Evidence map›Paper›PMID 42244858›Full record

ArticleFrontiers in aging2026

Racial disparities in pain and total knee arthroplasty across knee osteoarthritis phenotypes.

Ahmad Alkhatatbeh, Tariq Alkhatatbeh, Jiechen Chen, Hongjiang Chen, Jiankun Xu, Jun Hu

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Article in Frontiers in aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ahmad Alkhatatbeh *Department of Orthopedics Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Tariq Alkhatatbeh *Department of Joint Surgery, Center for Orthopedic Surgery, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, Guangdong, China.
Jiechen ChenDepartment of Orthopedics Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Hongjiang ChenDepartment of Orthopedics Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Jiankun XuDepartment of Orthopedics Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Jun HuDepartment of Orthopedics Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Knee osteoarthritis is heterogeneous in radiographic severity, pain, and multimorbidity, and racial disparities in pain and knee replacement are documented. We derived knee osteoarthritis phenotypes from electronic health record and radiograph data and assessed their associations with race and incident total knee arthroplasty. Methods: We conducted a retrospective cohort study in the Emory Knee Radiograph dataset of adults aged ≥40 years with knee osteoarthritis (n = 33,553). Among participants with pain scores within ±90 days of the index radiograph (n = 20,030), we applied k-means clustering to features including age, radiographic severity, pain measures, and comorbidity indicators to define phenotypes. Among participants without pre-index strict total knee arthroplasty, we used Cox proportional hazards models to evaluate time to incident post-index strict total knee arthroplasty, with follow-up censored at last observed contact and capped at the procedure-code ascertainment horizon. Results: Clustering identified three phenotypes: structural/metabolic disease characterized by radiographic severity and multimorbidity; younger trauma-associated mild disease; and pain-dominant disease with pain disproportionate to radiographic severity. Black individuals were overrepresented in the pain-dominant phenotype and underrepresented in the structural/metabolic phenotype, whereas White individuals showed the opposite pattern. In time-to-event models, higher radiographic severity and male sex were associated with faster time to total knee arthroplasty, whereas the younger trauma-associated and pain-dominant phenotypes were associated with lower hazard of total knee arthroplasty than the structural/metabolic phenotype. These findings were preserved in known-race-only and categorical Kellgren-Lawrence grade sensitivity analyses. Discussion: Routinely available electronic health record and radiographic data can identify clinically meaningful knee osteoarthritis phenotypes that differ by race and are independently associated with subsequent total knee arthroplasty. These findings support further validation of phenotype-aware approaches to studying disparities and guiding osteoarthritis care.

Indexed as

clinical phenotypeselectronic health recordshealth equityknee osteoarthritismachine learningunsupervised clustering

Identifiers

PMID42244858
PMCPMC13229871

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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.