Defining and evaluating a novel outcome measure representing end-stage knee osteoarthritis: data from the Osteoarthritis Initiative

Driban, Jeffrey B.; Price, Lori Lyn; Lynch, J.A.; Nevitt, M.; Lo, Grace H.; Eaton, Charles B.; McAlindon, Timothy E.

doi:10.1007/s10067-016-3299-5

Cited by 16 publications

(7 citation statements)

References 21 publications

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“…End-stage KOA has been viewed as a knee with considerable pain and functional limitations, accompanied by structural damage and/or other complications, such as flexion contractures and joint laxity, that prohibit the normal use of a joint (Driban et al, 2016). Generally, end-stage KOA is characterized as KL grade 4 on radiography (Guermazi et al, 2015).…”

Section: End-stage Koamentioning

confidence: 99%

Molecular Classification of Knee Osteoarthritis

Yang

et al. 2021

Front. Cell Dev. Biol.

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Knee osteoarthritis (KOA) is the most common form of joint degeneration with increasing prevalence and incidence in recent decades. KOA is a molecular disorder characterized by the interplay of numerous molecules, a considerable number of which can be detected in body fluids, including synovial fluid, urine, and blood. However, the current diagnosis and treatment of KOA mainly rely on clinical and imaging manifestations, neglecting its molecular pathophysiology. The mismatch between participants’ molecular characteristics and drug therapeutic mechanisms might explain the failure of some disease-modifying drugs in clinical trials. Hence, according to the temporal alteration of representative molecules, we propose a novel molecular classification of KOA divided into pre-KOA, early KOA, progressive KOA, and end-stage KOA. Then, progressive KOA is furtherly divided into four subtypes as cartilage degradation-driven, bone remodeling-driven, inflammation-driven, and pain-driven subtype, based on the major pathophysiology in patient clusters. Multiple clinical findings of representatively investigated molecules in recent years will be reviewed and categorized. This molecular classification allows for the prediction of high-risk KOA individuals, the diagnosis of early KOA patients, the assessment of therapeutic efficacy, and in particular, the selection of homogenous patients who may benefit most from the appropriate therapeutic agents.

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Section: End-stage Koamentioning

confidence: 99%

Molecular Classification of Knee Osteoarthritis

Yang

et al. 2021

Front. Cell Dev. Biol.

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“…The data we use in this study were collected according to the OAI protocol. As Driban et al mention, the OAI study was not designed to model eskOA 7 . In particular, the limited knee mobility condition in the eskOA definition uses variables that the OAI does not measure each year.…”

Section: Osteoarthritis Andcartilagementioning

confidence: 99%

“…To determine which patients have both advanced structural and symptomatic OA, we use an end-stage knee OA (eskOA) definition from Driban et al 7 that incorporates symptomatic, radiographic, and knee mobility criteria. Our model presents a risk score for this outcome.…”

Section: Introductionmentioning

confidence: 99%

Risk scoring for time to end-stage knee osteoarthritis: data from the Osteoarthritis Initiative

Dunn

Greenhouse

James

et al. 2020

Osteoarthritis and Cartilage

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Objective: This study constructs a risk score for patients' progression to end-stage knee osteoarthritis (OA) within 4 years. Design: The Osteoarthritis Initiative (OAI) was a longitudinal study of the onset and progression of knee OA. Using a recent definition of end-stage knee OA, we implement interval-censored survival forests to select predictors of this endpoint. We fit an interval-censored Cox model for time to end-stage knee OA, using the selected predictors. The risk score is the Cox model's fitted linear combination of the nine selected baseline structural and symptomatic knee OA variables. Results: We fit our models on a training set of 2,701 patients, and we evaluate on an independent test set of 1,436 patients. On the test sample, we observe a concordance index of 0.86 between risk score and time to end-stage, AUC of 0.87 for predicting end-stage within 24, 36, and 48 months, and positive predictive values that increase with the risk score. This risk stratification algorithm could enrich clinical trial patient enrollment. By enrolling test sample patients with scores above a threshold, a trial could have included 91% of test set patients who reach end-stage within 4 years while only enrolling 45% of the test sample. Conclusion: Using statistical methods, we construct and validate an interpretable risk score for time to end-stage knee OA. This score can help disease-modifying OA treatment developers to select candidates with the highest risk of fast-progressing knee OA.

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“…However, for all the mentioned treatment options, the guidelines do not provide clear guidance about when surgery is most beneficial for the individual patient. Consequently, under or over utilisation of healthcare resources may occur [2,[9][10][11]. Research suggests that up to 75% of patients could have postponed surgery and managed with physical training instead [12].…”

Section: Introductionmentioning

confidence: 99%

An AI-based patient-specific clinical decision support system for OA patients choosing surgery or not: study protocol for a single-centre, parallel-group, non-inferiority randomised controlled trial

Kastrup

Bjerregaard²,

Laursen

et al. 2023

Trials

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Background Osteoarthritis (OA) affects 20% of the adult Danish population, and the financial burden to society amounts to DKK 4.6 billion annually. Research suggests that up to 75% of surgical patients could have postponed an operation and managed with physical training. ERVIN.2 is an artificial intelligence (AI)-based clinical support system that addresses this problem by enhancing patient involvement in decisions concerning surgical knee and hip replacement. However, the clinical outcomes and cost-effectiveness of using such a system are scantily documented. Objective The primary objective is to investigate whether the usual care is non-inferior to ERVIN.2 supported care. The second objective is to determine if ERVIN.2 enhances clinical decision support and whether ERVIN.2 supported care is cost-effective. Methods This study used a single-centre, non-inferiority, randomised controlled in a two-arm parallel-group design. The study will be reported in compliance with CONSORT guidelines. The control group receives the usual care. As an add-on, the intervention group have access to baseline scores and predicted Oxford hip/knee scores and HRQoL for both the surgical and the non-surgical trajectory. A cost-utility analysis will be conducted alongside the trial using a hospital perspective, a 1-year time horizon and effects estimated using EQ-5D-3L. Results will be presented as cost per QALY gain. Discussion This study will bring knowledge about whether ERVIN.2 enhances clinical decision support, clinical effects, and cost-effectiveness of the AI system. The study design will not allow for the blinding of surgeons. Trial registration ClinicalTrials.gov NCT04332055. Registered on 2 April 2020.

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Defining and evaluating a novel outcome measure representing end-stage knee osteoarthritis: data from the Osteoarthritis Initiative

Cited by 16 publications

References 21 publications

Molecular Classification of Knee Osteoarthritis

Molecular Classification of Knee Osteoarthritis

Risk scoring for time to end-stage knee osteoarthritis: data from the Osteoarthritis Initiative

An AI-based patient-specific clinical decision support system for OA patients choosing surgery or not: study protocol for a single-centre, parallel-group, non-inferiority randomised controlled trial

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