More than 575,000 Americans have end-stage renal disease, and more than 350,000 receive life-saving dialysis treatment. Previous research has demonstrated that patients undergoing hemodialysis, especially those who are older, are at particular risk to have atrial fibrillation. Atrial fibrillation is associated with poor outcomes including high mortality (40% per year). Little is known about fixed and potentially modifiable ris factors for atrial fibrillation in older patients receiving hemodialysis; there is also only scant evidence about these patients' outcomes after their first diagnosis with atrial fibrillation. Fromalready collected Medicare insurance claims and medical records of a large dialysis provider, we propose to assemble a unique dataset that will provide unprecedented detail about these patients health and the health care they receive. In addition, we will have historical Medicare claims predating these patients' time of dialysis initiation by at least 2 years. Using this uniquedatabase, we will be in the unusual situation to be able to exclude patients who had atrial fibrillation before they started dialysis, and therefore be able to study incident atrial fibrillaton. We are proposing to identify potentially modifiable risk factors for incident atrial fibrillation, ith particular focus on laboratory measurements, vital signs, dialysis treatment- related factors, and dialysis facility practices. We will also launch a comprehensive assessment of the outcomes of patients once they are first diagnosed with atrial fibrillation, in comparison to similar patients who have not developed this arrhythmia. Outcomes of interest will cover all relevant domains: all-cause and cause-specific mortality; morbidity with focus on thromboembolic and hemorrhagic outcomes; patient-reported health-related quality of life outcomes; and health care utilization and cost. These studies will fill gaping holes in the currently available evidence. The perhaps most innovative aim will focus on deriving a prediction algorithm for near-term risk of new atrial fibrillation using the high-dimensional and extremely granular data in our dataset and novel bioinformatic methods. We will then validate the algorithm in a completely different later time period in data from the same provider as well as in another dataset from a different dialysis provider. If our approach proves successful in identifying patients at the highest near-term risk of incident atrial fibrillation, we are then in the position to test intervention studies to reducethe risk of imminent atrial fibrillation and thus avoid longer term sequelae of this arrhythmia in thes vulnerable patients. Findings from the proposed work have the potential to impact and improve the care that patients with end-stage renal disease receive. Our results may improve the quality of care received and, thus, the outcomes of this vulnerable patient population.
The aims and scope of work are in full congruence with the mission of the National Institutes of Diabetes and Digestive and Kidney Diseases, and more specifically the Division of Kidney, Urologic, and Hematologic Diseases, which will consider this application for funding.

Public Health Relevance

The kidneys of more than 575;000 Americans have irreversibly stopped working; which renders these patients dependent on receiving regular kidney dialysis. Atrial fibrillation is a common type of irregular heartbeat; but patients receivin kidney dialysis are particularly often affected. Atrial fibrillation may lead to stroke or death ina large proportion of these patients and therapies to reverse it or to prevent bad outcomes do not appear to work in dialysis patients. We propose to identify possibly preventable factors that make patients develop atrial fibrillation in the first place. This information will help identify hgh-risk patients in whom preventive measures can then be tested.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
5R01DK095024-04
Application #
8858625
Study Section
Cardiovascular and Sleep Epidemiology (CASE)
Program Officer
Abbott, Kevin C
Project Start
2012-08-01
Project End
2016-05-31
Budget Start
2015-06-01
Budget End
2016-05-31
Support Year
4
Fiscal Year
2015
Total Cost
$446,486
Indirect Cost
$162,100
Name
Stanford University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
Ullal, Aditya J; Kaiser, Daniel W; Fan, Jun et al. (2017) Safety and Clinical Outcomes of Catheter Ablation of Atrial Fibrillation in Patients With Chronic Kidney Disease. J Cardiovasc Electrophysiol 28:39-48
Yang, Felix; Hellyer, Jessica A; Than, Claire et al. (2017) Warfarin utilisation and anticoagulation control in patients with atrial fibrillation and chronic kidney disease. Heart 103:818-826
Goldstein, Benjamin A; Pencina, Michael J; Montez-Rath, Maria E et al. (2017) Predicting mortality over different time horizons: which data elements are needed? J Am Med Inform Assoc 24:176-181
Goldstein, Benjamin A; Pomann, Gina Maria; Winkelmayer, Wolfgang C et al. (2017) A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis. Stat Med 36:2750-2763
Jun, Min; James, Matthew T; Ma, Zhihai et al. (2017) Warfarin Initiation, Atrial Fibrillation, and Kidney Function: Comparative Effectiveness and Safety of Warfarin in Older Adults With Newly Diagnosed Atrial Fibrillation. Am J Kidney Dis 69:734-743
Tuohy, C Vaughan; Montez-Rath, Maria E; Turakhia, Mintu et al. (2016) Sleep disordered breathing and cardiovascular risk in older patients initiating dialysis in the United States: a retrospective observational study using medicare data. BMC Nephrol 17:16
Lenihan, Colin R; Montez-Rath, Maria E; Shen, Jenny I et al. (2015) Correlates and outcomes of warfarin initiation in kidney transplant recipients newly diagnosed with atrial fibrillation. Nephrol Dial Transplant 30:321-9
Goldstein, Benjamin A; Chang, Tara I; Winkelmayer, Wolfgang C (2015) Classifying individuals based on a densely captured sequence of vital signs: An example using repeated blood pressure measurements during hemodialysis treatment. J Biomed Inform 57:219-24
Goldstein, Benjamin A; Assimes, Themistocles; Winkelmayer, Wolfgang C et al. (2015) Detecting clinically meaningful biomarkers with repeated measurements: An illustration with electronic health records. Biometrics 71:478-86
Shen, Jenny I; Montez-Rath, Maria E; Lenihan, Colin R et al. (2015) Outcomes After Warfarin Initiation in a Cohort of Hemodialysis Patients With Newly Diagnosed Atrial Fibrillation. Am J Kidney Dis 66:677-88

Showing the most recent 10 out of 23 publications