Financial and resource costs of transvenous lead extraction in a high-volume lead extraction centre

Staphylococcus aureus bacteraemia, cardiac implantable electronic device, extraction, and the risk of recurrent infection; a retrospective population-based cohort study

BACKGROUND

Patients with cardiac implantable electronic device (CIED) and Staphylococcus aureus bacteraemia (SAB) are at risk of having CIED infection, pocket infection or endocarditis. To avoid treatment failures, guidelines recommend that the CIED should be extracted in all cases of SAB butrecent studies indicate low extraction rates and low risk of relapse. The aim of the study was to describe a Swedish population-based cohort of patients with CIED and SAB, the rate of extraction, and treatment failure measured as recurrent SAB.

METHODS

Patients identified to have SAB in the Karolinska Laboratory database, serving a population of 1.9 million, from January 2015 through December 2019 were matched to the Swedish ICD and Pacemaker Registry. Patients with CIED and SAB were included. Clinical data were collected from medical records.

RESULTS

A cohort of 274 patients was identified and 38 patients (14%)had the CIED extracted. Factors associated with extraction were lower age, lower Charlson comorbidity index, shorter time since CIED implantation, and non-nosocomial acquisition, but not mortality. No patient was put on lifelong antibiotic treatment. Sixteen patients (6%) had a recurrent SAB within one year, two in patients subjected to extraction (5%) and 14 in patients not subjected to CIED-extraction (6%). Three of the 14 patients were found to have definite endocarditis during the recurrent episode.

CONCLUSIONS

Despite a low extraction rate, there were few recurrences. We suggest that extraction of the CIED might be omitted if pocket infection, changes on the CIED, or definite endocarditis are not detected.

Enhancing transvenous lead extraction risk prediction: Integrating imaging biomarkers into machine learning models

BACKGROUND

Machine learning (ML) models have been proposed to predict risk related to transvenous lead extraction (TLE).

OBJECTIVE

The purpose of this study was to test whether integrating imaging data into an existing ML model increases its ability to predict major adverse events (MAEs; procedure-related major complications and procedure-related deaths) and lengthy procedures (≥100 minutes).

METHODS

We hypothesized certain features-(1) lead angulation, (2) coil percentage inside the superior vena cava (SVC), and (3) number of overlapping leads in the SVC-detected from a pre-TLE plain anteroposterior chest radiograph (CXR) would improve prediction of MAE and long procedural times. A deep-learning convolutional neural network was developed to automatically detect these CXR features.

RESULTS

A total of 1050 cases were included, with 24 MAEs (2.3%) . The neural network was able to detect (1) heart border with 100% accuracy; (2) coils with 98% accuracy; and (3) acute angle in the right ventricle and SVC with 91% and 70% accuracy, respectively. The following features significantly improved MAE prediction: (1) ≥50% coil within the SVC; (2) ≥2 overlapping leads in the SVC; and (3) acute lead angulation. Balanced accuracy (0.74-0.87), sensitivity (68%-83%), specificity (72%-91%), and area under the curve (AUC) (0.767-0.962) all improved with imaging biomarkers. Prediction of lengthy procedures also improved: balanced accuracy (0.76-0.86), sensitivity (75%-85%), specificity (63%-87%), and AUC (0.684-0.913).

CONCLUSION

Risk prediction tools integrating imaging biomarkers significantly increases the ability of ML models to predict risk of MAE and long procedural time related to TLE.

Intracardiac leadless versus transvenous permanent pacemaker implantation: Impact on clinical outcomes and healthcare utilization

BACKGROUND

Transvenous permanent pacemakers are used frequently to treat cardiac rhythm disorders. Recently, intracardiac leadless pacemakers offer potential treatment using an alternative insertion procedure due to their novel design. Literature comparing outcomes between the two devices is scarce. We aim to assess the impact of intracardiac leadless pacemakers on readmissions and hospitalization trends.

METHODS

We analyzed the National Readmissions Database from 2016 to 2019, seeking patients admitted for sick sinus syndrome, second-degree-, or third-degree atrioventricular block who received either a transvenous permanent pacemaker or an intracardiac leadless pacemaker. Patients were stratified by device type and assessed for 30-day readmissions, inpatient mortality, and healthcare utilization. Descriptive statistics, Cox proportional hazards, and multivariate regressions were used to compare the groups.

RESULTS

Between 2016 and 2019, 21,782 patients met the inclusion criteria. The mean age was 81.07 years, and 45.52 % were female. No statistical difference was noted for 30-day readmissions (HR 1.14, 95 % CI 0.92-1.41, p = 0.225) and inpatient mortality (HR 1.36, 95 % CI 0.71-2.62, p = 0.352) between the transvenous and intracardiac groups. Multivariate linear regression revealed that length of stay was 0.54 (95 % CI 0.26-0.83, p < 0.001) days longer for the intracardiac group.

CONCLUSION

Hospitalization outcomes associated with intracardiac leadless pacemakers are comparable to traditional transvenous permanent pacemakers. Patients may benefit from using this new device without incurring additional resource utilization. Further studies are needed to compare long-term outcomes between transvenous and intracardiac pacemakers.

Efficacy of the antibacterial envelope to prevent cardiac implantable electronic device infection in a high-risk population

AIMS

Infection is a serious complication of cardiac implantable electronic device (CIED) therapy. An antibiotic-eluting absorbable envelope has been developed to reduce the infection rate, but studies investigating the efficacy and a reasonable number needed to treat in high-risk populations for infections are limited.

METHODS AND RESULTS

One hundred and forty-four patients undergoing CIED implantation who received the antibacterial envelope were compared with a matched cohort of 382 CIED patients from our institution. The primary outcome was the occurrence of local infection, and secondary outcomes were any CIED-related local or systemic infections, including endocarditis, and all-cause mortality. The results were stratified by a risk score for CIED infection, PADIT. The envelope group had a higher PADIT score, 5.9 ± 3.1 vs. 3.9 ± 3.0 (P < 0.0001). For the primary endpoint, no local infections occurred in the envelope group, compared with 2.6% in the control group (P = 0.04), with a more pronounced difference in the stratum with a high (>7 points) PADIT score, 0 vs. 9.9% (P = 0.01). The total CIED-related infections were similar between groups, 6.3% compared with 5.0% (P = 0.567). Mortality after 1600 days of follow-up did not differ between groups, 22.9 vs. 26.4%, P = 0.475.

CONCLUSION

Our study confirms the clinical efficacy of an antibacterial envelope in the prevention of local CIED infection in patients with a higher risk according to the PADIT score. In an effort to improve cost-benefit ratios, ration of use guided by the PADIT score is advocated. Further prospective randomized studies in high-risk populations are called for.

Machine learning-derived major adverse event prediction of patients undergoing transvenous lead extraction: Using the ESC EHRA EORP European lead extraction ConTRolled ELECTRa registry

BACKGROUND

Transvenous lead extraction (TLE) remains a high-risk procedure.

OBJECTIVE

The purpose of this study was to develop a machine learning (ML)-based risk stratification system to predict the risk of major adverse events (MAEs) after TLE. A MAE was defined as procedure-related major complication and procedure-related death.

METHODS

We designed and evaluated an ML-based risk stratification system trained using the European Lead Extraction ConTRolled (ELECTRa) registry to predict the risk of MAEs in 3555 patients undergoing TLE and tested this on an independent registry of 1171 patients. ML models were developed, including a self-normalizing neural network (SNN), stepwise logistic regression model ("stepwise model"), support vector machines, and random forest model. These were compared with the ELECTRa Registry Outcome Score (EROS) for MAEs.

RESULTS

There were 53 MAEs (1.7%) in the training cohort and 24 (2.4%) in the test cohort. Thirty-two clinically important features were used to train the models. ML techniques were similar to EROS by balanced accuracy (stepwise model: 0.74 vs EROS: 0.70) and superior by area under the curve (support vector machines: 0.764 vs EROS: 0.677). The SNN provided a finite risk for MAE and accurately identified MAE in 14 of 169 "high (>80%) risk" patients (8.3%) and no MAEs in all 198 "low (<20%) risk" patients (100%).

CONCLUSION

ML models incrementally improved risk prediction for identifying those at risk of MAEs. The SNN has the additional advantage of providing a personalized finite risk assessment for patients. This may aid patient decision making and allow better preoperative risk assessment and resource allocation.

Monitoring excimer laser-guided cardiac lead extractions by uniportal video-assisted thoracoscopy: A single center experience

BACKGROUND

Though laser guided extractions of cardiac implantable electronic devices leads have become a routine procedure, the severe complications are associated with a high mortality. Here, we report our single center experience using uniportal video-assisted thoracoscopy for laser lead extraction and compare it to stand-alone laser lead extraction.

METHODS

The intraoperative data and postoperative clinical outcomes of patients undergoing laser lead extraction with concomitant thoracoscopy (N = 28) or without (N = 43) in our institution were analyzed retrospectively.

RESULTS

Neither the median x-ray time (612.0 s for the thoracoscopy group vs. 495.5 s for the non-thoracoscopy group, p = 0.962), length of the operation (112.5 vs. 100.0 min, p = 0.676) or the median length of hospital stay (9.0 vs. 10.0 days, p = 0.990) differed significantly. The mean intensive care unit stay was longer for patients in the non-thoracoscopy group (0.8 vs. 2.5 days, p = 0.005). The 30-day-mortality in the thoracoscopy group was zero, whereas five patients died in the non-thoracoscopy group. Furthermore, four patients in the non-thoracoscopy group had encountered haemothorax, while none were observed in the thoracoscopy group (p = 0.148).

CONCLUSIONS

The adoption of uniportal video-assisted thoracoscopy during laser-guided lead extraction of cardiac implantable electronic devices can be considered safe and does not lengthen the operating time or hospital stay. It might be useful in the detection of severe complications and, in experienced hands, possibly allow direct bleeding control.