Principal component modeling of isokinetic moment curves for discriminating between the injured and healthy knees of unilateral ACL deficient patients |
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| Institution: | 1. Resource Environmental Associates Ltd., Markham, Ontario, Canada;2. School of Kinesiology and Health Studies, Queen’s University, Kingston, Ontario, Canada;3. Department of Mechanical and Materials Engineering, Queen’s University, Kingston, Ontario, Canada;4. Clinical Research Centre, Kingston General Hospital, Kingston, ON, Canada;5. Division of Orthopaedic Surgery, School of Medicine, Queen’s University & Kingston General Hospital, Kingston, Ontario, Canada;6. Human Mobility Research Centre, Syl & Molly Apps Medical Research Centre, Kingston General Hospital, Kingston, Ontario, Canada;7. Department of Physical Therapy, The Stanley Steyer School of Health Professions, Sackler Faculty of Medicine, Tel Aviv University, Israel;1. School of Mechanical and Aerospace Engineering, Seoul National University/IAMD, Seoul, Republic of Korea;2. Department of Rehabilitation Medicine, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam, Republic of Korea;3. Department of Physical Medicine and Rehabilitation, Haeundae Paik Hospital, Inje University of Medicine, Busan, Republic of Korea;1. Rehabilitation Medicine, Department of Biomedical Sciences for Health, Università degli Studi, Milano, Italy;2. Department of Neurorehabilitation Sciences, Istituto Auxologico Italiano-IRCCS, Milano, Italy;3. School of Specialisation, Physical and Rehabilitation Medicine, Università degli Studi, Milano, Italy;4. University Rehabilitation Institute, Republic of Slovenia, Ljubljana, Slovenia;5. University of Ljubljana, Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Ljubljana, Slovenia;1. University Outpatient Clinic, Sports Medicine & Sports Orthopaedics, University of Potsdam, Germany;2. Bern University of Applied Sciences, Health, Physiotherapy, Bern, Switzerland;1. Ohio Musculoskeletal and Neurological Institute (OMNI) at Ohio University, Athens, OH, United States;2. School of Applied Health Sciences and Wellness at Ohio University, Athens, OH, United States;3. Department of Biomedical Sciences at Ohio University, Athens, OH, United States;1. Clinic for Orthopedics and Trauma Surgery, Heidelberg University Hospital Schlierbacher Landstr. 200a, 69118 Heidelberg, Germany;2. Marmara University, Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation, Istanbul, Turkey;3. Universitäts-Kinderspital Zürich, Steinwiesstrasse 75, 8032, Zürich, Switzerland;1. Department of Information Engineering, University of Padova, Padova, Italy;2. Department of Medicine, DIMED, University of Padova, Padova, Italy;3. Ibirapuera University, São Paulo, Brazil;4. Physical Therapy, Speech and Occupational Therapy Department, School of Medicine, University of Sᾶo Paulo, São Paulo, Brazil;5. Federal University of Sao Paulo, Santos, São Paulo, Brazil |
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| Abstract: | Bilateral knee strength evaluations of unilateral anterior cruciate ligament (ACL) deficient patients using isokinetic dynamometry are commonly performed in rehabilitation settings. The most frequently-used outcome measure is the peak moment value attained by the knee extensor and flexor muscle groups. However, other strength curve features may also be of clinical interest and utility. The purpose of this investigation was to identify, using Principal Component Analysis (PCA), strength curve features that explain the majority of variation between the injured and uninjured knee, and to assess the capabilities of these features to detect the presence of injury. A mixed gender cohort of 43 unilateral ACL deficient patients performed 6 continuous concentric knee extension and flexion repetitions bilaterally at 60° s−1 and 180° s−1 within a 90° range of motion. Moment waveforms were analyzed using PCA, and binary logistic regression was used to develop a discriminatory decision rule. For all directions and speeds, a statistically significant overall reduction in strength was noted for the involved knee in comparison to the uninvolved knee. The discriminatory decision rule yielded a specificity and sensitivity of 60.5% and 60.5%, respectively, corresponding to an accuracy of ∼62%. As such, the curve features extracted using PCA enabled only limited clinical usefulness in discerning between the ACL deficient and contra lateral, healthy knee. Improvement in discrimination capabilities may perhaps be achieved by consideration of different testing speeds and contraction modes, as well as utilization of other data analysis techniques. |
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| Keywords: | Anterior cruciate ligament Knee Strength Isokinetic dynamometry Diagnostics Sensitivity Specificity Classification |
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