Open AccessAcute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort With External Validation
Author(s) -
Brown Jeremiah R.,
MacKenzie Todd A.,
Maddox Thomas M.,
Fly James,
Tsai Thomas T.,
Plomondon Mary E.,
Nielson Christopher D.,
Siew Edward D.,
Resnic Frederic S.,
Baker Clifton R.,
Rumsfeld John S.,
Matheny Michael E.
Publication year - 2015
Publication title -
journal of the american heart association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.494
H-Index - 85
ISSN - 2047-9980
DOI - 10.1161/jaha.115.002136
Subject(s) - medicine , cohort , acute kidney injury , administration (probate law) , coronary angiography , emergency medicine , cohort study , cardiology , myocardial infarction , political science , law
Background Acute kidney injury ( AKI ) occurs frequently after cardiac catheterization and percutaneous coronary intervention. Although a clinical risk model exists for percutaneous coronary intervention, no models exist for both procedures, nor do existing models account for risk factors prior to the index admission. We aimed to develop such a model for use in prospective automated surveillance programs in the Veterans Health Administration. Methods and Results We collected data on all patients undergoing cardiac catheterization or percutaneous coronary intervention in the Veterans Health Administration from January 01, 2009 to September 30, 2013, excluding patients with chronic dialysis, end‐stage renal disease, renal transplant, and missing pre‐ and postprocedural creatinine measurement. We used 4 AKI definitions in model development and included risk factors from up to 1 year prior to the procedure and at presentation. We developed our prediction models for postprocedural AKI using the least absolute shrinkage and selection operator ( LASSO ) and internally validated using bootstrapping. We developed models using 115 633 angiogram procedures and externally validated using 27 905 procedures from a New England cohort. Models had cross‐validated C‐statistics of 0.74 (95% CI : 0.74–0.75) for AKI , 0.83 (95% CI : 0.82–0.84) for AKIN 2, 0.74 (95% CI : 0.74–0.75) for contrast‐induced nephropathy, and 0.89 (95% CI : 0.87–0.90) for dialysis. Conclusions We developed a robust, externally validated clinical prediction model for AKI following cardiac catheterization or percutaneous coronary intervention to automatically identify high‐risk patients before and immediately after a procedure in the Veterans Health Administration. Work is ongoing to incorporate these models into routine clinical practice.