Evidence map›Paper›PMID 30729143›Full record

ArticleOrthopaedic journal of sports medicine2019

A Comparison of Knee Abduction Angles Measured by a 3D Anatomic Coordinate System Versus Videographic Analysis: Implications for Anterior Cruciate Ligament Injury.

Zoë A Englander, Hattie C Cutcliffe, Gangadhar M Utturkar, William E Garrett, Charles E Spritzer, Louis E DeFrate

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Article in Orthopaedic journal of sports medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

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7citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Zoë A EnglanderDepartment of Orthopaedics, Duke University, Durham, North Carolina, USA.
Hattie C CutcliffeDepartment of Orthopaedics, Duke University, Durham, North Carolina, USA.
Gangadhar M UtturkarDepartment of Orthopaedics, Duke University, Durham, North Carolina, USA.
William E GarrettDepartment of Orthopaedics, Duke University, Durham, North Carolina, USA.
Charles E SpritzerDepartment of Radiology, Duke University, Durham, North Carolina, USA.
Louis E DeFrateDepartment of Orthopaedics, Duke University, Durham, North Carolina, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundKnee positions involved in noncontact anterior cruciate ligament (ACL) injury have been studied via analysis of injury videos. Positions of high ACL strain have been identified in vivo. These methods have supported different hypotheses regarding the role of knee abduction in ACL injury. PURPOSE/HYPOTHESIS: The purpose of this study was to compare knee abduction angles measured by 2 methods: using a 3-dimensional (3D) coordinate system based on anatomic features of the bones versus simulated 2-dimensional (2D) videographic analysis. We hypothesized that knee abduction angles measured in a 2D videographic analysis would differ from those measured from 3D bone anatomic features and that videographic knee abduction angles would depend on flexion angle and on the position of the camera relative to the patient. STUDY

designDescriptive laboratory study.

methodsModels of the femur and tibia were created from magnetic resonance images of 8 healthy male participants. The models were positioned to match biplanar fluoroscopic images obtained as participants posed in lunges of varying flexion angles (FLAs). Knee abduction angle was calculated from the positioned models in 2 ways: (1) varus-valgus angle (VVA), defined as the angle between the long axis of the tibia and the femoral transepicondylar axis by use of a 3D anatomic coordinate system; and (2) coronal plane angle (CPA), defined as the angle between the long axis of the tibia and the long axis of the femur projected onto the tibial coronal plane to simulate a 2D videographic analysis. We then simulated how changing the position of the camera relative to the participant would affect knee abduction angles.

resultsDuring flexion, when CPA was calculated from a purely anterior or posterior view of the joint-an ideal scenario for measuring knee abduction from 2D videographic analysis-CPA was significantly different from VVA (

conclusionHow closely CPA (derived from 2D videographic analysis) relates to VVA (derived from a 3D anatomic coordinate system) depends on FLA and camera orientation. CLINICAL RELEVANCE: This study provides a novel comparison of knee abduction angles measured from 2D videographic analysis and those measured within a 3D anatomic coordinate system. Consideration of these findings is important when interpreting 2D videographic data regarding knee abduction angle in ACL injury.

Indexed as

collapseimaginginjurymechanismMRIrupturevalgus

Identifiers

PMID30729143
PMCPMC6350144

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.