In Response to: Frog-Leg Test Maneuver for the Diagnosis of Injuries to the Posterolateral Corner of the Knee: A Diagnostic Accuracy Study

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Excerpt

Dear Editor-in-Chief:
The diagnosis of posterolateral corner (PLC) injuries remains challenging because the validity and reliability of most orthopedic tests is unreported in the literature. Identification and validation of easily applied clinical tests are therefore important to assist the clinician in reaching a confident diagnosis and applying appropriate treatment.
This study introduces the “frog-leg test,” which exerts a varus force to both knees while the posterolateral structures are palpated for joint gapping and tissue integrity. A side-to-side difference constitutes a positive test, which is considered diagnostic of posterolateral instability and indicates that the patient will most likely require surgical intervention.
Cooper et al1 identified the lateral collateral ligament (LCL), popliteus tendon (PLT), and popliteofibular ligament (PFL) as the 3 main static structures of the PLC. Biomechanical studies2 demonstrated that maximal loads are exerted on the LCL during varus or external tibial rotation at 30 degrees of knee flexion, whereas external tibial rotation is the only applied force that substantially loads the PLT and PFL. Varus instability at 30 degrees is therefore indicative of an isolated LCL injury, but the assessment of external tibial rotation is also required for the complete assessment of the PLC structures. With the femur externally rotated and the soles of feet together during the frog-leg maneuver, the tibia will most likely assume a relative internally rotated position, which is unlikely to stress the PLT or PFL.
Given the above considerations, we feel that the frog-leg test is more diagnostic of an isolated LCL injury than a complete PLC injury. Although the LCL is an important component of the PLC, intraoperatively, we have observed PLC injuries without the involvement of the LCL3; it is therefore important to differentiate these injury patterns to determine appropriate treatment and optimize patient outcomes.
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