Evidence map›Paper›PMID 38658876›Full record

ArticleBMC musculoskeletal disorders2024

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

Hong Yeol Yang, Yong Gwan Shin, Hyun Ho Shin, Ji Hoon Choi, Jong Keun Seon

Abstract read
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Article in BMC musculoskeletal disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Hong Yeol Yang *Department of Orthopaedic Surgery, Chonnam National University Medical School and Hospital, 322, Seoyang-ro 322 Hwasun-gun, Chonnam, 58128, Republic of Korea.
Yong Gwan Shin *R&D Center, XRAI inc, Gwangju, 61186, Republic of Korea.
Hyun Ho ShinDepartment of Orthopaedic Surgery, Chonnam National University Medical School and Hospital, 322, Seoyang-ro 322 Hwasun-gun, Chonnam, 58128, Republic of Korea.
Ji Hoon ChoiDepartment of Orthopaedic Surgery, Chonnam National University Medical School and Hospital, 322, Seoyang-ro 322 Hwasun-gun, Chonnam, 58128, Republic of Korea.
Jong Keun SeonDepartment of Orthopaedic Surgery, Chonnam National University Medical School and Hospital, 322, Seoyang-ro 322 Hwasun-gun, Chonnam, 58128, Republic of Korea. seonbell@chonnam.ac.kr.

Funding

Korea Medical Device Development Fund Project Number: 1711139068, KMDF_PR_20210525-KD000001
6 · The paper itself

Abstract

backgroundAlthough 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.

methodsThis 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).

resultsA 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/m

conclusionsThe 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.

Indexed as

Machine LearningOsteoarthritis, KneeOsteotomyTibiaAgedArthroplasty, Replacement, KneeFemaleHumansKnee JointMaleMiddle AgedRetrospective StudiesTreatment OutcomeHigh tibial osteotomyKnee osteoarthritisMachine learningPredictionRandom forestTreatment success

Identifiers

PMID38658876
PMCPMC11040853

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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.