Identification of high-risk preoperative blood indicators and baseline characteristics for multiple postoperative complications in rheumatoid arthritis patients undergoing total knee arthroplasty: a multi-machine learning feature contribution analysis.
10.7507/1002-1892.202508008
- Author:
Kejia ZHU
1
;
Zhiyang HUANG
1
;
Biao WANG
2
;
Hang LI
1
;
Yuangang WU
1
;
Bin SHEN
1
;
Yong NIE
1
Author Information
1. Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu Sichuan, 610041, P. R. China.
2. West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu Sichuan, 610041, P. R. China.
- Publication Type:Journal Article
- Keywords:
Rheumatoid arthritis;
blood biomarkers;
machine learning;
postoperative complications;
preoperative risk factors;
total knee arthroplasty
- MeSH:
Humans;
Arthroplasty, Replacement, Knee/adverse effects*;
Arthritis, Rheumatoid/blood*;
Machine Learning;
Postoperative Complications/blood*;
Female;
Male;
Retrospective Studies;
Middle Aged;
Aged;
Risk Factors;
Preoperative Period;
C-Reactive Protein/analysis*;
Risk Assessment
- From:
Chinese Journal of Reparative and Reconstructive Surgery
2025;39(12):1532-1542
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To explore, identify, and develop novel blood-based indicators using machine learning algorithms for accurate preoperative assessment and effective prediction of postoperative complication risks in patients with rheumatoid arthritis (RA) undergoing total knee arthroplasty (TKA).
METHODS:A retrospective cohort study was conducted including RA patients who underwent unilateral TKA between January 2019 and December 2024. Inpatient and 30-day postoperative outpatient follow-up data were collected. Six machine learning algorithms, including decision tree, random forest, logistic regression, support vector machine, extreme gradient boosting, and light gradient boosting machine, were used to construct predictive models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, and recall. SHapley Additive exPlanations (SHAP) values were employed to interpret and rank the importance of individual variables.
RESULTS:According to the inclusion criteria, a total of 1 548 patients were enrolled. Ultimately, 18 preoperative indicators were identified as effective predictive features, and 8 postoperative complications were defined as prediction labels for inclusion in the study. Within 30 days after surgery, 453 patients (29.2%) developed one or more complications. Considering overall accuracy, precision, recall, and F1-score, the random forest model [AUC=0.930, 95% CI (0.910, 0.950)] and the extreme gradient boosting model [AUC=0.909, 95% CI (0.880, 0.938)] demonstrated the best predictive performance. SHAP analysis revealed that anti-cyclic citrullinated peptide antibody, C-reactive protein, rheumatoid factor, interleukin-6, body mass index, age, and smoking status made significant contributions to the overall prediction of postoperative complications.
CONCLUSION:Machine learning-based models enable accurate prediction of postoperative complication risks among RA patients undergoing TKA. Inflammatory and immune-related blood biomarkers, such as anti-cyclic citrullinated peptide antibody, C-reactive protein, and rheumatoid factor, interleukin-6, play key predictive roles, highlighting their potential value in perioperative risk stratification and individualized management.
