New Tool Using Electronic Health Records Can Reliably Detect Infections after Cardiac Device Procedures
Healthcare associated infections are among the top 10 causes of death in the U.S., accounting for more than 99,000 deaths and more than $10 billion in costs annually; however, few resources are dedicated to infection surveillance of non-surgical procedures, including cardiovascular implantable electronic device (CIED) procedures, such as pacemaker or cardiac defibrillator implantations. This multi-center, national cohort study sought to develop and validate an electronic detection tool that accurately and reliably flags cases with true post-procedure cardiac device infection, leveraging the strengths of VA’s electronic health record. Inputs for the electronic flagging tool included structured data collected as part of usual clinical care, and identification of key words documented in clinical notes.
- Combining structured data, such as microbiology results, with text note searches was highly efficient for identifying true post-procedural infection.
- Among all 19,212 cardiac device procedures performed within VA in FY16-17, investigators reviewed 744 cases and identified 154 true procedure-related infections. The positive predictive validity of the tool was 44%, and overall sensitivity and specificity were 94% and 49%, respectively, indicating that the tool is useful for flagging cardiac device infection cases.
- The cardiac device infection detection tool included 90-day mortality, congestive heart failure and non-metastatic tumor comorbidities, CIED or surgical site infection ICD-10 diagnosis codes, antibiotic treatment of Staphylococci, a microbiology test of a cardiac specimen, and text documentation of infection in specific clinical notes (e.g., cardiology, infectious diseases, inpatient discharge summaries).
- This novel measurement tool, which adds data collected in clinical notes to flag cardiac device infections, has the potential to significantly reduce the burden of manual review for infection surveillance. Similar tools that combine structured data and key words from clinical notes could be developed to enhance infection detection, improve early event reporting, and support infection control efforts for other types of infections.
- These findings may overestimate the predictive value of the algorithm in non-closed healthcare settings, where patients may receive subsequent care away from the initial healthcare system.
This study was partly funded by HSR&D (PPO 18-031). Drs. Mull and Branch-Elliman, and Mses. Stolzmann and Shin are part of HSR&D’s Center for Healthcare Organization & Implementation Research (CHOIR) in Boston and Bedford, MA.
Mull H, Stolzmann K, Shin M, Kalver E, Schweizer M, and 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. September 21, 2020;3(9):e2012264.