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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.

journal of the american heart association

Acute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort With External Validation