Back

Predictive models for worsening heart failure events and death in adults with mild-to-moderate chronic kidney disease

19 May 2025 (16:00 - 16:08)
Organised by: Logo
Congress Presentation Part of: Heart failure, chronic kidney disease, and beyond: understanding risk, prognosis, and outcomes Comorbidities HFA Premium Access Heart Failure 2025 Heart Failure Association (HFA)

About the speaker

Photo
Doctor Andrew Ambrosy

Kaiser Permanente, San Francisco Medical Centre and Medical Offices, San Francisco (United States of America)

6 More presentations in this session

Presentation thumbnail
Prognostic value of handgrip strength in older patients with heart failure: a post-hoc analysis of FRAGILE-HF
Doctor Y. Akama (Tokyo, JP)
Abstract Slides Video
Presentation thumbnail
Incidence of cancer in heart failure across ejection fraction phenotypes and sexes: a nationwide cohort analysis
Doctor D. Stolfo (Udine, IT)
Abstract Slides Video
Presentation thumbnail
Frailty and cognitive function in patients with heart failure and preserved ejection fraction: insights from PERSPECTIVE
Doctor L. Shen (Glasgow, CN)
Abstract Slides Video
Presentation thumbnail
Therapeutic challenges and mortality outcomes in HFrEF patients with CKD: a multi-centre and multi-ethnic experience
Doctor A. Koh (Kuching, MY)
Abstract Slides Video
Presentation thumbnail
Changes of iron status during acutely decompensated heart failure hospitalisation and its association with adverse events after discharge
Mr R. Murai (Kobe, JP)
Abstract Slides Video

Access the full session

Session thumbnail
Heart failure, chronic kidney disease, and beyond: understanding risk, prognosis, and outcomes
Video

The Event

Event poster

Heart Failure 2025

19 May 2025 16:00 CET
You may be interested in
thumbnail
Congress Session
Nursing and Allied Professions Investigator Award
thumbnail
Congress Presentation
Role of mitral regurgitation in a new predictive model for mortality and hospitalization risk in heart failure
thumbnail
Congress Presentation
Interpretable machine learning model for predicting long-term cardiovascular mortality risk in chronic kidney disease patients using SHAP explainers

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources