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Permanent pacemaker implantation (PPMI) reduction and optimal management of newly acquired conduction disturbances after transcatheter aortic valve implantation (TAVI) are crucial. We sought to evaluate the relation between transcatheter heart valve (THV) implantation depth and baseline and newly acquired conduction disturbances on PPMI after TAVI. This study included 1,026 consecutive patients with severe symptomatic aortic stenosis (mean age 79.7 ± 8.4 years; 47.4% female) who underwent TAVI with the newer-generation self-expanding THVs Primary outcomes were early and late PPMI defined as the need for PPMI during the index admission and between discharge and 30 days, respectively. Early and late PPMI was required for 115 (11.2%) and 21 patients (2.0%), respectively. Early PPMI rates decreased from 26.7% in 2015 and 2016 to 5.7% in 2021, and so did the mean THV depth from 4.4 ± 2.4 mm to 1.8 ± 1.6 mm. Receiver operator characteristics curve analyses showed THV depth had significant discriminatory value for early and late PPMI with cutoff values of 3.0 and 2.2 mm, respectively. Rates of early and late PPMI were significantly lower for patients with shallower compared with deeper implantations (5.1% vs 22.6% and 0.4% vs 4.1%, p 0.001 for both, respectively). Furthermore, rates of early PPMI were lower with shallower implantations in patients with new left bundle branch block after TAVI (2.4% vs 15.9%; p 0.001) and those with baseline right bundle branch block (7.5% vs 29.6%; p = 0.017). Lower rates of PPMI with shallower THV implantation were consistently observed, including in patients with baseline and newly acquired conduction disturbances. Our findings might help optimize the management of a temporary pacemaker after TAVI.

Introduction: Patients with cancer are at increased cardiovascular (CV) risk. Both Coronary Artery Calcium (CAC) and 12-lead ECG features predict CV risk in the general population. However, the impact of these measures has not been investigated in patients with cancer. Hypothesis: We hypothesized that machine learning-based analysis of automatically-extracted ECG features, when added to CAC, can improve CV risk stratification in patients with cancer. Methods: We analyzed 1825 cancer patients with at least 1 CV risk factor who underwent CAC scoring and a standard 12-ECG as part of the CLARIFY (ClinicalTrials.gov Identifier: NCT04075162) registry. Events were defined as myocardial infarction, revascularization, stroke or death. A total of 445 ECG features were automatically extracted using a commercially available software (ECG Muse, GE healthcare). A LASSO-Cox method was used with 10-fold cross validation to select the most important ECG features with a novel ECG score derived using individual feature coefficients. We evaluated the ability of the combined ECG score with age, sex, and CAC to prognosticate a CV event in comparison to a model that was limited to age, sex, and CAC alone. The models were compared using an ANOVA test. Results: Over a median follow-up of 14 months, 224 patients had an event. Using the LASSO-Cox model, 25 ECG features were selected. The novel ECG score was associated with CV events (HR 8.6, 95% CI: 5.1-14.3, P Conclusions: A novel ML-based ECG risk score using automatically extracted ECG features can improve CV risk stratification when added to CAC and demographics in patients with cancer.

We performed a health needs assessment for five Plain communities in Pennsylvania from a random sample of households, comparing them to the general population of Pennsylvania adults. Plain respondents were more likely to drink well water, as likely to eat fruit and vegetables and much more likely to drink raw milk and be exposed to agricultural chemicals. Plain respondents were less likely to receive screening exams compared to the general population and there was variation from settlement to settlement in whether respondents had a regular doctor, whether they received preventive screenings or had their children vaccinated, with Mifflin County Amish generally lowest in these and Plain Mennonites highest. Plain respondents reported good physical and mental health compared to the general population but Groffdale Mennonite respondents had a high proportion of diagnoses of depression and were more likely to be receiving treatment for a mental health condition. Most Plain respondents would want a spouse tested for genetic disease with Mifflin County Amish least in favor of these tests. Despite their geographic and genetic isolation, the health of Plain communities in Pennsylvania is similar to that of other adults in the state.