? ? Acute Kidney Injury (AKI) is a common complication among hospitalized patients and heralds a 3-5 fold increase in mortality, increased costs, and potential lifelong dependence on dialysis. While clinical decision support systems (CDSS) have previously assisted with the medical management of chronic kidney disease, there has been no validated approach for a CDSS to identify AKI and facilitate a medical intervention at an early stage. We propose to develop an AKI detection algorithm using laboratory and bedside measurements that are typically available in electronic medical records. We will validate the algorithm against the judgment of an expert nephrology review panel and use the latest published standards for AKI staging. As a second step, we plan to create models (using both traditional and machine learning approaches) that predict progression of AKI by incorporating comorbidities, admission diagnoses, and exposure to nephrotoxic therapies. Once the electronic criteria for early AKI are defined, we will build and evaluate a hospital-wide CDSS for managing early AKI. The goal is to prevent AKI progression and the associated complications of hypokalemia, acidemia, and medication toxicity. ? ? ?

Public Health Relevance

Hospitalized patients who develop impaired kidney function are at increased risk of dying or developing adverse events. Computerized systems of care are needed to quickly alert hospital care teams to which patients are developing kidney injury in order to tailor medications and other therapies.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
1R01LM009965-01
Application #
7567679
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
Project Start
2008-09-30
Project End
2011-08-31
Budget Start
2008-09-30
Budget End
2009-08-31
Support Year
1
Fiscal Year
2008
Total Cost
$333,863
Indirect Cost
Name
Vanderbilt University Medical Center
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
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Matheny, Michael E; Miller, Randolph A; Ikizler, T Alp et al. (2010) Development of inpatient risk stratification models of acute kidney injury for use in electronic health records. Med Decis Making 30:639-50

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