Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20-25% the number of patients who show no progression. This result might lead to more efficient clinical trials.Comment: 22 pages, 12 figures, 10 table

Novel Approach to Estimate Osteoarthritis Progression:Use of the Reliable Change Index in the Evaluation of Joint Space Loss

OBJECTIVE: Osteoarthritis-related changes in joint space measurements over time are small and sensitive to measurement error. The Reliable Change Index (RCI) determines whether the magnitude of change observed in an individual can be attributed to true change. This study aimed to examine the RCI as a novel approach to estimating osteoarthritis progression.METHODS: Data were from 167 men and 392 women with knee osteoarthritis (diagnosed using the American College of Rheumatology criteria) randomized to the placebo arm of the 3-year Strontium Ranelate Efficacy in Knee Osteoarthritis trial (SEKOIA) and assessed annually. The RCI was used to determine whether the magnitude of change in joint space width (JSW) on radiographs between study years was likely to be true or due to measurement error.RESULTS: Between consecutive years, 57-69% of participants had an apparent decrease (change &lt;0) in JSW, while 31-43% of participants had annual changes indicating improvement in JSW. The RCI identified JSW decreases in only 6.0% of patients between baseline and year 1, and in 4.5% of patients between the remaining study years. The apparent increases in JSW were almost eliminated between baseline and year 1, and between years 1 and 2 only 1.3% of patients had a significant increase, dropping to 0.9% between years 2 and 3.CONCLUSION: The RCI provides a method to identify change in JSW, removing many apparent changes that are likely to be due to measurement error. This method appears to be useful for assessing change in JSW from radiographs in clinical and research settings.</p

Statin use and knee osteoarthritis progression: Results from a post-hoc analysis of the SEKOIA trial

Objective Epidemiological and experimental studies have suggested that lipid disorders might be involved in the pathophysiology of knee osteoarthritis (OA). Studies assessing the effect of statins on knee OA progression have shown conflicting results. We investigated the impact of statin use on radiological progression in patients with radiological and symptomatic knee OA. Methods In total, 336 patients from the placebo arm of SEKOIA trial completed the 3-year follow-up and were included in this post-hoc analysis. Statin use was recorded at baseline interview. Minimal medial tibiofemoral joint space was measured on plain radiographs by an automated method at baseline and then annually. Radiologic progression was defined as joint space narrowing ≥ 0.5 mm over 3 years. Results Overall, 71 patients were statin users (21.1%). They had a higher BMI (31.1 ± 5.3 vs. 29.3 ± 5.2 kg/m2, P = 0.008), a higher sum of metabolic factors (≥ 3 factors: 43.7% vs 7.2%; P for trend &lt; 0.001) and a higher rate of radiological progression (49.3% vs. 32.1%, P = 0.007) as compared to statin non-users. The significant association between radiological progression and statin use was independent of age, gender, WOMAC global score, disease duration, baseline joint space width, hypertension, type 2 diabetes, obesity (BMI &gt; 30 kg/m2) and cardiovascular diseases [relative risk 1.49 (95% CI: 1.10–2.02), P = 0.010]. Conclusion Among patients with knee OA, statin use was associated with radiological worsening over 3 years, regardless of other potential confounding factors (obesity, type 2 diabetes, hypertension, disease duration, symptom intensity and radiological severity)

Determining individual trajectories of joint space loss: improved statistical methods for monitoring knee osteoarthritis disease progression

ObjectivesKnee osteoarthritis (KOA) progression is frequently monitored by calculating the change in knee joint space width (JSW) measurements. Such differences are small and sensitive to measurement error. We aimed to assess the utility of two alternative statistical modelling methods for monitoring KOA.Material and methodsWe used JSW on radiographs from both the control arm of the Strontium Ranelate Efficacy in Knee Osteoarthritis trial (SEKOIA), a 3-year multicentre, double-blind, placebo-controlled phase three trial, and the Osteoarthritis Initiative (OAI), an open-access longitudinal dataset from the USA comprising participants followed over 8 years. Individual estimates of annualised change obtained from frequentist linear mixed effect (LME) and Bayesian hierarchical modelling, were compared with annualised crude change, and the association of these parameters with change in WOMAC pain was examined.ResultsMean annualised JSW changes were comparable for all estimates, a reduction of around 0.14 mm/y in SEKOIA and 0.08 mm/y in OAI. The standard deviation (SD) of change estimates was lower with LME and Bayesian modelling than crude change (SEKOIA SD = 0.12, 0.12 and 0.21 respectively; OAI SD = 0.08, 0.08 and 0.11 respectively). Estimates from LME and Bayesian modelling were statistically significant predictors of change in pain in SEKOIA (LME P-value = 0.04, Bayes P-value = 0.04), while crude change did not predict change in pain (P-value = 0.10).ConclusionsImplementation of LME or Bayesian modelling in clinical trials and epidemiological studies, would reduce sample sizes by enabling all study participants to be included in analysis regardless of incomplete follow up, and precision of change estimates would improve. They provide increased power to detect associations with other measures

Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data

Abstract Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20–25% the number of patients who show no progression. This result might lead to more efficient clinical trials

Can we identify patients with high risk of osteoarthritis progression who will respond to treatment? A focus on biomarkers ans frailty

Osteoarthritis (OA), a disease affecting different patient phenotypes, appears as an optimal candidate for personalized healthcare. The aim of the discussions of the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) working group was to explore the value of markers of different sources in defining different phenotypes of patients with OA. The ESCEO organized a series of meetings to explore the possibility of identifying patients who would most benefit from treatment for OA, on the basis of recent data and expert opinion. In the first meeting, patient phenotypes were identified according to the number of affected joints, biomechanical factors, and the presence of lesions in the subchondral bone. In the second meeting, summarized in the present article, the working group explored other markers involved in OA. Profiles of patients may be defined according to their level of pain, functional limitation, and presence of coexistent chronic conditions including frailty status. A considerable amount of data suggests that magnetic resonance imaging may also assist in delineating different phenotypes of patients with OA. Among multiple biochemical biomarkers identified, none is sufficiently validated and recognized to identify patients who should be treated. Considerable efforts are also being made to identify genetic and epigenetic factors involved in OA, but results are still limited. The many potential biomarkers that could be used as potential stratifiers are promising, but more research is needed to characterize and qualify the existing biomarkers and to identify new candidates

Novel Approach to Estimate Osteoarthritis Progression: Use of the Reliable Change Index in the Evaluation of Joint Space Loss

International audienceObjective Osteoarthritis-related changes in joint space measurements over time are small and sensitive to measurement error. The Reliable Change Index (RCI) determines whether the magnitude of change observed in an individual can be attributed to true change. This study aimed to examine the RCI as a novel approach to estimating osteoarthritis progression. Methods Data were from 167 men and 392 women with knee osteoarthritis (diagnosed using the American College of Rheumatology criteria) randomized to the placebo arm of the 3-year Strontium Ranelate Efficacy in Knee Osteoarthritis trial (SEKOIA) and assessed annually. The RCI was used to determine whether the magnitude of change in joint space width (JSW) on radiographs between study years was likely to be true or due to measurement error. Results Between consecutive years, 57-69% of participants had an apparent decrease (change <0) in JSW, while 31-43% of participants had annual changes indicating improvement in JSW. The RCI identified JSW decreases in only 6.0% of patients between baseline and year 1, and in 4.5% of patients between the remaining study years. The apparent increases in JSW were almost eliminated between baseline and year 1, and between years 1 and 2 only 1.3% of patients had a significant increase, dropping to 0.9% between years 2 and 3. Conclusion The RCI provides a method to identify change in JSW, removing many apparent changes that are likely to be due to measurement error. This method appears to be useful for assessing change in JSW from radiographs in clinical and research settings