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Development of an Automated Nephrotoxicity Pharmacosurveillance System
Michael E Matheny, MD MS MPH
Tennessee Valley Healthcare System Nashville Campus, Nashville, TN
Funding Period: September 2009 - August 2014
The mission of the Veterans Health Administration to provide high-quality care to the nation's Veterans is challenged by the medical complexity of the population; however, routinely collected data from the electronic health record provides unique opportunities for knowledge discovery and continuous quality improvement initiatives. For example, acute kidney injury (AKI) among hospitalized patients is associated with significantly elevated inpatient mortality risk and post-discharge morbidity. The incidence of AKI among the general U.S. population ranges from 1-5% in all hospital admissions and up to 20% in intensive care patients. In addition, increasing rates of AKI have been noted in patients with additional comorbidities and particularly those with polypharmacy. The veteran population is more complex than the general population and is at increased risk for AKI.
The objective of this project is to establish the necessary informatics infrastructure and clinical expertise needed to develop prospective surveillance of medication-related AKI in VA. This will require a literature review of the evidence on risk factors for AKI and development of automated data extraction protocols for VA data. The retrospective database will then be evaluated to establish risk adjustment models and expected event rates for AKI. Finally, the release of a new nephrotoxic medication will be simulated in these data and evaluated with my automated surveillance application.
This project represents an opportunity to quantify the magnitude of risk for known factors and identify new risk factors for AKI among the Veteran patient cohort, as well as advance patient safety by allowing identification of high risk inpatients with a risk prediction model. In future work, an automated surveillance system using the products of this research could provide complementary post-marketing medication surveillance to the FDA's adverse event reporting system.
The project is comprised of five studies: (1) Clinical Domain Synthesis, (2) Data
Processing and Validation, (3) Retrospective Evaluation, (4) Risk Prediction Modeling, and (5) Simulated Prospective Monitoring.
This grant will execute a series of objectives in order to pilot a prospective medication surveillance system using the VA informatics infrastructure. Data during 2002-2009 from the VISN 9 regional VA network will be used in this proposal. First, I must address data requirements from routine clinical data including parsing free text, integrating a large variety of data sources and identifying combinations of data elements indicative of clinical diagnoses. Next, I propose a series of hypothesis-driven and knowledge discovery retrospective evaluations of the resulting organized data, including nephrotoxicity synergy from multiple concurrent medications that affect the kidney through a pre-renal mechanism, evaluating the effect of hyperglycemia on AKI when patients are administered nephrotoxic agents and generating a risk prediction model for the development of AKI. Last, I propose to adapt state-of-the-art surveillance methods and an automated application for VA surveillance of AKI using the data infrastructure developed in this application and performing risk adjustment using the developed risk models. Lastly, I will pilot the system with a simulated prospective evaluation in which AKI events related to a medication class are inserted into the retrospective data in order to determine the sensitivity and specificity of the system as a proof of concept.
Not yet available.
Each phase of the proposal can provide benefits to the VA population. Development of the data processing infrastructure necessary for research can facilitate a wide variety of research endeavors beyond the scope of this application. Retrospective evaluation of the data, in addition to being preparatory to the prospective system, can identify new risk factors for AKI in the VA population as well as characterize the magnitude of risk for known nephrotoxic medications. Such information can be used to inform therapies for patients. The risk prediction models can be used to predict hospitalized patients at high risk for AKI. Establishing a medication surveillance system in VA could identify a number of unknown risks for AKI among VA patients over time, leading to improved overall patient safety.
External Links for this Project
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DRA: Health Systems, Kidney Disorders
MeSH Terms: none