File Download

There are no files associated with this item.

Supplementary

Conference Paper: Radiographic parameters for predicting anterior cruciate ligament status in osteoarthritis of the knee

TitleRadiographic parameters for predicting anterior cruciate ligament status in osteoarthritis of the knee
Authors
Issue Date5-Nov-2023
Abstract

Introduction: This study aimed to determine the accuracy of different radiographic parameters in predicting the functional deficiency of the anterior cruciate ligament (ACL) and to investigate whether a prediction model constructed by integrating significant radiographic signs can improve the predictive ability.
Methods: We recruited 95 patients who underwent primary osteoarthritis surgery at The Duchess of Kent Children’s Hospital at Sandy Bay between January 18, 2023, and May 11, 2021. The ACL status was determined by intra-operative assessment and divided into four categories: intact, frayed, disrupted, and absent. Radiographic measurements, including the coronal tibiofemoral subluxation (CTFS), hip–knee–ankle angle (HKA), mechanical proximal tibial angle (mPTA),
mechanical lateral distal femoral angle (mLDFA), maximum wear point of the proximal tibia% (MWPPT%), and posterior tibial slope (PTS), were measured using X-rays. Univariate analysis was used to compare these variables between groups with different ACL statuses, and significant variables (p<0.05) were further analysed using multiple logistic regression analysis. A logistic regression model was constructed using multivariable regression with generalised estimating models.
Results: The results showed that HKA, and PTS were significant predictive indicators of ACLD, with odds ratios (OR) of 1.51, and 1.88, respectively. Other parameters MWPPT%, CTFS, mPTA, and mLDFA did not show significant predictive value. Multiple logistic regression analysis was then used to construct a predictive model of ACLD using significant imaging indicators.
Discussion and Conclusion: HKA and PTS were identified as predictive factors for ACLD. And the predictive model could be used as a diagnostic tool.


Persistent Identifierhttp://hdl.handle.net/10722/339945

 

DC FieldValueLanguage
dc.contributor.authorFu, Chun Him Henry-
dc.contributor.authorCheung, Yim Ling Amy-
dc.contributor.authorLuk, Michelle Hilda-
dc.contributor.authorCheung, Man Hong-
dc.contributor.authorChan, Ping Keung-
dc.contributor.authorChiu, Kwong Yuen Peter-
dc.contributor.authorYiu, On Lap-
dc.date.accessioned2024-03-11T10:40:31Z-
dc.date.available2024-03-11T10:40:31Z-
dc.date.issued2023-11-05-
dc.identifier.urihttp://hdl.handle.net/10722/339945-
dc.description.abstract<p>Introduction: This study aimed to determine the accuracy of different radiographic parameters in predicting the functional deficiency of the anterior cruciate ligament (ACL) and to investigate whether a prediction model constructed by integrating significant radiographic signs can improve the predictive ability.<br>Methods: We recruited 95 patients who underwent primary osteoarthritis surgery at The Duchess of Kent Children’s Hospital at Sandy Bay between January 18, 2023, and May 11, 2021. The ACL status was determined by intra-operative assessment and divided into four categories: intact, frayed, disrupted, and absent. Radiographic measurements, including the coronal tibiofemoral subluxation (CTFS), hip–knee–ankle angle (HKA), mechanical proximal tibial angle (mPTA),<br>mechanical lateral distal femoral angle (mLDFA), maximum wear point of the proximal tibia% (MWPPT%), and posterior tibial slope (PTS), were measured using X-rays. Univariate analysis was used to compare these variables between groups with different ACL statuses, and significant variables (p<0.05) were further analysed using multiple logistic regression analysis. A logistic regression model was constructed using multivariable regression with generalised estimating models.<br>Results: The results showed that HKA, and PTS were significant predictive indicators of ACLD, with odds ratios (OR) of 1.51, and 1.88, respectively. Other parameters MWPPT%, CTFS, mPTA, and mLDFA did not show significant predictive value. Multiple logistic regression analysis was then used to construct a predictive model of ACLD using significant imaging indicators.<br>Discussion and Conclusion: HKA and PTS were identified as predictive factors for ACLD. And the predictive model could be used as a diagnostic tool.<br></p>-
dc.languageeng-
dc.relation.ispartof43rd Annual Congress of The Hong Kong Orthopaedic Association (04/11/2023-05/11/2023, Hong Kong)-
dc.titleRadiographic parameters for predicting anterior cruciate ligament status in osteoarthritis of the knee-
dc.typeConference_Paper-
dc.identifier.spage85-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats