Almosnino S et al. Principal component modeling of isokinetic moment curves for discriminating between the injured and healthy knees of unilateral ACL deficient patients. J Electromyogr Kinesiol. 2014 Feb;24(1):134-43. PMID 24280243.

J Electromyogr Kinesiol. 2014 Feb;24(1):134-43. doi: 10.1016/j.jelekin.2013.10.012. Epub 2013 Nov 11.

Principal component modeling of isokinetic moment curves for discriminating between the injured and healthy knees of unilateral ACL deficient patients.

Almosnino S(1), Brandon SC(2), Day AG(3), Stevenson JM(4), Dvir Z(5), Bardana DD(6).

Author information:

 
(1)Resource Environmental Associates Ltd., Markham, Ontario, Canada; School of Kinesiology and Health Studies, Queen's University, Kingston, Ontario, Canada; Human Mobility Research Centre, Syl & Molly Apps edical Research Centre, Kingston General Hospital, Kingston, Ontario, Canada. Electronic address: salmosnino@rea.ca.

 

(2)Department of Mechanical and Materials Engineering, Queen's University, Kingston, Ontario, Canada; Human Mobility Research Centre, Syl &Molly Apps Medical Research Centre, Kingston General Hospital, Kingston, Ontario, Canada.

 

(3)Clinical Research Centre, Kingston General Hospital, Kingston, ON, Canada.

 

(4)School of Kinesiology and Health Studies, Queen's University, Kingston, Ontario, Canada; Human Mobility Research Centre, Syl & Molly Apps Medical Research Centre, Kingston General Hospital, Kingston, Ontario, Canada.

 

(5)School of Kinesiology and Health Studies, Queen's University, Kingston, Ontario, Canada; Department of Physical Therapy, The Stanley Steyer School of Health Professions, Sackler Faculty of Medicine, Tel Aviv University, Israel.

 

(6)Division of Orthopaedic Surgery, School of Medicine, Queen's University &Kingston General Hospital, Kingston, Ontario, Canada; Human Mobility Research Centre, Syl & Molly Apps Medical Research Centre, Kingston General Hospital, Kingston, Ontario, Canada.

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.

Copyright © 2013 Elsevier Ltd. All rights reserved.

PMID: 24280243 [PubMed - indexed for MEDLINE]

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