Evidence map›Paper›PMID 39896932›Full record

ArticleOsteoarthritis and cartilage open2025

Association of radiographic structure deformity phenotypes of knee OA to clinical symptoms and risk for progression: Proposing a modification of Kellgren-Lawrence grade - Data from the Osteoarthritis Initiative and the MOST study.

Sunho Ko, Yunhee Choi, Hyuk-Soo Han, Seong Hwan Kim, Du Hyun Ro

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Article in Osteoarthritis and cartilage open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing 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

3 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.

Sunho KoDepartment of Orthopedic Surgery, Seoul National University Hospital, Seoul, South Korea.
Yunhee ChoiDivision of Medical Statistics, Medical Research Collaborating Center, Seoul National University Hospital, Seoul, South Korea.
Hyuk-Soo HanDepartment of Orthopedic Surgery, Seoul National University Hospital, Seoul, South Korea.
Seong Hwan KimDepartment of Orthopedic Surgery, Chung-Ang University Hospital, Seoul, South Korea.
Du Hyun RoDepartment of Orthopedic Surgery, Seoul National University Hospital, Seoul, South Korea.

Funding

Multicenter Osteoarthritis Study (MOST) Second RenewalU01AG019069 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NEVITT, MICHAEL C · 2001 to 2020
$21.7M
Multicenter Osteoarthritis Study (MOST) Second Renewal: Iowa Clinical CenterU01AG018832 · NIA · UNIVERSITY OF IOWA · PI SEGAL, NEIL A, TORNER, JAMES C · 2001 to 2020
$14.1M
Multicenter Osteoarthritis Study (MOST) Second Renewal - Boston UniversityU01AG018820 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI FELSON, DAVID TOBIN · 2001 to 2019
$13.6M
Multicenter Osteoarthritis Study (MOST) Second Renewal - UAB Clinical CenterU01AG018947 · NIA · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI LEWIS, CORA E · 2001 to 2020
$13.2M
NIA NIH HHS U01 AG018820NIA NIH HHS U01 AG018832NIA NIH HHS U01 AG018947NIA NIH HHS U01 AG019069
6 · The paper itself

Abstract

Objective: This study aims to define Kellgren-Lawrence grade (KLG) using OARSI grade and stratify radiographic knee osteoarthritis (KOA) into distinct phenotypes based on radiographic structure changes and compare clinical symptoms and disease progression. Design: We used radiographic grading data provided by the OAI and MOST study. Decision tree was used to (1) Find OARSI grade criteria for each KLG and (2) Phenotype early osteoarthritic knees (=KLG1, 2) by the radiographic structure changes. Pain, function, and progression to KLG ≥3 were compared between phenotypes. Results: 10,804 knees from 5802 patients were included. The mean follow-up duration was 55.6 ​± ​24.5 months. Criteria for KLG1 was: (1) Joint space narrowing (JSN) grade (more severe grade among medial and lateral compartments) ​= ​1 without osteophytes (i.e., KLG1Jt) (2) A single grade 1 osteophyte without JSN (i.e., KLG1Ost). Criteria for KLG2 was (1) JSN ​= ​1 with a sum of osteophyte grades ≥1 (i.e., KLG2Jt): (2) Sum of osteophyte grades ≥2 without JSN (i.e., KLG2Ost). In terms of pain and function, there was no difference between KLG1Ost and KLG1Jt or between KLG2Ost and KLG2Jt. For progression to KLG ≥3, the mean survival time of KLG1Ost was 1.87-fold (95 ​% CI: 1.31-2.67) longer than that of KLG1Jt, while KLG2Ost was 5.42-fold (95 ​% CI: 3.69-7.96) longer than KLG2Jt. Conclusions: We proposed the criteria for each KLG using OARSI grade and phenotypes characterized by radiographic structure deformity in early KLG. Within the same KLG, the rate of disease progression was different depending on the structural deformity.

Indexed as

Artificial intelligenceKellgren-LawrenceKnee osteoarthritisMachine learningOARSI

Identifiers

PMID39896932
PMCPMC11787473

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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.