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Predicting Frailty Trajectories Using Interpretable Machine Learning Among Older Adults Following Hip Surgery: Prospective Longitudinal Study.

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Predicting Frailty Trajectories Using Interpretable Machine Learning Among Older Adults Following Hip Surgery: Prospective Longitudinal Study.

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Related Experiment Video

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

Predicting Frailty Trajectories Using Interpretable Machine Learning Among Older Adults Following Hip Surgery:

Jingying Huang¹, Qinqin Fan², Huiqin Shi³

¹Sir Run Run Shaw Hospital, No. 3, Qingchun East Road, Hangzhou, Zhejiang, China, 86 17757173794.

|June 16, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study identified three frailty trajectories after hip surgery in older adults. An interpretable machine learning model predicts these trajectories, aiding early intervention for better geriatric care.

Keywords:

Extreme Gradient Boosting SHAP analysis Shapley Additive Explanations XGBoost aging frailty frailty trajectory hip surgery older adults predictive model

Related Experiment Videos

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

Area of Science:

Geriatric Medicine
Orthopedic Surgery
Data Science in Healthcare

Background:

Postoperative frailty is common in older adults after hip surgery, linked to poor outcomes.
Temporal patterns of frailty progression and predictive models are underexplored.
Validated tools for early risk stratification of frailty trajectories are lacking.

Purpose of the Study:

Identify distinct frailty trajectories within six months post-hip surgery.
Explore predictors associated with these frailty trajectories.
Develop and validate an interpretable machine learning model for individualized risk prediction.

Main Methods:

Prospective longitudinal observational study of 209 older adults undergoing hip surgery.
Frailty assessed preoperatively and at 1, 3, and 6 months postoperatively using the Frailty Index.
Group-based trajectory modeling and Extreme Gradient Boosting (XGBoost) with SHAP for prediction and interpretability.

Main Results:

Three frailty trajectories identified: low-fluctuation (26%), high-improvement (39%), and high-deterioration (35%).
Twelve predictors from the Health Ecology Model were selected.
XGBoost model showed excellent discrimination (AUC 0.98 training, 0.93 test) and clinical utility.

Conclusions:

An interpretable XGBoost model accurately predicts frailty trajectories post-hip surgery.
Early identification of at-risk patients enables targeted interventions.
A web-based calculator supports personalized perioperative care for geriatric populations.