NDLI: Factors to improve odds of success following medial opening-wedge high tibial osteotomy: a machine learning analysis

Content Provider	Springer Nature : BioMed Central
Author	Yang, Hong Yeol Shin, Yong Gwan Shin, Hyun Ho Choi, Ji Hoon Seon, Jong Keun
Abstract	Background Although high tibial osteotomy (HTO) is an established treatment option for medial compartment osteoarthritis, predictive factors for HTO treatment success remain unclear. This study aimed to identify informative variables associated with HTO treatment success and to develop and internally validate machine learning algorithms to predict which patients will achieve HTO treatment success for medial compartmental osteoarthritis. Methods This study retrospectively reviewed patients who underwent medial opening-wedge HTO (MOWHTO) at our center between March 2010 and December 2015. The primary outcomes were a lack of conversion to total knee arthroplasty (TKA) and achievement of a minimal clinically important difference of improvement in the Knee Injury and Osteoarthritis Outcome Score (KOOS) at a minimum of five years postoperatively. Recursive feature selection was used to identify the combination of variables from an initial pool of 25 features that optimized model performance. Five machine learning algorithms (XGBoost, multilayer perception, support vector machine, elastic-net penalized logistic regression, and random forest) were trained using five-fold cross-validation three times and applied to an independent test set of patients. The performance of the model was evaluated by the area under the receiver operating characteristic curve (AUC). Results A total of 231 patients were included, and 200 patients (86.6%) achieved treatment success at the mean of 9 years of follow-up. A combination of seven variables optimized algorithm performance, and the following specific cutoffs increased the likelihood of MOWHTO treatment success: body mass index (BMI) ≤ 26.8 kg/m2, preoperative KOOS for pain ≤ 46.0, preoperative KOOS for quality of life ≤ 33.0, preoperative International Knee Documentation Committee score ≤ 42.0, preoperative Short-Form 36 questionnaire (SF-36) score > 42.25, three-month postoperative hip-knee-ankle angle > 1.0°, and three-month postoperative medial proximal tibial angle (MPTA) > 91.5° and ≤ 94.7°. The random forest model demonstrated the best performance (F1 score: 0.93; AUC: 0.81) and was transformed into an online application as an educational tool to demonstrate the capabilities of machine learning. Conclusions The random forest machine learning algorithm best predicted MOWHTO treatment success. Patients with a lower BMI, poor clinical status, slight valgus overcorrection, and postoperative MPTA < 94.7 more frequently achieved a greater likelihood of treatment success. Level of evidence Level III, retrospective cohort study.
Related Links	https://bmcmusculoskeletdisord.biomedcentral.com/counter/pdf/10.1186/s12891-024-07441-x.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	14712474
DOI	10.1186/s12891-024-07441-x
Journal	BMC Musculoskeletal Disorders
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2024-04-24
Access Restriction	Open
Subject Keyword	Orthopedics Rehabilitation Rheumatology Sports Medicine Internal Medicine Epidemiology Treatment success High tibial osteotomy Knee osteoarthritis Machine learning Prediction Random forest
Content Type	Text
Resource Type	Article
Subject	Orthopedics and Sports Medicine Rheumatology
Journal Impact Factor	2.2/2023
5-Year Journal Impact Factor	2.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Is single-stage bilateral medial opening wedge high tibial osteotomy advisable?

High tibial osteotomy: closed wedge versus combined wedge osteotomy

Simoultaneous bilateral medial opening wedge high tibial osteotomy can be performed safely and effectively without bone grafting: analysis of a monocentric retrospective series

Change in adduction moment following medial open wedge high tibial osteotomy: a meta-analysis

Effect of posteriorly inclined sagittal osteotomy on posterior tibial slope in biplanar medial opening wedge high tibial osteotomy: a case series study

Closing-wedge high tibial osteotomy: survival and risk factor analysis at long-term follow up

Comparison of short-term clinical outcomes between open-wedge high tibial osteotomy and tibial condylar valgus osteotomy

Prognostic factors for medial open-wedge high tibial osteotomy with spacer implantation in patients with medial compartmental knee osteoarthritis

Potential anatomic risk factors resulting oversized postoperative medial proximal tibial angle after double level osteotomy

Factors to improve odds of success following medial opening-wedge high tibial osteotomy: a machine learning analysis

Similar Documents

Is single-stage bilateral medial opening wedge high tibial osteotomy advisable?

High tibial osteotomy: closed wedge versus combined wedge osteotomy

Simoultaneous bilateral medial opening wedge high tibial osteotomy can be performed safely and effectively without bone grafting: analysis of a monocentric retrospective series

Change in adduction moment following medial open wedge high tibial osteotomy: a meta-analysis

Effect of posteriorly inclined sagittal osteotomy on posterior tibial slope in biplanar medial opening wedge high tibial osteotomy: a case series study

Closing-wedge high tibial osteotomy: survival and risk factor analysis at long-term follow up

Comparison of short-term clinical outcomes between open-wedge high tibial osteotomy and tibial condylar valgus osteotomy

Prognostic factors for medial open-wedge high tibial osteotomy with spacer implantation in patients with medial compartmental knee osteoarthritis

Potential anatomic risk factors resulting oversized postoperative medial proximal tibial angle after double level osteotomy

Factors to improve odds of success following medial opening-wedge high tibial osteotomy: a machine learning analysis