Lifetime trauma exposure and arthritis in older adults.

OBJECTIVE: Experiencing potentially traumatic events across one's lifecourse increases risk for poor physical health outcomes. Existing models emphasize the effects of any lifetime trauma exposure, risk accumulation (multiple traumas over time), and sensitive periods of exposure (specific exposure timepoints leading to lasting consequences). We examined how different indices of trauma exposure across the lifecourse were associated with later life arthritis, a common and debilitating health condition. METHODS: Data include 5,717 Health and Retirement Study participants (age mean = 65.3, SD = 12.9) who reported on lifetime adversity and trauma in 2006-2008. Lifetime trauma exposure was modeled as any trauma, accumulation of traumas, and lifecourse profiles (no exposure, childhood only, adulthood only, childhood and adulthood exposure). Outcomes included prevalent arthritis at baseline and incident arthritis across 12 years of follow-up. Covariate-adjusted generalized linear models for prevalence ratios (PR) and Cox proportional hazards models for hazard ratios (HR) were conducted. RESULTS: Any lifetime trauma was associated with both prevalent arthritis at baseline (PR = 1.13, 95%CI 1.05-1.22) and incident arthritis over 12 years (HR = 1.25, 95%CI 1.17-1.47). Greater trauma accumulation was significantly associated with both prevalent and incident arthritis. Childhood exposure was particularly strongly associated with prevalent and incident cases, with adulthood exposure being unassociated with incident arthritis. Across models, trauma exposure was associated with prevalent cases of both immune-related and osteoarthritis types. CONCLUSIONS: Higher lifetime trauma burden, especially during childhood, may predispose individuals to arthritis later in life. Early intervention or prevention efforts should identify trauma as an important risk factor for musculoskeletal health across the lifecourse.

Structuring medication signeturs as a language regression task: comparison of zero- and few-shot GPT with fine-tuned models.

Importance: Electronic health record textual sources such as medication signeturs (sigs) contain valuable information that is not always available in structured form. Commonly processed through manual annotation, this repetitive and time-consuming task could be fully automated using large language models (LLMs). While most sigs include simple instructions, some include complex patterns. Objectives: We aimed to compare the performance of GPT-3.5 and GPT-4 with smaller fine-tuned models (ClinicalBERT, BlueBERT) in extracting the average daily dose of 2 immunomodulating medications with frequent complex sigs: hydroxychloroquine, and prednisone. Methods: Using manually annotated sigs as the gold standard, we compared the performance of these models in 702 hydroxychloroquine and 22 104 prednisone prescriptions. Results: GPT-4 vastly outperformed all other models for this task at any level of in-context learning. With 100 in-context examples, the model correctly annotates 94% of hydroxychloroquine and 95% of prednisone sigs to within 1 significant digit. Error analysis conducted by 2 additional manual annotators on annotator-model disagreements suggests that the vast majority of disagreements are model errors. Many model errors relate to ambiguous sigs on which there was also frequent annotator disagreement. Discussion: Paired with minimal manual annotation, GPT-4 achieved excellent performance for language regression of complex medication sigs and vastly outperforms GPT-3.5, ClinicalBERT, and BlueBERT. However, the number of in-context examples needed to reach maximum performance was similar to GPT-3.5. Conclusion: LLMs show great potential to rapidly extract structured data from sigs in no-code fashion for clinical and research applications.

Proton Pump Inhibitor Use and Bone Health in Patients With Rheumatic Diseases: A Cross-Sectional Study.

OBJECTIVE: To assess the effect of proton pump inhibitor (PPI) use on bone mineral density (BMD) and bone microarchitecture as measured by the trabecular bone score (TBS) in patients with inflammatory rheumatic and musculoskeletal diseases (iRMDs). METHODS: Cross-sectional data from a prospective single-center cohort (2015 to 2022) of patients with iRMDs were used to evaluate 3 co-primary outcomes: BMD of the left femoral neck and the lumbar spine (as T-scores) and the TBS. Inverse probability weighting adjusted for numerous confounders including age, sex, body mass index, current and cumulative glucocorticoid (GC) dose, C-reactive protein levels, disability, and others. Analyses were based on general linear models, following a prespecified statistical analysis plan. RESULTS: The study included 1495 patients (75% women; mean age, 62.6±13.1 years; 49% and 63% with regular PPI and GC use, respectively). The PPI users had lower BMD at both spine (adjusted contrast -0.25; 95% CI, -0.47 to -0.04; P=.02) and femoral neck (-0.17 [-0.35 to 0.01]; P=.07). Differences between PPI users and nonusers were statistically significant only in patients concurrently using GCs at more than 7.5 mg/d prednisone equivalent. The TBS was similar in PPI users and nonusers (adjusted contrast, 0.00 [-0.04 to 0.04]; P=.97). CONCLUSION: Our results suggest that PPIs lead to a loss of BMD rather than an impairment of bone microarchitecture in patients with iRMDs. The negative association between PPI use and BMD appears to be dependent on concurrent GC use. Clinicians should carefully review the indication for PPI use in patients with iRMDs, especially in those receiving higher dose GCs.

Development and validation of a risk scoring system to identify patients with lupus nephritis in electronic health record data.

OBJECTIVE: Accurate identification of lupus nephritis (LN) cases is essential for patient management, research and public health initiatives. However, LN diagnosis codes in electronic health records (EHRs) are underused, hindering efficient identification. We investigated the current performance of International Classification of Diseases (ICD) codes, 9th and 10th editions (ICD9/10), for identifying prevalent LN, and developed scoring systems to increase identification of LN that are adaptable to settings with and without LN ICD codes. METHODS: Training and test sets derived from EHR data from a large health system. An external set comprised data from the EHR of a second large health system. Adults with ICD9/10 codes for SLE were included. LN cases were ascertained through manual chart reviews conducted by rheumatologists. Two definitions of LN were used: strict (definite LN) and inclusive (definite, potential or diagnostic uncertainty). Gradient boosting models including structured EHR fields were used for predictor selection. Two logistic regression-based scoring systems were developed ('LN-Code' included LN ICD codes and 'LN-No Code' did not), calibrated and validated using standard performance metrics. RESULTS: A total of 4152 patients from University of California San Francisco Medical Center and 370 patients from Zuckerberg San Francisco General Hospital and Trauma Center met the eligibility criteria. Mean age was 50 years, 87% were female. LN diagnosis codes demonstrated low sensitivity (43-73%) but high specificity (92-97%). LN-Code achieved an area under the curve (AUC) of 0.93 and a sensitivity of 0.88 for identifying LN using the inclusive definition. LN-No Code reached an AUC of 0.91 and a sensitivity of 0.95 (0.97 for the strict definition). Both scoring systems had good external validity, calibration and performance across racial and ethnic groups. CONCLUSIONS: This study quantified the underutilisation of LN diagnosis codes in EHRs and introduced two adaptable scoring systems to enhance LN identification. Further validation in diverse healthcare settings is essential to ensure their broader applicability.

Using the technology acceptance model to assess clinician perceptions and experiences with a rheumatoid arthritis outcomes dashboard: qualitative study.

BACKGROUND: Improving shared decision-making using a treat-to-target approach, including the use of clinical outcome measures, is important to providing high quality care for rheumatoid arthritis (RA). We developed an Electronic Health Record (EHR) integrated, patient-facing sidecar dashboard application that displays RA outcomes, medications, and lab results for use during clinical visits ("RA PRO dashboard"). The purpose of this study was to assess clinician perceptions and experiences using the dashboard in a university rheumatology clinic. METHODS: We conducted focus group (FG) discussions with clinicians who had access to the dashboard as part of a randomized, stepped-wedge pragmatic trial. FGs explored clinician perceptions towards the usability, acceptability, and usefulness of the dashboard. FG data were analyzed thematically using deductive and inductive techniques; generated themes were categorized into the domains of the Technology Acceptance Model (TAM). RESULTS: 3 FG discussions were conducted with a total of 13 clinicians. Overall, clinicians were enthusiastic about the dashboard and expressed the usefulness of visualizing RA outcome trajectories in a graphical format for motivating patients, enhancing patient understanding of their RA outcomes, and improving communication about medications. Major themes that emerged from the FG analysis as barriers to using the dashboard included inconsistent collection of RA outcomes leading to sparse data in the dashboard and concerns about explaining RA outcomes, especially to patients with fibromyalgia. Other challenges included time constraints and technical difficulties refreshing the dashboard to display real-time data. Methods for integrating the dashboard into the visit varied: some clinicians used the dashboard at the beginning of the visit as they documented RA outcomes; others used it at the end to justify changes to therapy; and a few shared it only with stable patients. CONCLUSIONS: The study provides valuable insights into clinicians' perceptions and experiences with the RA PRO dashboard. The dashboard showed promise in enhancing patient-clinician communication, shared decision-making, and overall acceptance among clinicians. Addressing challenges related to data collection, education, and tailoring dashboard use to specific patient populations will be crucial for maximizing its potential impact on RA care. Further research and ongoing improvements in dashboard design and implementation are warranted to ensure its successful integration into routine clinical practice.

Latent Tuberculosis Screening Among New Users of a Biologic or Targeted Synthetic Disease-Modifying Antirheumatic Drug: Gaps in Screening Overall and Among Janus Kinase Inhibitors.

OBJECTIVE: We combined claims and electronic health record (EHR) data to provide contemporary and accurate estimates of latent tuberculosis (TB) screening among new users of a biologic or targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) and assess potential gaps in testing by drug type, patient characteristics, and practice. METHODS: Our denominator population was patients in the Rheumatology Informatics System for Effectiveness (RISE) registry and Medicare using a b/tsDMARD in 2018 without a claim or prescription in the year prior. TB screening was assessed in both Medicare and RISE 1 and 3 years before the medication start date. We calculated the proportion screened overall, by medication class, and by practice. We tested for demographic differences in screening using logistic regression. RESULTS: In the year before drug starts, 65.6% of patients had any TB screening; in a 3-year window, 72.9% had any TB screening. Rates of screening within 1 year by drug type were greater or equal to the overall screening rate for most drugs except for JAK inhibitors (JAKis) (46%) and interleukin-17 inhibitors (IL-17is) (11.5%). A lower proportion of Hispanic and Asian patients were screened compared with White patients. Practice screening rates ranged from 20.0% to 92.9% of patients within 1 year. CONCLUSION: We report higher screening rates than have previously been published because of combining claims and EHR data. However, important safety gaps remain, namely, reduced screening among new users of a JAKi or IL-17i and among Asian and Hispanic patients, as well as low-performing practices. Educational initiatives, team-based care delivery, task shifting, and technological interventions to address observed gaps in patient safety procedures are needed.

Diabetes and incidence of breast cancer and its molecular subtypes: A systematic review and meta-analysis.

Diabetes mellitus (DM) has been proposed to be positively associated with breast cancer (BCa) risk due to shared risk factors, metabolic dysfunction, and the use of antidiabetic medications. We conducted a systematic review and meta-analysis to evaluate the association between DM and BCa risk. We searched PubMed, Embase, and Web of Science for cohort and case-control studies assessing the association between DM and BCa published before 10 December 2021. Two reviewers independently screened the studies for inclusion, abstracted article data, and rated study quality. Random effects models were used to estimate summary risk ratios (RRs) and 95% confidence intervals (CIs). From 8396 articles identified in the initial search, 70 independent studies were included in the meta-analysis. DM was associated with an overall increased risk of BCa (RR = 1.20, 95% CI: 1.11-1.29). The 24 case-control studies demonstrated a stronger association (RR = 1.26, 95% CI: 1.13-1.40) than the 46 cohort studies (RR = 1.15, 95% CI: 1.05-1.27). Studies reporting risk by menopausal status found that postmenopausal women had an elevated risk of developing BCa (RR = 1.12, 95% CI: 1.07-1.17). No association between DM and BCa risk was observed among premenopausal women (RR = 0.95, 95% CI: 0.85-1.05). In addition, DM was associated with significantly increased risks of oestrogen receptor (ER)+ (RR = 1.09, 95% CI: 1.00-1.20), ER- (RR = 1.16, 95% CI: 1.04-1.30), and triple negative BCa (RR = 1.41, 95% CI: 1.01-1.96). The association estimate for human epidermal growth factor 2-positive BCa was also positive (RR = 1.21, 95% CI: 0.52-2.82), but the CI was wide and crossed the null. Our meta-analysis confirms a modest positive association between DM and BCa risk. In addition, our results suggest that the association between DM and BCa may be modified by menopausal status, and that DM may be differentially associated with BCa subtypes defined by receptor status. Additional studies are warranted to investigate the mechanisms underlying these associations and any influence of DM on BCa receptor expression.

Incidence Rate and Factors Associated With Fractures Among Medicare Beneficiaries With Ankylosing Spondylitis in the United States.

OBJECTIVE: We evaluated the incidence rate and factors associated with fractures among adults with ankylosing spondylitis (AS). METHODS: We performed a retrospective cohort study with data from the Rheumatology Informatics System for Effectiveness registry linked to Medicare claims from 2016 to 2018. Patients were required to have two AS International Classification of Diseases codes 30 or more days apart and a subsequent Medicare claim. Then, 1 year of baseline characteristics were included, after which patients were observed for fractures. First, we calculated the incidence rate of fractures. Second, we constructed logistic regression models to identify factors associated with the fracture, including age, sex, race and ethnicity, body mass index, Medicare/Medicaid dual eligibility, area deprivation index, Charlson comorbidity index, smoking status, osteoporosis, historical fracture, and use of osteoporosis treatment, glucocorticoids, and opioids. RESULTS: We identified 1,426 adults with prevalent AS. Mean ± SD age was 69.4 ± 9.8 years, 44.3% were female, and 77.3% were non-Hispanic White. Fractures occurred in 197 adults with AS. The overall incidence rate of fractures was 76.7 (95% confidence interval [CI] 66.4-88.6) per 1,000 person-years. Older age (odds ratio [OR] 2.8, 95% CI 1.39-5.65), historical fracture (OR 5.24, 95% CI 3.44-7.99), and use of more than 30 mg morphine equivalent (OR 1.86, 95% CI 1.08-3.19) conferred increased odds of fracture. CONCLUSIONS: In this large sample of Medicare beneficiaries with AS, increasing age, historical fracture, and use of opioids had higher odds of fracture. Men and women were equally likely to have a fracture. Because opioid use was associated with fracture in AS, this high-risk population should be considered for interventions to mitigate risk.

Peer Support in Rheumatic Diseases: A Narrative Literature Review.

Rheumatic diseases are a group of chronic conditions that are associated with significant morbidity, impaired physical function, psychosocial stress, and cost to the healthcare system. Peer support interventions have been shown to have a positive impact on health outcomes in several chronic conditions, but no review has specifically assessed the impact of peer support on rheumatic conditions. The aim of this narrative literature review was to understand how peer support has been applied in the field of rheumatology, with a specific focus on the impact of observational and randomized studies of direct peer support interventions on various outcome measures across rheumatic conditions. We also examined studies exploring patient attitudes and preferences toward peer support. The majority of studies included focused on peer support in rheumatoid arthritis and systemic lupus erythematosus. Generally, patients across the spectrum of rheumatic disease perceive peer support as a useful tool. Peer support interventions, while highly variable, were generally associated with positive impacts on health-related quality of life metrics (both perceived and measured), although these differences were not always statistically significant. Important limitations include variability in study design, selection bias among study participants, and short follow-up periods across most peer support interventions.

Association between self-reported race and ethnicity and myositis-specific autoantibodies in a diverse cohort of patients with inflammatory myopathy.

Myositis-specific autoantibodies (MSAs) are highly specific biomarkers for idiopathic inflammatory myopathies (IIMs). We investigated whether self-reported race and ethnicity were associated with the presence of specific MSAs. Charts of patients with IIM seen at 3 large healthcare systems in the same US city were reviewed. Demographic data and MSA test results were abstracted. Associations between race and ethnicity and presence of MSAs were analyzed using bivariate analysis and further characterized using separate unadjusted and adjusted logistic regression models. One hundred twenty-one subjects were included (19% Asian, 10% Black or African American, 27% Latinx or Hispanic, 36% non-Hispanic White, and 7% Other). In a bivariate analysis, anti-Jo-1 and anti-MDA5 autoantibodies were associated with race and ethnicity (p = 0.03 and 0.02, respectively). Black or African American subjects had increased odds of a positive anti-Jo-1 result compared to non-Hispanic White subjects on unadjusted logistic regression analysis (OR 8.61, 95% CI 1.61-46.07), although after adjustment for age and gender this finding was not significant. Subjects categorized as Other had increased odds of a positive anti-MDA5 result compared to non-Hispanic White subjects on both unadjusted (OR 55.0, 95% CI 2.02-1493) and adjusted analyses (OR 44.8, 95% CI 1.55-1298). Anti-Jo-1 and anti-MDA5 autoantibodies were significantly associated with race and ethnicity on bivariate analysis. Black or African American subjects had increased odds of positive anti-Jo-1 autoantibody on unadjusted, but not adjusted, logistic regression analysis. Subjects characterized as Other had increased odds of positive anti-MDA5 autoantibody, although confidence intervals were wide. Key Points • Association found between MSAs and race and ethnicity in diverse US cohort • Anti-Jo-1 and anti-MDA5 associated with race and ethnicity in bivariate analyses.

Diabetes mellitus and risk of breast cancer: a large-scale, prospective, population-based study.

BACKGROUND: The objective of this study was to evaluate associations of diabetes overall, type 1 diabetes (T1D), and type 2 diabetes (T2D) with breast cancer (BCa) risk. METHODS: We included 250,312 women aged 40-69 years between 2006 and 2010 from the UK Biobank cohort. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) were calculated for associations of diabetes and its two major types with the time from enrollment to incident BCa. RESULTS: We identified 8182 BCa cases during a median follow-up of 11.1 years. We found no overall association between diabetes and BCa risk (aHR = 1.02, 95% CI = 0.92-1.14). When accounting for diabetes subtype, women with T1D had a higher risk of BCa than women without diabetes (aHR = 1.52, 95% CI = 1.03-2.23). T2D was not associated with BCa risk overall (aHR = 1.00, 95% CI = 0.90-1.12). However, there was a significantly increased risk of BCa in the short time window after T2D diagnosis. CONCLUSIONS: Though we did not find an association between diabetes and BCa risk overall, an increased risk of BCa was observed shortly after T2D diagnosis. In addition, our data suggest that women with T1D may have an increased risk of BCa.

Development of American College of Rheumatology Quality Measures for Systemic Lupus Erythematosus: A Modified Delphi Process With Rheumatology Informatics System for Effectiveness (RISE) Registry Data Review.

OBJECTIVE: We aimed to develop readily measurable digital quality measure statements for clinical care in systemic lupus erythematosus (SLE) using a multistep process guided by consensus methods. METHODS: Using a modified Delphi process, an American College of Rheumatology (ACR) workgroup of SLE experts reviewed all North American and European guidelines from 2000 to 2020 on treatment, monitoring, and phenotyping of patients with lupus. Workgroup members extracted quality constructs from guidelines, rated these by importance and feasibility, and generated evidence-based quality measure statements. The ACR Rheumatology Informatics System for Effectiveness (RISE) Registry was queried for measurement data availability. In 3 consecutive Delphi sessions, a multidisciplinary Delphi panel voted on the importance and feasibility of each statement. Proposed measures with consensus on feasibility and importance were ranked to identify the top 3 measures. RESULTS: Review of guidelines and distillation of 57 quality constructs resulted in 15 quality measure statements. Among these, 5 met high consensus for importance and feasibility, including 2 on treatment and 3 on laboratory monitoring measures. The 3 highest-ranked statements were recommended for further measure specification as SLE digital quality measures: 1) hydroxychloroquine use, 2) limiting glucocorticoid use >7.5 mg/day to <6 months, and 3) end-organ monitoring of kidney function and urine protein excretion at least every 6 months. CONCLUSION: The Delphi process selected 3 quality measures for SLE care on hydroxychloroquine, glucocorticoid reduction, and kidney monitoring. Next, measures will undergo specification and validity testing in RISE and US rheumatology practices as the foundation for national implementation and use in quality improvement programs.

An Electronic Dashboard to Improve Dosing of Hydroxychloroquine Within the Veterans Health Care System: Time Series Analysis.

BACKGROUND: Hydroxychloroquine (HCQ) is commonly used for patients with autoimmune conditions. Long-term use of HCQ can cause retinal toxicity, but this risk can be reduced if high doses are avoided. OBJECTIVE: We developed and piloted an electronic health record-based dashboard to improve the safe prescribing of HCQ within the Veterans Health Administration (VHA). We observed pilot facilities over a 1-year period to determine whether they were able to improve the proportion of patients receiving inappropriate doses of HCQ. METHODS: Patients receiving HCQ were identified from the VHA corporate data warehouse. Using PowerBI (Microsoft Corp), we constructed a dashboard to display patient identifiers and the most recent HCQ dose and weight (flagged if ≥5.2 mg/kg/day). Six VHA pilot facilities were enlisted to test the dashboard and invited to participate in monthly webinars. We performed an interrupted time series analysis using synthetic controls to assess changes in the proportion of patients receiving HCQ ≥5.2 mg/kg/day between October 2020 and November 2021. RESULTS: At the start of the study period, we identified 18,525 total users of HCQ nationwide at 128 facilities in the VHA, including 1365 patients at the 6 pilot facilities. Nationwide, at baseline, 19.8% (3671/18,525) of patients were receiving high doses of HCQ. We observed significant improvements in the proportion of HCQ prescribed at doses ≥5.2 mg/kg/day among pilot facilities after the dashboard was deployed (-0.06; 95% CI -0.08 to -0.04). The difference in the postintervention linear trend for pilot versus synthetic controls was also significant (-0.06; 95% CI -0.08 to -0.05). CONCLUSIONS: The use of an electronic health record-based dashboard reduced the proportion of patients receiving higher than recommended doses of HCQ and significantly improved performance at 6 VHA facilities. National roll-out of the dashboard will enable further improvements in the safe prescribing of HCQ.

Spectrum of Clinical Presentations, Imaging Findings, and HLA Types in Immune Checkpoint Inhibitor-Induced Hypophysitis.

Context: Hypophysitis is a known immune-related adverse event (irAE) of immune checkpoint inhibitors (CPIs), commonly associated with CTLA-4 inhibitors and less often with PD-1/PD-L1 inhibitors. Objective: We aimed to determine clinical, imaging, and HLA characteristics of CPI-induced hypophysitis (CPI-hypophysitis). Methods: We examined the clinical and biochemical characteristics, magnetic resonance imaging (MRI) of the pituitary, and association with HLA type in patients with CPI-hypophysitis. Results: Forty-nine patients were identified. Mean age was 61.3 years, 61.2% were men, 81.6% were Caucasian, 38.8% had melanoma, and 44.5% received PD-1/PD-L1 inhibitor monotherapy while the remainder received CTLA-4 inhibitor monotherapy or CTLA-4/PD-1 inhibitor combination therapy. A comparison of CTLA-4 inhibitor exposure vs PD-1/PD-L1 inhibitor monotherapy revealed faster time to CPI-hypophysitis (median 84 vs 185 days, P < .01) and abnormal pituitary appearance on MRI (odds ratio 7.00, P = .03). We observed effect modification by sex in the association between CPI type and time to CPI-hypophysitis. In particular, anti-CTLA-4 exposed men had a shorter time to onset than women. MRI changes of the pituitary were most common at the time of hypophysitis diagnosis (55.6% enlarged, 37.0% normal, 7.4% empty or partially empty) but persisted in follow-up (23.8% enlarged, 57.1% normal, 19.1% empty or partially empty). HLA typing was done on 55 subjects; HLA type DQ0602 was over-represented in CPI-hypophysitis relative to the Caucasian American population (39.4% vs 21.5%, P = 0.01) and CPI population. Conclusion: The association of CPI-hypophysitis with HLA DQ0602 suggests a genetic risk for its development. The clinical phenotype of hypophysitis appears heterogenous, with differences in timing of onset, changes in thyroid function tests, MRI changes, and possibly sex related to CPI type. These factors may play an important role in our mechanistic understanding of CPI-hypophysitis.

Practical Considerations for Developing Clinical Natural Language Processing Systems for Population Health Management and Measurement.

Experts have noted a concerning gap between clinical natural language processing (NLP) research and real-world applications, such as clinical decision support. To help address this gap, in this viewpoint, we enumerate a set of practical considerations for developing an NLP system to support real-world clinical needs and improve health outcomes. They include determining (1) the readiness of the data and compute resources for NLP, (2) the organizational incentives to use and maintain the NLP systems, and (3) the feasibility of implementation and continued monitoring. These considerations are intended to benefit the design of future clinical NLP projects and can be applied across a variety of settings, including large health systems or smaller clinical practices that have adopted electronic medical records in the United States and globally.

Frequency of Contraception Documentation in Women With Systemic Lupus Erythematosus and Rheumatoid Arthritis Within the Rheumatology Informatics System for Effectiveness Registry.

OBJECTIVE: We sought to understand the frequency of contraception documentation for women with systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) in a large US electronic health record (EHR)-based registry and to identify disparities by teratogen prescription and patient race and ethnicity. METHODS: Contraception documentation from structured data fields within the Rheumatology Informatics System for Effectiveness (RISE) registry was collected for women of childbearing age (18-45 years) in 2018 who had at least 2 visits with International Classification of Diseases, Ninth Revision or Tenth Revision, diagnosis codes for SLE or RA (at any time). Univariate and multivariate analyses compared the frequency of contraception documentation based on patient characteristics including diagnosis, age, race, and teratogenicity of prescribed antirheumatic medications. RESULTS: In 2018, there were 9,826 women of childbearing age with SLE and 19,009 with RA, of whom 9.1% had any contraception documented. Rates of contraceptive documentation were significantly lower for women with SLE (adjusted odds ratio [OR] 0.84 [95% confidence interval (95% CI) 0.76-0.92]). Women of Hispanic ethnicity and Black and Asian race were all less likely than White women to have contraception documentation. Teratogen prescription was associated with higher rates of contraception documentation for women with RA but not SLE (RA adjusted OR 1.31 [95% CI 1.16-1.47]; SLE adjusted OR 1.08 [95% CI 0.91-1.28]). CONCLUSION: There are large gaps in contraception documentation within the RISE registry that are particularly stark among women of color. Although these data likely underestimate contraception use, they highlight that most rheumatologists do not have a systematic approach to collecting and recording this information in the EHR.

Development of a Natural Language Processing System for Extracting Rheumatoid Arthritis Outcomes From Clinical Notes Using the National Rheumatology Informatics System for Effectiveness Registry.

OBJECTIVE: To accelerate the use of outcome measures in rheumatology, we developed and evaluated a natural language processing (NLP) pipeline for extracting these measures from free-text outpatient rheumatology notes within the American College of Rheumatology's Rheumatology Informatics System for Effectiveness (RISE) registry. METHODS: We included all patients in RISE (2015-2018). The NLP pipeline extracted scores corresponding to 8 measures of rheumatoid arthritis (RA) disease activity (DA) and functional status (FS) documented in outpatient rheumatology notes. Score extraction performance was evaluated by chart review, and we assessed agreement with scores documented in structured data. We conducted an external validation of our NLP pipeline using data from rheumatology notes from an academic medical center that is not included in the RISE registry. RESULTS: We processed over 34 million notes from 854,628 patients, 158 practices, and 24 electronic health record (EHR) systems from RISE. Manual chart review revealed a sensitivity, positive predictive value (PPV), and F1 score of 95%, 87%, and 91%, respectively. Substantial agreement was observed between scores extracted from RISE notes and scores derived from structured data (κ = 0.43-0.68 among DA and 0.86-0.98 among FS measures). In the external validation, we found a sensitivity, PPV, and F1 score of 92%, 69%, and 79%, respectively. CONCLUSION: We developed an NLP pipeline to extract RA outcome measures from a national registry of notes from multiple EHR systems and found it to have good internal and external validity. This pipeline can facilitate measurement of clinical- and patient-reported outcomes for use in research and quality measurement.