Literature DB >> 22879400

Application of a clinic-based algorithm as a tool to identify female athletes at risk for anterior cruciate ligament injury: a prospective cohort study with a nested, matched case-control analysis.

John Goetschius1, Helen C Smith, Pamela M Vacek, Leigh Ann Holterman, Sandra J Shultz, Timothy W Tourville, James Slauterbeck, Robert J Johnson, Bruce D Beynnon.   

Abstract

BACKGROUND: When landing from a jump, the production of increased intersegmental knee abduction moments and coupled valgus motions has been associated with an increased risk of suffering a noncontact anterior cruciate ligament (ACL) injury in one study. This research has led to the development of a clinic-based algorithm that utilizes measures of knee valgus motion, knee flexion range of motion, body mass, tibial length, and quadriceps-to-hamstring strength ratio data to predict the probability of a high knee abduction moment (pKAM) when landing from a jump in female athletes. The ability of this algorithm to identify athletes at increased risk of suffering ACL injury has not been assessed. HYPOTHESIS: The pKAM is associated with ACL injury in female athletes. STUDY
DESIGN: Case-control study; Level of evidence, 3.
METHODS: This study was based on secondary analysis of data obtained from a previous investigation that focused on the use of the drop vertical jump (DVJ) test to assess the risk of ACL injury in female athletes. The DVJ screenings were performed on 1855 female high school and college athletes over 3 years. Knee valgus motion, knee flexion range of motion, and tibial length were measured from videos of the DVJ obtained during preseason screenings. Mass was measured using a physician's scale, and quadriceps-to-hamstring strength ratio was included using a surrogate value. These data were entered into the clinic-based algorithm that determined the pKAM. The association of pKAM with ACL injury was assessed using conditional logistic regression.
RESULTS: A total of 20 athletes sustained ACL injury and were matched with 45 uninjured control athletes who were recruited from the same teams. There was no relationship between the risk of suffering ACL injury and pKAM, as determined by the clinic-based algorithm.
CONCLUSION: The pKAM was not associated with noncontact ACL injury in our group of injured athletes and matched controls.

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Mesh:

Year:  2012        PMID: 22879400      PMCID: PMC6503969          DOI: 10.1177/0363546512456972

Source DB:  PubMed          Journal:  Am J Sports Med        ISSN: 0363-5465            Impact factor:   6.202


  19 in total

1.  A Sex-Stratified Multivariate Risk Factor Model for Anterior Cruciate Ligament Injury.

Authors:  Bruce D Beynnon; Daniel R Sturnick; Erin C Argentieri; James R Slauterbeck; Timothy W Tourville; Sandra J Shultz; Pamela M Vacek
Journal:  J Athl Train       Date:  2015-09-04       Impact factor: 2.860

Review 2.  A Systematic Evaluation of Field-Based Screening Methods for the Assessment of Anterior Cruciate Ligament (ACL) Injury Risk.

Authors:  Aaron S Fox; Jason Bonacci; Scott G McLean; Michael Spittle; Natalie Saunders
Journal:  Sports Med       Date:  2016-05       Impact factor: 11.136

3.  Predicting Injury: Challenges in Prospective Injury Risk Factor Identification.

Authors:  Daniel R Clifton; Dustin R Grooms; Jay Hertel; James A Onate
Journal:  J Athl Train       Date:  2016-08       Impact factor: 2.860

4.  Risk Factors for Lower Limb Injury in Female Team Field and Court Sports: A Systematic Review, Meta-analysis, and Best Evidence Synthesis.

Authors:  Tyler J Collings; Matthew N Bourne; Rod S Barrett; William du Moulin; Jack T Hickey; Laura E Diamond
Journal:  Sports Med       Date:  2021-01-05       Impact factor: 11.136

5.  Drop-Jump Landing Varies With Baseline Neurocognition: Implications for Anterior Cruciate Ligament Injury Risk and Prevention.

Authors:  Daniel C Herman; Jeffrey T Barth
Journal:  Am J Sports Med       Date:  2016-07-29       Impact factor: 6.202

6.  Tibial articular cartilage and meniscus geometries combine to influence female risk of anterior cruciate ligament injury.

Authors:  Daniel R Sturnick; Robert Van Gorder; Pamela M Vacek; Michael J DeSarno; Mack G Gardner-Morse; Timothy W Tourville; James R Slauterbeck; Robert J Johnson; Sandra J Shultz; Bruce D Beynnon
Journal:  J Orthop Res       Date:  2014-08-06       Impact factor: 3.494

7.  Knee Kinematics During Noncontact Anterior Cruciate Ligament Injury as Determined From Bone Bruise Location.

Authors:  Sophia Y Kim; Charles E Spritzer; Gangadhar M Utturkar; Alison P Toth; William E Garrett; Louis E DeFrate
Journal:  Am J Sports Med       Date:  2015-08-11       Impact factor: 6.202

8.  NON-CONTACT ANTERIOR CRUCIATE LIGAMENT AND LOWER EXTREMITY INJURY RISK PREDICTION USING FUNCTIONAL MOVEMENT SCREEN AND KNEE ABDUCTION MOMENT: AN EPIDEMIOLOGICAL OBSERVATION OF FEMALE INTERCOLLEGIATE ATHLETES.

Authors:  Scott E Landis; Russell T Baker; Jeffrey G Seegmiller
Journal:  Int J Sports Phys Ther       Date:  2018-12

9.  Defending Puts the Anterior Cruciate Ligament at Risk During Soccer: A Gender-Based Analysis.

Authors:  Robert H Brophy; Jeffrey G Stepan; Holly J Silvers; Bert R Mandelbaum
Journal:  Sports Health       Date:  2015-05       Impact factor: 3.843

10.  Biomechanical Comparison of Single- and Double-Leg Jump Landings in the Sagittal and Frontal Plane.

Authors:  Jeffrey B Taylor; Kevin R Ford; Anh-Dung Nguyen; Sandra J Shultz
Journal:  Orthop J Sports Med       Date:  2016-06-28
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