Automatic Prediction of Rheumatoid Arthritis Disease Activity from the Electronic Medical Records

Objective: We aimed to mine the data in the Electronic Medical Record to automatically discover patients' Rheumatoid Arthritis disease activity at discrete rheumatology clinic visits. We cast the problem as a document classification task where the feature space includes concepts from the clinical narrative and lab values as stored in the Electronic Medical Record. Materials and Methods The Training Set consisted of 2792 clinical notes and associated lab values. Test Set 1 included 1749 clinical notes and associated lab values. Test Set 2 included 344 clinical notes for which there were no associated lab values. The Apache clinical Text Analysis and Knowledge Extraction System was used to analyze the text and transform it into informative features to be combined with relevant lab values. Results: Experiments over a range of machine learning algorithms and features were conducted. The best performing combination was linear kernel Support Vector Machines with Unified Medical Language System Concept Unique Identifier features with feature selection and lab values. The Area Under the Receiver Operating Characteristic Curve (AUC) is 0.831 (σ = 0.0317), statistically significant as compared to two baselines (AUC = 0.758, σ = 0.0291). Algorithms demonstrated superior performance on cases clinically defined as extreme categories of disease activity (Remission and High) compared to those defined as intermediate categories (Moderate and Low) and included laboratory data on inflammatory markers. Conclusion: Automatic Rheumatoid Arthritis disease activity discovery from Electronic Medical Record data is a learnable task approximating human performance. As a result, this approach might have several research applications, such as the identification of patients for genome-wide pharmacogenetic studies that require large sample sizes with precise definitions of disease activity and response to therapies

The Influence of Polygenic Risk Scores on Heritability of Anti-CCP Level in RA

Objective: To study genetic factors that influence quantitative anti-cyclic citrullinated peptide (anti-CCP) antibody levels in RA patients. Methods: We carried out a genome wide association study (GWAS) meta-analysis using 1,975 anti-CCP+ RA patients from 3 large cohorts, the Brigham Rheumatoid Arthritis Sequential Study (BRASS), North American Rheumatoid Arthritis Consortium (NARAC), and the Epidemiological Investigation of RA (EIRA). We also carried out a genome-wide complex trait analysis (GCTA) to estimate the heritability of anti-CCP levels. Results: GWAS-meta analysis showed that anti-CCP levels were most strongly associated with the human leukocyte antigen (HLA) region with a p-value of 2×10−11 for rs1980493. There were 112 SNPs in this region that exceeded the genome-wide significance threshold of 5×10−8, and all were in linkage disequilibrium (LD) with the HLA- DRB1*03 allele with LD r2 in the range of 0.25-0.88. Suggestive novel associations outside of the HLA region were also observed for rs8063248 (near the GP2 gene) with a p-value of 3×10−7. None of the known RA risk alleles (~52 loci) were associated with anti-CCP level. Heritability analysis estimated that 44% of anti-CCP variation was attributable to genetic factors captured by GWAS variants. Conclusions: Anti-CCP level is a heritable trait. HLA-DR3 and GP2 are associated with lower anti-CCP levels

Effects of Achieving Target Measures in Rheumatoid Arthritis on Functional Status, Quality of Life, and Resource Utilization: Analysis of Clinical Practice Data

Objective: To evaluate associations between achieving guideline‐recommended targets of disease activity, defined by the Disease Activity Score in 28 joints using C‐reactive protein level (DAS28‐CRP) <2.6, the Simplified Disease Activity Index (SDAI) ≤3.3, or the Clinical Disease Activity Index (CDAI) ≤2.8, and other health outcomes in a longitudinal observational study. Methods: Other defined thresholds included low disease activity (LDA), moderate (MDA), or severe disease activity (SDA). To control for intraclass correlation and estimate effects of independent variables on outcomes of the modified Health Assessment Questionnaire (M‐HAQ), the EuroQol 5‐domain (EQ‐5D; a quality‐of‐life measure), hospitalization, and durable medical equipment (DME) use, we employed mixed models for continuous outcomes and generalized estimating equations for binary outcomes. Results: Among 1,297 subjects, achievement (versus nonachievement) of recommended disease targets was associated with enhanced physical functioning and lower health resource utilization. After controlling for baseline covariates, achievement of disease targets (versus LDA) was associated with significantly enhanced physical functioning based on SDAI ≤3.3 (ΔM‐HAQ −0.047; P = 0.0100) and CDAI ≤2.8 (−0.073; P = 0.0003) but not DAS28‐CRP <2.6 (−0.022; P = 0.1735). Target attainment was associated with significantly improved EQ‐5D (0.022–0.096; P < 0.0030 versus LDA, MDA, or SDA). Patients achieving guideline‐recommended disease targets were 36–45% less likely to be hospitalized (P < 0.0500) and 23–45% less likely to utilize DME (P < 0.0100). Conclusion: Attaining recommended target disease‐activity measures was associated with enhanced physical functioning and health‐related quality of life. Some health outcomes were similar in subjects attaining guideline targets versus LDA. Achieving LDA is a worthy clinical objective in some patients

Genome-Wide Association Study and Gene Expression Analysis Identifies CD84 as a Predictor of Response to Etanercept Therapy in Rheumatoid Arthritis

Anti-tumor necrosis factor alpha (anti-TNF) biologic therapy is a widely used treatment for rheumatoid arthritis (RA). It is unknown why some RA patients fail to respond adequately to anti-TNF therapy, which limits the development of clinical biomarkers to predict response or new drugs to target refractory cases. To understand the biological basis of response to anti-TNF therapy, we conducted a genome-wide association study (GWAS) meta-analysis of more than 2 million common variants in 2,706 RA patients from 13 different collections. Patients were treated with one of three anti-TNF medications: etanercept (n = 733), infliximab (n = 894), or adalimumab (n = 1,071). We identified a SNP (rs6427528) at the 1q23 locus that was associated with change in disease activity score (ΔDAS) in the etanercept subset of patients (P = 8×10-8), but not in the infliximab or adalimumab subsets (P>0.05). The SNP is predicted to disrupt transcription factor binding site motifs in the 3′ UTR of an immune-related gene, CD84, and the allele associated with better response to etanercept was associated with higher CD84 gene expression in peripheral blood mononuclear cells (P = 1×10-11 in 228 non-RA patients and P = 0.004 in 132 RA patients). Consistent with the genetic findings, higher CD84 gene expression correlated with lower cross-sectional DAS (P = 0.02, n = 210) and showed a non-significant trend for better ΔDAS in a subset of RA patients with gene expression data (n = 31, etanercept-treated). A small, multi-ethnic replication showed a non-significant trend towards an association among etanercept-treated RA patients of Portuguese ancestry (n = 139, P = 0.4), but no association among patients of Japanese ancestry (n = 151, P = 0.8). Our study demonstrates that an allele associated with response to etanercept therapy is also associated with CD84 gene expression, and further that CD84 expression correlates with disease activity. These findings support a model in which CD84 genotypes and/or expression may serve as a useful biomarker for response to etanercept treatment in RA patients of European ancestry. © 2013 Cui et al

PTPN22.6, a Dominant Negative Isoform of PTPN22 and Potential Biomarker of Rheumatoid Arthritis

PTPN22 is a tyrosine phosphatase and functions as a damper of TCR signals. A C-to-T single nucleotide polymorphism (SNP) located at position 1858 of human PTPN22 cDNA and converting an arginine (R620) to tryptophan (W620) confers the highest risk of rheumatoid arthritis among non-HLA genetic variations that are known to be associated with this disease. The effect of the R-to-W conversion on the phosphatase activity of PTPN22 protein and the impact of the minor T allele of the C1858T SNP on the activation of T cells has remained controversial. In addition, how the overall activity of PTPN22 is regulated and how the R-to-W conversion contributes to rheumatoid arthritis is still poorly understood. Here we report the identification of an alternative splice form of human PTPN22, namely PTPN22.6. It lacks the nearly entire phosphatase domain and can function as a dominant negative isoform of the full length PTPN22. Although conversion of R620 to W620 in the context of PTPN22.1 attenuated T cell activation, expression of the tryptophan variant of PTPN22.6 reciprocally led to hyperactivation of human T cells. More importantly, the level of PTPN22.6 in peripheral blood correlates with disease activity of rheumatoid arthritis. Our data depict a model that can reconcile the conflicting observations on the functional impact of the C1858T SNP and also suggest that PTPN22.6 is a novel biomarker of rheumatoid arthritis

A functional RANKL polymorphism associated with younger age at onset of rheumatoid arthritis

We previously reported association of co-occurrence of HLA-DRB1 shared epitope (SE) and RANKL SNPs with younger age of RA onset in 182 rheumatoid factor positive (RF) European American (EA) early RA patients. Here, we fine-mapped the 48 kb RANKL region in the extended 210 EA RF-positive early RA cohort, sought replication of RA-associated SNPs in additional 501 EA and 298 African-Americans (AA) RA cohorts, and explored functional consequences of RA-associated SNPs

A School-Based Intervention to Increase Lyme Disease Preventive Measures Among Elementary School-Aged Children

Abstract Purpose: Educational interventions to reduce Lyme disease (LD) among at-risk school children have had little study. The purpose of this study was to evaluate whether a short in-class LD education program based on social learning theory and the Health Belief Model (HBM) impacted a child's knowledge, attitude, and preventive behavior. Methods: Students in grades 2–5 in 19 elementary schools were selected in an area that was highly endemic for LD. The children received an educational intervention or were on a wait list as controls. Their knowledge, attitudes, and self-reported preventive behaviors were surveyed before implementing the program and 1 year later. General linear regression analyses adjusting for age, gender, and baseline variables were used to measure the impact of the intervention. Results: There were 3570 participants in the study: 1562 received the intervention, and 2008 were controls. The mean age for both groups was 9.1 years, with 53% women in the intervention group and 50% women in the control group. The children in the intervention group increased their overall knowledge of LD more than the children in the control group (overall knowledge score improvement, mean difference (SD) 1.38 (1.3) vs. 0.36 (1.3) p < 0.0001). All children in classes receiving the intervention reported an increase in precautionary behavior, positive attitude toward taking precautions, and self-efficacy compared with the wait list controls. Two LD cases were confirmed during the follow-up period, one in the intervention group and one in the controls. Conclusions: These findings demonstrate that a short in-class educational program that includes elements of the HBM, including: (1) awareness and knowledge about the disease, (2) benefits of preventive behavior, and (3) confidence in ability to perform preventive behaviors can improve knowledge, attitude, and self-reported precautionary behavior among at-risk children. www.clinicaltrials.gov: NCT0059499