Development and Validation of a Semi-Automated Surveillance Algorithm for Cardiac Device Infections: Insights from the VA CART program

Archana Asundi,P Michael Ho,Westyn Branch-Elliman,Anna E Barón,Kalpana Gupta,Payal Mehta,Maggie Stanislawski,Marin L Schweizer,Hillary J Mull

doi:10.1038/s41598-020-62083-y

Abstract

Procedure-related cardiac electronic implantable device (CIED) infections have high morbidity and mortality, highlighting the urgent need for infection prevention efforts to include electrophysiology procedures. We developed and validated a semi-automated algorithm based on structured electronic health records data to reliably identify CIED infections. A sample of CIED procedures entered into the Veterans’ Health Administration Clinical Assessment Reporting and Tracking program from FY 2008–2015 was reviewed for the presence of CIED infection. This sample was then randomly divided into training (2/3) validation sets (1/3). The training set was used to develop a detection algorithm containing structured variables mapped from the clinical pathways of CIED infection. Performance of this algorithm was evaluated using the validation set. 2,107 unique CIED procedures from a cohort of 5,753 underwent manual review; 97 CIED infections (4.6%) were identified. Variables strongly associated with true infections included presence of a microbiology order, billing codes for surgical site infections and post-procedural antibiotic prescriptions. The combined algorithm to detect infection demonstrated high c-statistic (0.95; 95% confidence interval: 0.92–0.98), sensitivity (87.9%) and specificity (90.3%) in the validation data. Structured variables derived from clinical pathways can guide development of a semi-automated detection tool to surveil for CIED infection.

Highlights

Prevention programs in the electrophysiology suite is crucial for minimizing morbidity and mortality among cardiac electronic implantable device (CIED) implantation recipients
A sample of cardiac procedures collected as part of the Veterans Health Administration (VA) Clinical Assessment Reporting and Tracking (CART) quality program underwent manual review for the presence of infection; these cases were used in the development and validation of the semi-automated tool
From a large sampling of VA CIED procedures, we found that a combination of clinically-oriented variables from CIED infection diagnostic and treatment pathways and administrative billing codes demonstrated clinically useful sensitivity and specificity for flagging true cases of CIED infection (Fig. 4)

Summary

Introduction

Prevention programs in the electrophysiology suite is crucial for minimizing morbidity and mortality among CIED implantation recipients. The increasing dissemination of electronic health records (EHRs), a promising strategy for expanding surveillance to uncovered procedural areas is the development of surveillance tools that leverage clinical data warehouses to measure and track infections. These tools could be used as stand-alone systems or could be used in a semi-automated method to augment and triage the manual review process toward cases at highest probability of having an adverse event[7,13]. We sought to develop and validate a surveillance tool for CIED infection surveillance using structured data elements from the Veterans Health Administration (VA)’s Corporate Data Warehouse (CDW) in order to triage cases for manual review

Methods

Results

Discussion

Conclusion