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Aim The Prevention of Arrhythmia Device Infection Trial (PADIT) infection risk score, developed based on a large prospectively collected data set, identified five independent predictors of cardiac implantable electronic device (CIED) infection. We performed an independent validation of the risk score in a data set extracted from U.S. healthcare claims. Methods and results Retrospective identification of index CIED procedures among patients aged ≥18 years with at least one record of a CIED procedure between January 2011 and September 2014 in a U.S health claims database. PADIT risk factors and major CIED infections (with system removal, invasive procedure without system removal, or infection-attributable death) were identified through diagnosis and procedure codes. The data set was randomized by PADIT score into Data Set A (60%) and Data Set B (40%). A frailty model allowing multiple procedures per patient was fit using Data Set A, with PADIT score as the only predictor, excluding patients with prior CIED infection. A data set of 54 042 index procedures among 51 623 patients with 574 infections was extracted. Among patients with no history of prior CIED infection, a 1 unit increase in the PADIT score was associated with a relative 28% increase in infection risk. Prior CIED infection was associated with significant incremental predictive value (HR 5.66, P, Graphical Abstract

Funding Acknowledgements This work was supported by Medtronic Background/Introduction: The increasing number of cardiac implantable electronic device (CIED) infections has led to increased interest in the identification of patients who may benefit from additional infection prevention measures. Purpose The purpose of this evaluation was to validate the predictive value of the Prevention of Arrhythmia Device Infection Trial (PADIT) risk score to identify patients at increased risk of CIED infection using a U.S. health claims data set. Methods A retrospective analysis using the Optum® Clinformatics® claims database was conducted to create a dataset of index procedures which either did or did not result in an infection. The study population included both commercial and Medicare Advantage patients aged ≥18 years with at least one record of a CIED procedure between January 2011 and September 2014. Major CIED infections, defined as an infection associated with system removal, invasive procedure without system removal, or death attributable to infection, were identified through diagnosis and procedure codes. The dataset was randomized (stratified by PADIT score, which included prior procedures, age, depressed renal function, immunocompromised, and procedure type) into a Development Dataset (60%) and a Validation dataset (40%). A frailty model allowing multiple procedures per patient was fit using the Development Dataset, with PADIT score as the only predictor, excluding patients with prior infection. Prior CIED infection, which was not available in the original PADIT data, was examined for additional predictive value. Results The data extraction resulted in a dataset of 53,554 index procedures among 51,583 patients, with 30,950 patients randomized to the Development Dataset. The distribution of procedures was pacemakers (52%), ICD (20%), CRT (12%), and Revision/Upgrade (16%), while prior procedures were none (62%), 1 (37%), and 2 (1%). Among patients with no history of prior CIED infection, the frailty model showed that a 1 unit increase in the PADIT score predicts higher infection risk (20%) in the U.S. claims data set (Table 1). Prior CIED infection was associated with strong additional predictive value (HR 4.77, p Conclusion In the largest external validation of a CIED risk score, the PADIT risk score predicts increased CIED infection risk, identifying higher risk patients that can benefit from targeted interventions to reduce the risk of CIED infection. Prior CIED infection brings additional predictive value to the PADIT score.