Neila Mezghani1, Youssef Ouakrim2, Alexandre Fuentes3, Amar Mitiche4, Nicola Hagemeister5, Pascal-André Vendittoli6, Jacques A de Guise7. 1. Centre de recherche LICEF, Université TÉLUQ, 5800 Rue Saint-Denis, Montreal, QC H2S 3L4, Canada. Electronic address: Neila.Mezghani@teluq.ca. 2. Centre de recherche LICEF, Université TÉLUQ, 5800 Rue Saint-Denis, Montreal, QC H2S 3L4, Canada. Electronic address: oua.youssef@gmail.com. 3. Centre du genou EMOVI, 3095 Laval Autoroute West, Laval, Que H7P 4W5, Canada. Electronic address: afuentes@emovi.ca. 4. INRS - Centre énergie, matériaux et télécommunications, 800 Rue de la Gauchetière West, Montréal, Que H5A, Canada. Electronic address: mitiche@emt.inrs.ca. 5. Laboratoire de recherche en imagerie et orthopédie, École de technologie supérieur, CRCHUM, 900 Rue Saint-Denis, Montréal, Que H2X 0A9, Canada. Electronic address: Nicola.Hagemeister@etsmtl.ca. 6. Centre de recherche Hôpital Maisonneuve-Rosemont, 5689 Boulevard Rosemont Montréal, Que H1T 3W5, Canada. Electronic address: pa_vendittoli@hotmail.com. 7. Laboratoire de recherche en imagerie et orthopédie, École de technologie supérieur, CRCHUM, 900 Rue Saint-Denis, Montréal, Que H2X 0A9, Canada. Electronic address: Jacques.deGuise@etsmtl.ca.
Abstract
OBJECTIVE: To investigate, as a discovery phase, if 3D knee kinematics assessment parameters can serve as mechanical biomarkers, more specifically as diagnostic biomarker and burden of disease biomarkers, as defined in the Burden of Disease, Investigative, Prognostic, Efficacy of Intervention and Diagnostic classification scheme for osteoarthritis (OA) (Altman et al., 1986). These biomarkers consist of a set of biomechanical parameters discerned from 3D knee kinematic patterns, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation measurements, during gait recording. METHODS: 100 medial compartment knee OA patients and 40 asymptomatic control subjects participated in this study. OA patients were categorized according to disease severity, by the Kellgren and Lawrence grading system. The proposed biomarkers were identified by incremental parameter selection in a regression tree of cross-sectional data. Biomarker effectiveness was evaluated by receiver operating characteristic curve analysis, namely, the area under the curve (AUC), sensitivity and specificity. RESULTS: Diagnostic biomarkers were defined by a set of 3 abduction/adduction kinematics parameters. The performance of these biomarkers reached 85% for the AUC, 80% for sensitivity and 90% for specificity; the likelihood ratio was 8%. Burden of disease biomarkers were defined by a 3-decision tree, with sets of kinematics parameters selected from all 3 movement planes. CONCLUSION: The results demonstrate, as part of a discovery phase, that sets of 3D knee kinematic parameters have the potential to serve as diagnostic and burden of disease biomarkers of medial compartment knee OA.
OBJECTIVE: To investigate, as a discovery phase, if 3D knee kinematics assessment parameters can serve as mechanical biomarkers, more specifically as diagnostic biomarker and burden of disease biomarkers, as defined in the Burden of Disease, Investigative, Prognostic, Efficacy of Intervention and Diagnostic classification scheme for osteoarthritis (OA) (Altman et al., 1986). These biomarkers consist of a set of biomechanical parameters discerned from 3D knee kinematic patterns, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation measurements, during gait recording. METHODS: 100 medial compartment knee OA patients and 40 asymptomatic control subjects participated in this study. OA patients were categorized according to disease severity, by the Kellgren and Lawrence grading system. The proposed biomarkers were identified by incremental parameter selection in a regression tree of cross-sectional data. Biomarker effectiveness was evaluated by receiver operating characteristic curve analysis, namely, the area under the curve (AUC), sensitivity and specificity. RESULTS: Diagnostic biomarkers were defined by a set of 3 abduction/adduction kinematics parameters. The performance of these biomarkers reached 85% for the AUC, 80% for sensitivity and 90% for specificity; the likelihood ratio was 8%. Burden of disease biomarkers were defined by a 3-decision tree, with sets of kinematics parameters selected from all 3 movement planes. CONCLUSION: The results demonstrate, as part of a discovery phase, that sets of 3D knee kinematic parameters have the potential to serve as diagnostic and burden of disease biomarkers of medial compartment knee OA.
Authors: Eni Halilaj; Apoorva Rajagopal; Madalina Fiterau; Jennifer L Hicks; Trevor J Hastie; Scott L Delp Journal: J Biomech Date: 2018-09-13 Impact factor: 2.712
Authors: Tiago Franco; Leonardo Sestrem; Pedro Rangel Henriques; Paulo Alves; Maria João Varanda Pereira; Diego Brandão; Paulo Leitão; Alfredo Silva Journal: Sensors (Basel) Date: 2022-10-07 Impact factor: 3.847