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- Angelini, Federico, et al.
(författare)
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Osteoarthritis endotype discovery via clustering of biochemical marker data
- 2022
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Ingår i: Annals of the Rheumatic Diseases. - : BMJ. - 0003-4967 .- 1468-2060. ; 81:5, s. 666-675
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Tidskriftsartikel (refereegranskat)abstract
- Objectives Osteoarthritis (OA) patient stratification is an important challenge to design tailored treatments and drive drug development. Biochemical markers reflecting joint tissue turnover were measured in the IMI-APPROACH cohort at baseline and analysed using a machine learning approach in order to study OA-dominant phenotypes driven by the endotype-related clusters and discover the driving features and their disease-context meaning. Method Data quality assessment was performed to design appropriate data preprocessing techniques. The k-means clustering algorithm was used to find dominant subgroups of patients based on the biochemical markers data. Classification models were trained to predict cluster membership, and Explainable AI techniques were used to interpret these to reveal the driving factors behind each cluster and identify phenotypes. Statistical analysis was performed to compare differences between clusters with respect to other markers in the IMI-APPROACH cohort and the longitudinal disease progression. Results Three dominant endotypes were found, associated with three phenotypes: C1) low tissue turnover (low repair and articular cartilage/subchondral bone turnover), C2) structural damage (high bone formation/resorption, cartilage degradation) and C3) systemic inflammation (joint tissue degradation, inflammation, cartilage degradation). The method achieved consistent results in the FNIH/OAI cohort. C1 had the highest proportion of non-progressors. C2 was mostly linked to longitudinal structural progression, and C3 was linked to sustained or progressive pain. Conclusions This work supports the existence of differential phenotypes in OA. The biomarker approach could potentially drive stratification for OA clinical trials and contribute to precision medicine strategies for OA progression in the future. Trial registration number NCT03883568.
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| 2. |
- Haugen, Ida K., et al.
(författare)
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Development of radiographic classification criteria for hand osteoarthritis : a methodological report (Phase 2)
- 2022
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Ingår i: RMD Open. - : BMJ. - 2056-5933. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- ObjectivesIn Phase 1 of developing new hand osteoarthritis (OA) classification criteria, features associated with hand OA were identified in a population with hand complaints. Radiographic findings could better discriminate patients with hand OA and controls than clinical examination findings. The objective of Phase 2 was to achieve consensus on the features and their weights to be included in three radiographic criteria sets of overall hand OA, interphalangeal OA and thumb base OA.MethodsMultidisciplinary, international expert panels were convened. Patient vignettes were used to identify important features consistent with hand OA. A consensus-based decision analysis approach implemented using 1000minds software was applied to identify the most important features and their relative importance influencing the likelihood of symptoms being due to hand OA. Analyses were repeated for interphalangeal and thumb base OA. The reliability and validity of the proposed criteria sets were tested.ResultsThe experts agreed that the criteria sets should be applied in a population with pain, aching or stiffness in hand joint(s) not explained by another disease or acute injury. In this setting, five additional criteria were considered important: age, morning stiffness, radiographic osteophytes, radiographic joint space narrowing and concordance between symptoms and radiographic findings. The reliability and validity were very good.ConclusionRadiographic features were considered critical when determining whether a patient had symptoms due to hand OA. The consensus-based decision analysis approach in Phase 2 complemented the data-driven results from Phase 1, which will form the basis of the final classification criteria sets.
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