Background: Nationally-derived models predicting 30-day readmissions following heart failure (HF) hospitalizations yield insufficient discrimination for institutional use.
Objective: Develop a customized readmission risk model from Medicare-employed and institutionally-customized risk factors and compare the performance against national models in a medical center.
Methods: Medicare patients age ≥ 65 years hospitalized for HF (n = 1,454) were studied in a derivation cohort and in a separate validation cohort (n = 243). All 30-day hospital readmissions were documented. The primary outcome was risk discrimination (c-statistic) compared to national models.
Results: A customized model demonstrated improved discrimination (c-statistic 0.72; 95% CI 0.69 - 0.74) compared to national models (c-statistics of 0.60 and 0.61) with a c-statistic of 0.63 in the validation cohort. Compared to national models, a customized model demonstrated superior readmission risk profiling by distinguishing a high-risk (38.3%) from a low-risk (9.4%) quartile.
Conclusions: A customized model improved readmission risk discrimination from HF hospitalizations compared to national models.
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http://dx.doi.org/10.1016/j.hrtlng.2018.05.012 | DOI Listing |
Int J Obstet Anesth
January 2025
Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, USA.
Background: Disparities in labor epidural analgesia (LEA) management could reduce maternal satisfaction and increase risk. We compared times from the first administration of breakthrough pain medication (top-up) to LEA replacement to evaluate disparities across race.
Methods: In this retrospective cohort study (01-01-2018 to 12-31-2022), all patients with LEA and maternal race/ethnicity of non-Hispanic White or Black were eligible.
J Med Internet Res
January 2025
Vibrent Health, Inc, Fairfax, VA, United States.
Background: Longitudinal cohort studies have traditionally relied on clinic-based recruitment models, which limit cohort diversity and the generalizability of research outcomes. Digital research platforms can be used to increase participant access, improve study engagement, streamline data collection, and increase data quality; however, the efficacy and sustainability of digitally enabled studies rely heavily on the design, implementation, and management of the digital platform being used.
Objective: We sought to design and build a secure, privacy-preserving, validated, participant-centric digital health research platform (DHRP) to recruit and enroll participants, collect multimodal data, and engage participants from diverse backgrounds in the National Institutes of Health's (NIH) All of Us Research Program (AOU).
J Med Internet Res
January 2025
School of Public Health, Capital Medical University, Beijing, China.
Background: Health inequalities among older adults become increasingly pronounced as aging progresses. In the digital era, some researchers argue that access to and use of digital technologies may contribute to or exacerbate these existing health inequalities. Conversely, other researchers believe that digital technologies can help mitigate these disparities.
View Article and Find Full Text PDFACS Nano
January 2025
National Engineering Research Center for Biomaterials, Sichuan University, 29 Wangjiang Road, Chengdu, Sichuan 610064, P. R. China.
Inadequate vascularization significantly hampers wound recovery by limiting nutrient delivery. To address this challenge, we extracted membrane vesicles from (LMVs) and identified their angiogenic potential via transcriptomic analysis. We further developed a composite hydrogel system (Gel-LMVs) by anchoring LMVs within carboxylated chitosan and cross-linking it with oxidized hyaluronic acid through a Schiff base reaction.
View Article and Find Full Text PDFRev Bras Enferm
January 2025
Universidade Federal de Santa Catarina, Colégio de Aplicação. Santa Catarina, Santa Catarina, Brazil.
Objective: To analyze the new roles of community health workers as outlined in the 2017 National Primary Care Policy (PNAB) from the perspectives of both nurses and community health workers.
Methods: This qualitative study involved nurses and community health workers from Family Health teams, conducted through semi-structured interviews via videoconference between August 2021 and April 2022. The data were analyzed using thematic content analysis.
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