Mikkelsen Hart (throatpeak8)

Background - Current expert consensus recommends remote monitoring (RM) for cardiac implantable electronic devices, with at least annual in-office follow-up. We studied safety and resource consumption of exclusive remote follow-up (RFU) in pacemaker patients for two years. Methods - In Japan, consecutive pacemaker patients committed to RM were randomized to either RFU or conventional in-office follow-up (CFU) at twice-yearly intervals. RFU patients were only seen if indicated by remote monitoring. All returned to hospital after two years. The primary endpoint was a composite of death, stroke, or cardiovascular events requiring surgery, and the primary hypothesis was non-inferiority with 5% margin. Results - Of 1274 randomized patients (50.4% female, age 77±10 years), 558 (RFU) and 550 (CFU) patients reached either the primary endpoint or 24 months follow-up. The primary endpoint occurred in 10.9% and 11.8%, resp. (P=0.0012 for non-inferiority). The median (IQR) number of in-office follow-ups was 0.50 (0.50 - 0.63) in RFU and 2.01 (1.93 - 2.05) in CFU per patient-year (P less then 0.001). Insurance claims for follow-ups and directly related diagnostic procedures were 18,800 Yen (16,500 - 20,700 Yen) in RFU and 21,400 Yen (16,700 - 25,900 Yen) in CFU (P less then 0.001). selleck compound Only 1.4% of remote follow-ups triggered an unscheduled in-office follow-up, and only 1.5% of scheduled in-office follow-ups were considered actionable. Conclusions - Replacing periodic in-office follow-ups with remote follow-ups for 2 years in pacemaker patients committed to RM does not increase the occurrence of major cardiovascular events and reduces resource consumption. Clinical Trial Registration - The trial was registered at https//clinicaltrials.gov; Unique Identifier NCT01523704.Actigraphy is widely used in sleep studies but lacks a universal unsupervised algorithm for sleep/wake identification. An unsupervised algorithm is useful in large-scale population studies and in cases where polysomnography (PSG) is unavailable, as it does not require sleep outcome labels to train the model but utilizes information solely contained in actigraphy to learn sleep and wake characteristics and separate the two states. In this study, we proposed a machine learning unsupervised algorithm based on the Hidden Markov Model (HMM) for sleep/wake identification. The proposed algorithm is also an individualized approach that takes into account individual variabilities and analyzes each individual actigraphy profile separately to infer sleep and wake states. We used Actiwatch and PSG data from 43 individuals in the Multi-Ethnic Study of Atherosclerosis study to evaluate the method performance. Epoch-by-epoch comparisons and sleep variable comparisons were made between our algorithm, the unsupervised algoritorithm achieved better performance than the commonly used Actiwatch software algorithm and the pre-trained UCSD algorithm. HMM can help expand the application of actigraphy in cases where PSG is hard to acquire and supervised methods cannot be trained. In addition, the estimated HMM parameters can characterize individual activity patterns and sedentary tendencies that can be further utilized in downstream analysis.Our study aims to define and identify correlates of social isolation among people living with HIV (PLHIV). The Longitudinal Investigation into Supportive and Ancillary health services (LISA) study provided a cross-sectional analytic sample of 996 PLHIV in British Columbia, Canada (sampled between 2007 and 2010). Individuals marginalized by socio-structural inequities were oversampled; sampling bias was addressed through inverse probability of participation weighting. Through latent class analysis, three groups were identified Socially Connected (SC) (n = 364, 37%), Minimally Isolated (MI) (n = 540, 54%) and Socially Isolated (SI) (n = 92, 9%). Correlates of the SI and MI classes, determined through multivariable multinomial regression using the