Nutritional status, inflammatory burden, and CT-derived muscle phenotypes for predicting chronic postsurgical pain after total hip arthroplasty: an explainable machine learning study
- 1Knee Preservation Center, The Affiliated Hospital of Qingdao University, Qingdao , ,
- 2Department of Sports Medicine, The Affiliated Hospital of Qingdao University, Qingdao , ,
- 3School of Mathematical Sciences University of Electronic Science and Technology of China , ,
Abstract
Background: Hip fracture (HF) is a common fragility fracture in older adults and may lead to persistent pain and impaired recovery. Chronic postsurgical pain (CPSP) after total hip arthroplasty (THA) negatively affects rehabilitation and quality of life. This study aimed to develop and validate a machine learning model integrating clinical variables, peri-hip muscle characteristics, and nutrition-related inflammatory indicators for CPSP prediction after THA in patients with HF.
Methods: This single-center cohort study included patients with HF undergoing primary unilateral THA between October 2022 and March 2026. Patients from October 2022 to September 2024 were randomly divided into training and internal validation sets, while those from October 2024 to March 2026 served as an independent temporal test set. CPSP was defined as surgery-related pain persisting for ≥3 months with NRS ≥4. Candidate predictors included clinical, psychological, nutritional, inflammatory, and CT-derived muscle parameters. LASSO and Boruta were used for feature selection, and six machine learning algorithms were compared. SHAP analysis was applied to interpret the optimal model.
Results: Among 881 patients, 137/529 (25.9%) developed CPSP in the development cohort. Seven predictors were selected: GNRI, gluteus medius/minimus muscle density, gluteus medius/minimus cross-sectional area, NLR, age, preoperative anxiety, and PNI. The SVM model showed the best performance, achieving an AUC of 0.854 in the temporal test set, with accuracy of 0.770, sensitivity of 0.789, specificity of 0.763, NPV of 0.913, F1-score of 0.637, and Brier score of 0.125. SHAP analysis identified GNRI as the most influential predictor (35.4%), followed by muscle density (13.6%) and NLR (12.5%).
Conclusions: A machine learning model integrating clinical, CT-derived muscle, and nutrition-related inflammatory features showed good predictive performance for CPSP after THA in patients with HF and may support early risk stratification and individualized perioperative management.
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BibTeX
@article{jia2026nutritional,
title = {{Nutritional status, inflammatory burden, and CT-derived muscle phenotypes for predicting chronic postsurgical pain after total hip arthroplasty: an explainable machine learning study}},
author = {Xiaodong Jia and Xinkun Han and Xinyu Cui and Yun Zhao and Yingze Zhang and Kuishuai Xu},
journal = {Frontiers in nutrition},
year = {2026},
month = oct,
doi = {10.3389/fnut.2026.1958865},
url = {https://europepmc.org/article/PMC/PMC13727434},
note = {CC BY},
}
RIS
TY - JOUR
TI - Nutritional status, inflammatory burden, and CT-derived muscle phenotypes for predicting chronic postsurgical pain after total hip arthroplasty: an explainable machine learning study
AU - Xiaodong Jia
AU - Xinkun Han
AU - Xinyu Cui
AU - Yun Zhao
AU - Yingze Zhang
AU - Kuishuai Xu
PY - 2026
DA - 2026/10/20
JO - Frontiers in nutrition
DO - 10.3389/fnut.2026.1958865
UR - https://europepmc.org/article/PMC/PMC13727434
AB - Background: Hip fracture (HF) is a common fragility fracture in older adults and may lead to persistent pain and impaired recovery. Chronic postsurgical pain (CPSP) after total hip arthroplasty (THA) negatively affects rehabilitation and quality of life. This study aimed to develop and validate a machine learning model integrating clinical variables, peri-hip muscle characteristics, and nutrition-related inflammatory indicators for CPSP prediction after THA in patients with HF. Methods: This single-center cohort study included patients with HF undergoing primary unilateral THA between October 2022 and March 2026. Patients from October 2022 to September 2024 were randomly divided into training and internal validation sets, while those from October 2024 to March 2026 served as an independent temporal test set. CPSP was defined as surgery-related pain persisting for ≥3 months with NRS ≥4. Candidate predictors included clinical, psychological, nutritional, inflammatory, and CT-derived muscle parameters. LASSO and Boruta were used for feature selection, and six machine learning algorithms were compared. SHAP analysis was applied to interpret the optimal model. Results: Among 881 patients, 137/529 (25.9%) developed CPSP in the development cohort. Seven predictors were selected: GNRI, gluteus medius/minimus muscle density, gluteus medius/minimus cross-sectional area, NLR, age, preoperative anxiety, and PNI. The SVM model showed the best performance, achieving an AUC of 0.854 in the temporal test set, with accuracy of 0.770, sensitivity of 0.789, specificity of 0.763, NPV of 0.913, F1-score of 0.637, and Brier score of 0.125. SHAP analysis identified GNRI as the most influential predictor (35.4%), followed by muscle density (13.6%) and NLR (12.5%). Conclusions: A machine learning model integrating clinical, CT-derived muscle, and nutrition-related inflammatory features showed good predictive performance for CPSP after THA in patients with HF and may support early risk stratification and individualized perioperative management.
SN - 2296-861X
ER -