Predicting atrial fibrillation and flutter using Electronic Health Records

2012 ◽  
Author(s):  
S. Karnik ◽  
Sin Lam Tan ◽  
B. Berg ◽  
I. Glurich ◽  
Jinfeng Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health ◽  
Atrial Fibrillation And Flutter

scholarly journals Combining Inpatient and Outpatient Data for Diagnosis of Non-Valvular Atrial Fibrillation Using Electronic Health Records: A Validation Study

2020 ◽  
Vol Volume 12 ◽  
pp. 477-483
Author(s):  
Orna Reges ◽  
Hagay Weinberg ◽  
Moshe Hoshen ◽  
Philip Greenland ◽  
Hana'a Rayyan-Assi ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Electronic Health Records ◽  
Validation Study ◽  
Health Records ◽  
Electronic Health

scholarly journals Defining Disease Phenotypes Using National Linked Electronic Health Records: A Case Study of Atrial Fibrillation

PLoS ONE ◽  
2014 ◽  
Vol 9 (11) ◽  
pp. e110900 ◽  
Author(s):  
Katherine I. Morley ◽  
Joshua Wallace ◽  
Spiros C. Denaxas ◽  
Ross J. Hunter ◽  
Riyaz S. Patel ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Electronic Health Records ◽  
Health Records ◽  
Disease Phenotypes ◽  
Electronic Health

scholarly journals Statins and new-onset atrial fibrillation in a cohort of patients with hypertension. Analysis of electronic health records, 2006–2015

PLoS ONE ◽  
2017 ◽  
Vol 12 (10) ◽  
pp. e0186972 ◽  
Author(s):  
Lia Alves-Cabratosa ◽  
Maria García-Gil ◽  
Marc Comas-Cufí ◽  
Anna Ponjoan ◽  
Ruth Martí-Lluch ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health ◽  
Onset Atrial Fibrillation ◽  
New Onset

scholarly journals Bisoprolol transdermal patch treatment for patients with atrial fibrillation after noncardiac surgery: A single-center retrospective study of 61 patients

2020 ◽  
Vol 8 ◽  
pp. 205031212090781
Author(s):  
Taku Yasui ◽  
Toru Oka ◽  
Wataru Shioyama ◽  
Makiko Oboshi ◽  
Masashi Fujita
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Electronic Health Records ◽  
Sinus Rhythm ◽  
Noncardiac Surgery ◽  
Additional Treatment ◽  
Postoperative Atrial Fibrillation ◽  
Transdermal Patch ◽  
Health Records ◽  
Electronic Health

Objectives: Atrial fibrillation after surgery is associated with increased rates of heart failure and ischemic stroke, and extension of hospitalization. Bisoprolol is a β-blocker used to reduce heart rate and manage arrhythmias during atrial fibrillation. However, the safety and efficacy of bisoprolol transdermal patch treatment in patients with postoperative atrial fibrillation remain unclear. Methods: We retrospectively assessed the electronic health records of our hospital between September 2013 and July 2018 and identified patients with postoperative atrial fibrillation who had been treated with a bisoprolol transdermal patch. We excluded patients with sinus rhythm using bisoprolol transdermal patch to prevent atrial fibrillation recurrence and those with sustained atrial fibrillation before surgery. Data on heart rhythm, heart rate, and blood pressure at the baseline and after 24 h of treatment were obtained from the electronic health records. Results: Of the 603 patients treated with the bisoprolol transdermal patch, 61 patients with postoperative atrial fibrillation after noncardiac surgery were included. The bisoprolol transdermal patch was discontinued due to bradycardia in two patients (3.3%). In both cases, the heart rate increased after the removal of the bisoprolol transdermal patch and no additional treatment was necessary. Among the 61 patients, sinus rhythm was restored within 24 h of bisoprolol treatment in 47 patients (77.0%). The heart rate significantly decreased from 124.8 ± 26.3 bpm at the baseline to 78.9 ± 16.6 bpm at 24 h after treatment ( p < 0.001). There were no significant differences in the systolic and diastolic blood pressures between patients before and at 24 h after treatment. Conclusion: The results of this study indicate that the bisoprolol transdermal patch is well tolerated and effective in patients with atrial fibrillation after noncardiac surgery.

scholarly journals Development and Validation of a Prediction Model for Atrial Fibrillation Using Electronic Health Records

2019 ◽  
Vol 5 (11) ◽  
pp. 1331-1341 ◽  
Author(s):  
Olivia L. Hulme ◽  
Shaan Khurshid ◽  
Lu-Chen Weng ◽  
Christopher D. Anderson ◽  
Elizabeth Y. Wang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Electronic Health Records ◽  
Prediction Model ◽  
Health Records ◽  
Electronic Health ◽  
Development And Validation

scholarly journals Atrial fibrillation and oral anticoagulation in older people with frailty: a nationwide primary care electronic health records cohort study

2020 ◽  
Author(s):  
Chris Wilkinson ◽  
Andrew Clegg ◽  
Oliver Todd ◽  
Kenneth Rockwood ◽  
Mohammad E Yadegarfar ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Electronic Health Records ◽  
Older People ◽  
Oral Anticoagulation ◽  
Stroke Prevention ◽  
Stroke Risk ◽  
Health Records ◽  
Risk Of Death ◽  
Increased Risk ◽  
Electronic Health

Abstract Background Atrial fibrillation (AF) is common in older people and is associated with increased stroke risk that may be reduced by oral anticoagulation (OAC). Frailty also increases with increasing age, yet the extent of OAC prescription in older people according to extent of frailty in people with AF is insufficiently described. Methods An electronic health records study of 536,955 patients aged ≥65 years from ResearchOne in England (384 General Practices), over 15.4 months, last follow-up 11th April 2017. OAC prescription for AF with CHA2DS2-Vasc ≥2, adjusted (demographic and treatments) risk of all-cause mortality, and subsequent cerebrovascular disease, bleeding and falls were estimated by electronic frailty index (eFI) category of fit, mild, moderate and severe frailty. Results AF prevalence and mean CHA2DS2-Vasc for those with AF increased with increasing eFI category (fit 2.9%, 2.2; mild 11.2%, 3.2; moderate 22.2%, 4.0; and severe 31.5%, 5.0). For AF with CHA2DS2-Vasc ≥2, OAC prescription was higher for mild (53.2%), moderate (55.6%) and severe (53.4%) eFI categories than fit (41.7%). In those with AF and eligible for OAC, frailty was associated with increased risk of death (HR for severe frailty compared with fit 4.09, 95% confidence interval 3.43–4.89), gastrointestinal bleeding (2.17, 1.45–3.25), falls (8.03, 4.60–14.03) and, among women, stroke (3.63, 1.10–12.02). Conclusion Among older people in England, AF and stroke risk increased with increasing degree of frailty; however, OAC prescription approximated 50%. Given competing demands of mortality, morbidity and stroke prevention, greater attention to stratified stroke prevention is needed for this group of the population.

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