A Prediction Score for Cardiovascular Implantable Electronic Device Infection in Non-Staphylococcus aureus Bacteremia.

Abstract

Background: Patients with cardiovascular implantable electronic devices (CIEDs) who develop bacteremia require risk stratification for device infection. Although prediction scores exist for Staphylococcus aureus bacteremia, no comparable CIED-specific tool exists for non-S. aureus bacteremia. Our study aimed to estimate the probability of definite CIED infection in adults with non-S. aureus bacteremia without pocket infection.

Methods: We performed a prediction-model analysis of adults with CIEDs and non-S. aureus bacteremia, comprising gram-negative bacteremia (GNB) and gram-positive cocci other than S. aureus (GPC) bacteremia, at Mayo Clinic Rochester during 2012-2019. Patients with pocket infection were excluded. Definite CIED infection was classified using 2019 European Heart Rhythm Association criteria. Penalized logistic regression was used to construct two prediction models for definite infection: a full model with nine prespecified predictors, and a reduced model from which a point-based risk score could be obtained.

Results: A total of 282 patients with CIEDs developed non-S. aureus bacteremia. Median age was 75 years; 69% were men. GPC bacteremia occurred in 156 patients (55%) and GNB in 126 (45%). Sixty patients (21%) had definite CIED infection. Both the full and reduced models demonstrated excellent predictive discrimination (apparent area under receiver operating characteristic curve 0.946 and 0.940). The reduced model retained four predictors: GPC bacteremia, persistent bacteremia, longer time to positivity, and unknown bacteremia source.

Conclusion: A four-variable risk score incorporating organism category, persistent bacteremia, time to positivity, and unknown bacteremia source may guide early device-focused evaluation, but external validation is required before clinical adoption.