Ugochukwu Ugwuowo1, Yu Yamamoto2, Tanima Arora2, Ishan Saran3, Caitlin Partridge4, Aditya Biswas2, Melissa Martin2, Dennis G Moledina5, Jason H Greenberg6, Michael Simonov5, Sherry G Mansour2, Ricardo Vela7, Jeffrey M Testani8, Veena Rao8, Keith Rentfro9, Wassim Obeid10, Chirag R Parikh10, F Perry Wilson11. 1. Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, CT; Clinical and Translational Research Accelerator, Yale University School of Medicine, New Haven, CT; Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD. 2. Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, CT; Clinical and Translational Research Accelerator, Yale University School of Medicine, New Haven, CT. 3. Department of Physics, Emory University, Atlanta, GA. 4. Joint Data Analytics Team, Yale University School of Medicine, New Haven, CT. 5. Clinical and Translational Research Accelerator, Yale University School of Medicine, New Haven, CT. 6. Clinical and Translational Research Accelerator, Yale University School of Medicine, New Haven, CT; Department of Pediatrics, Yale University School of Medicine, New Haven, CT. 7. Department of Mechanical Engineering, University of Texas at El Paso. El Paso, TX. 8. Section of Cardiology, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT. 9. Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, CT. 10. Johns Hopkins University School of Medicine, Baltimore, MD; Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD. 11. Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, CT; Clinical and Translational Research Accelerator, Yale University School of Medicine, New Haven, CT. Electronic address: francis.p.wilson@yale.edu.
Abstract
RATIONALE & OBJECTIVE: Acute kidney injury (AKI) is diagnosed based on changes in serum creatinine concentration, a late marker of this syndrome. Algorithms that predict elevated risk for AKI are of great interest, but no studies have incorporated such an algorithm into the electronic health record to assist with clinical care. We describe the experience of implementing such an algorithm. STUDY DESIGN: Prospective observational cohort study. SETTING & PARTICIPANTS: 2,856 hospitalized adults in a single urban tertiary-care hospital with an algorithm-predicted risk for AKI in the next 24 hours>15%. Alerts were also used to target a convenience sample of 100 patients for measurement of 16 urine and 6 blood biomarkers. EXPOSURE: Clinical characteristics at the time of pre-AKI alert. OUTCOME: AKI within 24 hours of pre-AKI alert (AKI24). ANALYTICAL APPROACH: Descriptive statistics and univariable associations. RESULTS: At enrollment, mean predicted probability of AKI24 was 19.1%; 18.9% of patients went on to develop AKI24. Outcomes were generally poor among this population, with 29% inpatient mortality among those who developed AKI24 and 14% among those who did not (P<0.001). Systolic blood pressure<100mm Hg (28% of patients with AKI24 vs 18% without), heart rate>100 beats/min (32% of patients with AKI24 vs 24% without), and oxygen saturation<92% (15% of patients with AKI24 vs 6% without) were all more common among those who developed AKI24. Of all biomarkers measured, only hyaline casts on urine microscopy (72% of patients with AKI24 vs 25% without) and fractional excretion of urea nitrogen (20% [IQR, 12%-36%] among patients with AKI24 vs 34% [IQR, 25%-44%] without) differed between those who did and did not develop AKI24. LIMITATIONS: Single-center study, reliance on serum creatinine level for AKI diagnosis, small number of patients undergoing biomarker evaluation. CONCLUSIONS: A real-time AKI risk model was successfully integrated into the EHR.
RATIONALE & OBJECTIVE: Acute kidney injury (AKI) is diagnosed based on changes in serum creatinine concentration, a late marker of this syndrome. Algorithms that predict elevated risk for AKI are of great interest, but no studies have incorporated such an algorithm into the electronic health record to assist with clinical care. We describe the experience of implementing such an algorithm. STUDY DESIGN: Prospective observational cohort study. SETTING & PARTICIPANTS: 2,856 hospitalized adults in a single urban tertiary-care hospital with an algorithm-predicted risk for AKI in the next 24 hours>15%. Alerts were also used to target a convenience sample of 100 patients for measurement of 16 urine and 6 blood biomarkers. EXPOSURE: Clinical characteristics at the time of pre-AKI alert. OUTCOME: AKI within 24 hours of pre-AKI alert (AKI24). ANALYTICAL APPROACH: Descriptive statistics and univariable associations. RESULTS: At enrollment, mean predicted probability of AKI24 was 19.1%; 18.9% of patients went on to develop AKI24. Outcomes were generally poor among this population, with 29% inpatient mortality among those who developed AKI24 and 14% among those who did not (P<0.001). Systolic blood pressure<100mm Hg (28% of patients with AKI24 vs 18% without), heart rate>100 beats/min (32% of patients with AKI24 vs 24% without), and oxygen saturation<92% (15% of patients with AKI24 vs 6% without) were all more common among those who developed AKI24. Of all biomarkers measured, only hyaline casts on urine microscopy (72% of patients with AKI24 vs 25% without) and fractional excretion of urea nitrogen (20% [IQR, 12%-36%] among patients with AKI24 vs 34% [IQR, 25%-44%] without) differed between those who did and did not develop AKI24. LIMITATIONS: Single-center study, reliance on serum creatinine level for AKI diagnosis, small number of patients undergoing biomarker evaluation. CONCLUSIONS: A real-time AKI risk model was successfully integrated into the EHR.
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