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Radiologists should use the hip-knee-ankle angle rather than the mechanical axis deviation to describe knee alignment
Priyam Choudhury1, Scott J Billings2, Joseph M Bestic2
1Department of Radiology, Mayo Clinic, Jacksonville, FL, 32224, USA. priyamchoudhury28@gmail.com.
Skeletal Radiology
|November 14, 2022
Summary
The hip-knee-ankle angle (HKAA) is a more clinically relevant measure for total knee arthroplasty (TKA) than mechanical axis deviation (MAD). While MAD strongly correlates with HKAA, its accuracy varies by patient height and sex.
Area of Science:
- Orthopedic surgery
- Radiology
- Biomechanical analysis
Background:
- Coronal knee alignment is critical for total knee arthroplasty (TKA) success.
- Mechanical axis deviation (MAD) is frequently reported, but its clinical relevance compared to hip-knee-ankle angle (HKAA) is debated.
- Understanding alignment predictors is key to improving TKA outcomes and longevity.
Purpose of the Study:
- To assess the predictive accuracy of mechanical axis deviation (MAD) for hip-knee-ankle angle (HKAA).
- To determine if patient height and sex influence the MAD's ability to predict HKAA.
- To compare the measurement time efficiency of MAD versus HKAA.
Main Methods:
- Retrospective review of 200 hip-to-ankle radiographs for TKA planning.
- Measurement of MAD and HKAA using PACS tools.
- Analysis using receiver operator characteristic (ROC) curves and linear modeling; paired t tests for time comparison.
Main Results:
- Strong correlation found between MAD and HKAA (r=0.99, p<0.001).
- MAD's predictive performance for HKAA was significantly affected by patient height (p=0.005) and sex (p<0.001).
- No significant difference in measurement time between MAD and HKAA (p>0.05).
Conclusions:
- Hip-knee-ankle angle (HKAA) is recommended over mechanical axis deviation (MAD) for assessing coronal knee alignment in TKA.
- HKAA offers greater clinical relevance and comparable measurement efficiency.
- Accurate alignment assessment is crucial for optimizing TKA outcomes and minimizing failure rates.

