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| http://dx.doi.org/10.1073/pnas.1901163116 | DOI Listing |
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Development and validation of an EHR-based risk prediction model for geriatric patients undergoing urgent and emergency surgery.
BMC Anesthesiol
January 2025
Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA, 94612, USA.
Background: Clinical determination of patients at high risk of poor surgical outcomes is complex and may be supported by clinical tools to summarize the patient's own personalized electronic health record (EHR) history and vitals data through predictive risk models. Since prior models were not readily available for EHR-integration, our objective was to develop and validate a risk stratification tool, named the Assessment of Geriatric Emergency Surgery (AGES) score, predicting risk of 30-day major postoperative complications in geriatric patients under consideration for urgent and emergency surgery using pre-surgical existing electronic health record (EHR) data.
Methods: Patients 65-years and older undergoing urgent or emergency non-cardiac surgery within 21 hospitals 2017-2021 were used to develop the model (randomly split: 80% training, 20% test).
Perspective: Challenges for Personalized Nutrition in the Current U.S. Regulatory Framework and Future Opportunities.
Adv Nutr
January 2025
Pharmavite, LLC, West Hills, CA, USA. Electronic address:
Personalized Nutrition (PN) aims to provide tailored dietary recommendations to improve a person's health outcomes by integrating a multitude of individual-level information and support desired behavior changes. The field is rapidly evolving with technological advances. As new biomarkers are discovered, wearables and other devices can now provide up-to-the-minute insights, and artificial intelligence (AI) and machine learning (ML) models support recommendations and lifestyle behavior change.
View Article and Find Full Text PDFMachine-learning based risk prediction of outcomes in patients hospitalised with COVID-19 in Australia: the AUS-COVID score.
J Am Med Inform Assoc
January 2025
Department of Cardiology, Royal North Shore Hospital, Sydney, NSW, Australia.
Objective: We aimed to develop a highly interpretable and effective, machine-learning based risk prediction algorithm to predict in-hospital mortality, intubation and adverse cardiovascular events in patients hospitalised with COVID-19 in Australia (AUS-COVID Score).
Materials And Methods: This prospective study across 21 hospitals included 1714 consecutive patients aged ≥ 18 in their index hospitalization with COVID-19. The dataset was separated into training (80%) and test sets (20%).
What Kills Mature Street and Park Trees in Cities? Systematic Quantitative Review of Published Case Studies.
Environ Manage
January 2025
School of Environment and Science, Griffith University, 170 Kessels Road, Nathan, 4111, Australia.
Street and park trees often endure harsher conditions, including increased temperatures and drier soil and air, than those found in urban or natural forests. These conditions can lead to shorter lifespans and a greater vulnerability to dieback. This literature review aimed to identify confirmed causes of street and park tree dieback in urban areas from around the world.
View Article and Find Full Text PDFAn Asset-Based Examination of Contextual Factors Influencing Nutrition Security: The Case of Rural Northern New England.
Nutrients
January 2025
Food Systems Program, University of Vermont, Burlington, VT 05405, USA.
Background/objectives: Rural communities face a disproportionate burden in terms of diet-related health challenges and have been identified as a target for the U.S. Department of Agriculture's nutrition security initiatives.
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