ≥6 months (HR=2.16, p<0.001), compared to off OACs for less than a week. No significant effect was found among low-risk patients (CHA2DS2-VASc 0 or 1). Switching to another OAC was related to 22% increased risk of stroke (p=0.05). Conclusions: Despite their greater convenience relative to warfarin, discontinuation and nonadherence to NOACs is high. Lack of persistence and adherence was associated with increased risk of ischemic stroke and TIA among patients with elevated baseline risk.

Introduction: Recent studies showed post-operative atrial fibrillation (POAF) is associated with increased stroke risk - even approaching that of unprovoked AF. This raises the question of whether patients with POAF should receive anticoagulation. We aim to compare different anticoagulation strategies in patients with POAF after cardiac and non-cardiac surgeries. Methods: Using a large national administrative database (OptumLabs Data Warehouse), we identified 33,981 patients with POAF between 1/1/2013-9/30/2019. Patients were divided to four groups based on the initial treatment within 30 days of POAF: (1) no anticoagulation (N=28,807) (2) apixaban (N=2178) (3) rivaroxaban (N=1320) (4) warfarin (N=1676). Follow up started at the 30 th day after POAF to exclude patients who died shortly after surgery and minimize immortal time bias. The follow up continued until patients changed treatment strategies, insurance, or died. 1:1 propensity score matching was performed between each anticoagulant to no treatment to balance on 86 baseline characteristics. Results: The mean age was 71.1±11.2 yr; mean CHA 2 DS 2 -VASc was 4.1±1.8; 29.1% received cardiac surgeries; 15.2% received anticoagulation within 30 days of POAF. The event rate per 100 person years was 1.58 for stroke in no-anticoagulation group, and 1.29 for anticoagulation group; 2.86 for major bleeding in no-anticoagulation group and 4.50 for anticoagulation group. After matching, apixaban and rivaroxaban were associated with a lower risk of stroke, but a higher risk of major bleeding; warfarin was not associated with a lower risk of stroke, but a higher risk of major bleeding (Figure). The effect was consistent across subgroups, e.g., age and surgery type. Conclusions: In patients with POAF, the stroke rate was lower than what was typically observed in clinical AF. Treatment with apixaban and rivaroxaban immediately after POAF was associated with a reduced risk of stroke but an increased risk of major bleeding.

Introduction: Watchman has been increasingly used in AF patients who have difficulties taking anticoagulation, but the RCTs only compared Watchman to warfarin. There was also a signal of increased ischemic stroke with Watchman in comparison to warfarin in RCTs, which made some clinicians question the fundamental premise that Watchman can reduce stroke. We aimed to compare Watchman to no anticoagulation in a large national cohort of patients in the US. Methods: We identified AF patients treated with Watchman (N=7,309) or no anticoagulation (N=1,234,543) between 2015-2017 from the OptumLabs Data Warehouse, which is the first to link private insurance, Medicare Advantage, and Medicare Fee-for-Service records at patient level longitudinally. One-to-one propensity score matching was used to balance patients on demographics and medical history. The primary outcomes were ischemic stroke or systemic embolism (hereafter referred to as stroke) and major bleeding. Secondary outcomes included all-cause mortality and intracranial bleeding. Results: In the unmatched cohort, the mean age was 76.6±6.4 yr in the Watchman group and 76.6 ±8.5 yr in the no anticoagulation group, but Watchman patients were more likely to have a history of bleeding (44.9% vs 13.7%) and intracranial bleeding (9.5% vs 2.6%). After matching, the two groups were similar on 18 baseline characteristics. Watchman was associated with a lower risk of stroke and mortality, but a higher risk of major bleeding and a similar risk of intracranial bleeding. The associations were largely consistent across subgroups, including those with relative contraindications to anticoagulation (Figure). Conclusions: In AF patients who had difficulties taking anticoagulation, Watchman offers an alternative approach to reduce stroke and mortality. The results provide important evidence to inform clinical decision making in the absence of RCTs, since 40% of AF patients in routine practice are not on anticoagulation.

Introduction: An artificial intelligence (AI) algorithm applied to electrocardiography (ECG) during normal sinus rhythm (NSR) has been shown to predict concomitant atrial fibrillation (AF). We sought to characterize the value of AI-ECG as a predictor of future AF and assess its performance compared to other clinical prediction scores in a population-based sample. Methods: We calculated the AI-ECG probability during NSR in patients who enrolled in the population-based Mayo Clinic Study of Aging with at least one ECG in NSR within two years prior and no history of AF at the time of the baseline study visit. The cumulative incidence of AF was estimated for strata defined by AI-ECG probability and CHARGE-AF score. Cox proportional hazards were fit to assess the independent prognostic value and interaction of AI-ECG probability and CHARGE-AF score. Concordance (c) statistics were calculated for AI-ECG probability, CHARGE-AF score and combined AI-ECG and CHARGE-AF score. Results: A total of 1,936 patients with a median age 75.8 (quartile range [QR] 70.4, 81.8) years, median CHARGE-AF score 14.0 (QR 13.2, 14.7) and median CHADS2VASC score 3 (QR 2, 4) were included in the analysis. The cumulative incidence of AF increased in a stepwise fashion across quartiles of AI-ECG probability and CHARGE-AF score (Figure 1). When compared in the same model, both AI-ECG probability (hazard ratio [HR] 1.76, 95% confidence interval [CI] 1.51-2.04) and CHARGE-AF score (HR 1.90, 95% CI 1.58-2.28) independently predicted AF without significant interaction (p=0.54). C statistics were 0.69 (95% CI 0.66-0.72) for AI-ECG probability, 0.69 (95% CI 0.66-0.71) for CHARGE-AF and 0.72 (95% CI 0.69-0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the Mayo Clinic Study of Aging, both the AI-ECG probability and CHARGE-AF score independently predicted time to AF. The AI-ECG may offer a means to assess risk with a single test without requiring manual or automated clinical data abstraction.

The possibilities and implementation of wearable cardiac monitoring beyond atrial fibrillation are increasing continuously. This review focuses on the real-world use and evolution of these devices for other arrhythmias, cardiovascular diseases and some of their risk factors beyond atrial fibrillation. The management of nonatrial fibrillation arrhythmias represents a broad field of wearable technologies in cardiology using Holter, event recorder, electrocardiogram (ECG) patches, wristbands and textiles. Implementation in other patient cohorts, such as ST-elevation myocardial infarction (STEMI), heart failure or sleep apnea, is feasible and expanding. In addition to appropriate accuracy, clinical studies must address the validation of clinical pathways including the appropriate device and clinical decisions resulting from the surrogate assessed.

Background: Atrial fibrillation (AF) is an established risk factor for ischemic stroke, but it can be paroxysmal and may go undiagnosed. An artificial intelligence (AI)-enabled ECG acquired during normal sinus rhythm was recently shown to detect silent AF. The objective of this study was to determine if AI-ECG AF score is associated with presence of cerebral infarcts. Methods: Participants from a population-based study ages 30 to 95 years with T2 fluid attenuation inversion recovery (FLAIR) MRI obtained between October 10, 2011, and November 2, 2017 were considered for inclusion. Participants without ECG were excluded. AI-ECG score was calculated using most recent ECG with normal sinus rhythm at the time of MRI. Presence of infarcts was determined on FLAIR MRI scans. Logistic regression was run to evaluate the relationship between AI-ECG AF score and presence of cerebral infarcts. Similar analyses were performed using history of AF rather than AI-ECG AF score as predictor. Age and sex were included as covariates. We also examined whether a high-threshold AI-ECG score was associated with infarcts. In a prior study, an AI-ECG AF score > 0.5 was associated with a cumulative incidence of AF of 21.5% at 2 years and 52.2% at 10 years. Results: This study included 1,373 individuals. Average age was 69.6 years and 53% of participants were male. There were 136 (10%) individuals with ECG-confirmed AF; 1237 (90%) participants had no AF history. Of participants with AF, 23% (n=31) were on anticoagulation, 47% (n=64) were on antiplatelet and 18% (n=24) were on dual therapy. Only 1.3% (n=16) of patients without AF were on anticoagulation and 47% (n=578) were on antiplatelet therapy. Ischemic infarcts were detected in 214 (15.6%) patients. As a continuous measure AI-ECG was associated with infarcts but not after adjusting for age and sex (p=0.46). AI-ECG AF score > 0.5 was associated with infarcts ( p < 0.001); even after adjusting for age and sex ( p = 0.03). History of AF was also associated with infarcts after adjusting for age and sex ( p = 0.018). Conclusion: AI-ECG AF score and history of AF were associated with presence of cerebral infarcts after adjusting for age and sex. This tool could be useful in select patients with cryptogenic stroke but further investigation would be required.

Abstract Rationale In October 2014, the antifibrotic medications pirfenidone and nintedanib became the first medications approved by the U.S. Food and Drug Administration for use in patients with idiopathic pulmonary fibrosis (IPF). Since approval, there has been no nonregistry analysis of the real-world adoption of these medications in everyday clinical practice. Objectives To evaluate the adoption, persistence, and out-of-pocket (OOP) costs of pirfenidone and nintedanib since their approval in the United States in 2014. Methods A retrospective cohort analysis was performed by identifying privately insured and Medicare Advantage beneficiaries with IPF. We then split the patients into three cohorts: those who were untreated and those who filled a prescription for either pirfenidone or nintedanib between October 1, 2014, and July 31, 2019. The primary outcome was adoption of the medications. Secondary outcomes included medication persistence and prescription drug costs. Results A total of 10,996 patients with IPF were identified in the data set. A minority of patients (26.4%) with IPF identified in the cohort had started either medication since approval in 2014, with the adoption of both medications being comparable at around 13.2%. Those receiving the medications were younger (72 vs. 73.9 yr; P &lt; 0.0001) and healthier (3.9 vs. 4.9 comorbidities; P &lt; 0.0001) than those not receiving treatment. Men were significantly more likely to receive treatment than woman (30.0% vs. 21.9%; P &lt; 0.0001). Among treated patients, 42.8% discontinued the medications during the study period. Patients’ OOP expenses per month were high for both drugs (mean, $397.51 for nintedanib; mean, $394.49 for pirfenidone). Conclusions The adoption of both the antifibrotic medications in the United States in everyday practice has been low since approval and may be associated with the high OOP cost.

Background: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI–enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology–AF) in a population-based sample. Methods: We calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score. Results: A total of 1936 participants with median age 75.8 (interquartile range, 70.4–81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2–14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51–2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58–2.28]) independently predicted future AF without significant interaction ( P =0.54). C statistics were 0.69 (95% CI, 0.66–0.72) for AI-ECG AF model output, 0.69 (95% CI, 0.66–0.71) for CHARGE-AF, and 0.72 (95% CI, 0.69–0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.