Predicting Functional Outcomes of Total Hip Arthroplasty Using Machine Learning: A Systematic Review
Abstract
1. Introduction
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Author, Year, and Evidence Level | Data Size | Data Source | Variables Included | Primary Outcome and Endpoint | Missing Data Handled | Test/Train Ratio | AI Models Used | Reliability Test Group (AUC) | Included Logistic Regression? | Better than Logistic Regression? | External Validation? |
---|---|---|---|---|---|---|---|---|---|---|---|
Fontana et al., 2019 [17] Level 3 | 7239 | Single institution | Demographic *, medical, outcome scores | MCID at 2 years: SF-36 PCS (5.0), SF-36 MCS (5.0), HOOS Jr (17.7) | Imputation | 80:20 | LASSO, random forest, linear support vector machine | SF-36 PCS: 0.78 SF-36 MCS: 0.89 HOOS Jr: 0.78 | No | Not Available | No |
Huber et al., 2019 [18] Level 3 | 31,905 | National Registry | Demographic *, outcome scores | MID at 6 months: EQ-VAS (11.0), OHS (8.0) | Removed prior to analysis | ~50:50 (two separate years) | Extreme gradient boosting (EGB), multi-step elastic net, random forest, neural net, naïve Bayes, k-nearest neighbours | Only reported for training group EQ-VAS: 0.87 for EGB OHS: 0.78 for EGB | Yes | Not clearly stated but regression “followed closely” the best-performing model (EGB) | No |
Kunze et al., 2020 [19] Level 3 | 616 | Single institution | Demographic *, medical, preoperative health state | MCID at minimum 2 years: EQ-VAS (half standard deviation) | Multiple imputation (range of movement excluded missing >30%) | 70:30 | Random forest, stochastic gradient boosting, support vector machine, neural network, elastic net penalized logistic regression (ENPLR) | 0.97 for random forest, 0.92 for neural network, 0.92 for stochastic gradient boosting, 0.90 for support vector machine, 0.87 for ENPLR | No | Not Available | No |
Schwartz et al., 1997 [20] Level 3 | 221 | Single institution | Demographic *, preoperative pain | Improvement in SF-36 pain at 1 year | Mean of missing variable | Not reported | Neural network | 0.79 | Yes | Yes (AUC: 0.79 vs. 0.74) | No |
Sniderman et al. 2021 [21] Level 2 | 160 | Single institution | Demographic *, medical, cognitive, surgical approach | 3 months postoperative: HOOS | Less than 5% | 67:33 | LASSO | N/A | Yes | Not Available | No |
Klemt et al., 2023 [22] Level 3 | 2137 | Single institution | Demographic *, medical comorbidity, medications, surgical parameters | 1 year postoperative: HOOS SF10A physical PROMIS physical PROMIS mental | Not stated | 80:20 | Random forest, support vector machine, neural network, elastic net–penalized logistic regression (ENPLR) | 0.85 for random forest, 0.84 for support vector machine, 0.87 for neural network, 0.86 for ENPLR | No | Not Available | No |
Langenberger et al., 2023 [23] Level 2 | 1843 | Multicentre | Demographic *, activity level, outcome scores | MCID at 1 year: EQ-5D (0.2), EQ-VAS (5.86), HOOS (10.01) | <30% missing = imputed using missForest | 80:20 | Neural network, gradient boosting, LASSO, ridge, elastic net, random forest | Best-performing: EQ-5D: 0.81 EQ-VAS: 0.84 HOOS: 0.71 | Yes | EQ-5D: 0.81 vs. 0.81 EQ-VAS: 0.84 vs. 0.84 HOOS: 0.71 vs. 0.67 | No |
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Clement, N.D.; Clement, R.; Clement, A. Predicting Functional Outcomes of Total Hip Arthroplasty Using Machine Learning: A Systematic Review. J. Clin. Med. 2024, 13, 603. https://doi.org/10.3390/jcm13020603
Clement ND, Clement R, Clement A. Predicting Functional Outcomes of Total Hip Arthroplasty Using Machine Learning: A Systematic Review. Journal of Clinical Medicine. 2024; 13(2):603. https://doi.org/10.3390/jcm13020603
Chicago/Turabian StyleClement, Nick D., Rosie Clement, and Abigail Clement. 2024. "Predicting Functional Outcomes of Total Hip Arthroplasty Using Machine Learning: A Systematic Review" Journal of Clinical Medicine 13, no. 2: 603. https://doi.org/10.3390/jcm13020603
APA StyleClement, N. D., Clement, R., & Clement, A. (2024). Predicting Functional Outcomes of Total Hip Arthroplasty Using Machine Learning: A Systematic Review. Journal of Clinical Medicine, 13(2), 603. https://doi.org/10.3390/jcm13020603