BACKGROUND/RATIONALE:
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.

OBJECTIVE(S):
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.

METHODS:
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.

FINDINGS/RESULTS:
Not yet available.

IMPACT:
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.

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External Links for this Project

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PUBLICATIONS:

Journal Articles

1. Schildcrout JS, Haneuse S, Peterson JF, Denny JC, Matheny ME, Waitman LR, Miller RA. Analyses of longitudinal, hospital clinical laboratory data with application to blood glucose concentrations. Statistics in medicine. 2011 Nov 30; 30(27):3208-20. [view]
2. Griffith ML, Boord JB, Eden SK, Matheny ME. Clinical inertia of discharge planning among patients with poorly controlled diabetes mellitus. The Journal of clinical endocrinology and metabolism. 2012 Jun 1; 97(6):2019-26. [view]
3. Siew ED, Matheny ME, Ikizler TA, Lewis JB, Miller RA, Waitman LR, Go AS, Parikh CR, Peterson JF. Commonly used surrogates for baseline renal function affect the classification and prognosis of acute kidney injury. Kidney international. 2010 Mar 1; 77(6):536-42. [view]
4. Liu M, McPeek Hinz ER, Matheny ME, Denny JC, Schildcrout JS, Miller RA, Xu H. Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records. Journal of the American Medical Informatics Association : JAMIA. 2013 May 1; 20(3):420-6. [view]
5. Reeves RM, Ong FR, Matheny ME, Denny JC, Aronsky D, Gobbel GT, Montella D, Speroff T, Brown SH. Detecting temporal expressions in medical narratives. International journal of medical informatics. 2013 Feb 1; 82(2):118-27. [view]
6. Matheny ME, Fitzhenry F, Speroff T, Hathaway J, Murff HJ, Brown SH, Fielstein EM, Dittus RS, Elkin PL. Detection of blood culture bacterial contamination using natural language processing. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium. 2009 Nov 14; 2009:411-5. [view]
7. Matheny ME, Fitzhenry F, Speroff T, Green JK, Griffith ML, Vasilevskis EE, Fielstein EM, Elkin PL, Brown SH. Detection of infectious symptoms from VA emergency department and primary care clinical documentation. International journal of medical informatics. 2012 Mar 1; 81(3):143-56. [view]
8. Matheny ME, Miller RA, Ikizler TA, Waitman LR, Denny JC, Schildcrout JS, Dittus RS, Peterson JF. Development of Inpatient Risk Stratification Models of Acute Kidney Injury for Use in Electronic Health Records. Medical Decision Making. 2010 Nov 1. [view]
9. Siew ED, Ikizler TA, Matheny ME, Shi Y, Schildcrout JS, Danciu I, Dwyer JP, Srichai M, Hung AM, Smith JP, Peterson JF. Estimating baseline kidney function in hospitalized patients with impaired kidney function. Clinical journal of the American Society of Nephrology : CJASN. 2012 May 1; 7(5):712-9. [view]
10. Matheny ME, Normand SL, Gross TP, Marinac-Dabic D, Loyo-Berrios N, Vidi VD, Donnelly S, Resnic FS. Evaluation of an automated safety surveillance system using risk adjusted sequential probability ratio testing. BMC medical informatics and decision making. 2011 Dec 14; 11:75. [view]
11. FitzHenry F, Murff HJ, Matheny ME, Gentry N, Fielstein EM, Brown SH, Reeves RM, Aronsky D, Elkin PL, Messina VP, Speroff T. Exploring the frontier of electronic health record surveillance: the case of postoperative complications. Medical care. 2013 Jun 1; 51(6):509-16. [view]
12. Ohno-Machado L, Bafna V, Boxwala AA, Chapman BE, Chapman WW, Chaudhuri K, Day ME, Farcas C, Heintzman ND, Jiang X, Kim H, Kim J, Matheny ME, Resnic FS, Vinterbo SA, iDASH team. iDASH: integrating data for analysis, anonymization, and sharing. Journal of the American Medical Informatics Association : JAMIA. 2012 Mar 1; 19(2):196-201. [view]
13. Siew ED, Peterson JF, Eden SK, Hung AM, Speroff T, Ikizler TA, Matheny ME. Outpatient nephrology referral rates after acute kidney injury. Journal of the American Society of Nephrology : JASN. 2012 Feb 1; 23(2):305-12. [view]
14. Resnic FS, Wang TY, Arora N, Vidi V, Dai D, Ou FS, Matheny ME. Quantifying the learning curve in the use of a novel vascular closure device: an analysis of the NCDR (National Cardiovascular Data Registry) CathPCI registry. JACC. Cardiovascular interventions. 2012 Jan 1; 5(1):82-9. [view]
15. Tiroch KA, Matheny ME, Resnic FS. Quantitative impact of cardiovascular risk factors and vascular closure devices on the femoral artery after repeat cardiac catheterization. American heart journal. 2010 Jan 1; 159(1):125-30. [view]
16. Siew ED, Peterson JF, Eden SK, Moons KG, Ikizler TA, Matheny ME. Use of multiple imputation method to improve estimation of missing baseline serum creatinine in acute kidney injury research. Clinical journal of the American Society of Nephrology : CJASN. 2013 Jan 1; 8(1):10-8. [view]
17. Reeves RM, FitzHenry F, Brown SH, Kotter K, Gobbel GT, Montella D, Murff HJ, Speroff T, Matheny ME. Who said it? Establishing professional attribution among authors of Veterans' Electronic Health Records. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium. 2013 Jul 30; 2012:753-62. [view]

DRA: Health Systems, Kidney Disorders
DRE: Epidemiology
Keywords: Pharmaceuticals
MeSH Terms: none