Temporal trends in guideline-recommended medical therapy after an acute heart failure decompensation event: an observational analysis from Generator Heart Failure DataMart by Antonio Gasbarrini, Giuseppe Patti, Filippo Crea, Giovanni Scambia, Domenico D'Amario, Vincenzo Valentini, Gianluigi Savarese, Alfredo Cesario, Jacopo Lenkowicz, Stefano Patarnello, Renzo Laborante, Agni Delvinioti, Andrada Mihaela Tudor, Chiara Iacomini, Antonio Iaconelli, Donato Antonio Paglianiti, Mattia Galli, Daniele Rodolico, Attilio Restivo, Giuseppe Ciliberti, Gaetano Rizzo, Emiliano Bianchini, Matteo Busti, Luca Sensini

“…Objectives To evaluate the trend of prescription of the four foundational therapies, and their impact on 30-day urgent re-admissions and all-cause death in patients with heart failure and reduced ejection fraction (HFrEF) following an acute decompensation event.Design Retrospective.Setting One tertiary referral centre.Participants 999 consecutively patients admitted with a primary diagnosis of HFrEF between January 2020 and June 2023 were identified through a validated, high‐performance technology infrastructure based on artificial intelligence. The entire cohort was divided into three time periods based on two time points: September 2021 (ie, the release of the latest European guidelines) and January 2022 (ie, reimbursement for sodium-glucose cotransporter 2 (SGLT2) inhibitors).Primary and secondary outcome measures Trends and predictors of the prescription of each of the four foundational therapies and of the composite of all-cause death and rehospitalisation for urgent causes at 30 days.Results Among the 999 included patients, β-blockers were prescribed in 93% of patients, ACE inhibitor (ACEi)/angiotensin receptor blocker (ARB)/angiotensin–neprilysin receptor inhibitor (ARNi) in 73%, mineralocorticoid receptor antagonist in 30% and SGLT2 inhibitors in 18%. …”
Get full text

Integrated Framework and Technical Path for Multi-level Nested Assessment of Landscape Character by Yuncai WANG, Qizhen DONG

“…The multi-level nested assessment framework and technical path integrating LCA and LPA can accurately grasp the local characteristics of landscape through quantitative assessment and spatial mapping using artificial intelligence technology at different scales of practical needs. …”
Get full text

TAME 2.0: expanding and improving online data science training for environmental health research by Alexis Payton, Alexis Payton, Alexis Payton, Elise Hickman, Elise Hickman, Elise Hickman, Jessie Chappel, Jessie Chappel, Jessie Chappel, Kyle Roell, Kyle Roell, Lauren E. Koval, Lauren E. Koval, Lauren A. Eaves, Lauren A. Eaves, Chloe K. Chou, Chloe K. Chou, Chloe K. Chou, Allison Spring, Allison Spring, Sarah L. Miller, Sarah L. Miller, Sarah L. Miller, Oyemwenosa N. Avenbuan, Oyemwenosa N. Avenbuan, Oyemwenosa N. Avenbuan, Rebecca Boyles, Rebecca Boyles, Paul Kruse, Cynthia V. Rider, Grace Patlewicz, Caroline Ring, Cavin Ward-Caviness, David M. Reif, Ilona Jaspers, Ilona Jaspers, Ilona Jaspers, Ilona Jaspers, Ilona Jaspers, Rebecca C. Fry, Rebecca C. Fry, Rebecca C. Fry, Julia E. Rager, Julia E. Rager, Julia E. Rager, Julia E. Rager

“…Training topics were prioritized based upon ongoing engagement with trainees, professional colleague feedback, and emerging topics in the field of environmental health research (e.g., artificial intelligence and machine learning). TAME 2.0 is a significant expansion upon the original TAME training resource pilot. …”
Get full text