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      Incidence, recurrence, and outcome of postrace atrial fibrillation in Thoroughbred horses

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          Abstract

          Background

          Atrial fibrillation (AF) impacts performance and horse and jockey safety. Understanding the outcomes of AF identified postrace will better inform regulatory policy.

          Hypothesis/Objectives

          To investigate the outcomes after episodes of AF identified postrace and determine whether affected horses are at increased risk of additional episodes compared to the general racing population.

          Animals

          Total of 4684 Thoroughbred racehorses.

          Methods

          Race records for Thoroughbred horses racing in Hong Kong from 2007 to 2017 were reviewed. Horses that performed below expectation were examined by cardiac auscultation and ECG. Incidence and recurrence of AF were compared between horses with and without a history of AF and between horses with paroxysmal and persistent episodes using Fisher's exact test.

          Results

          There were 96 135 race starts during the study. Atrial fibrillation was identified in 4.9% of horses, with an overall incidence of 2.7 episodes per 1000 starts. The incidence of AF in horses after any previous episode (12.8 per 1000 starts) was higher than for horses with no previous episode (2.4 per 1000 starts; odds ratio [OR], 5.3; 95% confidence interval [CI], 3.8‐7.6). Recurrence was seen in 64% of horses previously treated for persistent AF, which was higher than recurrence in horses with paroxysmal AF (23%; OR, 5.9; 95% CI, 1.6‐21.2). Median duration between episodes was 343 days (range, 34‐1065).

          Conclusions and Clinical Importance

          Thoroughbreds are at increased risk of recurrent AF after both paroxysmal and persistent episodes, but the duration of time between episodes varies widely. These findings support a substantial burden of AF among individual Thoroughbred racehorses.

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          Most cited references65

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          Statistical notes for clinical researchers: Chi-squared test and Fisher's exact test

          When we try to compare proportions of a categorical outcome according to different independent groups, we can consider several statistical tests such as chi-squared test, Fisher's exact test, or z-test. The chi-squared test and Fisher's exact test can assess for independence between two variables when the comparing groups are independent and not correlated. The chi-squared test applies an approximation assuming the sample is large, while the Fisher's exact test runs an exact procedure especially for small-sized samples. Chi-squared test 1. Independency test The chi-squared test is used to compare the distribution of a categorical variable in a sample or a group with the distribution in another one. If the distribution of the categorical variable is not much different over different groups, we can conclude the distribution of the categorical variable is not related to the variable of groups. Or we can say the categorical variable and groups are independent. For example, if men have a specific condition more than women, there is bigger chance to find a person with the condition among men than among women. We don't think gender is independent from the condition. If there is equal chance of having the condition among men and women, we will find the chance of observing the condition is the same regardless of gender and can conclude their relationship as independent. Examples 1 and 2 in Table 1 show perfect independent relationship between condition (A and B) and gender (male and female), while example 3 represents a strong association between them. In example 3, women had a greater chance to have the condition A (p = 0.7) compared to men (p = 0.3). The chi-squared test performs an independency test under following null and alternative hypotheses, H0 and H1, respectively. H0: Independent (no association) H1: Not independent (association) The test statistic of chi-squared test: χ 2 = ∑ ( 0 - E ) 2 E ~ χ 2 with degrees of freedom (r - 1)(c - 1), Where O and E represent observed and expected frequency, and r and c is the number of rows and columns of the contingency table. The first step of the chi-squared test is calculation of expected frequencies (E). E is calculated under the assumption of independent relation or, in other words, no association. Under independent relationship, the cell frequencies are determined only by marginal proportions, i.e., proportion of A (60/200 = 0.3) and B (1400/200 = 0.7) in example 2. In example 2, the expected frequency of the male and A cell is calculated as 30 that is the proportion of 0.3 (proportion of A) in 100 Males. Similarly, the expected frequency of the male and A cell is 50 that is the proportion of 0.5 (proportion of A = 100/200 = 0.5) in 100 Males in example 3 (Table 1). Expected frequency (E) of Male & A = Number of A * Number of Male Total number = p A * p male * total number The second step is obtaining (O - E)2/E for each cell and summing up the values over each cell. The final summed value follows chi-squared distribution. For the ‘male and A’ cell in example 3, (O - E)2/E = (30 - 50)2/50 = 8. Chi-squared statistic calculated = ∑ ( 0 - E ) 2 E = 8 + 8 + 8 + 8 = 32 in example 3. For examples 1 and 2, the chi-squared statistics equal zero. A big difference between observed value and expected value or a large chi-squared statistic implies that the assumption of independency applied in calculation of expected value is irrelevant to the observed data that is being tested. The degrees of freedom is one as the data has two rows and two columns: (r - 1) * (c - 1) = (2 - 1) * (2 - 1) = 1. The final step is making conclusion referring to the chi-squared distribution. We reject the null hypothesis of independence if the calculated chi-squared statistic is larger than the critical value from the chi-squared distribution. In the chi-squared distribution, the critical values are 3.84, 5.99, 7.82, and 9.49, with corresponding degrees of freedom of 1, 2, 3, and 4, respectively, at an alpha level of 0.5. Larger chi-square statistics than these critical values of specific corresponding degrees of freedom lead to the rejection of null hypothesis of independence. In examples 1 and 2, the chi-squared statistic is zero which is smaller than the critical value of 3.84, concluding independent relationship between gender and condition. However, data in example 3 have a large chi-squared statistic of 32 which is larger than 3.84; it is large enough to reject the null hypothesis of independence, concluding a significant association between two variables. The chi-squared test needs an adequate large sample size because it is based on an approximation approach. The result is relevant only when no more than 20% of cells with expected frequencies < 5 and no cell have expected frequency < 1.1 2. Effect size As the significant test does not tell us the degree of effect, displaying effect size is helpful to show the magnitude of effect. There are three different measures of effect size for chi-squared test, Phi (φ), Cramer's V (V), and odds ratio (OR). Among them φ and OR can be used as the effect size only in 2 × 2 contingency tables, but not for bigger tables. φ = χ 2 n V = χ 2 n · d f , where n is total number of observation, and df is degrees of freedom calculated by (r - 1) * (c - 1). Here, r and c are the numbers of rows and columns of the contingency table. In example 3, we can calculate them as φ = χ 2 n = 32 200 = 0.4 , V = χ 2 n · d f = 32 200 · 1 = 0.4 , and O R = 70 · 70 30 · 30 = 5.44 . Referring to Table 2, the effect size V = 0.4 is interpreted medium to large. If number of rows and/or columns are larger than 2, only Cramer's V is available. 3. Post-hoc pairwise comparison of chi-squared test The chi-squared test assesses a global question whether relation between two variables is independent or associated. If there are three or more levels in either variable, a post-hoc pairwise comparison is required to compare the levels of each other. Let's say that there are three comparative groups like control, experiment 1, and experiment 2 and we try to compare the prevalence of a certain disease. If the chi-squared test concludes that there is significant association, we may want to know if there is any significant difference in three compared pairs, between control and experiment 1, between control and experiment 2, and between experiment 1 and experiment 2. We can reduce the table into multiple 2 × 2 contingency tables and perform the chi-squared test with applying the Bonferroni corrected alpha level (corrected α = 0.05/3 compared pairs = 0.017). Fisher's exact test Fisher's exact test is practically applied only in analysis of small samples but actually it is valid for all sample sizes. While the chi-squared test relies on an approximation, Fisher's exact test is one of exact tests. Especially when more than 20% of cells have expected frequencies < 5, we need to use Fisher's exact test because applying approximation method is inadequate. Fisher's exact test assesses the null hypothesis of independence applying hypergeometric distribution of the numbers in the cells of the table. Many packages provide the results of Fisher's exact test for 2 × 2 contingency tables but not for bigger contingency tables with more rows or columns. For example, the SPSS statistical package automatically provides an analytical result of Fisher's exact test as well as chi-squared test only for 2 × 2 contingency tables. For Fisher's exact test of bigger contingency tables, we can use web pages providing such analyses. For example, the web page ‘Social Science Statistics’ (http://www.socscistatistics.com/tests/chisquare2/Default2.aspx) permits performance of Fisher exact test for up to 5 × 5 contingency tables. The procedure of chi-squared test and Fisher's exact test using IBM SPSS Statistics for Windows Version 23.0 (IBM Corp., Armonk, NY, USA) is as follows:
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            Progression from paroxysmal to persistent atrial fibrillation clinical correlates and prognosis.

            We investigated clinical correlates of atrial fibrillation (AF) progression and evaluated the prognosis of patients demonstrating AF progression in a large population. Progression of paroxysmal AF to more sustained forms is frequently seen. However, not all patients will progress to persistent AF. We included 1,219 patients with paroxysmal AF who participated in the Euro Heart Survey on AF and had a known rhythm status at follow-up. Patients who experienced AF progression after 1 year of follow-up were identified. Progression of AF occurred in 178 (15%) patients. Multivariate analysis showed that heart failure, age, previous transient ischemic attack or stroke, chronic obstructive pulmonary disease, and hypertension were the only independent predictors of AF progression. Using the regression coefficient as a benchmark, we calculated the HATCH score. Nearly 50% of the patients with a HATCH score >5 progressed to persistent AF compared with only 6% of the patients with a HATCH score of 0. During follow-up, patients with AF progression were more often admitted to the hospital and had more major adverse cardiovascular events. A substantial number of patients progress to sustained AF within 1 year. The clinical outcome of these patients regarding hospital admissions and major adverse cardiovascular events was worse compared with patients demonstrating no AF progression. Factors known to cause atrial structural remodeling (age and underlying heart disease) were independent predictors of AF progression. The HATCH score may help to identify patients who are likely to progress to sustained forms of AF in the near future. Copyright (c) 2010 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
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              Initiation of atrial fibrillation by ectopic beats originating from the pulmonary veins: electrophysiological characteristics, pharmacological responses, and effects of radiofrequency ablation.

              Atrial fibrillation (AF) can be initiated by ectopic beats originating from the atrial or great venous tissues. This study investigated the anatomic characteristics and electrophysiological properties of pulmonary veins (PVs), as well as the possible mechanisms and response to drugs of ectopic foci, and assessed the effects of radiofrequency (RF) ablation on AF initiated by ectopic beats originating from PVs. Seventy-nine patients with frequent episodes of paroxysmal AF and 10 control patients were included. Distal PVs showed the shortest effective refractory periods (ERPs), and right superior PVs showed a higher incidence of intra-PV conduction block than left superior PVs. Superior and left PVs had longer myocardial sleeves than inferior and right PVs, respectively. These electrophysiological characteristics were similar between AF and control patients. Propranolol, verapamil, and procainamide suppressed ectopic beats that originated from the PVs. Of 116 ectopic foci that initiated AF, 103 (88.8%) originated from PVs. A mean of 7+/-3 RF applications completely eliminated 110 ectopic foci (94.8%). During the 6+/-2-month follow-up period, 68 patients (86. 1%) were free of AF without any antiarrhythmic drugs. Follow-up transesophageal echocardiogram showed 42.4% of ablated PVs had focal stenosis. One patient had mild exertional dyspnea after ablation, but it resolved 3 months later; 1 patient had onset of mild exertional dyspnea 5 months after ablation. Electrophysiological characteristics of PVs are different from those in the atria. Ectopic beats from PVs can initiate AF, and beta-adrenergic receptor blocker, calcium channel blockers, and sodium channel blockers can suppress these ectopic beats. Careful mapping and elimination of these ectopic foci can cure paroxysmal AF.
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                Author and article information

                Contributors
                laura.nath@adelaide.edu.au
                Journal
                J Vet Intern Med
                J Vet Intern Med
                10.1111/(ISSN)1939-1676
                JVIM
                Journal of Veterinary Internal Medicine
                John Wiley & Sons, Inc. (Hoboken, USA )
                0891-6640
                1939-1676
                19 February 2021
                Mar-Apr 2021
                : 35
                : 2 ( doiID: 10.1111/jvim.v35.2 )
                : 1111-1120
                Affiliations
                [ 1 ] School of Animal & Veterinary Sciences University of Adelaide Roseworthy South Australia Australia
                [ 2 ] Centre for Heart Rhythm Disorders, Adelaide Medical School University of Adelaide Adelaide South Australia Australia
                [ 3 ] Hong Kong Jockey Club, Veterinary Clinical Services Equine Hospital Hong Kong SAR Hong Kong
                [ 4 ] Veterinary Regulation Hong Kong Jockey Club Hong Kong SAR Hong Kong
                [ 5 ] EVC Global LTD Hong Kong Hong Kong
                Author notes
                [*] [* ] Correspondence

                Laura C. Nath, School of Animal & Veterinary Sciences, University of Adelaide, Roseworthy, South Australia, Australia.

                Email: laura.nath@ 123456adelaide.edu.au

                Author information
                https://orcid.org/0000-0001-9685-4673
                Article
                JVIM16063
                10.1111/jvim.16063
                7995445
                33604980
                43dbc828-58e0-4c40-94eb-742375c5d143
                © 2021 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals LLC. on behalf of the American College of Veterinary Internal Medicine.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 25 January 2021
                : 06 August 2020
                : 02 February 2021
                Page count
                Figures: 4, Tables: 3, Pages: 10, Words: 7988
                Funding
                Funded by: Agrifutures Australia , open-funder-registry 10.13039/501100009207;
                Funded by: Racing Victoria , open-funder-registry 10.13039/100010313;
                Categories
                Standard Article
                EQUINE
                Standard Articles
                Cardiology
                Custom metadata
                2.0
                March/April 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.9 mode:remove_FC converted:26.03.2021

                Veterinary medicine
                arrhythmia,atrial fibrillation,cardiology,electrocardiography,epidemiology,equine
                Veterinary medicine
                arrhythmia, atrial fibrillation, cardiology, electrocardiography, epidemiology, equine

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