Abstract Aims Device implantation is growing exponentially, as well as associated infections, ranging from isolated pocket erosion to endocarditis and bacteraemia, all worsening the prognosis of patients with frailty and comorbidity. Transvenous lead extraction (TLE) can resolve the complications, although a 1-year mortality risk of up to 25% is reported; despite higher health costs, prolonged hospitalization, and poor quality of life, strategies for predicting increased infection risk and reduced infection incidence are yet missing. Currently applied clinical scores do not consider etiologic microbial agents. We aimed to assess whether PADIT and UPCM scores could be implemented when bacteria or fungi are known to be causative of infection, and how these agents affected the outcome. Methods and results A retrospective analysis of patients undergone cardiac implantable electronic device (CIED) pocket revision, and/or TLE between 2016 and 2021 was performed. For each procedure, microbiological samples of both generator pocket tissue and intracardiac portions of the leads were analysed. In addition, blood cultures were performed in three sets. Transesophageal echocardiography was performed in all cases for ruling out suspected endocarditis. Spearman ad Pearson coefficients were tested for correlation among microorganism, prior infection and/or procedure, PADIT and UPCM scores; a P-value less than 0.05 was considered significant. We analysed 14 patients (10 males, 4 females, mean age ± SD: 72 ± 13): one case (4%) affected by pocket erosion, seven cases (50%) affected by both pocket site and lead infection (with associated bacteraemia in one subject), and one case (4%) due to lead-related infective endocarditis. Of these, five (36%) underwent device replacement, while nine (64%) to extraction or pocket/lead revision. Nine (64%) patients had positive culture examinations (Figure 1). The correlation method gave a statistically significant association between Gram- infection and prior sepsis (r 0.63; P-value 0.02). We considered the number of procedures on the same pocket and/or CIED previous infections as markers of frailty and increased infectious risk. As expected, the PADIT score, but not UPCM, significantly correlated with the number of previous procedures (r 0.70; P-value 0.006). Indeed, both scores had a similar infectious risk prediction. Conclusions In our analysis, predictive PADIT score of infectious risk performed better than UPCM, while both proved their reliability in identifying high-risk patients. The absence of correlation between UPCM score and infective agents is not conclusive, but probably due to the small sample size. Interestingly, growing rate of device reinfection correlates with the risk of Gram- bacterial infection. Thus, the integration of the microbiological data in the current prediction models could significantly increase their performance.