@article{huberman_reich_bondell_2022, title={Nonparametric conditional density estimation in a deep learning framework for short-term forecasting (May, 10.1007/s10651-021-00499-z, 2021)}, volume={8}, ISSN={["1573-3009"]}, DOI={10.1007/s10651-022-00543-6}, journal={ENVIRONMENTAL AND ECOLOGICAL STATISTICS}, author={Huberman, David B. and Reich, Brian J. and Bondell, Howard D.}, year={2022}, month={Aug} }
 @article{huberman_reich_bondell_2021, title={Nonparametric conditional density estimation in a deep learning framework for short-term forecasting}, volume={5}, ISSN={["1573-3009"]}, DOI={10.1007/s10651-021-00499-z}, abstractNote={Short-term forecasting is an important tool in understanding environmental processes. In this paper, we incorporate machine learning algorithms into a conditional distribution estimator for the purposes of forecasting tropical cyclone intensity. Many machine learning techniques give a single-point prediction of the conditional distribution of the target variable, which does not give a full accounting of the prediction variability. Conditional distribution estimation can provide extra insight on predicted response behavior, which could influence decision-making and policy. We propose a technique that simultaneously estimates the entire conditional distribution and flexibly allows for machine learning techniques to be incorporated. A smooth model is fit over both the target variable and covariates, and a logistic transformation is applied on the model output layer to produce an expression of the conditional density function. We provide two examples of machine learning models that can be used, polynomial regression and deep learning models. To achieve computational efficiency, we propose a case–control sampling approximation to the conditional distribution. A simulation study for four different data distributions highlights the effectiveness of our method compared to other machine learning-based conditional distribution estimation techniques. We then demonstrate the utility of our approach for forecasting purposes using tropical cyclone data from the Atlantic Seaboard. This paper gives a proof of concept for the promise of our method, further computational developments can fully unlock its insights in more complex forecasting and other applications.}, journal={ENVIRONMENTAL AND ECOLOGICAL STATISTICS}, author={Huberman, David B. and Reich, Brian J. and Bondell, Howard D.}, year={2021}, month={May} }
 @article{huberman_reich_pacifici_collazo_2020, title={Estimating the drivers of species distributions with opportunistic data using mediation analysis}, volume={11}, ISSN={["2150-8925"]}, url={https://doi.org/10.1002/ecs2.3165}, DOI={10.1002/ecs2.3165}, abstractNote={Abstract Ecological occupancy modeling has historically relied on high‐quality, low‐quantity designed‐survey data for estimation and prediction. In recent years, there has been a large increase in the amount of high‐quantity, unknown‐quality opportunistic data. This has motivated research on how best to combine these two data sources in order to optimize inference. Existing methods can be infeasible for large datasets or require opportunistic data to be located where designed‐survey data exist. These methods map species occupancies, motivating a need to properly evaluate covariate effects (e.g., land cover proportion) on their distributions. We describe a spatial estimation method for supplementarily including additional opportunistic data using mediation analysis concepts. The opportunistic data mediate the effect of the covariate on the designed‐survey data response, decomposing it into a direct and indirect effect. A component of the indirect effect can then be quickly estimated via regressing the mediator on the covariate, while the other components are estimated through a spatial occupancy model. The regression step allows for use of large quantities of opportunistic data that can be collected in locations with no designed‐survey data available. Simulation results suggest that the mediated method produces an improvement in relative MSE when the data are of reasonable quality. However, when the simulated opportunistic data are poorly correlated with the true spatial process, the standard, unmediated method is still preferable. A spatiotemporal extension of the method is also developed for analyzing the effect of deciduous forest land cover on red‐eyed vireo distribution in the southeastern United States and find that including the opportunistic data do not lead to a substantial improvement. Opportunistic data quality remains an important consideration when employing this method, as with other data integration methods.}, number={6}, journal={ECOSPHERE}, publisher={Wiley}, author={Huberman, David B. and Reich, Brian J. and Pacifici, Krishna and Collazo, Jaime A.}, year={2020}, month={Jun} }
 @article{orgel_wojdyla_huberman_halperin_breithardt_singer_fox_hankey_mahaffey_jones_et al._2017, title={Noncentral Nervous System Systemic Embolism in Patients With Atrial Fibrillation Results From ROCKET AF (Rivaroxaban Once Daily, Oral, Direct Factor Xa Inhibition Compared With Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation)}, volume={10}, ISSN={["1941-7713"]}, DOI={10.1161/circoutcomes.116.003520}, abstractNote={HomeCirculation: Cardiovascular Quality and OutcomesVol. 10, No. 5Noncentral Nervous System Systemic Embolism in Patients With Atrial Fibrillation Free AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessResearch ArticlePDF/EPUBNoncentral Nervous System Systemic Embolism in Patients With Atrial FibrillationResults From ROCKET AF (Rivaroxaban Once Daily, Oral, Direct Factor Xa Inhibition Compared With Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation) Ryan Orgel, MD, Daniel Wojdyla, MS, David Huberman, BS, Jonathan L. Halperin, MD, Günter Breithardt, MD, Daniel E. Singer, MD, Keith A.A. Fox, MBChB, Graeme J. Hankey, MD, Kenneth W. Mahaffey, MD, W. Schuyler Jones, MD and Manesh R. Patel, MD Ryan OrgelRyan Orgel From the Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (R.O., D.W., W.S.J., M.R.P.); Department of Statistics, North Carolina State University, Raleigh (D.H.); Mount Sinai Medical Center, New York, NY (J.L.H.); Department of Cardiovascular Medicine, Hospital of the University of Münster, Germany (G.B.); Massachusetts General Hospital and Harvard Medical School, Boston (D.E.S.); University of Edinburgh and Royal Infirmary of Edinburgh, Scotland, United Kingdom (K.A.A.F.); School of Medicine and Pharmacology, University of Western Australia, Crawley (G.J.H.); and Stanford University School of Medicine, CA (K.W.M.). Search for more papers by this author , Daniel WojdylaDaniel Wojdyla From the Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (R.O., D.W., W.S.J., M.R.P.); Department of Statistics, North Carolina State University, Raleigh (D.H.); Mount Sinai Medical Center, New York, NY (J.L.H.); Department of Cardiovascular Medicine, Hospital of the University of Münster, Germany (G.B.); Massachusetts General Hospital and Harvard Medical School, Boston (D.E.S.); University of Edinburgh and Royal Infirmary of Edinburgh, Scotland, United Kingdom (K.A.A.F.); School of Medicine and Pharmacology, University of Western Australia, Crawley (G.J.H.); and Stanford University School of Medicine, CA (K.W.M.). Search for more papers by this author , David HubermanDavid Huberman From the Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (R.O., D.W., W.S.J., M.R.P.); Department of Statistics, North Carolina State University, Raleigh (D.H.); Mount Sinai Medical Center, New York, NY (J.L.H.); Department of Cardiovascular Medicine, Hospital of the University of Münster, Germany (G.B.); Massachusetts General Hospital and Harvard Medical School, Boston (D.E.S.); University of Edinburgh and Royal Infirmary of Edinburgh, Scotland, United Kingdom (K.A.A.F.); School of Medicine and Pharmacology, University of Western Australia, Crawley (G.J.H.); and Stanford University School of Medicine, CA (K.W.M.). Search for more papers by this author , Jonathan L. HalperinJonathan L. Halperin From the Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (R.O., D.W., W.S.J., M.R.P.); Department of Statistics, North Carolina State University, Raleigh (D.H.); Mount Sinai Medical Center, New York, NY (J.L.H.); Department of Cardiovascular Medicine, Hospital of the University of Münster, Germany (G.B.); Massachusetts General Hospital and Harvard Medical School, Boston (D.E.S.); University of Edinburgh and Royal Infirmary of Edinburgh, Scotland, United Kingdom (K.A.A.F.); School of Medicine and Pharmacology, University of Western Australia, Crawley (G.J.H.); and Stanford University School of Medicine, CA (K.W.M.). Search for more papers by this author , Günter BreithardtGünter Breithardt From the Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (R.O., D.W., W.S.J., M.R.P.); Department of Statistics, North Carolina State University, Raleigh (D.H.); Mount Sinai Medical Center, New York, NY (J.L.H.); Department of Cardiovascular Medicine, Hospital of the University of Münster, Germany (G.B.); Massachusetts General Hospital and Harvard Medical School, Boston (D.E.S.); University of Edinburgh and Royal Infirmary of Edinburgh, Scotland, United Kingdom (K.A.A.F.); School of Medicine and Pharmacology, University of Western Australia, Crawley (G.J.H.); and Stanford University School of Medicine, CA (K.W.M.). Search for more papers by this author , Daniel E. SingerDaniel E. Singer From the Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (R.O., D.W., W.S.J., M.R.P.); Department of Statistics, North Carolina State University, Raleigh (D.H.); Mount Sinai Medical Center, New York, NY (J.L.H.); Department of Cardiovascular Medicine, Hospital of the University of Münster, Germany (G.B.); Massachusetts General Hospital and Harvard Medical School, Boston (D.E.S.); University of Edinburgh and Royal Infirmary of Edinburgh, Scotland, United Kingdom (K.A.A.F.); School of Medicine and Pharmacology, University of Western Australia, Crawley (G.J.H.); and Stanford University School of Medicine, CA (K.W.M.). Search for more papers by this author , Keith A.A. FoxKeith A.A. Fox From the Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (R.O., D.W., W.S.J., M.R.P.); Department of Statistics, North Carolina State University, Raleigh (D.H.); Mount Sinai Medical Center, New York, NY (J.L.H.); Department of Cardiovascular Medicine, Hospital of the University of Münster, Germany (G.B.); Massachusetts General Hospital and Harvard Medical School, Boston (D.E.S.); University of Edinburgh and Royal Infirmary of Edinburgh, Scotland, United Kingdom (K.A.A.F.); School of Medicine and Pharmacology, University of Western Australia, Crawley (G.J.H.); and Stanford University School of Medicine, CA (K.W.M.). Search for more papers by this author , Graeme J. HankeyGraeme J. Hankey From the Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (R.O., D.W., W.S.J., M.R.P.); Department of Statistics, North Carolina State University, Raleigh (D.H.); Mount Sinai Medical Center, New York, NY (J.L.H.); Department of Cardiovascular Medicine, Hospital of the University of Münster, Germany (G.B.); Massachusetts General Hospital and Harvard Medical School, Boston (D.E.S.); University of Edinburgh and Royal Infirmary of Edinburgh, Scotland, United Kingdom (K.A.A.F.); School of Medicine and Pharmacology, University of Western Australia, Crawley (G.J.H.); and Stanford University School of Medicine, CA (K.W.M.). Search for more papers by this author , Kenneth W. MahaffeyKenneth W. Mahaffey From the Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (R.O., D.W., W.S.J., M.R.P.); Department of Statistics, North Carolina State University, Raleigh (D.H.); Mount Sinai Medical Center, New York, NY (J.L.H.); Department of Cardiovascular Medicine, Hospital of the University of Münster, Germany (G.B.); Massachusetts General Hospital and Harvard Medical School, Boston (D.E.S.); University of Edinburgh and Royal Infirmary of Edinburgh, Scotland, United Kingdom (K.A.A.F.); School of Medicine and Pharmacology, University of Western Australia, Crawley (G.J.H.); and Stanford University School of Medicine, CA (K.W.M.). Search for more papers by this author , W. Schuyler JonesW. Schuyler Jones From the Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (R.O., D.W., W.S.J., M.R.P.); Department of Statistics, North Carolina State University, Raleigh (D.H.); Mount Sinai Medical Center, New York, NY (J.L.H.); Department of Cardiovascular Medicine, Hospital of the University of Münster, Germany (G.B.); Massachusetts General Hospital and Harvard Medical School, Boston (D.E.S.); University of Edinburgh and Royal Infirmary of Edinburgh, Scotland, United Kingdom (K.A.A.F.); School of Medicine and Pharmacology, University of Western Australia, Crawley (G.J.H.); and Stanford University School of Medicine, CA (K.W.M.). Search for more papers by this author and Manesh R. PatelManesh R. Patel From the Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (R.O., D.W., W.S.J., M.R.P.); Department of Statistics, North Carolina State University, Raleigh (D.H.); Mount Sinai Medical Center, New York, NY (J.L.H.); Department of Cardiovascular Medicine, Hospital of the University of Münster, Germany (G.B.); Massachusetts General Hospital and Harvard Medical School, Boston (D.E.S.); University of Edinburgh and Royal Infirmary of Edinburgh, Scotland, United Kingdom (K.A.A.F.); School of Medicine and Pharmacology, University of Western Australia, Crawley (G.J.H.); and Stanford University School of Medicine, CA (K.W.M.). Search for more papers by this author Originally published11 May 2017https://doi.org/10.1161/CIRCOUTCOMES.116.003520Circulation: Cardiovascular Quality and Outcomes. 2017;10:e003520IntroductionAtrial fibrillation (AF) is common and occurs in 2% to 4% of adults 60 years of age or older.1 Thromboembolic events, including stroke and noncentral nervous system (CNS) systemic embolism (SE), are common complications. Non-CNS SE accounts for ≈10% of all thromboembolic events2 and is important to identify because they are associated with high morbidity and mortality. Using data from ROCKET AF (Rivaroxaban Once Daily, Oral, Direct Factor Xa Inhibition Compared With Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation; NCT00403767), we describe the incidence, location, diagnosis, treatment, and outcomes in patients with non-CNS SE. Baseline characteristics of patients with non-CNS SE are presented and discussed to identify those at increased risk of such an event.Methods and ResultsThe design and methods of ROCKET AF have been described.3,4 In brief, it was a multicenter, randomized, double-blind, event-driven trial conducted at 1178 participating sites in 45 countries.3 Included patients had AF and were at moderate-to-high risk for stroke as defined by a CHADS2 score (Congestive Heart Failure, Hypertension, Age, Diabetes Mellitus, Stroke 2 Score) ≥2. A total of 14 264 patients were randomly assigned to receive fixed-dose rivaroxaban 20 mg daily (15 mg daily in patients with creatinine clearance 30–49 mL/min) or dose-adjusted warfarin (target international normalized ratio 2.0–3.0). Patients were intended to continue study drug throughout the trial unless discontinuation was clinically indicated (eg, safety concern, pregnancy, stroke or non-CNS SE, HIV diagnosis, abnormal liver function, creatinine clearance <25 mL/min on 2 consecutive measurements, or need for excluded medication).4Non-CNS SE was defined as abrupt vascular insufficiency associated with clinical or radiological evidence of arterial occlusion in the absence of other likely mechanisms. In the presence of atherosclerotic peripheral artery disease (PAD), diagnosis of embolism to the lower extremities required angiographic demonstration of acute arterial occlusion.4 For patients who met the definition of non-CNS SE, the anatomic location, method of diagnosis, presence of atherosclerosis, and recent trauma or instrumentation of the affected arterial territory were documented.Institutional review board approval was obtained at all sites, and all patients provided written informed consent.The current analysis is a post hoc subgroup analysis of the intention-to-treat population. Categorical variables are summarized as counts (percentages), and continuous variables are summarized as medians (25th, 75th percentiles) or means (SDs). All analyses were performed with SAS software version 9.2 (SAS Institute, Inc, Cary, NC).Of 14 264 randomized patients in ROCKET AF, 47 experienced non-CNS SE, corresponding to a rate of 0.183 (95% confidence interval, 0.136–0.241) per 100 patient-years of follow-up. (Patient characteristics are shown in Table.)Table. Baseline Characteristics of Patients With and Without Non-CNS SECharacteristicPatients With Non-CNS SE (n=47)Patients Without Non-CNS SE (n=14 124)Treatment Rivaroxaban20 (42.6%)7061 (50.0%) Warfarin27 (57.4%)7063 (50.0%)Age, y75 (68, 80)73 (65, 78)Female24 (51.1%)5581 (39.5%)Types of atrial fibrillation Persistent39 (83.0%)11 446 (81.0%) Paroxysmal8 (17.0%)2482 (17.6%) New onset/newly diagnosed0 (0.0%)196 (1.4%)CHADS2 score, mean (SD)3.7 (1.1)3.5 (0.9)CHADS2 score 10 (0.0%)3 (0.0%) 26 (12.8%)1851 (13.1%) 313 (27.7%)6156 (43.6%) 419 (40.4%)4048 (28.7%) 55 (10.6%)1792 (12.7%) 64 (8.5%)274 (1.9%)Presenting characteristics Creatinine clearance*, mL/min55 (44, 75)67 (52, 87)Baseline comorbidities Previous stroke, TIA, or non-CNS embolism32 (68.1%)7735 (54.8%) Peripheral arterial disease6 (12.8%)826 (5.8%) Hypertension43 (91.5%)12 781 (90.5%) Diabetes mellitus14 (29.8%)5633 (39.9%) Previous myocardial infarction16 (34.0%)2430 (17.2%) Congestive heart failure2 (7.4%)1190 (13.5%) Chronic obstructive pulmonary disease7 (14.9%)1474 (10.4%)Medications Previous vitamin K antagonist use33 (70.2%)8820 (62.4%) Previous chronic aspirin use17 (36.2%)5167 (36.6%)Categorical variables are shown as a number (%), and continuous variables are shown as a median (25th, 75th percentiles), unless otherwise noted. CHADS2 indicates Congestive Heart Failure, Hypertension, Age, Diabetes Mellitus, Stroke 2 Score; CNS, central nervous system; SE, systemic embolism; and TIA, transient ischemic attack.*Calculated using the Cockcroft-Gault equation.Of the 47 non-CNS SE events, 29 occurred in lower extremities, 8 in mesenteric arteries, 6 in upper extremities, 2 in renal arteries, and 1 in the splenic artery; location was not recorded for 1 non-CNS SE.Methods of diagnosis of non-CNS SE varied, but the most common were ultrasound (34%), computed tomography/angiography (21%), invasive angiography (17%), and magnetic resonance imaging/angiography (2%). Multiple diagnosis methods could be used for a single patient, or patients could be diagnosed clinically. The initial diagnostic test was not standardized or dictated by the study protocol. There were 16 patients in whom no diagnostic test was performed, and although clinical diagnosis was not a prespecified method, this can be inferred.The use of study medication (rivaroxaban or warfarin) was variable at the time of non-CNS SE. Before the occurrence of non-CNS SE, 21 patients had permanently discontinued study drug, and 2 had temporarily discontinued. Reasons for permanent discontinuation included bleeding adverse events (n=3), nonbleeding adverse events (n=2), noncompliance with study medication (n=1), withdrawn consent (n=5), investigator decision (n=2 [not protocol related]), or the clinical efficacy end point (ie, stroke) was reached (n=8). The median time from permanent discontinuation to non-CNS SE was 24 days (25th, 75th percentiles: 6, 92). However, 168 of the 534 patients who experienced stroke in ROCKET AF permanently stopped study drug before the stroke (31.5%).After non-CNS SE, 14 patients stopped study medication permanently, 1 temporarily discontinued use, and 7 continued study medication throughout the study. Study medication use was not adequately characterized in 2 patients.There were 53 interventions in 47 patients; multiple interventions could be attempted in the same patient when clinically indicated. The most common interventions were surgery (47%), medical management (28%), and percutaneous intervention (13%); 11% of interventions were not described.A total of 11 patients died (n=6 rivaroxaban, n=5 warfarin); 7 died within 30 days of non-CNS SE, 2 died between 30 days and 6 months, and 2 died >6 months after the non-CNS SE event. Of the 11 fatal events, 5 occurred in lower extremities, 5 in mesenteric arteries, and 1 in upper extremities.Among those who survived the non-CNS SE event, 2 patients had a subsequent stroke, 1 required amputation, and 3 experienced major bleeding events. Of those who experienced major bleeding, 1 patient had multiple major bleeding events, and 2 patients had major bleeding on the same day as the non-CNS SE event.CommentIn patients enrolled in ROCKET AF, non-CNS SE occurred infrequently, at a rate of 0.183 (95% confidence interval, 0.136–0.241) per 100 patient-years. Importantly, non-CNS SE accounted for only 8.2% of the total thromboembolic events, whereas stroke accounted for the remainder, which is similar to previous estimates.2 The difference in frequency between stroke and non-CNS SE is multifactorial. One explanation for this discrepancy is that the higher prevalence of symptomatic embolization of carotid arteries versus subclavian arteries can be associated with the low resistance and increased volume of blood flow in cerebral arteries.5 Alternatively, it is reasonable to assume that the disparity is at least partly because of the difficulty in detecting non-CNS events. Cerebral tissue is particularly sensitive to ischemia when compared with other organs. Visceral or mesenteric SE detection is impacted by asymptomatic or nonspecific symptoms, and many of these locations have a rich vascular network leading to collateral circulation and compensation. Consequently, the prevalence of non-CNS SE events likely will remain understated.5 The vast majority of non-CNS SE events occurred in lower extremity arterial distributions, which is consistent with previous data.5–7 PAD and previous atherosclerosis in the affected arterial distribution seem to be associated with the occurrence of non-CNS SE, which may suggest that non-CNS SE occurs more readily in areas of diseased vasculature. The mechanism explaining this finding may be that occlusion of the vasculature because of embolic material occurs at areas of atherosclerotic plaque, causing total obstruction of blood flow.8 Alternatively, some of the association with PAD may be related to thrombosis of a diseased vessel rather than a true embolic event.5,8Bleeding was a major adverse event associated with non-CNS SE (n=3 [6.4%]), and although this sample size is small, the rate of major bleeding seems to be increased compared with the intention-to-treat population (3.6% of patients on rivaroxaban and 3.4% of patients on warfarin).3 Given the small sample size, it is not possible to draw a conclusion, although patients with non-CNS SE likely received additional procedures and antithrombotic therapy as treatment, thus placing them at increased bleeding risk. There was also 1 patient (2.8%) who required amputation after lower extremity SE (n=35 for all extremity SE). Amputation is required in ≈7% of limb ischemia cases because of PAD.9There was a high rate of mortality associated with non-CNS SE, as 11 patients (23.4%) who experienced it died. Significantly, the majority of patients died within 30 days of the event. Even more striking was the difference in mortality based on location of SE. For lower extremity SE, the rate of mortality in ROCKET AF was 17.2%, which is similar to previous reports for acute limb ischemia; previous studies suggest that the mortality rate of acute limb ischemia in the community is ≈9% in hospital and 42.5% at 1 year post-event.2,10 Visceral SE (including mesenteric, renal, and splenic) has a much higher rate of mortality, with 45.5% of patients experiencing such an event in ROCKET AF eventually dying. This is a representative of previous literature, in which reported mortality rates of visceral or mesenteric ischemia are 55% to 60%.2Non-CNS SE is less commonly recognized clinically than ischemic stroke but is associated with significant morbidity and mortality. Although the majority of these events occur in the extremities, visceral events also occur and are associated with high mortality rates. Many patients permanently stopped study medication before non-CNS SE, emphasizing the importance of uninterrupted anticoagulation in patients with AF. There is some suggestion that PAD and previous atherosclerosis may predispose patients to non-CNS SE; additional studies are needed to identify patients at risk and guide appropriate treatment.Sources of FundingROCKET AF (Rivaroxaban Once Daily, Oral, Direct Factor Xa Inhibition Compared With Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation) was supported by Johnson & Johnson Pharmaceutical Research and Development and Bayer HealthCare. D. Huberman was supported by the National Institutes of Health (grant T32 HL079896).DisclosuresDr Halperin acts as a consultant/advisory board for Bayer AG HealthCare, Boehringer Ingelheim, Daiichi Sankyo, Johnson & Johnson, Ortho-McNeil-Janssen Pharmaceuticals, Pfizer, Sanofi-Aventis, Biotronik, Boston Scientific, Janssen, and Medtronic. Dr Breithardt receives institutional research grant from BMS/Pfizer, Sanofi-Aventis, St. Jude; Speaker bureau: Bayer, BMS/Pfizer, and Daiichi Sankyo and acts as a consultant/advisory board for Bayer, BMS/Pfizer. Dr Singer receives institutional research grant from Johnson & Johnson, Bristol-Myers Squibb, Boehringer Ingelheim, and Medtronic and acts as a consultant/advisory board for Boehringer Ingelheim, Bristol-Myers Squibb, CVS Health, Johnson & Johnson, Merck, Pfizer, and St. Jude Medical. K.A.A. Fox receives institutional research grant from Bayer, Janssen, AstraZeneca; receives honoraria from Bayer, AstraZeneca, GlaxoSmithKline, Janssen, and Sanofi and acts as a consultant/advisory board for Bayer, Lilly, AstraZeneca, and Sanofi. Dr Hankey is on Speaker bureau for Bayer; acts as a consultant/advisory board for Bayer, Sanofi; and in AC Immune (Chair, Data Monitoring Committee). Dr Mahaffey acts as a consultant for Eli Lilly, ACC, AstraZeneca, BAROnova, Bayer, Bio2 Medical, Boehringer Ingelheim, Bristol-Myers Squibb, Cubist, Elsevier (AHJ), Epson, Forest, GlaxoSmithKline, Johnson & Johnson, MEDTRONIC, Merck, Mt. Sinai, MyoKardia, Omthera, Portola, Purdue Pharma, Spring Publishing, The Medicines Company, Vindico, and WebMD; has an ownership interest in BioPrint Fitness; and receives institutional research grants from Amgen, Boehringer Ingelheim, Daiichi Sankyo, Johnson & Johnson, MEDTRONIC, St. Jude, and Tenax. Dr Jones receives institutional research grant from American Heart Association, AstraZeneca, Bristol-Myers Squibb, and Boston Scientific Corp; receives honoraria from American College of Physicians, American College of Radiology, and American Physician Institute. Dr Patel receives institutional research grant from AstraZeneca, CSL, HeartFlow, Janssen Research & Development, Johnson & Johnson, Maquet, Medtronic, and NHLBI and acts as a consultant for AstraZeneca, Bayer, CSL, Genzyme, Janssen Research & Development, Medtronic, and Merck. The other author reports no conflicts.FootnotesCorrespondence to W. Schuyler Jones, MD, Duke University Medical Center, Box 3330, Durham, NC 27710. E-mail [email protected]References1. Petersen P. Thromboembolic complications in atrial fibrillation.Stroke. 1990; 21:4–13.LinkGoogle Scholar2. Bekwelem W, Connolly SJ, Halperin JL, Adabag S, Duval S, Chrolavicius S, Pogue J, Ezekowitz MD, Eikelboom JW, Wallentin LG, Yusuf S, Hirsch AT. Extracranial systemic embolic events in patients with nonvalvular atrial fibrillation: incidence, risk factors, and outcomes.Circulation. 2015; 132:796–803. doi: 10.1161/CIRCULATIONAHA.114.013243.LinkGoogle Scholar3. 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Zhong L, Yin X and Xie Z (2020) Safety of radiofrequency ablation for reducing inflammatory cytokine levels and the left atrial diameter in patients with atrial fibrillation, Journal of International Medical Research, 10.1177/0300060520949760, 48:9, (030006052094976), Online publication date: 1-Sep-2020. Ramasamy S, Patel P, Gupta A, Okin P, Murthy S, Navi B, Kamel H and Merkler A (2019) Association Between Troponin Levels and Visceral Infarction in Patients with Acute Ischemic Stroke, Journal of Stroke and Cerebrovascular Diseases, 10.1016/j.jstrokecerebrovasdis.2019.104449, 28:12, (104449), Online publication date: 1-Dec-2019. Finn C, Hung P, Patel P, Gupta A and Kamel H (2018) Relationship Between Visceral Infarction and Ischemic Stroke Subtype, Stroke, 49:3, (727-729), Online publication date: 1-Mar-2018. Mel'nikov M, Apresyan A, Sotnikov A, Kozhevnikov D and Papava G (2021) Emergency care for patients with embolism of the aorta and main limb arteries in St. Petersburg: an analysis of 3498 cases over 50 years, Grekov's Bulletin of Surgery, 10.24884/0042-4625-2021-180-4-28-34, 180:4, (28-34) Mel’nikov M, Sotnikov A, Kozhevnikov D, Solov’yeva M and Boldueva S (2022) Embolism to the main limb arteries in patients with atrial fibrillation, Regional blood circulation and microcirculation, 10.24884/1682-6655-2021-20-4-14-20, 20:4, (14-20) May 2017Vol 10, Issue 5 Advertisement Article InformationMetrics © 2017 American Heart Association, Inc.https://doi.org/10.1161/CIRCOUTCOMES.116.003520PMID: 28495674 Originally publishedMay 11, 2017 PDF download Advertisement SubjectsArrhythmiasEmbolism}, number={5}, journal={CIRCULATION-CARDIOVASCULAR QUALITY AND OUTCOMES}, author={Orgel, Ryan and Wojdyla, Daniel and Huberman, David and Halperin, Jonathan L. and Breithardt, Guenter and Singer, Daniel E. and Fox, Keith A. A. and Hankey, Graeme J. and Mahaffey, Kenneth W. and Jones, W. Schuyler and et al.}, year={2017}, month={May} }