School of Sport and Exercise SciencesFACULTY OF SCIENCE

Evaluation of a clinical algorithm predicting high knee loads in female athletes

Nicole PetchCo-authors

Raihana Sharir, Radin Rafeeudin, Jos Vanrenterghem, Mark A. Robinson

School of Sport and Exercise SciencesFACULTY 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.

School of Sport and Exercise SciencesFACULTY 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)

School of Sport and Exercise SciencesFACULTY 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

School of Sport and Exercise SciencesFACULTY OF SCIENCE

Landing Biomechanics – Knee Flexion ROMThree-dimensional motion capture (250 Hz) and force analysis (1500 Hz) were combined in Visual 3D (C-Motion)

A B

ᶿ1 ᶿ2

TOUCHDOWN MAXIMUM FLEXION

School of Sport and Exercise SciencesFACULTY 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

School of Sport and Exercise SciencesFACULTY 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

School of Sport and Exercise SciencesFACULTY OF SCIENCE

0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-990

1

2

3

4

5

6

7

High Knee Load Probability

Num

ber

of A

thle

tes

Female Probability of High Knee Load Distribution

9 High Probability 6 Low

Probability

School of Sport and Exercise SciencesFACULTY OF SCIENCE

KAM Predicted Vs Measured

0 10 20 30 40 50 60 70 80 90 10005

1015202530354045

R² = 0.42667045523841

Probality of High Knee Load (%)

Knee

Abd

ucti

on M

omen

t (N

m)

Linear regression was used to examine the predicted versus measured variables.   

School of Sport and Exercise SciencesFACULTY OF SCIENCE

KAM Predicted Vs Measured

0 10 20 30 40 50 60 70 80 90 10005

1015202530354045

R² = 0.42667045523841

Probability of High Knee Load (%)

Knee

Abd

uctio

n M

omen

t (N

m)

Linear regression was used to examine the predicted versus measured variables.   

School of Sport and Exercise SciencesFACULTY OF SCIENCE

H:Q Predicted Vs Measured

0.90 1.10 1.30 1.50 1.70 1.901.50

1.60

1.70

1.80

1.90

2.00

2.10

R² = 0.00933516103972665

Measured H:Q Ratio

Pred

icte

d H

:Q R

atio

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.   

School of Sport and Exercise SciencesFACULTY 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.

School of Sport and Exercise SciencesFACULTY 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.

School of Sport and Exercise SciencesFACULTY 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.

School of Sport and Exercise SciencesFACULTY OF SCIENCE

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