Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort

ClinicalTrials.gov, https://clinicaltrials.gov, NCT03883568[Abstract] Objectives: The IMI-APPROACH knee osteoarthritis study used machine learning (ML) to predict structural and/or pain progression, expressed by a structural (S) and pain (P) predicted-progression score, to select patients from existing cohorts. This study evaluates the actual 2-year progression within the IMI-APPROACH, in relation to the predicted-progression scores. Methods: Actual structural progression was measured using minimum joint space width (minJSW). Actual pain (progression) was evaluated using the Knee injury and Osteoarthritis Outcomes Score (KOOS) pain questionnaire. Progression was presented as actual change (Δ) after 2 years, and as progression over 2 years based on a per patient fitted regression line using 0, 0.5, 1 and 2-year values. Differences in predicted-progression scores between actual progressors and non-progressors were evaluated. Receiver operating characteristic (ROC) curves were constructed and corresponding area under the curve (AUC) reported. Using Youden's index, optimal cut-offs were chosen to enable evaluation of both predicted-progression scores to identify actual progressors. Results: Actual structural progressors were initially assigned higher S predicted-progression scores compared with structural non-progressors. Likewise, actual pain progressors were assigned higher P predicted-progression scores compared with pain non-progressors. The AUC-ROC for the S predicted-progression score to identify actual structural progressors was poor (0.612 and 0.599 for Δ and regression minJSW, respectively). The AUC-ROC for the P predicted-progression score to identify actual pain progressors were good (0.817 and 0.830 for Δ and regression KOOS pain, respectively). Conclusion: The S and P predicted-progression scores as provided by the ML models developed and used for the selection of IMI-APPROACH patients were to some degree able to distinguish between actual progressors and non-progressors

Neuropathic Pain in the IMI-APPROACH Knee Osteoarthritis Cohort: Prevalence and Phenotyping

The study is registered under clinicaltrials.gov nr: NCT03883568.[Abstract] Objectives: Osteoarthritis (OA) patients with a neuropathic pain (NP) component may represent a specific phenotype. This study compares joint damage, pain and functional disability between knee OA patients with a likely NP component, and those without a likely NP component. Methods: Baseline data from the Innovative Medicines Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway knee OA cohort study were used. Patients with a painDETECT score ≥19 (with likely NP component, n=24) were matched on a 1:2 ratio to patients with a painDETECT score ≤12 (without likely NP component), and similar knee and general pain (Knee Injury and Osteoarthritis Outcome Score pain and Short Form 36 pain). Pain, physical function and radiographic joint damage of multiple joints were determined and compared between OA patients with and without a likely NP component. Results: OA patients with painDETECT scores ≥19 had statistically significant less radiographic joint damage (p≤0.04 for Knee Images Digital Analysis parameters and Kellgren and Lawrence grade), but an impaired physical function (p<0.003 for all tests) compared with patients with a painDETECT score ≤12. In addition, more severe pain was found in joints other than the index knee (p≤0.001 for hips and hands), while joint damage throughout the body was not different. Conclusions: OA patients with a likely NP component, as determined with the painDETECT questionnaire, may represent a specific OA phenotype, where local and overall joint damage is not the main cause of pain and disability. Patients with this NP component will likely not benefit from general pain medication and/or disease-modifying OA drug (DMOAD) therapy. Reserved inclusion of these patients in DMOAD trials is advised in the quest for successful OA treatments

The Epidemiology of Hip and Major Osteoporotic Fractures in a Dutch Population of Community-Dwelling Elderly : Implications for the Dutch FRAX (R) Algorithm

Background Incidence rates of non-hip major osteoporotic fractures (MOF) remain poorly characterized in the Netherlands. The Dutch FRAX (R) algorithm, which predicts 10-year probabilities of hip fracture and MOF (first of hip, humerus, forearm, clinical vertebral), therefore incorporates imputed MOF rates. Swedish incidence rate ratios for hip fracture to MOF (Malmo 1987-1996) were used to perform this imputation. However, equality of these ratios between countries is uncertain and recent evidence is scarce. Aims were to estimate incidence rates of hip fracture and MOF and to compare observed MOF rates to those predicted by the imputation method for the Netherlands. Methods Using hospitalisation and general practitioner records from the Dutch PHARMO Database Network (2002-2011) we calculated age-and-sex-specific and age-standardized incidence rates (IRs) of hip and other MOFs (humerus, forearm, clinical vertebral) and as used in FRAX (R). Observed MOF rates were compared to those predicted among community-dwelling individuals >= 50 years by the standardized incidence ratio (SIR; 95% CI). Results Age-standardized IRs (per 10,000 person-years) of MOF among men and women >= 50 years were 25.9 and 77.0, respectively. These numbers were 9.3 and 24.0 for hip fracture. Among women 55-84 years, observed MOF rates were significantly higher than predicted (SIR ranged between 1.12-1.50, depending on age). In men, the imputation method performed reasonable. Conclusion Observed MOF incidence was higher than predicted for community-dwelling women over a wide age-range, while it agreed reasonable for men. As miscalibration may influence treatment decisions, there is a need for confirmation of results in another data source. Until then, the Dutch FRAX1output should be interpreted with caution

The Epidemiology of Hip and Major Osteoporotic Fractures in a Dutch Population of Community-Dwelling Elderly : Implications for the Dutch FRAX (R) Algorithm

Background Incidence rates of non-hip major osteoporotic fractures (MOF) remain poorly characterized in the Netherlands. The Dutch FRAX (R) algorithm, which predicts 10-year probabilities of hip fracture and MOF (first of hip, humerus, forearm, clinical vertebral), therefore incorporates imputed MOF rates. Swedish incidence rate ratios for hip fracture to MOF (Malmo 1987-1996) were used to perform this imputation. However, equality of these ratios between countries is uncertain and recent evidence is scarce. Aims were to estimate incidence rates of hip fracture and MOF and to compare observed MOF rates to those predicted by the imputation method for the Netherlands. Methods Using hospitalisation and general practitioner records from the Dutch PHARMO Database Network (2002-2011) we calculated age-and-sex-specific and age-standardized incidence rates (IRs) of hip and other MOFs (humerus, forearm, clinical vertebral) and as used in FRAX (R). Observed MOF rates were compared to those predicted among community-dwelling individuals >= 50 years by the standardized incidence ratio (SIR; 95% CI). Results Age-standardized IRs (per 10,000 person-years) of MOF among men and women >= 50 years were 25.9 and 77.0, respectively. These numbers were 9.3 and 24.0 for hip fracture. Among women 55-84 years, observed MOF rates were significantly higher than predicted (SIR ranged between 1.12-1.50, depending on age). In men, the imputation method performed reasonable. Conclusion Observed MOF incidence was higher than predicted for community-dwelling women over a wide age-range, while it agreed reasonable for men. As miscalibration may influence treatment decisions, there is a need for confirmation of results in another data source. Until then, the Dutch FRAX1output should be interpreted with caution

Initiating tocilizumab, with or without methotrexate, compared with starting methotrexate with prednisone within step-up treatment strategies in early rheumatoid arthritis : an indirect comparison of effectiveness and safety of the U-Act-Early and CAMERA-II treat-to-target trials

OBJECTIVES: Methotrexate (MTX), often combined with low moderately dosed prednisone, is still the cornerstone of initial treatment for early rheumatoid arthritis (RA). It is not known how this strategy compares with initial treatment with a biological. We therefore compared the effectiveness of tocilizumab (TCZ), or TCZ plus MTX (TCZ+MTX) with MTX plus 10 mg prednisone (MTX+pred), all initiated within a treat-to-target treatment strategy in early RA. METHODS: Using individual patient data of two trials, we indirectly compared tight-controlled treat-to-target strategies initiating TCZ (n=103), TCZ+MTX (n=106) or MTX+pred (n=117), using initiation of MTX (n=227) as reference. Primary outcome was Disease Activity Score assessing 28 joints (DAS28) over 24 months. To assess the influence of acute phase reactants (APRs), a disease activity composite outcome score without APR (ie, modification of the Clinical Disease Activity Index (m-CDAI)) was analysed. Secondary outcomes were remission (several definitions), physical function and radiographic progression. Multilevel models were used to account for clustering within trials and patients over time, correcting for relevant confounders. RESULTS: DAS28 over 24 months was lower for TCZ+MTX than for MTX+Pred (mean difference: -0.62 (95% CI -1.14 to -0.10)). Remission was more often achieved in TCZ+MTX and in TCZ versus MTX+pred (p=0.02/0.05, respectively). Excluding APRs from the disease activity outcome score, TCZ-based strategies showed a slightly higher m-CDAI compared with MTX+pred, but this was not statistically significant. Other outcomes were also not statistically significantly different between the strategies. CONCLUSIONS: In patients with early RA, although TCZ-based strategies resulted in better DAS28 and remission rates compared with MTX+pred, at least part of these effects may be due to a specific effect of TCZ on APRs

Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials:the IMI-APPROACH study

Abstract Objectives: To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study. Design: We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression. Results: From 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P ​+ ​S, 5%), and a proportion of non-progressors (N, 52%) ∼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81–0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52–0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P ​+ ​S (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57). Conclusions: The machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial