The new donor heart allocation system prioritizes candidates supported by temporary devices. However, waitlist and posttransplant outcomes in candidates with durable left ventricular assist device (LVAD) remain to be elucidated. The United Network for Organ Sharing database was queried for adults listed from October 2015 to March 2020 for a single-organ, first-time heart transplant (HTx) with a durable LVAD. Waitlist removal within 1 year because of death or clinical deterioration and HTx was analyzed using competing risks regression. Candidates who underwent HTx within 1 year of listing were identified for examination of post-HTx survival using the Kaplan-Meier method and Cox proportional hazards models. Compared with candidates listed under the old system (n = 2,122), candidates listed under the new system (n = 1,562) were slightly younger ( p = 0.04) but had higher body mass index ( p < 0.01). Those listed under the new system were significantly less likely to experience waitlist removal because of death or clinical deterioration (subhazard ratio [HR] 0.68, 95% CI 0.52-0.90) but were also less likely to undergo HTx (sub-HR 0.91, 95% CI 0.83-0.998). Those who survived to HTx were more likely to experience death or need for re-HTx within 1 year of HTx under the new system (adjusted HR 1.50, 95% CI 1.11-2.03). Candidates with durable LVAD experience favorable waitlist outcomes under the new allocation system, although those who undergo HTx may be at increased mortality risk. Thus, candidates with a durable LVAD should be carefully selected for HTx listing under the new allocation system.
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http://dx.doi.org/10.1097/MAT.0000000000001599 | DOI Listing |
Previous findings have raised doubt in whether comparable conformity effects can be obtained for information from humans and computers or other systems of little or no social importance. In the present study, we compared the impact of "other choices" (i.e.
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January 2025
Department of Computing, University of North Florida, 1 UNF Dr., Jacksonville, 32246, FL, USA.
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions.
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Methods: A total of 884 patients (490 diagnosed with OSA) were included, which was divided into the training, validation, and test sets.
Planta
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Laboratory of Environmental Microbiology and Biotechnology, Universidade Vila Velha (UVV), Vila Velha, ES, Brazil.
Both, Serendipita indica and AMF, show promise as sustainable biofertilizers for reforestation, improving nutrient uptake and stress tolerance, despite contrasting effects on photosynthetic capacity and biomass allocation. Reclaiming degraded areas is essential for biodiversity conservation and enhancing ecosystem services enhancement, especially when using native species. This study investigated Schinus terebinthifolius Raddi, a native Brazilian species, and its compatibility with plant growth-promoting microorganisms (PGPM), including an endophytic fungus (Serendipita indica) and a consortium of arbuscular mycorrhizal fungi (AMF), to identify effective strategies for reforestation in nutrient-poor environments.
View Article and Find Full Text PDFHeliyon
January 2025
Guangzhou Xinhua University, School of Resources and Planning, Guangzhou, 510520, China.
Emergency shelters are multifunctional spaces that provide safe refuge, essential life protection, and rescue command for residents in case of urban disaster. These shelters constitute crucial components of urban public safety. This study, with Tianhe District in Guangzhou City as a case study, used data from emergency evacuation sites and other socio-economic sources to construct an evaluation system for spatial suitability evaluation and layout optimization of emergency shelters.
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