Valid measurement of erosion volume in rheumatoid arthritis (RA) will facilitate the testing of treatments that may help to heal erosion. This study was undertaken to develop and validate a software method to measure erosion volume on computed tomography (CT) scans of the hand and wrist.Duplicate CT acquisitions of both hands of 5 patients with RA were evaluated using a semiautomated software tool to measure erosion volume in the entire hand and wrist and in each of 6 subregions. Reproducibility was quantified using the intraclass correlation coefficient (ICC), root mean square standard deviation (RMSSD), and coefficient of variation (CV), and the analysis was performed at the level of the hand (n = 10) and the subject (n = 5).The ICCs between 2 repositioned acquisitions were excellent, ranging from 0.97 to 1.00. At the hand level, the RMSSD was 15.6 mm(3) with a CV of 7.3%, and the CVs at the 6 regions ranged from 7.6% to 21.0%. At the subject level, the RMSSD was 31.2 mm(3) with a CV of 3.7%, and the CVs at the 6 regions ranged from 0.5% to 15.8%.We have developed a novel semiautomated software method to measure erosion volume on hand and wrist CT scans. The method is reproducible and can be used to detect changes in erosion volume. This will facilitate the testing of treatments intended to reduce erosion volume.
Objective We aimed to determine how 2 definitions of end-stage knee osteoarthritis (esKOA) and each component (knee symptoms, persistent knee pain, radiographic severity, and presence of limited mobility or instability) related to future knee replacement (KR). Methods We performed knee-based analyses of Osteoarthritis Initiative data from baseline to the first 4 annual follow-up visits, and data on KR from baseline until the fifth yearly contact. We calculated a base model using common risk factors for KR in logistic regression models with generalized estimating equations. We assessed model performance with area under the receiver-operating characteristic curve (AUC) and Hosmer-Lemeshow test. We then added esKOA or each component from the visit (< 12 months) before a KR and change in the year before a KR. We calculated the net reclassification improvement (NRI) index and the integrated discrimination improvement (IDI) index. Results Our sample was mostly female (58%), ≥ 65 years old, White (82%), and without radiographic knee osteoarthritis (50%). At the visit before a KR, Kellgren-Lawrence (KL) grades (ordinal scale; AUC 0.88, NRI 1.12, IDI 0.11), the alternate definition of esKOA (AUC 0.84, NRI 1.16, IDI 0.12), and a model with every component of esKOA (AUC 0.91, NRI 1.30, IDI 0.17) had the best performances. During the year before a KR, change in esKOA status (alternate definition) had the best performance (AUC 0.86, NRI 1.24, IDI 0.12). Conclusion Radiographic severity may be a screening tool to find a knee that will likely receive a KR. However, esKOA may be an ideal outcome in clinical trials because a change in esKOA state predicts future KR.
We aimed to determine whether hand OA is characterized by systemic cartilage loss by assessing if radiographically normal joints had greater joint space width (JSW) loss over 4 years in hands with incident or prevalent OA elsewhere in the hand compared with hands without OA. We used semi-automated software to measure JSW in the distal and proximal IP joints of 3368 participants in the Osteoarthritis Initiative who had baseline and 48-month hand radiographs. A reader scored 16 hand joints (including the thumb base) for Kellgren-Lawrence (KL) grade. A joint had OA if scored as KL ≥2. We identified three groups based on longitudinal hand OA status: no hand OA (KL <2 in all 16 joints) at the baseline and 48-month visits, incident hand OA (KL <2 in all 16 joints at baseline and then one or more joints with KL ≥2 at 48 months) and prevalent hand OA (one or more joints with KL ≥2 at baseline and 48 months). We then assessed if JSW in radiographically normal joints (KL 0) differed across these three groups. We calculated unpooled effect sizes to help interpret the differences between groups. We observed small differences in JSW loss that are unlikely to be clinically important in radiographically normal joints between those without hand OA (n = 1054) and those with incident (n = 102) or prevalent hand OA (n = 2212) (effect size range -0.01-0.24). These findings were robust when examining JSW loss dichotomized based on meaningful change and in other secondary analyses. Hand OA is not a systemic disease of cartilage.
To identify the extent to which opioid prescribing rates for patients with rheumatoid arthritis (RA) vary in the US and to determine the implications of baseline opioid prescribing rates on the probability of future long-term opioid use.We identified patients with RA from physicians who contributed ≥10 patients within the first 12 months of participation in the Corrona RA Registry. The baseline opioid prescribing rate was calculated by dividing the number of patients with RA reporting opioid use during the first 12 months by the number of patients with RA providing data that year. To estimate odds ratios (ORs) for long-term opioid use, we used generalized linear mixed models.During the follow-up period, long-term opioid use was reported by 7.0% (163 of 2,322) of patients of physicians with a very low rate of opioid prescribing (referent) compared to 6.8% (153 of 2,254) of patients of physicians with a low prescribing rate, 12.5% (294 of 2,352) of patients of physicians with a moderate prescribing rate, and 12.7% (307 of 2,409) of patients of physicians with a high prescribing rate. The OR for long-term opioid use after the baseline period was 1.16 (95% confidence interval [95% CI] 0.79-1.70) for patients of low-intensity prescribing physicians, 1.89 (95% CI 1.27-2.82) for patients of moderate-intensity prescribing physicians, and 2.01 (95% CI 1.43-2.83) for patients of high-intensity prescribing physicians, compared to very low-intensity prescribing physicians.Rates of opioid prescriptions vary widely. Our findings indicate that baseline opioid prescribing rates are a strong predictor of whether a patient will become a long-term opioid user in the future, after controlling for patient characteristics.
Introduction: The use of statins after acute myocardial infarction (MI) has been shown to reduce the risk of recurrent MI and mortality. We examined the association between statin therapy and the risk of 1 year mortality after MI hospitalization. Methods: Data from the Veterans Health Administration was used to create a national sample of Veterans hospitalized for their first MI event between 2002 and 2015. Veterans with prevalent heart failure, stroke, or cancer diagnoses at the time of discharge for the index MI and prolonged hospitalization (greater than 30 days) were excluded. The statin therapy group was defined as Veterans having any statin prescription at the time of discharge. The primary outcome was all-cause mortality obtained from the National Death Index. We fitted a Cox regression model adjusted for age, length of hospital stay, peak cardiac troponin I ratio (the ratio of the peak measurement to the reference upper limit of normal for the assay) during hospitalization, statin use before admission, beta blocker prescription at discharge, liver disease, peripheral arterial disease, estimated glomerular filtration rate, high-density lipoprotein and total cholesterol levels. Billing codes were used to define exclusion criteria and co-morbidities. Results: Among 16,263 Veterans hospitalized for MI, mean age was 62 years and 98% were men. During 350 days mean follow up, 966 deaths occurred. In the statin therapy group 709/13,334 (5.3%) of Veterans died compared to 257/2,929 (8.8%) of Veterans without statin therapy. In an age-adjusted model, 1-year mortality was 35% lower (HR 0.65, 95%CI 0.56 - 0.75) for patients that were prescribed a statin at discharge compared to Veterans who did not receive a statin at discharge. In a multivariable model we observed a 27% (HR 0.73, 95% CI 0.63 - 0.85) lower risk of death for users of statin therapy compared to non-users (Figure). Conclusions: Statin therapy prescribed after a first MI event may reduce the 1 year risk of all-cause mortality.
Background: Patients with rheumatoid arthritis (RA) are at increased risk of serious infections, with considerable excess morbidity and mortality after pneumonia. RA-related autoantibodies such as anti-cyclic citrullinated peptide (CCP) and rheumatoid factor (RF) may be generated at inflamed pulmonary mucosa prior to clinical RA onset. Therefore, patients with seropositive RA may be at increased risk for pneumonia after RA diagnosis due to subclinical pulmonary injury. Objectives: We investigated whether seropositive RA was associated with increased pneumonia risk compared to seronegative RA. Methods: We performed a retrospective cohort study among RA patients seen at a health care system in Boston, MA. RA patients were identified using a previously validated electronic health record (EHR) algorithm incorporating billing codes, natural language processing (NLP) of notes, medications, and laboratory results at 97% specificity 1 . We constructed an incident RA cohort using NLP for the index date of initial mention of RA. All patients were required to have both CCP and RF data from clinical care to determine serologic RA phenotype. We used semi-supervised machine learning approaches to identify pneumonia using billing codes and terms extracted using NLP, with the Centers for Disease Control definition of pneumonia from medical record review as a gold standard. The area under the receiver operating curve (AUROC) for this billing code+NLP pneumonia algorithm was 0.94 compared to the standard rule-based pneumonia algorithm (billing code on inpatient discharge) AUROC of 0.86 (p<0.001). Smoking status was extracted using NLP methods. Other covariates, including a previous validated weighted RA multimorbidity score 2 , were determined using structured EHR data. We used Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for pneumonia adjusting for potential confounders. Results: We analyzed a total of 4,110 patients with incident RA and both CCP/RF data available. Mean age at index date was 53.0 years (SD 14.8), 77.2% were female, and 79.8% were CCP+ or RF+. During 32,248 patient-years of follow-up (mean 7.8 years/patient), we identified 240 pneumonia cases. Patients with seropositive RA had a HR of 1.99 (95%CI 1.30-3.01, Table) for pneumonia compared to patients with seronegative RA, adjusted for age, sex, smoking, index year, ESR level, glucocorticoid use, DMARD use, and weighted RA multimorbidity score. While CCP+ RA (HR 1.91, 95%CI 1.23-2.97) and RF+ RA (HR 2.07, 95%CI 1.35-3.16) had increased pneumonia risk compared to seronegative RA, the CCP+RF- RA subgroup had no association with pneumonia (HR 0.67, 95%CI 0.23-1.93). Conclusion: Patients with incident seropositive RA, particularly RF+ RA, had increased risk for pneumonia throughout the RA disease course that was not explained by measured confounders including smoking status, multimorbidity, medications, and ESR level. Further studies should investigate how RF+ may predispose RA patients to later develop pneumonia after clinical RA diagnosis. References: [1]Liao KP, Cai T, Gainer V, et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res. 2010;62(8):1120–1127. [2]Radner H, Yoshida K, Mjaavatten MD, et al. Development of a multimorbidity index: Impact on quality of life using a rheumatoid arthritis cohort. Semin Arthritis Rheum. 2015;45(2):167–173. Disclosure of Interests: Jeffrey Sparks Consultant of: Bristol-Myers Squibb, Optum, Janssen, Gilead, Weixing Huang: None declared, Bing Lu: None declared, Sicong Huang: None declared, Andrew Cagan: None declared, Vivian Gainer: None declared, Sean Finan: None declared, Guergana Savova: None declared, Daniel Solomon Grant/research support from: Funding from Abbvie and Amgen unrelated to this work, Elizabeth Karlson: None declared, Katherine Liao: None declared
To elucidate how postdiagnosis multimorbidity and lifestyle changes contribute to the excess mortality of rheumatoid arthritis (RA).We performed a matched cohort study among women in the Nurses' Health Study (1976-2018). We identified women with incident RA and matched each by age and year to 10 non-RA comparators at the RA diagnosis index date. Specific causes of death were ascertained via death certificates and medical record review. Lifestyle and morbidity factors were reported biennially; 61 chronic conditions were combined into the Multimorbidity Weighted Index (MWI). After adjusting for baseline confounders, we used inverse probability weighting analysis to examine the mediating influence of postindex MWI scores and lifestyle factors on total, cardiovascular, and respiratory mortality, comparing women with RA to their matched comparators.We identified 1,007 patients with incident RA and matched them to 10,070 non-RA comparators. After adjusting for preindex confounders, we found that hazard ratios (HRs) and 95% confidence intervals (95% CIs) were higher for total mortality (HR 1.46 [95% CI 1.32, 1.62]), as well as cardiovascular (HR 1.54 [95% CI 1.22, 1.94]) and respiratory (HR 2.75 [95% CI 2.05, 3.71]) mortality in patients with RA compared to non-RA comparators. Adjusting for postindex lifestyle factors (physical activity, body mass index, diet, smoking) attenuated but did not substantially account for this excess RA mortality. After additional adjustment for postindex MWI scores, patients with RA had HRs of 1.18 (95% CI 1.05, 1.32) for total, 1.19 (95% CI 0.94, 1.51) for cardiovascular, and 1.93 (95% CI 1.42, 2.62) for respiratory mortality.We found that MWI scores substantially accounted for the excess total and cardiovascular mortality among women with RA. This finding underscores the importance of monitoring for the total disease burden as a whole in monitoring patients with RA.
