1. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
Evaluation of a clinical algorithm predicting
high knee loads in female athletes
Nicole Petch
Co-authors
Raihana Sharir, Radin Rafeeudin, Jos Vanrenterghem, Mark A. Robinson

2. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
Background
ACL injuries are acute, immediately disabling, require surgical
intervention and lengthy rehabilitation (Lohmander et al., 2007;
Renstrom et al., 2008).
High peak knee abduction moments in particular have been identified
as an ACL injury risk factor (Hewett et al., 2005)
An algorithm has been developed (Myer et al., 2011) to predict the
peak knee abduction moment.
Easy-to-measure anthropometric, kinematic and strength
measurements, bridge the gap between laboratory and clinic-based
assessments.

3. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
Objectives
Evaluate the algorithm for use in university-
level female athletes by comparing its
prediction of:
1.Probability of a high knee load to
measured knee abduction moment.
2.Hamstrings: Quadriceps (H:Q) ratio to
measured H:Q ratio.
Hypothesis
1.There was a significant relationship between the predicted
probability of a high knee load and the measured peak
knee abduction moment.
2. There was a significant relationship between the predicted
H:Q ratio and the measured H:Q ratio.
Adapted Myer et al (2011)

4. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
Adapted Myer et al (2011)
Tibia Length: 34cm
Knee Valgus Motion: 4cm
Knee Flexion ROM: 75o
Mass:
60Kg
QuadHam Ratio: 1.4
Probability of high knee load: 0.28
Points plotted
summed
Total Points: 75

5. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
Landing Biomechanics – Knee Flexion ROM
Three-dimensional motion capture (250 Hz) and force analysis
(1500 Hz) were combined in Visual 3D (C-Motion)
A B
ᶿ1 ᶿ2
TOUCHDOWN MAXIMUM FLEXION

6. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
Landing Biomechanics – Knee Valgus Motion
A B
X1 X2
Peak knee abduction moment during the DVJ first landing phase
calculated in Visual 3D (C-Motion)
TOUCHDOWN MAXIMUM MEDIAL POSITION

7. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
Nomogram Results
46cm
7.3cm
80.6o
74.7Kg
1.3
143.5
0.99
32cm
4.2cm
61.4o
61.5Kg
1.4
74
0.28
High Risk Female Example Low Risk Female Example

8. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
0
1
2
3
4
5
6
7
0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-99
NumberofAthletes
High Knee Load Probability
Female Probability of High Knee Load Distribution
9 High
Probability6 Low
Probability

9. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
KAM Predicted Vs Measured
R² = 0.4267
0
5
10
15
20
25
30
35
40
45
0 20 40 60 80 100
KneeAbductionMoment
(Nm)
Probality of High Knee Load (%)
Linear regression was used to examine the predicted versus
measured variables.

10. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
KAM Predicted Vs Measured
R² = 0.4267
0
5
10
15
20
25
30
35
40
45
0 20 40 60 80 100
KneeAbductionMoment
(Nm)
Probability of High Knee Load (%)
Linear regression was used to examine the predicted versus
measured variables.

11. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
H:Q Predicted Vs Measured
R² = 0.0093
1.50
1.60
1.70
1.80
1.90
2.00
2.10
0.90 1.40 1.90
PredictedH:QRatio
Measured H:Q Ratio
Prediction method
over predicted actual
H:Q Ratio.
No correlation
between prediction
and measured.
Linear regression was used to examine the predicted versus
measured variables.

12. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
Findings
Algorithm significantly predicted KAM in female athletes (p=0.008,
r2=0.43). Estimated H:Q did not significantly relate to measured H:Q
ratio (p=0.68, r2=0.01).
Algorithm was highly sensitive (100%) at classifying individuals
with a >70% probability of high knee load with an actual knee
load >25.25Nm.
The sensitivity however was 60% with 3 Females predicted high
risk <70%, when actual knee load >25.25Nm.
Nomogram profile highly influenced by tibia length and valgus
motion respectively.

13. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
Conclusions
Classification of a high risk female athlete: <70% probability of
high knee load, <25.25Nm measured KAM
Although the relationships between the predicted and measured
variables were not strong, the algorithm seems to be a good
predictor of female university athletes with high KAM and could
therefore be useful as an ACL injury screening tool.
Future Research
Further research into improving the sensitivity of the clinical
algorithm. Application of the algorithm to evaluate prediction of
ACL injury.
Adapting the algorithm to reduce the influence of tibia length on
resultant predicted knee loads.

14. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
References
Hewett, T. E., Myer, G. D., Ford, K. R., Heid, R. S., Colosimo, A. J., McLean, S. G. et al. (2005).
Biomechanical Measures of Neuromuscular Control and Valgus Loading of the Knee Predict Anterior
Cruciate Ligament Injury Risk in Female Athletes. The American Journal of Sports Medicine, 33,
492-501.
Lohmander, L. S., Englund, P. M., Dahl, L. L., Roos, E. M. (2007). The Long-term Consequences of
Anterior Cruciate Ligament and Meniscus Injuries: Osteoarthritis. The American Journal of Sports
Medicine, 35, 1756-1769.
Myer, G. D., Ford, K. R., Khoury, J., Succop, P., & Hewett, T. E. (2011b). Biomechanical laboratory-
based prediction algorithm to identify female athletes with high knee loads that increase risk of
ACL injury. British Journal of Sports Medicine, 45, 245-252.
Renstrom, P., Ljungqvist, A., Arendt, E., Beynnon, B., Fukubayashi, T., Garrett, W. et al. (2008).
Non-contact ACL injuries in female athletes: an International Olympic Committee current concepts
statement. British Journal of Sports Medicine, 42, 394-412.

15. School of Sport and Exercise Sciences
FACULTY OF SCIENCE
Thank you for listening
Any questions?