External Validation of an AI-based Preoperative Frailty Index using Real-World Data.

Authors

Chen Bai, Feifei Xiao, Mohammad Al-Ani, Catherine C Price, Todd M Manini, Mamoun T Mardini

Published in

The journals of gerontology. Series A, Biological sciences and medical sciences. Jun 09, 2025. Epub Jun 09, 2025.

Abstract

Preoperative frailty assessment is crucial for surgical risk stratification in older adults. Traditional frailty measurements are often too time-consuming and resource-intensive in preoperative settings. This study aimed to externally validate an artificial intelligence (AI)-based frailty index developed using electronic health records (EHR).
We externally validated an AI-based frailty index, previously developed by our team, on a cohort of 152,364 surgical patients aged 65+ years from the OneFlorida+ Clinical Research Consortium. We examined the association between the predicted frailty and three postoperative outcomes: 30-day mortality, length of hospital stay, and discharge disposition. We also compared the predictive performance of general and service-specific frailty indices (the latter developed using data from patients undergoing specific surgeries) in predicting postoperative outcomes.
The AI-based frailty index demonstrated a strong and stepwise association with adverse postoperative outcomes. Patients in the highest frailty level (top 20%) had significantly higher odds of 30-day mortality (OR 4.33, 95% CI 3.91-4.80), longer hospital stays (2.53 times longer, 95% CI 2.47-2.60), and a higher likelihood of unfavorable discharge dispositions compared to the lowest frailty level, after adjusting for demographics and comorbidities. The general frailty index performed comparably to or slightly better than service-specific indices across surgical specialties.
The developed preoperative frailty index effectively predicts postoperative outcomes in a large and diverse external cohort. The index's efficiency and predictive performance in stratifying surgical risk can potentially improve surgical care and outcomes.

PMID:
40489638
Bibliographic data and abstract were imported from PubMed on 10 Jun 2025.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.