The ultimate goal of this project is to improve cardiovascular health by using advanced clinical informatics to identify procedure-associated cardiac implantable electronic device (CIED) infections. This is important because cardiac device infections are a common, highly morbid and costly complication of electrophysiology procedures, yet no standardized infection prevention programs exist to reduce them. Surveillance with feedback is an important aspect of infection prevention programs. Detection of infections allows problems to be identified and policies and procedures refined to improve practice and clinical outcomes. This I21 project will use flags left in the VA electronic medical record as part of diagnosis and treatment to identify cardiac device infections. In Aim I, a preliminary electronic CIED infection detection tool will be refined. In Aim II, the infection detection tool will be validated on a large, national cohort of VA patients. Future IIR proposals will measure the impact of implementing a surveillance system on CIED infections and will assess how information from surveillance systems can be used to improve outcomes. The PI, Dr. Branch-Elliman, is ideally suited to conduct the proposed work. Her background developing advanced medical informatics tools based on diagnostic and therapeutic pathways to detect other types of healthcare-associated infections within the VA healthcare system and expertise in infection prevention and antimicrobial stewardship in the cardiac electrophysiology laboratory greatly enhance the probability of success. The Specific Aims are: Specific Aim I: To improve the receiver operating characteristics (ROC) of the CIED infection detection tool to >0.85 using the existing Clinical Assessment Reporting and Tracking (CART-EP) database (~1500 manually reviewed cases). Structured variables that are part of the diagnostic and therapeutic pathway for managing CIED infections (e.g., antimicrobial orders, consult orders, microbiology results, laboratory results) will be tested to determine their impact on the ROC value and a refined tool will be developed. Specific Aim II: To test and validate the clinical informatics-based infection detection tool on FY 2016-17 data available in the VA Corporate Data Warehouse (CDW) (> 14,900 cases). The electronic detection tool will be applied to the CDW cohort and approximately 1000 charts will be selected for manual review to determine the presence/absence of infection. These 1000 charts will include an equal number of high, intermediate, and low probability cases. Cases categorized incorrectly by the infection detection tool (i.e., infection cases scored as low probability and non-infected cases scored as high probability) will be reviewed to identify the reason for the discordance. These findings will be used to inform a future implementation. Site-level performance variation will also be measured. Future Directions: A future IIR proposal will implement the tool in the 15-hospital Infection Control and Prevention Practice-Based Research Network (ICCPPBRN) to measure the impact of an electronic infection detection system on clinical outcomes and to measure how surveillance with feedback changes policies and practice. Impact: Expansion of infection prevention to currently uncovered outpatient areas, such as the electrophysiology laboratories, is important for improving patient safety. This research closes an important gap in the care of cardiovascular patients while also providing important insights into methods to expand surveillance of healthcare-associated infections to include outpatient areas.
External Links for this Project
Grant Number: I21HX002710-01
Dimensions for VA
- Mull HJ, Stolzmann KL, Shin MH, Kalver E, Schweizer ML, Branch-Elliman W. Novel Method to Flag Cardiac Implantable Device Infections by Integrating Text Mining With Structured Data in the Veterans Health Administration's Electronic Medical Record. JAMA Network Open. 2020 Sep 1; 3(9):e2012264.
- Mull HJ, Stolzmann K, Kalver E, Shin MH, Schweizer ML, Asundi A, Mehta P, Stanislawski M, Branch-Elliman W. Novel methodology to measure pre-procedure antimicrobial prophylaxis: integrating text searches with structured data from the Veterans Health Administration's electronic medical record. BMC medical informatics and decision making. 2020 Jan 30; 20(1):15.
Treatment - Observational, Technology Development and Assessment, TRL - Applied/Translational
Adverse Event Monitoring, Best Practices, Guideline Development and Implementation, Monitoring, Surveillance
None at this time.