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Atrial Fibrillation

Atrial Fibrillation is a common heart rhythm disorder characterized by irregular and often rapid heart rate.
It can lead to serious complications if left untreated.
PubCompare.ai's AI-driven platform helps researchers optimize Atrial Fibrillation studies by identifying the most effective treatments and products from literature, preprints, and patents.
The platform's intelligent comparisons empower researchers to make informed decisions and advance this important area of cardiovascular research.
Experience the power of AI-driven research optimization today.

Most cited protocols related to «Atrial Fibrillation»

1
Standardized GBD Modeling Methodologies
For most diseases and injuries, processed data are modelled using standardised tools to generate estimates of each quantity of interest by age, sex, location, and year. There are three main standardised tools: Cause of Death Ensemble model (CODEm), spatiotemporal Gaussian process regression (ST-GPR), and DisMod-MR. Previous publications2 (link), 3 (link), 12 and the appendix provide more details on these general GBD methods. Briefly, CODEm is a highly systematised tool to analyse cause of death data using an ensemble of different modelling methods for rates or cause fractions with varying choices of covariates that perform best with out-of-sample predictive validity testing. DisMod-MR is a Bayesian meta-regression tool that allows evaluation of all available data on incidence, prevalence, remission, and mortality for a disease, enforcing consistency between epidemiological parameters. ST-GPR is a set of regression methods that borrow strength between locations and over time for single metrics of interest, such as risk factor exposure or mortality rates. In addition, for select diseases, particularly for rarer outcomes, alternative modelling strategies have been developed, which are described in appendix 1 (section 3.2).
In GBD 2019, we designated a set of standard locations that included all countries and territories as well as the subnational locations for Brazil, China, India, and the USA. Coefficients of covariates in the three main modelling tools were estimated for these standard locations only—ie, we ignored data from subnational locations other than for Brazil, China, India, and the USA (appendix 1 section 1.1). Using this set of standard locations will prevent changes in regression coefficients from one GBD cycle to the next that are solely due to the addition of new subnational units in the analysis that might have lower quality data or small populations (appendix 1 section 1.1). Changes to CODEm for GBD 2019 included the addition of count models to the model ensemble for rarer causes. We also modified DisMod-MR priors to effectively increase the out-of-sample coverage of uncertainty intervals (UIs) as assessed in simulation testing (appendix 1 section 4.5).
For the cause Alzheimer's disease and other dementias, we changed the method of addressing large variations between locations and over time in the assignment of dementia as the underlying cause of death. Based on a systematic review of published cohort studies, we estimated the relative risk of death in individuals with dementia. We identified the proportion of excess deaths in patients with dementia where dementia is the underlying cause of death as opposed to a correlated risk factor (appendix 1 section 2.6.2). We changed the strategy of modelling deaths for acute hepatitis A, B, C, and E from a natural history model relying on inpatient case fatality rates to CODEm models after predicting type-specific acute hepatitis deaths from vital registration data with specified hepatitis type.
DisMod-MR was used to estimate deaths from three outcomes (dementia, Parkinson's, and atrial fibrillation), and to determine the proportions of deaths by underlying aetiologies of cirrhosis, liver cancer, and chronic kidney disease deaths.
Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. (2020). Lancet (London, England), 396(10258), 1204-1222.
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Publication 2020
Alzheimer's Disease Atrial Fibrillation Cancer of Liver Chronic Kidney Diseases Dementia Hepatitis A Injuries Inpatient Liver Cirrhosis Patients Population Group Presenile Dementia
2
Genetic Associations in Diverse Diseases
BioVU accrues DNA samples extracted from blood remaining from routine clinical testing after they have been retained for 3 days and are scheduled to be discarded. A full description of this resource and its associated ethical, privacy and other protections has been published elsewhere (Roden et al., 2008 (link)). All patients age ≥18 years with an outpatient laboratory draw, who have a signed consent to treatment form, and that have not made a formal indication to opt-out are potential inclusions in BioVU. The resource is linked to a de-identified version of the EMR called the synthetic derivative (Roden et al., 2008 (link)). As of January 18, 2010, the resource included 75 769 DNA samples.
The study population consisted of the first ∼6000 European–Americans accrued into BioVU. The only selection criteria were that they met the general conditions for eligibility for BioVU; no clinical inclusion or exclusion criteria were applied. In the current analysis, we selected five SNPs with previously known disease associations: rs1333049 [coronary artery disease (CAD) and carotid artery stenosis (CAS)], rs2200733 [atrial fibrillation (AF)], rs3135388 [multiple sclerosis (MS) and systemic lupus erythematosus (SLE)], rs6457620 [rheumatoid arthritis (RA)], and rs17234657 [Crohn's disease (CD)]. (Some other potential associations exist for some SNPs, such as progression of carotid atherosclerosis and rs1333049, but are not represented well through ICD9 codes.) The primary outcome of this study was identification of the prior statistical associations with MS, CD, AF, CAD, SLE, CAS and RA.
Genotyping was conducted by the Vanderbilt DNA Resources Core using the mid-throughput Sequenom® genotyping platform (rs1333049, rs3135388 and rs17234657; genotyping efficiency 98.4–100%), which is based on a single base primer extension reaction coupled with mass spectrometry, or using a TaqMan assay (rs6457620 and rs2200733; genotyping efficiency 99.4% and 99.0%, respectively). Quality control procedures included examination of marker and sample genotyping efficiency, allele frequency calculations and tests of Hardy–Weinberg equilibrium.
Denny J.C., Ritchie M.D., Basford M.A., Pulley J.M., Bastarache L., Brown-Gentry K., Wang D., Masys D.R., Roden D.M, & Crawford D.C. (2010). PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations. Bioinformatics, 26(9), 1205-1210.
Publication 2010
Atrial Fibrillation Biological Assay BLOOD Carotid Atherosclerosis Carotid Stenosis Coronary Artery Disease Crohn Disease Disease Progression Eligibility Determination Europeans Inclusion Bodies Lupus Erythematosus, Systemic Mass Spectrometry Multiple Sclerosis Oligonucleotide Primers Outpatients Patients Protective Agents Rheumatoid Arthritis Single Nucleotide Polymorphism
3
MASALA Study Eligibility Criteria
To be eligible for the MASALA study participants had to have (1) South Asian ancestry defined by having at least 3 grandparents born in one of the following countries: India, Pakistan, Bangladesh, Nepal, or Sri Lanka; and (2) age between 40 and 79 years; (3) ability to speak and/or read English, Hindi or Urdu. A pilot study called the Metabolic syndrome and Atherosclerosis in South Asians Living in America (NIH grant #K23 HL080026) had similar eligibility criteria and methods as this larger study. The 150 participants enrolled in the pilot study were eligible to enroll in the current MASALA study.
We used identical exclusion criteria to MESA12 (link) which included having a physician diagnosed heart attack, stroke or transient ischemic attack, heart failure, angina, use of nitroglycerin, or those with a history of cardiovascular procedures such as coronary artery bypass graft surgery, angioplasty, valve replacement, pacemaker or defibrillator implantation, or any surgery on the heart or arteries. Those with current atrial fibrillation or in active treatment for cancer were excluded. Those with life expectancy < 5 years due to a serious medical illness, impaired cognitive ability as judged by the reviewer, plans to move out of the study region in the next 5 years, or those living in a nursing home or on a waiting list were also excluded. Due to CT scanner limitations, those weighing over 300 lbs. were excluded.
Kanaya A.M., Kandula N., Herrington D., Budoff M.J., Hulley S., Vittinghoff E, & Liu K. (2013). Mediators of Atherosclerosis in South Asians Living in America (MASALA) Study: Objectives, Methods, and Cohort Description. Clinical Cardiology, 36(12), 713-720.
Publication 2013
Angina Pectoris Angioplasty Arteries Asian Persons Atherosclerosis Atrial Fibrillation Cardiovascular System CAT SCANNERS X RAY Cerebrovascular Accident Childbirth Cognition Congestive Heart Failure Coronary Artery Bypass Surgery Defibrillators Eligibility Determination Grandparent Infantile Neuroaxonal Dystrophy Malignant Neoplasms Metabolic Syndrome X Myocardial Infarction Nitroglycerin Ovum Implantation Pacemaker, Artificial Cardiac Physicians South Asian People Surgical Procedure, Cardiac Transient Ischemic Attack
4
Expanding PheWAS Phenotype Categorization
In this study, we revised and expanded our earlier PheWAS phenotype categorization to a total of 1,645 phenotypes identified from International Classification of Disease, Ninth revision, Clinical Modification (ICD9) codes. (Our initial PheWAS phenotype categorization included 744 phenotypes9 (link).) The ICD9 coding system is divided into four components: diseases, signs and symptoms (“three digit” codes, 001–999), external causes of injury (“E” codes), procedures (“two digit” codes 00.0–99.9) and supplemental classifications (“V” codes). The prior PheWAS code groupings included only diseases, signs and symptoms (three digit) ICD9 codes9 (link). We revised and expanded the PheWAS phenotypes by (i) adding V codes (commonly used to record personal histories of given diseases) and E codes (which refer to external causes of injury) to the PheWAS code mapping, (ii) redesigning the code system to be hierarchical, such that one phenotype could be a parent of another subphenotype (e.g., cardiac arrhythmias is a parent of atrial fibrillation, atrial flutter and other arrhythmias), and (iii) including more granular phenotypes into the coding system (e.g., “type 1 diabetes with ketoacidosis”). Creation of hierarchical phenotypes included creation of phenotypes not present in the ICD9 billing hierarchy, such as “inflammatory bowel disease” as the parent phenotype for “Crohn’s disease” and “ulcerative colitis.” In this process, we were guided by the hierarchical organization of the Clinical Classifications Software (CCS) produced by the Agency for Healthcare Research and Quality42 (link); the 2011 version of the CCS contains 727 phenotypes. The resulting PheWAS code group currently contains 1,645 phenotypes, 1,358 of which had at least 25 cases (a prevalence of 0.18% in our data set) in the eMERGE cohort, our threshold for these analyses. The current version of the PheWAS codes, with ICD9 mappings and control groups, is available from http://knowledgemap.mc.vanderbilt.edu/research/content/phewas.
Denny J.C., Bastarache L., Ritchie M.D., Carroll R.J., Zink R., Mosley J.D., Field J.R., Pulley J.M., Ramirez A.H., Bowton E., Basford M.A., Carrell D.S., Peissig P.L., Kho A.N., Pacheco J.A., Rasmussen L.V., Crosslin D.R., Crane P.K., Pathak J., Bielinski S.J., Pendergrass S.A., Xu H., Hindorff L.A., Li R., Manolio T.A., Chute C.G., Chisholm R.L., Larson E.B., Jarvik G.P., Brilliant M.H., McCarty C.A., Kullo I.J., Haines J.L., Crawford D.C., Masys D.R, & Roden D.M. (2013). Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nature biotechnology, 31(12), 1102-1110.
Publication 2013
Atrial Fibrillation Atrial Flutter Cardiac Arrhythmia Crohn Disease Diabetic Ketoacidosis Fingers Inflammatory Bowel Diseases Injuries Parent Phenotype Ulcerative Colitis
5
Atrial Fibrillation Anticoagulation Risk Factors

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Publication 2011
Adult Anemia Anti-Inflammatory Agents, Non-Steroidal Aspirin Atrial Fibrillation Clinical Laboratory Services Clopidogrel Creatinine Diagnosis Genotype Glomerular Filtration Rate Heart Atrium Hemoglobin Hospitalization Inpatient Outpatients Patient Discharge Patients Pharmaceutical Preparations Platelet Counts, Blood Pulmonary Embolism Renal Insufficiency Serum Thrombocytopenia Ticlopidine Transients Warfarin Woman

Most recents protocols related to «Atrial Fibrillation»

1
In Vivo Imaging of MMP Activation in MI
Not available on PMC !

Example 1

We have established the feasibility of in vivo imaging of MMP activation in pigs (Sahul et al. Circ Cardiovasc Imaging 2011, 4:381-391) and dogs (Liu et al. J Nucl Med 2011, 52(3):453-60) post-MI. The data derived in pigs involved surgical occlusion of two marginal branches of the left circumflex artery and resulted in regional activation of MMPs in the inferolateral wall. (Sahul et al. Circ Cardiovasc Imaging 2011, 4:381-391). This surgical model caused significant activation of MMPs in the surgical wound adjacent to both the atria and ventricles of heart, complicating in vivo imaging. The studies in dogs employed percutaneous balloon occlusion of left anterior descending artery, avoided the surgical intervention, and resulted in improved image quality. In these recently published porcine studies with serial SPECT/CT imaging, we demonstrated focal uptake of the MMP-targeted agent 99mTc-RP805 within the infarcted lateral wall, which peaked at ˜2 weeks post injury, and remained elevated at 4 weeks post occlusion. Early MMP activity at 1 week post-MI predicted late post MI ventricular remodeling (FIG. 1).

US11865195B2. Evaluation of presence of and vulnerability to atrial fibrillation and other indications using matrix metalloproteinase-based imaging (2024-01-09). Lantheus Medical Imaging, Inc. [US], Yale University [US]. Inventors: Albert J. Sinusas [US], Joseph G. Akar [US], Richard R. Cesati [US], Heike S. Radeke [US], Stephen B. Haber [US].
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Patent 2024
Arterial Occlusion Atrial Fibrillation Canis familiaris Dental Occlusion Heart Atrium Heart Ventricle Injuries Interstitial Collagenase MMP1 protein, human Operative Surgical Procedures Pigs Single Photon Emission Computed Tomography Computed Tomography Surgical Wound Sus scrofa
2
Imaging-Based AF Recurrence Prediction
Not available on PMC !

Example 3

A cohort of patients post cardioversion are administered an effective amount of the imaging agent of the invention, images of each patient's left atrium are obtained and the uptake of the imaging agent is quantified. A cut point for imaging agent uptake is then established such that the cut point separates the cohort into 2 populations; those above the cut point have recurrent AF while those below the cut point do not.

Based on the cut point levels determined above, future patients are then tested for MMP levels using the methods of the invention and those demonstrating above cut point levels are treated using one or more of the therapies described herein and known in the art for AF, including but not limited to pharmacological rate control therapy, pharmacological rhythm control therapy, ablation, and/or implantable pacer.

US11865195B2. Evaluation of presence of and vulnerability to atrial fibrillation and other indications using matrix metalloproteinase-based imaging (2024-01-09). Lantheus Medical Imaging, Inc. [US], Yale University [US]. Inventors: Albert J. Sinusas [US], Joseph G. Akar [US], Richard R. Cesati [US], Heike S. Radeke [US], Stephen B. Haber [US].
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Patent 2024
Atrial Fibrillation Atrium, Left Electric Countershock Matrix Metalloproteinase 3 Patients Population Group
3
Predicting Atrial Fibrillation Risk
Not available on PMC !

Example 4

A cohort of patients with a recent history of myocardial infarction are administered an effective amount of the imaging agent of the invention, images of each patient's left atrium are obtained and the uptake of the imaging agent is quantified. The patients also undergo a resting flurpiridaz F 18 myocardial perfusion study and the summed rest score determined for each patient. Logisitic regression analysis is performed to produce an equation expressing the likelihood of future AF as a function of summed rest score and quantified imaging agent uptake.

US11865195B2. Evaluation of presence of and vulnerability to atrial fibrillation and other indications using matrix metalloproteinase-based imaging (2024-01-09). Lantheus Medical Imaging, Inc. [US], Yale University [US]. Inventors: Albert J. Sinusas [US], Joseph G. Akar [US], Richard R. Cesati [US], Heike S. Radeke [US], Stephen B. Haber [US].
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Patent 2024
Atrial Fibrillation Atrium, Left Matrix Metalloproteinases Myocardial Infarction Myocardium Patients Perfusion
4
Cross-sectional study of Shanghai population
The study subjects (n = 13,750) were enrolled through cluster multistage and random sampling to community population from several districts of Shanghai in China in this cross-sectional study. The participants aged more than 18 years old were investigated in each center from May to September in 2016. Exclusion criteria included history of aortic dissection, history of amputation surgery, atrial fibrillation, mental disorder or lack of compliance. After the subjects with incomplete data or exclusion criteria were removed, there were totally 13,144 participants left (Fig. 1).

Flow chart of subjects enrollment

The study complied with the Declaration of Helsinki. It was also approved by the ethics committee of Shanghai Jiao Tong University and informed consent was obtained from all the participants prior to enrollment.
Wang Y., Guo X., Zhang Y., Zhang R, & Li J. (2023). Different associations of general and abdominal obesity with upper and lower extremity artery disease among a community population in China. Nutrition & Metabolism, 20, 14.
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Publication 2023
Amputation Atrial Fibrillation Dissecting Aneurysms Ethics Committees Mental Disorders
5
Long-term Conditions in Welsh Residents
Residents of Wales diagnosed for the first time with at least one of 17 long-term conditions between January 2000 and December 2021 were identified using ICD-10 or Read v2 codes (Supplementary Tables S2 and S3). The conditions included were anxiety disorders, asthma, atrial fibrillation, coronary heart disease (CHD), chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), dementia, depression, diabetes mellitus, epilepsy, heart failure, hypertension, inflammatory bowel disease (IBD), osteoporosis, peripheral vascular disease (PVD), rheumatoid arthritis, and stroke and transient ischaemic attack (TIA). These conditions comprise most of the general practice ‘Quality and Outcomes (QOF) Framework’.13 In addition, individuals diagnosed with three diabetes subtypes (type 1, type 2, undetermined) were identified using an algorithm.14 (link) ‘Undetermined type diabetes’ was assigned when criteria for type 1 or type 2 were not met.
The final study dataset excluded records missing week of birth or sex, or where the diagnosis date was before birth or after death dates.
Qi C., Osborne T., Bailey R., Cooper A., Hollinghurst J.P., Akbari A., Crowder R., Peters H., Law R.J., Lewis R., Smith D., Edwards A, & Lyons R.A. (2023). Impact of COVID-19 pandemic on incidence of long-term conditions in Wales: a population data linkage study using primary and secondary care health records. The British Journal of General Practice, 73(730), e332-e339.
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Publication 2023
Anxiety Disorders Asthma Atrial Fibrillation Cerebrovascular Accident Childbirth Chronic Kidney Diseases Chronic Obstructive Airway Disease Congestive Heart Failure Dementia Diabetes Mellitus Diagnosis Epilepsy Heart Disease, Coronary High Blood Pressures Inflammatory Bowel Diseases Osteoporosis Peripheral Vascular Diseases Rheumatoid Arthritis Training Programs Transient Ischemic Attack

Top products related to «Atrial Fibrillation»

1
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2
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R version 3.6.1 is a statistical computing and graphics software package. It is an open-source implementation of the S programming language and environment. R version 3.6.1 provides a wide range of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more.
5
Sas v9 by SAS Institute
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More about "Atrial Fibrillation"

Atrial fibrillation (AF) is a common and serious heart rhythm disorder characterized by an irregular and often rapid heart rate.
It is a type of arrhythmia that can lead to significant complications if left untreated, such as blood clots, stroke, heart failure, and other cardiovascular issues.
Researchers analyzing AF often utilize statistical software like SAS version 9.4, Stata, and R version 3.6.1 to examine data and identify effective treatments.
The PubCompare.ai platform helps optimize these studies by using AI-driven comparisons to locate the most promising protocols, therapies, and products from the literature, preprints, and patents.
This intelligent comparison functionality empowers researchers to make more informed decisions and advance the field of cardiovascular research focused on atrial fibrillation.
Key subtopics include risk factors, symptoms, diagnosis (e.g., using the Elecsys 2010 system), management strategies, and emerging treatments.
Researchers can experience the power of AI-driven research optimization by exploring the PubCompare.ai platform, which can help identify the most effective ways to prevent, manage, and treat this common and potentially serious heart rhythm disorder.
The platform's insights can be leveraged using SAS software version 9.4, SPSS version 26, R software, and other statistical tools to further advance understanding and improve patient outcomes.