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- Publisher Website: 10.1016/j.ocarto.2024.100440
- Scopus: eid_2-s2.0-85185469395
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Article: An interpretable knee replacement risk assessment system for osteoarthritis patients
Title | An interpretable knee replacement risk assessment system for osteoarthritis patients |
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Authors | |
Keywords | Knee osteoarthritis Machine learning Prognosis Self-administrable Survival analysis |
Issue Date | 1-Jun-2024 |
Publisher | Elsevier |
Citation | Osteoarthritis and Cartilage Open, 2024, v. 6, n. 2 How to Cite? |
Abstract | Objective: Knee osteoarthritis (OA) is a complex disease with heterogeneous representations. Although it is modifiable to prevention and early treatment, there still lacks a reliable and accurate prognostic tool. Hence, we aim to develop a quantitative and self-administrable knee replacement (KR) risk stratification system for knee osteoarthritis (KOA) patients with clinical features. Method: A total of 14 baseline features were extracted from 9592 cases in the Osteoarthritis Initiative (OAI) cohort. A survival model was constructed using the Random Survival Forests algorithm. The prediction performance was evaluated with the concordance index (C-index) and average receiver operating characteristic curve (AUC). A three-class KR risk stratification system was built to differentiate three distinct KR-free survival groups. Thereafter, Shapley Additive Explanations (SHAP) was introduced for model explanation. Results: KR incidence was accurately predicted by the model with a C-index of 0.770 (±0.0215) and an average AUC of 0.807 (±0.0181) with 14 clinical features. Three distinct survival groups were observed from the ten-point KR risk stratification system with a four-year KR rate of 0.79%, 5.78%, and 16.2% from the low, medium, and high-risk groups respectively. KR is mainly caused by pain medication use, age, surgery history, diabetes, and a high body mass index, as revealed by SHAP. Conclusion: A self-administrable and interpretable KR survival model was developed, underscoring a KR risk scoring system to stratify KOA patients. It will encourage regular self-assessments within the community and facilitate personalised healthcare for both primary and secondary prevention of KOA. |
Persistent Identifier | http://hdl.handle.net/10722/344632 |
DC Field | Value | Language |
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dc.contributor.author | Li, HHT | - |
dc.contributor.author | Chan, LC | - |
dc.contributor.author | Chan, PK | - |
dc.contributor.author | Wen, C | - |
dc.date.accessioned | 2024-07-31T06:22:40Z | - |
dc.date.available | 2024-07-31T06:22:40Z | - |
dc.date.issued | 2024-06-01 | - |
dc.identifier.citation | Osteoarthritis and Cartilage Open, 2024, v. 6, n. 2 | - |
dc.identifier.uri | http://hdl.handle.net/10722/344632 | - |
dc.description.abstract | Objective: Knee osteoarthritis (OA) is a complex disease with heterogeneous representations. Although it is modifiable to prevention and early treatment, there still lacks a reliable and accurate prognostic tool. Hence, we aim to develop a quantitative and self-administrable knee replacement (KR) risk stratification system for knee osteoarthritis (KOA) patients with clinical features. Method: A total of 14 baseline features were extracted from 9592 cases in the Osteoarthritis Initiative (OAI) cohort. A survival model was constructed using the Random Survival Forests algorithm. The prediction performance was evaluated with the concordance index (C-index) and average receiver operating characteristic curve (AUC). A three-class KR risk stratification system was built to differentiate three distinct KR-free survival groups. Thereafter, Shapley Additive Explanations (SHAP) was introduced for model explanation. Results: KR incidence was accurately predicted by the model with a C-index of 0.770 (±0.0215) and an average AUC of 0.807 (±0.0181) with 14 clinical features. Three distinct survival groups were observed from the ten-point KR risk stratification system with a four-year KR rate of 0.79%, 5.78%, and 16.2% from the low, medium, and high-risk groups respectively. KR is mainly caused by pain medication use, age, surgery history, diabetes, and a high body mass index, as revealed by SHAP. Conclusion: A self-administrable and interpretable KR survival model was developed, underscoring a KR risk scoring system to stratify KOA patients. It will encourage regular self-assessments within the community and facilitate personalised healthcare for both primary and secondary prevention of KOA. | - |
dc.language | eng | - |
dc.publisher | Elsevier | - |
dc.relation.ispartof | Osteoarthritis and Cartilage Open | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Knee osteoarthritis | - |
dc.subject | Machine learning | - |
dc.subject | Prognosis | - |
dc.subject | Self-administrable | - |
dc.subject | Survival analysis | - |
dc.title | An interpretable knee replacement risk assessment system for osteoarthritis patients | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.ocarto.2024.100440 | - |
dc.identifier.scopus | eid_2-s2.0-85185469395 | - |
dc.identifier.volume | 6 | - |
dc.identifier.issue | 2 | - |
dc.identifier.eissn | 2665-9131 | - |
dc.identifier.issnl | 2665-9131 | - |