Correction to: Identification of a Rule to Predict Response to Sarilumab in Patients with Rheumatoid Arthritis Using Machine Learning and Clinical Trial Data.
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SUBMITTER: Rehberg M
PROVIDER: S-EPMC8572238 | biostudies-literature | 2021 Dec
REPOSITORIES: biostudies-literature
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Correction to: Identification of a Rule to Predict Response to Sarilumab in Patients with Rheumatoid Arthritis Using Machine Learning and Clinical Trial Data.
Rehberg Markus M Giegerich Clemens C Praestgaard Amy A van Hoogstraten Hubert H Iglesias-Rodriguez Melitza M Curtis Jeffrey R JR Gottenberg Jacques-Eric JE Schwarting Andreas A Castañeda Santos S Rubbert-Roth Andrea A Choy Ernest H S EHS
Rheumatology and therapy 20211201 4
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Project description:IntroductionIn rheumatoid arthritis, time spent using ineffective medications may lead to irreversible disease progression. Despite availability of targeted treatments, only a minority of patients achieve sustained remission, and little evidence exists to direct the choice of biologic disease-modifying antirheumatic drugs in individual patients. Machine learning was used to identify a rule to predict the response to sarilumab and discriminate between responses to sarilumab versus adalimumab, with a focus on clinically feasible blood biomarkers.MethodsThe decision tree model GUIDE was trained using a data subset from the sarilumab trial with the most biomarker data, MOBILITY, to identify a rule to predict disease activity after sarilumab 200 mg. The training set comprised 18 categorical and 24 continuous baseline variables; some data were omitted from training and used for validation by the algorithm (cross-validation). The rule was tested using full datasets from four trials (MOBILITY, MONARCH, TARGET, and ASCERTAIN), focusing on the recommended sarilumab dose of 200 mg.ResultsIn the training set, the presence of anti-cyclic citrullinated peptide antibodies, combined with C-reactive protein > 12.3 mg/l, was identified as the "rule" that predicts American College of Rheumatology 20% response (ACR20) to sarilumab. In testing, the rule reliably predicted response to sarilumab in MOBILITY, MONARCH, and ASCERTAIN for many efficacy parameters (e.g., ACR70 and the 28-joint disease activity score using CRP [DAS28-CRP] remission). The rule applied less to TARGET, which recruited individuals refractory to tumor necrosis factor inhibitors. The potential clinical benefit of the rule was highlighted in a clinical scenario based on MONARCH data, which found that increased ACR70 rates could be achieved by treating either rule-positive patients with sarilumab or rule-negative patients with adalimumab.ConclusionsWell-established and clinically feasible blood biomarkers can guide individual treatment choice. Real-world validation of the rule identified in this post hoc analysis is merited.Clinical trial registrationNCT01061736, NCT02332590, NCT01709578, NCT01768572.
| S-EPMC8572308 | biostudies-literature
Project description: Not available
| S-EPMC10796306 | biostudies-literature
Project description:IntroductionClinical trial findings may not be generalizable to routine practice. This study evaluated sarilumab effectiveness in patients with rheumatoid arthritis (RA) and tested the real-world applicability of a response prediction rule, derived from trial data using machine learning (based on C-reactive protein [CRP] > 12.3 mg/l and seropositivity [anticyclic citrullinated peptide antibodies, ACPA +]).MethodsSarilumab initiators from the ACR-RISE Registry, with ≥ 1 prescription on/after its FDA approval (2017-2020), were divided into three cohorts based on progressively restrictive criteria: Cohort A (had active disease), Cohort B (met eligibility criteria of a phase 3 trial in RA patients with inadequate response/intolerance to tumor necrosis factor inhibitors [TNFi]), and Cohort C (characteristics matched to the phase 3 trial baseline). Mean changes in Clinical Disease Activity Index (CDAI) and Routine Assessment of Patient Index Data 3 (RAPID3) were evaluated at 6 and 12 months. In a separate cohort, predictive rule was tested based on CRP levels and seropositive status (ACPA and/or rheumatoid factor); patients were categorized into rule-positive (seropositive with CRP > 12.3 mg/l) and rule-negative groups to compare the odds of achieving CDAI low disease activity (LDA)/remission and minimal clinically important difference (MCID) over 24 weeks.ResultsAmong sarilumab initiators (N = 2949), treatment effectiveness was noted across cohorts, with greater improvement noted for Cohort C at 6 and 12 months. Among the predictive rule cohort (N = 205), rule-positive (vs. rule-negative) patients were more likely to reach LDA (odds ratio: 1.5 [0.7, 3.2]) and MCID (1.1 [0.5, 2.4]). Sensitivity analyses (CRP > 5 mg/l) showed better response to sarilumab in rule-positive patients.ConclusionsIn real-world setting, sarilumab demonstrated treatment effectiveness, with greater improvements in the most selective population, mirroring phase 3 TNFi-refractory and rule-positive RA patients. Seropositivity appeared a stronger driver for treatment response than CRP, although optimization of the rule in routine practice requires further data.
| S-EPMC10326227 | biostudies-literature
Project description:ObjectiveRecognizing that the interrelationships between chronic conditions that complicate rheumatoid arthritis (RA) are poorly understood, we aimed to identify patterns of multimorbidity and to define their prevalence in RA through machine learning.MethodsWe constructed RA and age- and sex-matched (1:1) non-RA cohorts within a large commercial insurance database (MarketScan) and the Veterans Health Administration (VHA). Chronic conditions (n = 44) were identified from diagnosis codes from outpatient and inpatient encounters. Exploratory factor analysis was performed separately in both databases, stratified by RA diagnosis and sex, to identify multimorbidity patterns. The association of RA with different multimorbidity patterns was determined using conditional logistic regression.ResultsWe studied 226,850 patients in MarketScan (76% female) and 120,780 patients in the VHA (89% male). The primary multimorbidity patterns identified were characterized by the presence of cardiopulmonary, cardiometabolic, and mental health and chronic pain disorders. Multimorbidity patterns were similar between RA and non-RA patients, female and male patients, and patients in MarketScan and the VHA. RA patients had higher odds of each multimorbidity pattern (odds ratios [ORs] 1.17-2.96), with mental health and chronic pain disorders being the multimorbidity pattern most strongly associated with RA (ORs 2.07-2.96).ConclusionCardiopulmonary, cardiometabolic, and mental health and chronic pain disorders represent predominant multimorbidity patterns, each of which is overrepresented in RA. The identification of multimorbidity patterns occurring more frequently in RA is an important first step in progressing toward a holistic approach to RA management and warrants assessment of their clinical and predictive utility.
| S-EPMC10009900 | biostudies-literature
Project description:In the last few decades, strategies for the management of rheumatoid arthritis (RA) have been increasingly oriented toward more comprehensive control of the disease, taking into account even RA extra-articular manifestations, comorbidities, and the patient's perception about the disease. The need for improving the shared decision-making process suggested by European League Against Rheumatism recommendations is leading to an increasing interest in the role of patient-reported outcomes (PROs) beside the usual more objective criteria for defining clinical response based on disease-activity composite indices. Measurement of such PROs as pain or fatigue may be significantly influenced by mood disorders often complicating RA, the pathogenesis of which is deeply interconnected with phlogistic processes mediated by proinflammatory cytokines. IL6 is a pleiotropic mediator involved in neuroendocrine and neuropsychological processes, besides its well known effects on immune, cardiovascular, and metabolic systems. Therefore, there is a growing body of evidence about the efficacy of IL6 blockade in PRO improvement in RA patients. Sarilumab is a monoclonal antibody binding both soluble and membrane-bound IL6R?, inhibiting the IL6-mediated signaling pathway with favorable efficacy and safety profile. This review analyzes the importance of PROs in strategies for the management of RA and the pathogenic mechanisms linking IL6 with the patient's perception of the disease. Moreover, the main findings from sarilumab randomized controlled trials are summarized in detail, emphasizing the potential role of this IL6 blocker in the holistic treatment of RA.
| S-EPMC6108331 | biostudies-literature
Project description:ObjectiveSarilumab, as monotherapy or in combination with conventional synthetic DMARDs, such as MTX, has demonstrated improvement in clinical outcomes in patients with RA. The primary objective of this post hoc analysis was to compare the efficacy of sarilumab (200 mg every 2 weeks) monotherapy (MONARCH study) with that of sarilumab and MTX combination therapy (MOBILITY study) at week 24.MethodsThe endpoints assessed were mean change from baseline in the Clinical Disease Activity Index (CDAI), 28-joint Disease Activity using CRP (DAS28-CRP), CRP, haemoglobin (Hb), pain visual analogue scale (VAS) and Functional Assessment of Chronic Illness Therapy (FACIT)-Fatigue. Least square (LS) mean change from baseline (95% CI) at week 24 for all endpoints was compared between the treatment arms for adjusted comparisons.ResultsThis analysis included 184 patients on sarilumab monotherapy and 399 patients on sarilumab plus MTX. Differences (P < 0.05) were observed in ethnicity, region, body mass index group, rheumatoid factor, anti-cyclic citrullinated peptide antibodies, swollen joint count, CRP, CDAI and oral glucocorticoid use between these treatment groups. After adjusting for these differences in a mixed-effect model repeated measure, LS mean change from baseline for all assessments was similar between the treatment groups with overlapping CIs: CDAI, -28.79 vs -26.21; DAS28-CRP, -2.95 vs -2.81; CRP, -18.31 vs -16.46; Hb, 6.59 vs 8.09; Pain VAS, -33.62 vs -31.66; FACIT-Fatigue, 9.90 vs 10.24.ConclusionThis analysis demonstrated that the efficacy of sarilumab monotherapy was similar to that of sarilumab and MTX combination therapy.
| S-EPMC9157062 | biostudies-literature
Project description:BackgroundWe sought to identify features that distinguish osteoarthritis (OA) and rheumatoid arthritis (RA) hematoxylin and eosin (H&E)-stained synovial tissue samples.MethodsWe compared fourteen pathologist-scored histology features and computer vision-quantified cell density (147 OA and 60 RA patients) in H&E-stained synovial tissue samples from total knee replacement (TKR) explants. A random forest model was trained using disease state (OA vs RA) as a classifier and histology features and/or computer vision-quantified cell density as inputs.ResultsSynovium from OA patients had increased mast cells and fibrosis (p < 0.001), while synovium from RA patients exhibited increased lymphocytic inflammation, lining hyperplasia, neutrophils, detritus, plasma cells, binucleate plasma cells, sub-lining giant cells, fibrin (all p < 0.001), Russell bodies (p = 0.019), and synovial lining giant cells (p = 0.003). Fourteen pathologist-scored features allowed for discrimination between OA and RA, producing a micro-averaged area under the receiver operating curve (micro-AUC) of 0.85±0.06. This discriminatory ability was comparable to that of computer vision cell density alone (micro-AUC = 0.87±0.04). Combining the pathologist scores with the cell density metric improved the discriminatory power of the model (micro-AUC = 0.92±0.06). The optimal cell density threshold to distinguish OA from RA synovium was 3400 cells/mm2, which yielded a sensitivity of 0.82 and specificity of 0.82.ConclusionsH&E-stained images of TKR explant synovium can be correctly classified as OA or RA in 82% of samples. Cell density greater than 3400 cells/mm2 and the presence of mast cells and fibrosis are the most important features for making this distinction.
| S-EPMC9979511 | biostudies-literature
Project description:Background and objectiveSarilumab binds to the interleukin-6 receptor with high affinity, inhibiting cis and trans signaling by interleukin-6. Sarilumab has demonstrated efficacy and safety in patients with rheumatoid arthritis. The objective of this study was to develop a population-pharmacokinetic model using data from 1770 patients with rheumatoid arthritis across phase I-III studies.MethodsPotential covariates were identified using a stepwise forward-addition and backward-deletion strategy, and the final model was evaluated by visual predictive check and bootstrap methods.ResultsSarilumab pharmacokinetics is described by a two-compartment model with first-order absorption and parallel linear and nonlinear Michaelis-Menten elimination. A subcutaneous dose of sarilumab 200 mg every 2 weeks resulted in more pronounced saturation of the nonlinear clearance pathway over the dosing interval than 150 mg every 2 weeks. Steady-state exposure (area under the plasma concentration-time curve from day 0 to day 14) increased twofold with dose escalation from 150 to 200 mg every 2 weeks. Body weight, anti-drug antibody status, sarilumab drug product, sex, creatinine clearance, albumin, and baseline C-reactive protein levels were identified as significant covariates according to the predefined statistical significance criteria in stepwise covariate searches. The main intrinsic source of pharmacokinetic variability in exposure was body weight. Compared with a typical 71-kg patient, the area under the plasma concentration-time curve from day 0 to day 14 was 20-23% lower for an 83-kg patient and 20-25% higher for a 62-kg patient.ConclusionsThese findings, combined with the safety and efficacy data, indicated limited clinical relevance of body-weight effect on sarilumab exposure. No adjustment in sarilumab dose is required for body weight or any other demographics assessed.
| S-EPMC6856490 | biostudies-literature
Project description:ObjectiveIn this study, we sought to refine histologic scoring of rheumatoid arthritis (RA) synovial tissue by training with gene expression data and machine learning.MethodsTwenty histologic features were assessed in 129 synovial tissue samples (n = 123 RA patients and n = 6 osteoarthritis [OA] patients). Consensus clustering was performed on gene expression data from a subset of 45 synovial samples. Support vector machine learning was used to predict gene expression subtypes, using histologic data as the input. Corresponding clinical data were compared across subtypes.ResultsConsensus clustering of gene expression data revealed 3 distinct synovial subtypes, including a high inflammatory subtype characterized by extensive infiltration of leukocytes, a low inflammatory subtype characterized by enrichment in pathways including transforming growth factor β, glycoproteins, and neuronal genes, and a mixed subtype. Machine learning applied to histologic features, with gene expression subtypes serving as labels, generated an algorithm for the scoring of histologic features. Patients with the high inflammatory synovial subtype exhibited higher levels of markers of systemic inflammation and autoantibodies. C-reactive protein (CRP) levels were significantly correlated with the severity of pain in the high inflammatory subgroup but not in the others.ConclusionGene expression analysis of RA and OA synovial tissue revealed 3 distinct synovial subtypes. These labels were used to generate a histologic scoring algorithm in which the histologic scores were found to be associated with parameters of systemic inflammation, including the erythrocyte sedimentation rate, CRP level, and autoantibody levels. Comparison of gene expression patterns to clinical features revealed a potentially clinically important distinction: mechanisms of pain may differ in patients with different synovial subtypes.
| S-EPMC6336443 | biostudies-literature
Project description:Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.
2014-02-21 | GSE55235 | GEO