Katz, Patricia

Objective Epidemiologic estimates for age of systemic lupus erythematosus (SLE) diagnosis are limited, particularly for men and racial and ethnic subpopulations in the United States. Leveraging the Centers for Disease Control and Prevention (CDC) National Lupus Registry network of population‐based SLE registries, meta‐analyses were performed estimating age‐specific incidence of SLE by sex, race, and ethnicity. Methods The CDC network of SLE registries includes five state‐based registries, plus a sixth Indian Health Service registry. Incidence periods spanned calendar years 2002‐2018. Registries provided age‐specific incidence of SLE, fulfilling the American College of Rheumatology (ACR) SLE classification criteria, per 100,000 person‐years, stratified by sex, race, and ethnicity for the meta‐analyses. Results Incidence rates among women were highest for those aged 30‐39 (12.7, 95%CI: 9.5‐16.8), while rates among men were highest for those aged 60‐69 (2.1, 95%CI: 1.3‐3.4). Black women aged 20‐39 had the highest SLE incidence rates (23.6, 95%CI: 20.7‐26.8). Among women diagnosed with SLE before age 20, incidence was highest among Asian (6.5, 95%CI: 2.8‐15.2) individuals. Among women diagnosed with SLE after age 60, incidence was highest among American Indian/Alaska Native (9.4, 95%CI: 3.4‐15.4) individuals. Weighted percentages show 10.0% of those with SLE were diagnosed before age 20, while 15.1% were diagnosed after age 60. The largest proportion of individuals with SLE, 22.1%, were diagnosed between 30‐39 years. Conclusions This study provides comprehensive sex‐ and race‐specific estimates of age at SLE diagnosis. The substantial later in life SLE incidence among men and disease burden in pediatric and older populations should raise awareness in considering SLE in the differential diagnosis in previously underrecognized groups.

Objective Cognitive impairment is prevalent in rheumatoid arthritis (RA), yet risk factors are not well understood. We explored associations between clinical characteristics and cognitive impairment in an RA cohort. Methods Data were from a longitudinal RA cohort at Veterans Affairs Puget Sound Health Care System. Cognition was evaluated using the Saint Louis University Mental Status (SLUMS) examination. Demographics and health factors, objectively measured physical function, participant‐reported symptoms, RA disease characteristics, and comorbidities were evaluated. Univariable linear regressions explored the association between clinical factors and SLUMS scores. Those with P < 0.1 in the univariable models were evaluated in separate multivariable linear regressions controlling for age, sex, and years of education. Results A total of 145 participants with RA were included, with a mean age of 64.5 (SD 11.6) years, and were predominantly male (74%). Using SLUMS, 42 participants (29%) had normal cognition, 83 (57%) had mild cognitive impairment, and 20 (14%) had dementia. Physical performance (aβ: 0.35, confidence interval [CI] 0.11 to 0.59), self‐reported exhaustion (aβ: −2.22, CI −3.44 to −0.99), pain (aβ: −0.25, CI −0.50 to −0.00), disability (aβ: −1.79, CI −3.16 to −0.42), and trouble falling asleep (aβ: −2.57, CI −4.00 to −1.14) were all independently associated with a lower SLUMS score (all P < 0.05). Conclusion Cognitive impairment was prevalent in our cohort of veterans with RA and was associated with several modifiable clinical factors. Future longitudinal studies are needed to determine the directionality of these associations and evaluate interventions for modifiable risk factors that may mitigate cognitive dysfunction. image

Objective As part of a Centers for Disease Control and Prevention–funded American College of Rheumatology (ACR) initiative, we sought to develop quality measures related to Patient Reported Outcome Measure (PROM) use for systemic lupus erythematosus (SLE) clinical care. Methods An expert workgroup composed of physician, patient, and researcher representatives convened to identify patient‐reported outcome (PRO) domains of greatest importance to people with SLE. A patient advisory panel separately ranked domains. PROMs assessing priority domains were identified through structured literature review, and detailed psychometric reviews were conducted for each PROM. In a Delphi process, the expert workgroup rated PROMs on content validity, psychometric quality, feasibility of implementation, and importance for guiding patient self‐management. The patient advisory panel reviewed PROMs in parallel and contributed to the final recommendations. Results Among relevant PRO domains, the workgroup and patient partners ranked depression, physical function, pain, cognition, and fatigue as high‐priority domains. The workgroup recommended at least once yearly measurement for (1) assessment of depression using the Patient Health Questionnaire or Patient Reported Outcomes Measurement Information System (PROMIS) depression scales; (2) assessment of physical function using PROMIS physical function scales or the Multi‐Dimensional Health Assessment Questionnaire; and (3) optional assessments of fatigue and cognition. Pain scales evaluated were not found to be sufficiently superior to what is already assessed in most SLE clinic visits. Conclusion Expert workgroup members and patient partners recommend that clinicians assess depression and physical function at least once yearly in all people with SLE. Additional PROMs addressing cognition and fatigue can also be assessed. Next steps are to incorporate PROM‐based quality measures into the ACR The Rheumatology Informatics System for Effectiveness registry.

ObjectiveSome patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID‐19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID‐19 ARDS in this population and to create a simple risk score calculator for use in clinical settings.MethodsData were derived from the COVID‐19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID‐19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier.ResultsThe study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67‐0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%‐83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator.ConclusionWe were able to predict ARDS with good sensitivity using information readily available at COVID‐19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID‐19 disease progression.