Abstract 14368: Artificial Intelligence-Electrocardiography to Predict Time to Atrial Fibrillation: An Analysis of Mayo Clinic Study of Aging

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Georgios Christopoulos ◽  
Camden L Lopez ◽  
Xiaoxi Yao ◽  
Zachi I Attia ◽  
Jonathan Graff-radford ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Mayo Clinic ◽  
Cumulative Incidence ◽  
Proportional Hazards ◽  
Normal Sinus Rhythm ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Normal Sinus ◽  
Clinic Study

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.

scholarly journals Artificial Intelligence–Electrocardiography to Predict Incident Atrial Fibrillation

2020 ◽  
Vol 13 (12) ◽  
Author(s):  
Georgios Christopoulos ◽  
Jonathan Graff-Radford ◽  
Camden L. Lopez ◽  
Xiaoxi Yao ◽  
Zachi I. Attia ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Proportional Hazards ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Hazard Ratio ◽  
Model Output ◽  
Genomic Epidemiology ◽  
Logit Transformation ◽  
Cox Proportional Hazards Models

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.

Abstract 57: Increased Fitness Attenuates the Risk for Developing Atrial Fibrillation in African-Americans

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Charles Faselis ◽  
Peter Kokkinos ◽  
John Peter Kokkinos ◽  
Apostolos Tsimploulis ◽  
Fiorina Kyritsi ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
African Americans ◽  
Exercise Capacity ◽  
Proportional Hazards ◽  
Normal Sinus Rhythm ◽  
Cardiac Risk ◽  
Cox Proportional Hazards ◽  
Normal Sinus ◽  
Metabolic Equivalents ◽  
Cox Proportional Hazards Models

Introduction: Atrial fibrillation (AF) is associated with increased mortality risk. Some studies indicate that endurance training may increase the rate of progression to AF. However, others dispute this notion. Furthermore, fitness status and atrial fibrillation in African-Americans has not been investigated. Methods: From 1986 to 2011, a total of 4,401 African-Americans with normal sinus rhythm (mean age 56±11) underwent a routine exercise tolerance testing at Veterans Affairs Medical Centers Washington DC. During a mean follow-up period of 8.0±4.7 years 361 (8.2%) developed AF. To assess the role of fitness status in the development of AF, we formed the following four fitness categories based on peak workload achieved (metabolic equivalents; METs): Least-Fit: ≤ 5METs (≤20%; n=531); Low-Fit: 5.5-7.2 METs (20.1%-50%; n=1,367); Moderate-Fit:7.3-9.0 METs (50.1%-80%; n=1,775); and High-Fit: >9 METs (>80%; n=728). Cox proportional hazards models were applied after adjusting for age, BMI, race, gender, CV disease, CV medications, and cardiac risk factors. P-values <0.05 using two sided tests were considered statistically significant. Results: The association between exercise capacity and the risk for developing AF was inverse and graded. For every 1-MET increase in exercise capacity the AF-risk was 25% lower (HR=0.75; CI: 0.71-0.81; p<0.001). When compared to the Least-Fit category, the risk for developing AF was 36% lower (HR=0.64; CI: 0.48-0.83; p=0.001) in Low-Fit; 66% (HR=0.44; CI: 0.33-0.60; p<0.001) in Moderate-Fit; and 76% (HR=0.24; CI: 0.14-0.40; p<0.001) in High-Fit individuals. Conclusions: Aerobic fitness is inversely and independently associated with lower risk for developing AF.

Abstract P005: Comparative Effectiveness of Rivaroxaban versus Warfarin for the Treatment of Patients with Non-valvular Atrial Fibrillation

Circulation ◽  
2016 ◽  
Vol 133 (suppl_1) ◽  
Author(s):  
Faye L Norby ◽  
Lindsay G Bengtson ◽  
Lin Y Chen ◽  
Richard F MacLehose ◽  
Pamela L Lutsey ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Real World ◽  
Proportional Hazards ◽  
Large Population ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Gi Bleeding ◽  
Cox Proportional Hazards Models ◽  
The Us

Background: Rivaroxaban is a novel oral anticoagulant approved in the US in 2011 for prevention of stroke and systemic embolism in patients with non-valvular atrial fibrillation (NVAF). Information on risks and benefits among rivaroxaban users in real-world populations is limited. Methods: We used data from the US MarketScan Commercial and Medicare Supplemental databases between 2010 and 2013. We selected patients with a history of NVAF and initiating rivaroxaban or warfarin. Rivaroxaban users were matched with up to 5 warfarin users by age, sex, database enrollment date and drug initiation date. Ischemic stroke, intracranial bleeding (ICB), myocardial infarction (MI), and gastrointestinal (GI) bleeding outcomes were defined by ICD-9-CM codes in an inpatient claim after drug initiation date. Cox proportional hazards models were used to assess the association between rivaroxaban vs. warfarin use and outcomes adjusting for age, sex, and CHA2DS2-VASc score. Separate models were used to compare a) new rivaroxaban users with new warfarin users, and b) switchers from warfarin to rivaroxaban to continuous warfarin users. Results: Our analysis included 34,998 rivaroxaban users matched to 102,480 warfarin users with NVAF (39% female, mean age 71), in which 487 ischemic strokes, 179 ICB, 647 MI, and 1353 GI bleeds were identified during a mean follow-up of 9 months. Associations of rivaroxaban vs warfarin were similar in new users and switchers; therefore we pooled both analyses. Rivaroxaban users had lower rates of ICB (hazard ratio (HR) (95% confidence interval (CI)) = 0.72 (0.46, 1.12))) and ischemic stroke (HR (95% CI) = 0.88 (0.68, 1.13)), but higher rates of GI bleeding (HR (95% CI) = 1.15 (1.01, 1.33)) when compared to warfarin users (table). Conclusion: In this large population-based study of NVAF patients, rivaroxaban users had a non-significant lower risk of ICB and ischemic stroke than warfarin users, but a higher risk of GI bleeding. These real-world findings are comparable to results reported in published clinical trials.

scholarly journals Physical activity types and atrial fibrillation risk in the middle-aged and elderly: The Rotterdam Study

2018 ◽  
Vol 25 (12) ◽  
pp. 1316-1323 ◽  
Author(s):  
Marijn Albrecht ◽  
Chantal M Koolhaas ◽  
Josje D Schoufour ◽  
Frank JA van Rooij ◽  
M Kavousi ◽  
...  
Keyword(s):  
Physical Activity ◽  
Atrial Fibrillation ◽  
Proportional Hazards ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Total Physical Activity ◽  
Rotterdam Study ◽  
Cox Proportional Hazards Models ◽  
Activity Types ◽  
Study Population

Background The association between physical activity and atrial fibrillation remains controversial. Physical activity has been associated with a higher and lower atrial fibrillation risk. These inconsistent results might be related to the type of physical activity. We aimed to investigate the association of total and types of physical activity, including walking, cycling, domestic work, gardening and sports, with atrial fibrillation. Design Prospective cohort study. Methods Our study was performed in the Rotterdam Study, a prospective population-based cohort. We included 7018 participants aged 55 years and older with information on physical activity between 1997–2001. Cox proportional hazards models were used to examine the association of physical activity with atrial fibrillation risk. Models were adjusted for biological and behavioural risk factors and the remaining physical activity types. Physical activity was categorised in tertiles and the low group was used as reference. Results During 16.8 years of follow-up (median: 12.3 years, interquartile range: 8.7–15.9 years), 800 atrial fibrillation events occurred (11.4% of the study population). We observed no association between total physical activity and atrial fibrillation risk in any model. After adjustment for confounders, the hazard ratio and 95% confidence interval for the high physical activity category compared to the low physical activity category was: 0.71 (0.80–1.14) for total physical activity. We did not observe a significant association between any of the physical activity types with atrial fibrillation risk. Conclusion Our results suggest that physical activity is not associated with higher or lower risk of atrial fibrillation in older adults. Neither total physical activity nor any of the included physical activity types was associated with atrial fibrillation risk.

Artificial intelligence-enabled detection of paroxysmal atrial fibrillation from normal sinus ECGs in patients with coronary microvascular dysfunction

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Ahmad ◽  
M Corban ◽  
T Toya ◽  
Z.I Attia ◽  
P Noseworthy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Functional Assessment ◽  
Paroxysmal Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Area Under The Curve ◽  
Microvascular Dysfunction ◽  
Persistent Atrial Fibrillation ◽  
Coronary Microvascular Dysfunction ◽  
Normal Sinus

Abstract Background Artificial Intelligence (AI) algorithms enabled the detection of patients with paroxysmal atrial fibrillation (PAF) from a single normal sinus rhythm (NSR) ECG. Coronary microvascular dysfunction (CMD) is a precursor for coronary artery disease, which is a known risk factor for AF. Purpose The aim of this study is to examine the probability of PAF, according to AI-enabled algorithm estimation, in patients with CMD. Methods 1858 patients without persistent atrial fibrillation with signs and/or symptoms of ischemia and with non-obstructive CAD (&lt;40% stenosis) who underwent invasive coronary microvascular functional assessment and the ECG closest to the functional assessment were included in this analysis. Patients with coronary flow velocity reserve (CFR) &lt;2 in response to adenosine were labelled as endothelial-independent CMD; % increase in coronary blood flow (%ΔCBF) &lt;50% in response to acetylcholine were labelled as endothelial-dependent CMD. Patients were categorized into 4 groups. G1: Normal (NL) CFR/NL %ΔCBF; G2: Abnormal (ABN) %ΔCBF only; G3: ABN CFR only; G4: ABL CFR & %ΔCBF. The probability of having PAF (%probAF) was calculated by a previously-trained and validated AI algorithm. AF Flag = %probAF &gt;9%; which is a pre-set cut-off found to have the highest accuracy of identifying patients with PAF (Area Under the Curve = 0.87). Results Mean age for patients was 51.2±12.4 and 66.3% were females. 835 (45%) were in G1, 39 (2%) in G2, 911 (49%) in G3, and 73 (4%) in G4. Compared to G1 and G2, G3 and G4 were older, had more diabetes and higher smoking rates (p&lt;0.05 for all). Furthermore, G4 had a significantly higher %probAF compared to other groups (Fig. 1). G4 were also more likely to be flagged by the algorithm as having PAF, even after adjusting for cardiovascular risk factors, with an odds ratio of 1.9 [CI 95% 1.1–3.3; p=0.03]) (Fig. 2). Conclusion Patients with combined CMD have a significantly higher probability of having PAF based on an AI-enabled algorithm. Further research is warranted to know if patients with CMD would benefit from formal AF screening at the time of diagnosis. Funding Acknowledgement Type of funding source: None

Association of irritable bowel syndrome in patients with atrial fibrillation: a nationwide population-based investigation

Perfusion ◽  
2020 ◽  
Vol 35 (8) ◽  
pp. 847-852
Author(s):  
Wei-Syun Hu ◽  
Cheng-Li Lin
Keyword(s):  
Atrial Fibrillation ◽  
Irritable Bowel Syndrome ◽  
Proportional Hazards ◽  
Female Gender ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Regression Methods ◽  
Proportional Hazards Regression ◽  
Kaplan Meier ◽  
Irritable Bowel

Objective: We seek to characterize the association between atrial fibrillation and irritable bowel syndrome. Methods: We identify 11,642 cases (atrial fibrillation) and 46,487 sex-, age-, and index year–matched controls (non-atrial fibrillation) from Longitudinal Health Insurance Database. Kaplan–Meier, Cox proportional hazards regression methods and competing risk analysis methods were used to assess the association of atrial fibrillation with outcome of irritable bowel syndrome. Results: After adjustment for gender, age, comorbidities and medications, patients with atrial fibrillation had a significant higher risk (adjusted hazard ratio = 1.12, p < 0.01) to develop irritable bowel syndrome than patients without atrial fibrillation. Compared to participants without atrial fibrillation, those with atrial fibrillation had 1.13-fold (p < 0.05) and 1.11-fold (p < 0.05) risk of irritable bowel syndrome in female and male subgroup, respectively. Among subjects aged ≥65 years, those with AF had 1.11-fold risk of irritable bowel syndrome than non-AF cohort (P < 0.01). Among participants with any one of the comorbidities, those with atrial fibrillation had 1.10-fold risk of irritable bowel syndrome than non-atrial fibrillation cohort (p < 0.05). Conclusion: We report that the presence of atrial fibrillation is associated with greater incidence of irritable bowel syndrome and the association is stronger among female gender, age 65 years or above, and with comorbidities.

scholarly journals Aspirin and Risk of Dementia in Patients with Late-Onset Depression: A Population-Based Cohort Study

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ya-Hsu Yang ◽  
Chih-Chiang Chiu ◽  
Hao-Wei Teng ◽  
Chun-Teng Huang ◽  
Chun-Yu Liu ◽  
...  
Keyword(s):  
Cumulative Incidence ◽  
Propensity Scores ◽  
Nearest Neighbor ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Late Onset ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Incident Dementia

Background. Late onset depression (LOD) often occurs in the context of vascular disease and may be associated with risk of dementia. Aspirin is widely used to reduce the risk of cardiovascular disease and stroke. However, its role in patients with LOD and risk of dementia remains inconclusive. Materials and Methods. A population-based study was conducted using data from National Health Insurance of Taiwan during 1996–2009. Patients fulfil diagnostic criteria for LOD with or without subsequent dementia (incident dementia) and among whom users of aspirin (75 mg daily for at least 6 months) were identified. The time-dependent Cox proportional hazards model was applied for multivariate analyses. Propensity scores with the one-to-one nearest-neighbor matching model were used to select matching patients. Cumulative incidence of incident dementia after diagnosis of LOD was calculated by Kaplan–Meier Method. Results. A total of 6028 (13.4%) and 40,411 (86.6%) patients were defined as, with and without diagnosis of LOD, among whom 2,424 (41.9%) were aspirin users. Patients with LOD had more comorbidities such as cardiovascular diseases, diabetes, and hypertension comparing to those without LOD. Among patients with LOD, aspirin users had lower incidence of subsequent incident dementia than non-users (Hazard Ratio = 0.734, 95% CI 0.641–0.841, p<0.001). After matching aspirin users with non-users by propensity scores-matching method, the cumulative incidence of incident dementia was significantly lower in aspirin users of LOD patients (p=0.022). Conclusions. Aspirin may be associated with a lower risk of incident dementia in patients with LOD. This beneficial effect of aspirin in LOD patients needs validation in prospective clinical trials and our results should be interpreted with caution.

scholarly journals Prediction of atrial fibrillation from normal ECG using artificial intelligence in patients with unexplained stroke

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
Y.S Baek ◽  
S.C Lee ◽  
W.I Choi ◽  
D.H Kim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Statistical Significance ◽  
Validation Dataset ◽  
Internal Validation ◽  
Normal Sinus ◽  
Group 2

Abstract Background Stroke related to embolic and of undetermined source constitute 20 to 30% of ischemic strokes. Many of these strokes are related to atrial fibrillation (AF), which might be underdetected due to its paroxysmal and silent nature. Purpose The aim of our study was to predict AF during normal sinus rhythm in a standard 12-lead ECG to train an artificial intelligence to train deep neural network in patients with unexplained stroke (embolic stroke of undetermined source; ESUS). Methods We analyzed digital raw data of 12-lead ECGs using artificial intelligence (AI) recurrent neural network (RNN) to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 12-lead ECGs. We included 2,585 cases aged 18 years or older with multiple ECGs at our university hospital between 2005 and 2017 validated by crossover analysis of two electrophysiologists. We defined the first recorded AF ECG as the index ECG and the first day of the window of interest as 14 days before the date of the index ECG. We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated recall, F1 score, and the area under the curve (AUC) of the receiver operatoring characteristic curve (ROC) for the internal validation dataset to select a probability threshold. We applied this developed AI program to 169 ESUS patients who has been diagnosed and had standard 12-lead ECGs in our hospital. Results We acquired 1,266 NSR ECSs from real normal subjects and 1,319 NSR ECGs form paroxysmal AF patients. RNN AI-enabled ECG identified atrial fibrillation with an AUC of 0.79, recall of 82%, specificity of 78%, F1 score of 75% and overall accuracy of 72.8% (Figure). ESUS patients were divided into three groups according to calculated probabilities of AF using AI guided RNN program: group 1 (35 patients with probability of 0–25% of paroxysmal AF), group 2 (86 patients with probability of 25–75% of paroxysmal AF) and group 3 (48 patients with probability of 75–100% of paroxysmal AF). In Kaplan-Meier estimates, Group 2 and 3 (more than 25% of PAF probabilities) tended to have higher AF incidence although it did not reach statistical significance (log-rank p 0.678) (Figure). Conclusion AI may discriminate subtle changes between real and paroxysmal NSR and can also be helpful in patients with ESUS to identify if AF is the underlying cause of the stroke. Further studies are needed in order to evaluate their possible use in future prognostic models. Funding Acknowledgement Type of funding source: None

The Risk of Acute Kidney Injury with Oral Anticoagulants in Elderly Adults with Atrial Fibrillation

2021 ◽  
pp. CJN.05920421
Author(s):  
Ziv Harel ◽  
Eric McArthur ◽  
Nivethika Jeyakumar ◽  
Manish Sood ◽  
Amit Garg ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Acute Kidney Injury ◽  
Lower Risk ◽  
Propensity Scores ◽  
Proportional Hazards ◽  
Oral Anticoagulants ◽  
Kidney Injury ◽  
International Normalized Ratio ◽  
Population Based ◽  
Cox Proportional Hazards

Background: Anticoagulation with either with a vitamin K antagonist or a direct oral anticoagulant (DOAC) may be associated with acute kidney injury (AKI). Our objective was to assess the risk of AKI among elderly individuals with atrial fibrillation (AF) newly prescribed a DOAC (dabigatran, rivaroxaban, or apixaban) versus warfarin. Methods: A population-based cohort study of 20,683 outpatients in Ontario, Canada, ≥66 years, with atrial fibrillation who were prescribed warfarin, dabigatran, rivaroxaban or apixaban between 2009- 2017. Inverse probability of treatment weighting based on derived propensity scores for the treatment with each DOAC was used to balance baseline characteristics among patients receiving each of the three DOACs, compared to warfarin. Cox proportional hazards regression was performed in the weighted population to compare the association between the prescribed anticoagulant and the outcomes of interest. The exposure was an outpatient prescription of warfarin, or one of the DOACs. The primary outcome was a hospital encounter with AKI, defined using KDIGO thresholds. Prespecified subgroup analyses were conducted by estimated glomerular filtration rate (eGFR) category, and by the percentage of international normalized ratio measurements in range, a validated marker of anticoagulation control. Results: : Each DOAC was associated with a significantly lower risk of AKI compared to warfarin (weighted HR 0.65; 95% CI 0.53 to 0.80 for dabigatran, weighted HR 0.85; 95% CI 0.73 to 0.98 for rivaroxaban; and weighted HR 0.81; 95% CI 0.72 to 0.93 for apixaban). In subgroup analysis, the lower risk of AKI associated with each DOAC was consistent across each eGFR strata. The risk of AKI was significantly lower among users of each of the DOACs compared to warfarin users who had a percentage of international normalized ratio measurements ≤56.1%. Conclusions: DOACs were associated with a lower risk of AKI compared to warfarin.

Abstract 16415: Racial Differences in Incidence of Atrial Fibrillation After Cryptogenic Stroke

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
David B Laslett ◽  
Abdullah Haddad ◽  
Dianna Gaballa ◽  
Hardik Mangrolia ◽  
Olivia Follis ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Racial Differences ◽  
Cumulative Incidence ◽  
Proportional Hazards ◽  
Current Knowledge ◽  
Cryptogenic Stroke ◽  
Cox Proportional Hazards ◽  
Kaplan Meier ◽  
Artery Disease

Introduction: The incidence of atrial fibrillation (AF) is lower among non-whites compared to whites, despite a higher burden of AF risk factors. Current knowledge of first detection of AF after cryptogenic stroke (CS) by an implantable cardiac monitor (ICM) is based on a predominantly white cohort. The incidence of new AF after CS among minorities is unknown. We hypothesized that the incidence of AF after CS would be lower in non-whites. Methods: We reviewed charts of all patients without a history of AF undergoing implantation of an ICM after CS at our hospital from July 2014 to December 2019. Incidence of AF was identified through review of ICM monitoring for each patient, including adjudication of AF episodes for accuracy. Kaplan Meier survival analysis was performed, and cumulative incidence of AF using adjusted Cox proportional hazards regression was compared by race. Results: We identified 417 patients who underwent ICM implant after CS during the study period, with a mean follow-up time of 1.5 ± 1.1 years. Mean age was 62 ± 12 years, and 46% (n=190) were male. The majority of patients were non-white (white, 15%, n=63; black, 59%, n=244; Hispanic, 26%, n=110). At baseline, blacks, Hispanics, and whites were of similar age (mean 62.2, 62.1, and 61.5 years, respectively), and blacks and Hispanics had more AF risk factors, including heart failure, hypertension, diabetes, chronic kidney disease, and higher BMI, than whites. Hispanics had more coronary artery disease than whites and blacks (25.5%, 17.5%, 9.1% respectively, p < 0.001). Among blacks, the cumulative incidence of newly detected AF at one, two, and three years was 13.0%, 18.9%, and 23%, which was similar to Hispanics (12.9%, 18.2%, and 20.9%). By comparison, the incidence in whites was significantly higher (20.8%, 34.3%, 40.3%; blacks p=0.02; Hispanics p=0.01) Conclusion: In patients undergoing ICM after CS, the incidence of newly detected AF is approximately double in whites compared to both blacks and Hispanics.

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