Plasma Matrix Metalloproteinase Concentrations and Risk of Interstitial Lung Disease in a Prospective Rheumatoid Arthritis Cohort.

OBJECTIVE: To evaluate the associations of plasma matrix metalloproteinases (MMPs) with prevalent and incident interstitial lung disease (ILD) in people with rheumatoid arthritis (RA). METHODS: Within a multicenter, prospective cohort of US veterans with RA, we performed a cross-sectional study of prevalent ILD and cohort study of incident ILD. ILD diagnoses were validated by medical record review of provider diagnoses and chest imaging and/or pathology reports. MMP-1, 3, 7, and 9 concentrations were measured in plasma samples, then standardized and categorized into quartiles. The associations of MMPs with prevalent and incident ILD were assessed with logistic (prevalent) and Cox (incident) regression models adjusted for RA-ILD risk factors. RESULTS: Among 2,312 participants (88.9% male; mean age 63.8 years), 96 had prevalent ILD. Incident ILD developed in 130 participants over 17,378 person-years of follow-up (crude incidence rate 7.5/1,000 person-years). Participants with the highest quartile of MMP-7 concentrations had a nearly four-fold increased odds of prevalent ILD (adjusted odds ratio 3.78 [95% confidence interval (95% CI) 1.86-7.65]) and over two-fold increased risk of incident ILD (adjusted hazard ratio 2.33 [95% CI 1.35-4.02]). Higher MMP-9 concentrations were also associated with prevalent and incident ILD, as well as negatively correlated with forced vital capacity among those with prevalent ILD (r = -0.30, P = 0.005). CONCLUSION: MMP-7 and MMP-9 were strongly associated with both prevalent and incident ILD in this large, multicenter RA cohort after adjustment for other RA-ILD risk factors. These population-level findings further support a potential pathogenic role for MMPs in RA-ILD and suggest that their measurement could facilitate RA-ILD risk stratification.

Development and Internal Validation of a Clinical and Genetic Risk Score for Rheumatoid Arthritis-Associated Interstitial Lung Disease.

OBJECTIVE: Although clinical and genetic risk factors have been identified for rheumatoid arthritis-associated interstitial lung disease (RA-ILD), there are no current tools allowing for risk stratification. We sought to develop and validate an ILD risk model in a large, multicentre, prospective RA cohort. METHODS: Participants in the Veterans Affairs RA (VARA) registry were genotyped for 12 single nucleotide polymorphisms (SNPs) associated with idiopathic pulmonary fibrosis. ILD was validated through systematic record review. A genetic risk score (GRS) was computed from minor alleles weighted by effect size with ILD, using backward selection. The GRS was combined with clinical risk factors within a logistic regression model. Internal validation was completed using bootstrapping, and model performance was assessed by the area under the receiver operating curve (AUC). RESULTS: Of 2,386 participants (89% male, mean age 69.5 years), 9.4% had ILD. Following backward selection, five SNPs contributed to the GRS. The GRS and clinical factors outperformed clinical factors alone in discriminating ILD (AUC 0.675 vs 0.635, p< 0.001). The shrinkage-corrected performance for combined and clinical-only models was 0.667 (95% CI 0.628, 0.712) and 0.623 (95% CI 0.584, 0.651), respectively. Twenty percent of the cohort had a combined risk score below a cut-point with >90% sensitivity. CONCLUSION: A clinical and genetic risk model discriminated ILD in a large, multicentre RA cohort better than a clinical-only model, excluding 20% of the cohort from low-yield testing. These results demonstrate the potential utility of a GRS in RA-ILD and support further investigation into individualized risk stratification and screening.

Extracting forced vital capacity from the electronic health record through natural language processing in rheumatoid arthritis-associated interstitial lung disease.

PURPOSE: To develop a natural language processing (NLP) tool to extract forced vital capacity (FVC) values from electronic health record (EHR) notes in patients with rheumatoid arthritis-interstitial lung disease (RA-ILD). METHODS: We selected RA-ILD patients (n = 7485) in the Veterans Health Administration (VA) between 2000 and 2020 using validated ICD-9/10 codes. We identified numeric values in proximity to FVC string patterns from clinical notes in the EHR. Subsequently, we performed processing steps to account for variability in note structure, related pulmonary function test (PFT) output, and values copied across notes, then assigned dates from linked administrative procedure records. NLP-derived FVC values were compared to values recorded directly from PFT equipment available on a subset of patients. RESULTS: We identified 5911 FVC values (n = 1844 patients) from PFT equipment and 15 383 values (n = 4982 patients) by NLP. Among 2610 date-matched FVC values from NLP and PFT equipment, 95.8% of values were within 5% predicted. The mean (SD) difference was 0.09% (5.9), and values strongly correlated (r = 0.94, p < 0.001), with a precision of 0.87 (95% CI 0.86, 0.88). NLP captured more patients with longitudinal FVC values (n = 3069 vs. n = 1164). Mean (SD) change in FVC %-predicted per year was similar between sources (-1.5 [30.0] NLP vs. -0.9 [16.6] PFT equipment; standardized response mean = 0.05 for both). CONCLUSIONS: NLP of EHR notes increases the capture of accurate, longitudinal FVC values by three-fold over PFT equipment. Use of this NLP tool can facilitate pharmacoepidemiologic research in RA-ILD and other lung diseases by capturing this critical measure of disease severity.

Multimorbidity Patterns and Rheumatoid Arthritis Disease Outcomes: Findings From a Multicenter, Prospective Cohort.

OBJECTIVE: To determine whether unique multimorbidity patterns are associated with long-term rheumatoid arthritis (RA) disease severity. METHODS: We conducted a cohort study within the Veterans Affairs Rheumatoid Arthritis registry. We applied previously derived multimorbidity patterns based on the presence of diagnostic codes for relevant conditions prior to enrollment using linked administrative data. Disease activity and functional status were assessed longitudinally up to 5 years after enrollment. The association of multimorbidity patterns with disease activity and functional status were assessed using generalized estimating equations models adjusting for relevant confounders. RESULTS: We studied 2,956 participants, of which 88.2% were male, 76.9% reported white race, and 79.3% had a smoking history. Mental health and substance abuse (ß 0.12 [95% confidence interval {CI} 0.00, 0.23]), cardiovascular (ß 0.25 [95% CI 0.12, 0.38]), and chronic pain (ß 0.21 [95% CI 0.11, 0.31]) multimorbidity were associated with higher Disease Activity Score in 28 joints (DAS28) scores. Mental health and substance abuse (ß 0.09 [0.03, 0.15]), cardiovascular (ß 0.11 [95% CI 0.04, 0.17]), and chronic pain multimorbidity (ß 0.15 [95% CI 0.10, 0.20]) were also associated with higher Multidimensional Health Assessment Questionnaire (MDHAQ) scores. The metabolic pattern of multimorbidity was not associated with DAS28 or MDHAQ. The number of multimorbidity patterns present was highly associated with DAS28 and MDHAQ (P trend < 0.001), and patients with all four multimorbidity patterns had the highest DAS28 (ß 0.59 [95% CI 0.36, 0.83]) and MDHAQ (ß 0.27 [95% CI 0.16, 0.39]) scores. CONCLUSION: Mental health and substance abuse, chronic pain, and cardiovascular multimorbidity patterns are associated with increased RA disease activity and poorer functional status. Identifying and addressing these multimorbidity patterns may facilitate achieving RA treatment targets.

Aortic Stenosis Risk in Rheumatoid Arthritis.

Importance: Although an increased risk of ischemic cardiovascular disease has been associated with rheumatoid arthritis (RA), the risk of aortic stenosis (AS) is unknown. Objective: To examine the risk of incident AS, aortic valve intervention, AS-related death, and risk factors for AS development in patients with RA. Design, Setting, and Participants: This cohort study linked data from the Veterans Health Administration (VHA) and Centers for Medicare & Medicaid Services from 2000 to 2019. Patients with RA were matched by age, sex, and VHA enrollment year with up to 10 patients without RA. The cohort was followed until incident AS, aortic valve intervention, or death. Data were analyzed from August 23, 2022, to March 3, 2023. Exposures: the primary exposure was the presence of RA, defined using validated RA algorithms. Main Outcomes and Measures: Aortic stenosis was defined as a composite of inpatient or outpatient diagnoses, surgical or transcatheter aortic valve replacement, or AS-related death using diagnostic and procedural codes. Risk of AS development was assessed with multivariable Cox proportional hazards models adjusted for race, ethnicity, smoking status, body mass index, rurality, comorbidities, and health care use. Results: The cohort included 73 070 patients with RA (64 008 [87.6%] males; mean [SD] age, 63.0 [11.9] years) matched with 639 268 patients without RA (554 182 [86.7%] males; mean [SD] age, 61.9 [11.7] years) and 16 109 composite AS outcomes that occurred over 6 223 150 person-years. The AS incidence rate was 3.97 (95% CI, 3.81-4.13) per 1000 person-years in patients with RA and 2.45 (95% CI, 2.41-2.49) per 1000 person-years in the control patients (absolute difference, 1.52 per 1000 person-years). Rheumatoid arthritis was associated with an increased risk of composite AS (adjusted hazard ratio [AHR], 1.48; 95% CI, 1.41-1.55), aortic valve intervention (AHR, 1.34; 95% CI, 1.22-1.48), and AS-related death (AHR, 1.26; 95% CI, 1.04-1.54). Conclusions and Relevance: In this cohort study, RA was associated with a higher risk of developing AS and the subsequent risks of undergoing aortic valve intervention and suffering from AS-related death. Future studies are needed to confirm whether valvular heart disease, specifically AS, may be an overlooked cardiovascular disease complication in RA.

Enhancing the identification of rheumatoid arthritis-associated interstitial lung disease through text mining of chest computerized tomography reports.

OBJECTIVES: Algorithms have been developed to identify rheumatoid arthritis-interstitial lung disease (RA-ILD) in administrative data with positive predictive values (PPVs) between 70 and 80%. We hypothesized that including ILD-related terms identified within chest computed tomography (CT) reports through text mining would improve the PPV of these algorithms in this cross-sectional study. METHODS: We identified a derivation cohort of possible RA-ILD cases (n = 114) using electronic health record data from a large academic medical center and performed medical record review to validate diagnoses (reference standard). ILD-related terms (e.g., ground glass, honeycomb) were identified in chest CT reports by natural language processing. Administrative algorithms including diagnostic and procedural codes as well as specialty were applied to the cohort both with and without the requirement for ILD-related terms from CT reports. We subsequently analyzed similar algorithms in an external validation cohort of 536 participants with RA. RESULTS: The addition of ILD-related terms to RA-ILD administrative algorithms increased the PPV in both the derivation (improvement ranging from 3.6 to 11.7%) and validation cohorts (improvement 6.0 to 21.1%). This increase was greatest for less stringent algorithms. Administrative algorithms including ILD-related terms from CT reports exceeded a PPV of 90% (maximum 94.6% derivation cohort). Increases in PPV were accompanied by a decline in sensitivity (validation cohort -3.9 to -19.5%). CONCLUSIONS: The addition of ILD-related terms identified by text mining from chest CT reports led to improvements in the PPV of RA-ILD algorithms. With high PPVs, use of these algorithms in large data sets could facilitate epidemiologic and comparative effectiveness research in RA-ILD.

Updating and Validating the Rheumatic Disease Comorbidity Index to Incorporate ICD-10-CM Diagnostic Codes.

OBJECTIVE: To update and validate the Rheumatic Disease Comorbidity Index (RDCI) utilizing International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes. METHODS: We defined ICD-9-CM (n = 1,068) and ICD-10-CM (n = 1,425) era cohorts (n = 862 in both) spanning the ICD-9-CM to ICD-10-CM transition in a multicenter, prospective rheumatoid arthritis registry. Information regarding comorbidities was collected from linked administrative data over 2-year assessment periods. An ICD-10-CM code list was generated from crosswalks and clinical expertise. ICD-9- and ICD-10-derived RDCI scores were compared using intraclass correlation coefficients (ICC). The predictive ability of the RDCI for functional status and death during follow-up was assessed using multivariable regression models and goodness-of-fit statistics (Akaike's information criterion [AIC] and quasi information criterion [QIC]) in both cohorts. RESULTS: Mean ± SD RDCI scores were 2.93 ± 1.72 in the ICD-9-CM cohort and 2.92 ± 1.74 in the ICD-10-CM cohort. RDCI scores had substantial agreement in individuals who were in both cohorts (ICC 0.71 [95% confidence interval 0.68-0.74]). Prevalence of comorbidities was similar between cohorts with absolute differences <6%. Higher RDCI scores were associated with a greater risk of death and poorer functional status during follow-up in both cohorts. Similarly, in both cohorts, models including the RDCI score had the lowest QIC (functional status) and AIC (death) values, indicating better model performance. CONCLUSION: The newly proposed ICD-10-CM codes for the RDCI-generated comparable RDCI scores to those derived from ICD-9-CM codes and are highly predictive of functional status and death. The proposed ICD-10-CM codes for the RDCI can be used in rheumatic disease outcomes research spanning the ICD-10-CM era.

A Narrowing Mortality Gap: Temporal Trends of Cause-Specific Mortality in a National Matched Cohort Study in US Veterans With Rheumatoid Arthritis.

OBJECTIVE: To examine temporal trends in all-cause and cause-specific mortality in patients with rheumatoid arthritis (RA) in the Veterans Health Administration (VHA). METHODS: We conducted a matched cohort study in the VHA from January 1, 2000 to December 31, 2017. Incident RA patients were matched up to 1:10 on age, sex, and VHA enrollment year to non-RA patients, then followed until death or end of study period. Cause of death was obtained from the National Death Index. Multivariable Cox regression models stratified by RA diagnosis years were used to examine trends in RA-related risk of all-cause and cause-specific mortality. RESULTS: Among 29,779 incident RA patients (matched to 245,226 non-RA patients), 9,565 deaths occurred. RA patients were at increased risk of all-cause (adjusted hazard ratio [HRadj ] 1.23 [95% confidence interval (95% CI) 1.20-1.26]), cardiovascular (HRadj 1.19 [95% CI 1.14-1.23]), cancer (HRadj 1.19 [95% CI 1.14-1.24]), respiratory (HRadj 1.46 [95% CI 1.38-1.55]), and infection-related mortality (HRadj 1.59 [95% CI 1.41-1.80]). Interstitial lung disease was the cause of death most strongly associated with RA (HRadj 3.39 [95% CI 2.88-3.99]). Nearly 70% of excess deaths in RA were attributable to cardiopulmonary disease. All-cause mortality risk related to RA was lower among those diagnosed during 2012-2017 (HRadj 1.10 [95% CI 1.05-1.15]) compared to 2000-2005 (HRadj 1.31 [95% CI 1.26-1.36]), but still higher than for non-RA controls (P < 0.001). Cause-specific mortality trends were similar. CONCLUSION: Excess RA-related mortality was driven by cardiovascular, cancer, respiratory, and infectious causes, particularly cardiopulmonary diseases. Although our findings support that RA-related mortality risk is decreasing over time, a mortality gap remains for all-cause and cause-specific mortality in RA.

Identification of Multimorbidity Patterns in Rheumatoid Arthritis Through Machine Learning.

OBJECTIVE: Recognizing 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. METHODS: We 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. RESULTS: We 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). CONCLUSION: Cardiopulmonary, 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.

Risk of Prostate Cancer in US Veterans With Rheumatoid Arthritis.

OBJECTIVE: Patients with rheumatoid arthritis (RA) have an increased risk of select cancers, including lymphoma and lung cancer. Whether RA influences prostate cancer risk is uncertain. We aimed to determine the risk of prostate cancer in patients with RA compared to patients without RA in the Veterans Health Administration (VA). METHODS: We performed a matched (up to 1:5) cohort study of male patients with and without RA in the VA from 2000 to 2018. RA status, as well as covariates, were obtained from national VA databases. Prostate cancer was identified through linked VA cancer databases and the National Death Index. Multivariable Cox models compared prostate cancer risk between patients with RA and patients without RA, including models that accounted for retention in the VA system. RESULTS: We included 56,514 veterans with RA and 227,284 veterans without RA. During 2,337,104 patient-years of follow-up, 6,550 prostate cancers occurred. Prostate cancer incidence (per 1,000 patient-years) was 3.50 (95% confidence interval [95% CI] 3.32-3.69) in patients with RA and 2.66 (95% CI 2.58-2.73) in patients without RA. After accounting for confounders and censoring for attrition of VA health care, RA was modestly associated with a higher prostate cancer risk (adjusted HR [HRadj ] 1.12 [95% CI 1.04-1.20]). There was no association between RA and prostate cancer mortality (HRadj 0.92 [95% CI 0.73-1.16]). CONCLUSION: RA was associated with a modestly increased risk of prostate cancer, but not prostate cancer mortality, after accounting for relevant confounders and several potential sources of bias. However, even minimal unmeasured confounding could explain these findings.

Genetic, social, and environmental risk factors in rheumatoid arthritis-associated interstitial lung disease.

OBJECTIVE: MUC5B and TOLLIP single nucleotide polymorphisms (SNPs) and cigarette smoking were associated with rheumatoid arthritis-interstitial lung disease (RA-ILD) in a predominantly Northern European population. We evaluated whether RA-ILD is associated with these genetic variants and HLA-DRB1 shared epitope (SE) alleles in a large RA cohort stratified by race and smoking history. METHODS: HLA-DRB1 SE alleles and MUC5B rs35705950 and TOLLIP rs5743890 SNPs were genotyped in U.S. veterans with RA. ILD was validated through medical record review. Genetic associations with ILD were assessed in logistic regression models overall and in subgroups defined by race and smoking status, with additive interactions assessed by the relative excess risk of interaction (RERI). RESULTS: Of 2,556 participants (88% male, 77% White), 238 (9.3%) had ILD. The MUC5B variant was associated with ILD (OR 2.25 [95% CI 1.69, 3.02]), whereas TOLLIP and HLA-DRB1 SE were not. The MUC5B variant was less frequent among Black/African American participants (5.8% vs. 22.6%), though its association with RA-ILD was numerically stronger (OR 4.23 [1.65, 10.86]) compared to all other participants (OR 2.32 [1.70, 3.16]). Those with the MUC5B variant and a smoking history had numerically higher odds of ILD (OR 4.18 [2.53, 6.93]) than non-smokers (OR 2.41 [1.16, 5.04]). Additive interactions between MUC5B-race and MUC5B-smoking were not statistically significant. CONCLUSION: In this large RA cohort, the MUC5B promoter variant was associated with >2-fold higher odds of RA-ILD. While this variant is less common among Black/African American patients, its presence in this population carried >4-fold higher odds of RA-ILD.

The impact of disease severity measures on survival in U.S. veterans with rheumatoid arthritis-associated interstitial lung disease.

OBJECTIVES: To determine whether RA and interstitial lung disease (ILD) severity measures are associated with survival in patients with RA-ILD. METHODS: We studied US veterans with RA-ILD participating in a multicentre, prospective RA cohort study. RA disease activity (28-joint DAS [DAS28-ESR]) and functional status (multidimensional HAQ [MDHAQ]) were collected longitudinally while pulmonary function tests (forced vital capacity [FVC], diffusing capacity for carbon monoxide) were obtained from medical records. Vital status and cause of death were determined from the National Death Index and administrative data. Predictors of death were assessed using multivariable Cox regression models adjusting for age, sex, smoking status, ILD duration, comorbidity burden and medications. RESULTS: We followed 227 RA-ILD participants (93% male and mean age of 69 years) over 1073 person-years. Median survival after RA-ILD diagnosis was 8.5 years. Respiratory diseases (28%) were the leading cause of death, with ILD accounting for 58% of respiratory deaths. Time-varying DAS28-ESR (adjusted hazard ratio [aHR] 1.21; 95% CI: 1.03, 1.41) and MDHAQ (aHR 1.85; 95% CI: 1.29, 2.65) were separately associated with mortality independent of FVC and other confounders. Modelled together, the presence of either uncontrolled disease activity (moderate/high DAS28-ESR) or FVC impairment (<80% predicted) was significantly associated with mortality risk. Those with a combination of moderate/high disease activity and FVC <80% predicted had the highest risk of death (aHR 4.43; 95% CI: 1.70, 11.55). CONCLUSION: Both RA and ILD disease severity measures are independent predictors of survival in RA-ILD. These findings demonstrate the prognostic value of monitoring the systemic features of RA-ILD.

Risk of COVID-19 in Rheumatoid Arthritis: A National Veterans Affairs Matched Cohort Study in At-Risk Individuals.

OBJECTIVE: Rheumatoid arthritis (RA) and its treatments are associated with an increased risk of infection, but it remains unclear whether these factors have an impact on the risk or severity of COVID-19. The present study was undertaken to assess the risk and severity of COVID-19 in a US Department of Veterans Affairs (VA) cohort of patients with RA and those without RA. METHODS: A matched cohort study using national VA data was conducted. Patients diagnosed as having RA were identified among nondeceased individuals who were active in the VA health care system as of January 1, 2020 and who had received care in a VA medical center in 2019; patients for whom no RA diagnostic code was indicated were matched to the RA patients (1:1) by age, sex, and VA site (non-RA controls). Patients diagnosed as having COVID-19 and those with severe COVID-19 (defined as requiring hospitalization or leading to death) were ascertained from a national VA COVID-19 surveillance database through December 10, 2020. Multivariable Cox models were used to compare the risk of COVID-19 and COVID-19 hospitalization or death between RA patients and non-RA controls, after adjusting for demographic characteristics, comorbidities, health care utilization and access, and county-level COVID-19 incidence rates. RESULTS: This VA cohort of RA patients and non-RA controls (n = 33,886 subjects per group) predominantly comprised male patients (84.5%), and the mean age was 67.8 years. During follow-up, 1,503 patients in the cohort were diagnosed as having COVID-19; among them, 388 patients had severe COVID-19 (hospitalization or death), while in 228 patients, the deaths were not related to COVID-19. In the multivariable model, RA was associated with a higher risk of COVID-19 (adjusted hazard ratio [HR] 1.25 [95% confidence interval (95% CI) 1.13-1.39]) and a higher risk of COVID-19 hospitalization or death (adjusted HR 1.35 [95% CI 1.10-1.66]) as compared to non-RA controls. Use of disease-modifying antirheumatic drugs and prednisone, as well as self-reported Black race, self-reported Hispanic ethnicity, and presence of several chronic conditions, but not seropositivity for RA autoantibodies, were each associated with risk of COVID-19 and severe COVID-19 (hospitalization or death). CONCLUSION: Patients with RA are at higher risk of developing COVID-19 and severe COVID-19 (leading to hospitalization or death) compared to those without RA. With a risk of COVID-19 that approaches that of other recognized chronic conditions, these findings suggest that RA patients should be prioritized for COVID-19 prevention and management strategies.

Associations of serum cytokines and chemokines with the risk of incident cancer in a prospective rheumatoid arthritis cohort.

OBJECTIVES: We aimed to assess whether serum cytokine/chemokine concentrations predict incident cancer in RA patients. METHODS: Data from cancer-free enrollees in the Veterans Affairs Rheumatoid Arthritis (VARA) Registry were linked to a national VA oncology database and the National Death Index (NDI) to identify incident cancers. Seventeen serum cytokines/chemokines were measured from enrollment serum and an overall weighted cytokine/chemokine score (CK score) was calculated. Associations of cytokines/chemokines with all-site, lung, and lymphoproliferative cancers were assessed in Cox regression models accounting for relevant covariates including age, sex, RA disease activity, and smoking. RESULTS: In 1216 patients, 146 incident cancers (42 lung and 23 lymphoproliferative cancers) occurred over 10,072 patient-years of follow-up with a median time of 4.6 years from enrollment (cytokine/chemokine measurement) to cancer incidence. In fully adjusted models, CK score was associated with a higher risk of all-site (aHR 1.32, 95% CI 1.01-1.71, p < 0.001), lung (aHR 1.81, 1.40-2.34, p = 0.001), and lung/lymphoproliferative (aHR 1.54 [1.35-1.75], p < 0.001) cancer. The highest quartile of CK score was associated with a higher risk of all-site (aHR 1.91, 0.96-3.81, p = 0.07; p-trend = 0.005), lung (aHR 8.18, 1.63-41.23, p = 0.01; p-trend < 0.001), and lung/lymphoproliferative (aHR 4.56 [1.84-11.31], p = 0.001; p-trend < 0.001) cancer. Thirteen of 17 individual analytes were associated with incident cancer risk. CONCLUSION: Elevated cytokine/chemokine concentrations are predictive of future cancer in RA patients, particularly lung and lymphoproliferative cancers. These results suggest that the measurement of circulating cytokines/chemokines could be informative in cancer risk stratification and could provide insight into future cancer prevention strategies in RA, and possibly individuals without RA.

Burden and trajectory of multimorbidity in rheumatoid arthritis: a matched cohort study from 2006 to 2015.

OBJECTIVES: To compare the onset and trajectory of multimorbidity between individuals with and without rheumatoid arthritis (RA). METHODS: A matched, retrospective cohort study was completed in a large, US commercial insurance database (MarketScan) from 2006 to 2015. Using validated algorithms, patients with RA (overall and incident) were age-matched and sex-matched to patients without RA. Diagnostic codes for 44 preidentified chronic conditions were selected to determine the presence (≥2 conditions) and burden (count) of multimorbidity. Cross-sectional comparisons were completed using the overall RA cohort and conditional logistic and negative binomial regression models. Trajectories of multimorbidity were assessed within the incident RA subcohort using generalised estimating equations. RESULTS: The overall cohort (n=277 782) and incident subcohort (n=61 124) were female predominant (76.5%, 74.1%) with a mean age of 55.6 years and 54.5 years, respectively. The cross-sectional prevalence (OR 2.29, 95% CI 2.25 to 2.34) and burden (ratio of conditions 1.68, 95% CI 1.66 to 1.70) of multimorbidity were significantly higher in RA than non-RA in the overall cohort. Within the incident RA cohort, patients with RA had more chronic conditions than non-RA (ß 1.13, 95% CI 1.10 to 1.17), and the rate of accruing chronic conditions was significantly higher in RA compared with non-RA (RA × follow-up year, ß 0.21, 95% CI 0.20 to 0.21, p<0.001). Results were similar when including the pre-RA period and in several sensitivity analyses. CONCLUSIONS: Multimorbidity is highly prevalent in RA and progresses more rapidly in patients with RA than in patients without RA during and immediately following RA onset. Therefore, multimorbidity should be aggressively identified and targeted early in the RA disease course.

A needs assessment for simulation-based training of emergency medical providers in Nebraska, USA.

BACKGROUND: Training emergency medical services (EMS) workforce is challenging in rural and remote settings. Moreover, critical access hospitals (CAHs) struggle to ensure continuing medical education for their emergency department (ED) staff. This project collected information from EMS and ED providers across Nebraska to identify gaps in their skills, knowledge, and abilities and thus inform curriculum development for the mobile simulation-based training program. METHODS: The needs assessment used a three-step process: (1) four facilitated focus group sessions were conducted in distinct geographical locations across Nebraska to identify participants' perceived training gaps; (2) based on the findings from the focus group, a needs assessment survey was constructed and sent to all EMS and ED staff in Nebraska; and (3) 1395 surveys were completed and analyzed. RESULTS: Thematic areas of training gaps included cardiopulmonary conditions, diabetes management, mass casualty incidents (MCI), maternal health and child delivery, patient assessment, pediatric care (PC), and respiratory emergency care. Gaps in non-clinical skills were related to crisis management such as maintaining effective teamwork. Participants frequently identified cardiopulmonary care, PC, and MCI as highly needed trainings. Other needs included life support-related retaining courses, sessions informing protocol updates, the availability of retraining tailored for rural areas, substance use-related emergencies, and farming-related injuries. CONCLUSION: EMS and ED staff identified several skill gaps and training needs in the provision of emergency services in rural communities. These results allow for the development of customized training curricula and, with the help of an on-site simulation-based program, can identify gaps in health professionals' skills, knowledge, and abilities and thus help them respond to acute healthcare needs of rural communities.