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Enhancing Frailty Assessments for Transcatheter Aortic Valve Replacement Patients Using Structured and Unstructured

Mamoun T Mardini¹, Chen Bai¹, Anthony A Bavry²

¹Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States.

|November 27, 2024

Summary

This summary is machine-generated.

Integrating real-world data enhances frailty assessments for transcatheter aortic valve replacement (TAVR) candidates. Combining clinical notes and electronic health records improves patient selection for better outcomes.

Keywords:

EHR TAVR cardiac surgery cardiology clinical notes electronic health record frailty machine learning minimally invasive surgery real-world data topic modeling transcatheter aortic valve replacement

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Exergaming in Older People Living with HIV Improves Balance, Mobility and Ameliorates Some Aspects of Frailty

Exergaming in Older People Living with HIV Improves Balance, Mobility and Ameliorates Some Aspects of Frailty

Published on: October 6, 2016

Area of Science:

Cardiology
Geriatrics
Health Informatics

Background:

Transcatheter aortic valve replacement (TAVR) is a key treatment for severe aortic stenosis, predominantly affecting older adults.
Accurate frailty assessment is crucial for selecting TAVR candidates and optimizing procedural success.
Current frailty assessments may benefit from more comprehensive data integration.

Purpose of the Study:

To improve frailty assessments for TAVR candidates.
To integrate structured and unstructured real-world data for enhanced frailty prediction.
To evaluate the efficacy of combined data sources in predicting frailty.

Main Methods:

Analysis of 14,000 TAVR patient records (Jan 2018-Dec 2019).
Frailty assessment using Fried criteria (weight loss, exhaustion, walking speed, grip strength, physical activity).
Application of Latent Dirichlet allocation for topic modeling and Extreme Gradient Boosting for prediction on structured EHR and unstructured clinical notes, with LASSO for feature selection and nested cross-validation for evaluation.

Main Results:

Combined structured EHR and unstructured clinical notes improved frailty prediction (AUC 0.82) compared to EHR data alone (AUC 0.64).
Top frailty predictors identified include congestive heart failure management, back problems, and atrial fibrillation.
Latent Dirichlet allocation identified 7 key topics, revealing the impact of specific medical treatments on frailty prediction.

Conclusions:

Integrating unstructured clinical notes with structured EHR data significantly enhances frailty prediction for TAVR candidates.
This approach offers a potential pathway for standardizing frailty assessments using real-world data.
Improved frailty assessment can lead to better patient selection and outcomes for TAVR procedures.