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

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.

Novel Method to Flag Cardiac Implantable Device Infections by Integrating Text Mining With Structured Data in the Veterans Health Administration’s Electronic Medical Record

“…This was primarily due to a very high rate of antimicrobial prescriptions, most of which were for infections unrelated to the cardiac device. 26 We found that using a limited set of antimicrobials—those most commonly used to treat Staphylococcal infections—was operationally useful. 40 This finding is congruent with past studies evaluating the utility of a combination of microbiology results and antimicrobial orders for measuring methicillin-resistant S aureus infections.…”

“… 32 In our previous work using an algorithm based on structured clinical data from the EMR, PPV ranged from 30% to 40%; however, this study was based on a small sample size. 26 In the present study, we incorporated text mining strategies to discriminate procedure-related infection from other infection diagnoses in a national VA sample, and our algorithm had a PPV of 44% based on our threshold of predicted probability of infection 10% or greater. In practice, this approach was efficient—only 3% of CIED cases met our 10% threshold—and it has the potential to significantly reduce the burden of manual review for infection surveillance.…”

“…We used an iterative approach to develop an algorithm to predict CIED procedures with a true infection. We began with previously developed clinical diagnostic and treatment flags from structured data 26 : temperature at or higher than 100.3 °F (38.0 °C), any antibiotic prescribed for more than 3 days, antibiotics typically used to treat Staphylococcal infections, microbiology test order, and an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Clinical Modification ( ICD-10-CM ) code diagnosis of either an SSI, nonspecific infection, or cardiac device infection (eTable 2 in the Supplement ). Next, we considered other factors that might affect the presence of infection-related flags, including comorbidities, emergent procedure, and death within 90 days.…”

“… 22 , 23 , 24 Previously, diagnosis codes and medical record review of a random sample of CIED procedures were used to detect postprocedure infections, but both approaches had low positive predictive validity (PPV). 25 , 26 …”

A novel disinfection protocol using ATP testing for lead garments in the electrophysiology lab

“…We predict that this intervention would be cost-effective and feasible, given the affordability of disinfectant wipes and their widespread availability within hospitals [ 7 ]. Comparatively, cardiac device implantation infections cost upwards of $50,000 per event [ 9 ]. This study is limited by the imperfect correlation between ATP counts and degree of microbial contamination; however, a strict threshold of cleanliness was employed and very low readings were obtained post-intervention, which is associated with low aerobic colony counts [ 5 , 6 ].…”