Implant efficiency and clinical performance of Aveir™ VR and Micra™ VR leadless pacemaker: A multicenter comparative analysis of 67 patients.

INTRODUCTION: Leadless pacemaker (LP) is a novel pacemaker that has been proven to be effective and safe; however, the majority of LPs in previous reports were the Medtronic Micra™ VR LP. We aim to evaluate the implant efficiency and clinical performance of the Aveir™ VR LP compared to the Micra™ VR LP. METHOD: We performed a retrospective analysis in two healthcare systems (Sparrow Hospital and Ascension Health System, Michigan) in patients implanted with LPs between January 1, 2018, and April 1, 2022. The parameters were collected at implantation, 3 months and 6 months. RESULTS: A total of 67 patients were included in the study. The Micra™ VR group had shorter time in the electrophysiology lab (41 ± 12 vs. 55 ± 11.5 min, p = .008) and shorter fluoroscopic time (6.5 ± 2.2 vs. 11.5 ± 4.5 min, p < .001) compared to the Aveir™ VR group. The Aveir™ VR group had a significantly higher implant pacing threshold compared to the Micra™ VR group (0.74 ± 0.34 mA vs. 0.5 ± 0.18 mA at pulse width 0.4 ms, p < .001), but no difference was found at 3 months and 6 months. There was no significant difference in the R-wave sensing and impedance and pacing percentage at implantation, 3 months, and 6 months. Complications of the procedure were rare. The mean projected longevity of the Aveir™ VR group was longer than the Micra™ VR group (18.8 ± 4.3 vs. 7.7 ± 0.75 years, p < .001). CONCLUSION: Implantation of the Aveir™ VR required longer laboratory and fluoroscopic time, but showed longer longevity at 6 months follow-up, compare to the Micra™ VR. Complications and lead dislodgement are rare.

Dysbiosis in the Dead: Human Postmortem Microbiome Beta-Dispersion as an Indicator of Manner and Cause of Death.

The postmortem microbiome plays an important functional role in host decomposition after death. Postmortem microbiome community successional patterns are specific to body site, with a significant shift in composition 48 h after death. While the postmortem microbiome has important forensic applications for postmortem interval estimation, it also has the potential to aid in manner of death (MOD) and cause of death (COD) determination as a reflection of antemortem health status. To further explore this association, we tested beta-dispersion, or the variability of microbiomes within the context of the "Anna Karenina Principle" (AKP). The foundational principle of AKP is that stressors affect microbiomes in unpredictable ways, which increases community beta-dispersion. We hypothesized that cases with identified M/CODs would have differential community beta-dispersion that reflected antemortem conditions, specifically that cardiovascular disease and/or natural deaths would have higher beta-dispersion compared to other deaths (e.g., accidents, drug-related deaths). Using a published microbiome data set of 188 postmortem cases (five body sites per case) collected during routine autopsy in Wayne County (Detroit), MI, we modeled beta-dispersion to test for M/COD associations a priori. Logistic regression models of beta-dispersion and case demographic data were used to classify M/COD. We demonstrated that beta-dispersion, along with case demographic data, could distinguish among M/COD - especially cardiovascular disease and drug related deaths, which were correctly classified in 79% of cases. Binary logistic regression models had higher correct classifications than multinomial logistic regression models, but changing the defined microbial community (e.g., full vs. non-core communities) used to calculate beta-dispersion overall did not improve model classification or M/COD. Furthermore, we tested our analytic approach on a case study that predicted suicides from other deaths, as well as distinguishing MOD (e.g., homicides vs. suicides) within COD (e.g., gunshot wound). We propose an analytical workflow that combines postmortem microbiome indicator taxa, beta-dispersion, and case demographic data for predicting MOD and COD classifications. Overall, we provide further evidence the postmortem microbiome is linked to the host's antemortem health condition(s), while also demonstrating the potential utility of including beta-dispersion (a non-taxon dependent approach) coupled with case demographic data for death determination.

Cold Case Experiment Demonstrates the Potential Utility of Aquatic Microbial Community Assembly in Estimating a Postmortem Submersion Interval.

Microbial community assembly (MCA) of both human and nonhuman animal carcasses provides indicators useful for estimating the postmortem interval (PMI) in terrestrial settings. However, there are fewer studies estimating postmortem submersion intervals (PMSIs) in aquatic habitats. No aquatic studies to date assessed MCA in the context of a death investigation, with all previous studies focusing on important basic ecological questions. Within the context of a cold case investigation, we performed an experiment using replicate adult swine carcasses to describe postmortem MCA variability within a nonflowing aquatic habitat. Using high-throughput sequencing of carcass postmortem microbiomes, we described MCA variability and identified key taxa associated with decomposition in an aquatic habitat similar to the cold case body recovery site. We also modeled key taxa for estimating PMSIs, modeling within ±3 days (mean square error) postmortem using random forest regression. Our findings show significant changes in microbial communities as decomposition progressed, and several taxa were identified as important indicator taxa which may be useful for future estimates of PMSI. While descriptive, this study provides initial findings quantifying MCA variability within a nonflowing aquatic habitat. Within the context of the cold case investigation, we discuss how postmortem microbial samples collected at the time of body recovery could have been an important piece of evidence for understanding the PMSI of recovered remains. Additional experimental studies are needed to explicitly test and identify mechanisms associated with postmortem MCA variability in other habitats and under different temperature (e.g., seasons) conditions.