Long-term performance of single-connector (DF4) implantable defibrillator leads.

AIMS: Single-connector (DF4) defibrillator leads have become the predominantly implanted transvenous implantable cardioverter-defibrillator lead. However, data on their long-term performance are derived predominantly from manufacturer product performance reports. METHODS AND RESULTS: We reviewed medical records in 5289 patients with DF4 leads between 2011 and 2023 to determine the frequency of lead-related abnormalities. We defined malfunction as any single or combination of electrical abnormalities requiring revision including a sudden increase (≥2×) in stimulation threshold, a discrete jump in high-voltage impedance, or sensing of non-physiologic intervals or noise. We documented time to failure, predictors of failure, and management strategies. Mean follow-up after implant was 4.15 ± 3.6 years (median = 3.63), with 37% of leads followed for >5 years. A total of 80 (1.5%) leads demonstrated electrical abnormalities requiring revision with an average time to failure of 4 ± 2.8 years (median = 3.5). Of the leads that malfunctioned, 62/80 (78%) were extracted and replaced with a new lead and in the other 18 cases, malfunctioned DF4 leads were abandoned, and a new lead implanted. In multivariable models, younger age at implant (OR 1.03 per year; P < 0.001) and the presence of Abbott/St. Jude leads increased the risk of malfunction. CONCLUSION: DF4 defibrillator leads demonstrate excellent longevity with >98.3% of leads followed for at least 5 years still functioning normally. Younger age at implant and lead manufacturer are associated with an increased risk of DF4 lead malfunction. The differences in lead survival between manufacturers require further investigation.

Safety of same-day discharge after lead extraction procedures.

BACKGROUND: Same-day discharge (SDD) after cardiovascular procedures is rapidly gaining ground. OBJECTIVE: We sought to evaluate the safety of SDD after transvenous lead extraction (TLE). METHODS: We performed a retrospective chart review of patients who underwent elective TLE between January 2020 and October 2021 at our institution. The primary outcome was SDD, and major procedural complications and readmissions within 30 days of the procedure were secondary outcomes. RESULTS: In this analysis of 111 patients who underwent elective TLE, 80 patients (72%) were discharged on the same day (SDD group) while 31 patients (28%) stayed overnight (overnight group). Lead malfunction was the most common indication for TLE in both groups. Patients in the overnight group were more likely to have a lead dwell time of ≤10 years than those in the SDD group (38.7% vs 20% of all leads in each group; P = .042), have laser sheaths used for extraction and a higher number of leads extracted. No major complications were reported in both groups. In a multivariate analysis, lower body mass index and the use of laser sheath during TLE were predictors of overnight stay. Patients who underwent a procedure using advanced extraction techniques were 3.5 times more likely to stay overnight (95% confidence interval 1.27-9.78; P = .016). CONCLUSION: In appropriately selected patients undergoing elective lead extraction, SDD is feasible and safe. Higher body mass index, fewer extracted leads, shorter lead dwell times (<10 years), and less frequent use of laser-powered extraction sheaths were associated with an increased likelihood of SDD.

Patient-specific COVID-19 resource utilization prediction using fusion AI model.

The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based on past medical data stored in electronic medical records (EMR). We conducted this study on 3194 patients (46% male with mean age 56.7 (±16.8), 56% African American, 7% Hispanic) flagged as COVID-19 positive cases in 12 centers under Emory Healthcare network from February 2020 to September 2020, to assess whether a COVID-19 positive patient's need for hospitalization can be predicted at the time of RT-PCR test using the EMR data prior to the test. Five main modalities of EMR, i.e., demographics, medication, past medical procedures, comorbidities, and laboratory results, were used as features for predictive modeling, both individually and fused together using late, middle, and early fusion. Models were evaluated in terms of precision, recall, F1-score (within 95% confidence interval). The early fusion model is the most effective predictor with 84% overall F1-score [CI 82.1-86.1]. The predictive performance of the model drops by 6 % when using recent clinical data while omitting the long-term medical history. Feature importance analysis indicates that history of cardiovascular disease, emergency room visits in the past year prior to testing, and demographic factors are predictive of the disease trajectory. We conclude that fusion modeling using medical history and current treatment data can forecast the need for hospitalization for patients infected with COVID-19 at the time of the RT-PCR test.