Calculator Developed to Predict AKI Risk After Angiography in VA Population

Using data culled from the VA healthcare system, researchers have created a new tool for predicting acute kidney injury (AKI) in patients undergoing diagnostic catheterization or PCI.

The paper represents the first VA Cath Lab prediction model and tool within the national VA system, Jeremiah R. Brown, PhD, of Dartmouth-Hitchcock Medical Center (Lebanon, NH) told TCTMD.

The model is based on predictors available in real-time from electronic health records and the VA Clinical Assessment, Reporting, and Tracking program (CART).  “It is our hope that automated surveillance of AKI will help clinicians reduce the incidence of AKI and incorporate protocols to prevent AKI urgently among those patients identified as high risk,” he and his colleagues write.

There is already reporting of AKI from the NCDR, Brown told TCTMD; the current initiative, however, will develop these automated reports specifically for VA centers on a monthly or more rapid frequency, to be determined by CART.

Brown et al’s paper was published earlier this month in the Journal of the American Heart Association.

A Host of Variables

Investigators collected information on 115,633 angiograms done in VA patients—excluding those with existing kidney problems or missing creatinine measurements—between January 2009 and September 2013. They looked not only at procedural details but also at risk factors in the year before.

There were numerous variables associated with AKI, defined using Kidney Disease Improving Global Outcomes (KIDGO) criteria as ≥ 0.3 mg/dL within 48 hours of the procedure or ≥ 50% increase in serum creatinine from baseline to post-cardiac cath peak serum creatinine at any time during hospitalization or within 7 days post-procedure.

Risk factors included history of diabetes, prior instances of congestive heart failure and low albumin; previous AKI and chronic kidney disease; exposure to loop diuretics; and urgent catheterization, shock, ACS, and anemia.

Lower likelihood of AKI was predicted by prior CABG and PCI as well as by exposure to N-acetylcysteine, angiotensin receptor blockers, or hydroxymethyl glutaryl coenzyme A reductase inhibitors at presentation.

Externally validated against a cohort of 27,905 cases from New England, the model had a C-statistic of 0.74 (95% CI 0.74-0.75).

Additional models were designed for Acute Kidney Injury Network Stage 2, contrast-induced nephropathy, and dialysis. “Work is ongoing to incorporate these models into routine clinical practice,” the researchers report.

For now, says Brown, the model “is one of the most robust pre-procedural prediction tools for evaluating AKI risk prior to the procedure and can be used for clinical decision making.” This might include delaying procedures for patients at high risk, using less contrast dye, mandating hydration protocols, and/or staging PCI procedures following the diagnostic cath.

However, those benefits must be weighed against the potential risks, the authors add. “The unintended consequences of identifying high-risk patients for AKI may be delayed or postponed procedures,” they note. “Providers will need to balance the tradeoffs of readiness for cardiac catheterization in [such patients] and potential delays in the procedure with the other clinical needs and timing of revascularization.”

Brown JR, MacKenzie TA, Maddox TM, et al. Acute kidney injury risk prediction in patients undergoing angiography in a national Veterans Health Administration cohort with external validation. J Am Heart Assoc. 2015;4:e002136.

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  • Brown reports no relevant conflicts of interest.