Author: Mokhtar

According to new research at ACR Convergence 2023, the annual meeting of the American College of Rheumatology (ACR), use of tumor necrosis factor inhibitors during pregnancy is not associated with worse fetal or obstetric outcomes and may reduce the risk of serious maternal infections during pregnancy ( Abstract #0477).

Tumor necrosis factor (TNF) inhibitors such as adalimumab and infliximab are often prescribed for inflammatory forms of arthritis that have not improved with other treatments. Although research shows the drugs are safe during pregnancy, many women stop taking them for fear of harming the fetus. Unlike other medications used for inflammatory arthritis, such as methotrexate, which can cause serious fetal complications, TNF inhibitors are not known teratogens (an agent that causes abnormalities after fetal exposure during pregnancy).

To further test the safety of persistent TNF inhibitors during pregnancy, Anna Molto, MD, PhD, HDR, a rheumatologist and researcher at Cochin Hospital in Paris, France, and her colleagues used data from a nationwide French health insurance database to create a ​​to emulate a randomized database. clinical trial (RCT). This type of research relies on observational data to conduct a study when a gold standard RCT may not be ethical or feasible.

The researchers identified more than 2,000 women treated with TNF inhibitors for rheumatoid arthritis (579 patients) or spondyloarthritis (1,503 patients) between 2008 and 2017. Each had a singleton pregnancy, with 1,497 (72%) stopping treatment when they learned they were pregnant. The average age of the women at the start of pregnancy was 31 years±5 years and the average duration of illness was 4 ±5 years.

The results showed no statistically significant difference in poor obstetric, fetal or infant outcomes, including spontaneous abortion (a loss of pregnancy naturally before twenty weeks of gestation), medical termination of pregnancy, preeclampsia or eclampsia, gestational diabetes, premature birth, small birth weight or serious birth defects.

Interestingly, women who continued to use TNF inhibitors were less likely to be hospitalized for serious infections during pregnancy during six weeks postpartum compared to those who stopped treatment (0.2 versus 1.3 percent, respectively). Molto says this finding was the most surprising.

“Although we had assumed that pregnancy outcomes would be at least comparable in both groups, we did not expect that there would be a lower risk of maternal infections in patients who continued TNFi, as the risk of infection is known to be higher with these treatments” , she says. She speculates that the finding may be due to lower concomitant use of corticosteroids, but does not yet have results to confirm her theory.

Regarding the overall study results, Molto says: “These data add to the increasingly reassuring data on the use of TNFi during pregnancy. And most importantly, if a rheumatologist is considering stopping a TNFi during pregnancy because of the risk of infection, this study suggests that this may not be justified.”

Molto acknowledges the limitations of relying on claims data, noting that disease activity cannot be measured, but also points out that the use of a national database ensures that “all French participants are included, [thereby avoiding] selection bias.”

The next step, Molto says, is to test the hypothesis in a randomized controlled trial.

This study was carried out thanks to the funding program of the French Ministry of Health.

Researchers will present the first-ever study on fractures and calcium pyrophosphate deposition disease at ACR Convergence 2023, the annual meeting of the American College of Rheumatology (ACR). They report a doubled risk of fracture in patients with acute calcium pyrophosphate crystal arthritis compared with those without the disease (Abstract #0235).

Calcium pyrophosphate deposition disease (CPPD) occurs when calcium pyrophosphate (CPP) crystals form near cartilage cells, sometimes leading to joint inflammation, pain, and swelling. It is often called pseudogout because of its clinical similarity to gout, yet much less is known about CPPD than about gout and other forms of inflammatory arthritis.

Rheumatologist Sara Tedeschi, MD, MPH, her colleagues at Brigham and Women’s Hospital in Boston, and fellow at the Medical College of Wisconsin, wanted to expand the knowledge base by investigating whether patients with CPPD disease are at increased risk for fractures. Previous studies had shown a link between low bone density and CPPD. Recent data from experimental models suggest that increased formation of osteoclast (cells that break down old bone) due to loss of function of osteoprotegerin (a protein that normally inhibits bone resorption) may contribute to the pathogenesis of the disease.

To find out more, Tedeschi and her team conducted a matched cohort study using electronic health records (EHR) from Mass General Brigham’s health care system. The study included more than 1,100 patients who had at least one episode of acute CPP crystal arthritis; the acute inflammatory form of CPPD – between 1991 and 2017. They were compared with more than 3,300 comparison researchers who did not have acute CPP crystal arthritis, although they could have other types of arthritis. The average age in both groups was 73 years, and more than half were women.

The index date for patients with CPP crystal arthritis was either the first mention of pseudogout in their chart or the first synovial fluid analysis with the finding of CPP crystals. The period from registration of the EPD to the index date was at least 180 days. The index date for the matched comparators was a medical encounter within 30 days of the matched pseudogout patient’s index date.

The primary outcome of the study was the first fragility fracture (fractures resulting from a fall from standing height or lower) at the humerus, wrist, hip or pelvis. Secondary outcomes were the first fracture at each of these anatomic locations. For patients with more than one fracture, only the earliest fracture was used. Fragility fractures were identified using published algorithms with a positive predictive value of greater than 90%.

The researchers estimated the incidence rates and incidence ratios for each type of fracture and for fractures at each individual body location. They used Cox models (a statistical technique that can be used to measure time-to-event results on one or more predictors) to estimate adjusted risk ratios for fractures. Patients who had rheumatoid arthritis (RA) or were prescribed corticosteroid or osteoporosis treatment were excluded from the sensitivity analyzes in an attempt to rule out the influence of these diagnoses/medications, which are known to increase the risk of fracture.

The researchers found that the fracture rate was twice as high in the acute crystal CPP arthritis cohort as in the comparison group, after adjusting for traditional fracture risk factors: 11.2 per 1,000 person-years versus 5.6 per 1,000 person-years. The disparity between the two groups increased over time and the sensitivity analyzes yielded similar findings.

Tedeschi says the increased risk of fractures wasn’t particularly surprising, but the difference was large. Also surprising, she says, was “that differences in fracture risk were seen, of similar magnitude, after excluding patients who had used corticosteroids in the 90 days before the index date.” [Moreover]Fracture rates varied within the first months of follow-up, indicating a pre-existing difference in bone health between cohorts.”

Tedeschi notes that the study does not indicate whether patients with acute CPP crystal arthritis had repeat episodes or used corticosteroids after the index date, either of which could influence the findings. She adds that they could not assess falls, which would affect fracture risk and may have differed between CPPD and comparators. She concludes by noting: “The analysis did not assess vertebral fractures as they may be asymptomatic and not captured in diagnosis codes.”

Yet the findings are clear: patients with acute CPP crystal arthritis have a doubled risk of fragility fractures.

“At the very least, we hope that physicians will consider assessing bone mineral density in patients with CPPD to determine whether osteoporosis treatment is necessary,” says Tedeschi.

This research was supported by grants from the NIH’s National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS).

New research at ACR Convergence 2023, the annual meeting of the American College of Rheumatology (ACR), shows that ultrasound-detected salivary gland abnormalities in primary Sjögren’s become more severe over time and that the slowly progressive disease likely begins long before it reaches the is first. detected (summary #1371).

Sjögren’s disease, also called primary Sjögren’s syndrome, is a systemic autoimmune disease. It is characterized by inflammation of the tear and salivary glands, leading to chronic dry eyes and mouth. Fatigue is common and about a third of patients have complications affecting the lungs, skin, kidneys and joints. Up to 60% of patients may develop systemic symptoms.

Salivary gland ultrasound (SGUS) is a safe and non-invasive method for diagnosing and monitoring Sjögren’s disease. Still, it’s unclear whether the abnormalities it detects become more notable over time. Valérie Devauchelle-Pensec, M.D., Ph.D., professor of rheumatology in the Department of Clinical Immunology and Rheumatology at the University of Brest Occidentale and Cavale Blanche Hospital in Brest, France, designed a cross-sectional international study to find out.

I have been caring for patients with Sjögren’s disease for years and I am always surprised that when I see them at the beginning of their disease, their first ultrasound scan of the salivary gland shows severe lesions. I also have many patients with rheumatoid arthritis. In rheumatoid arthritis, the joints are destroyed, but not at the onset of the disease. Sjögren’s seems different. I wondered, ‘When does the disease really start and do the lesions evolve over time or not?’ Many of my colleagues, who are experts in Sjögren’s and ultrasound, agreed to participate [in the study].”

Valérie Devauchelle-Pensec, MD, Ph.D., professor of rheumatology, department of clinical immunology and rheumatology at the University of Brest Occidentale

Between May 2019 and February 2022, 247 patients from 11 international centers consecutively participated in the study. Most were women, with an average age of 58 years. Nearly 100% of patients reported dry mouth; 75% had abnormal saliva production and 85% were positive for anti-SSA autoantibodies, a hallmark of Sjögren’s. The median EULAR Sjögren’s disease activity score (ESSDAI) was 3, indicating low disease activity.

Ultrasound-detected functional abnormalities of the parotid and submandibular gland were classified according to the most recent Outcome Measures in Rheumatology (OMERACT) score, a four-grade semiquantitative scoring system. The patients were then grouped according to the duration of illness from the onset of dry mouth symptoms.

• Group A: less than five years (47 patients)
• Group B: five to nine years (69 patients)
• Group C: 10 to 20 years (78 patients)
• Group D: More than 20 years (53 patients)

When the researchers looked at the most serious node for each patient, they found a significant association between disease duration and the OMERACT score. The odds ratio for progression over a five-year interval was 1.23.

There was no statistical difference between the groups with regard to the various ultrasound parameters, with the exception of the proportion of hyperechoic bands, which are associated with damage in established Sjögren’s patients.

“We hypothesized that hyperechoic bands represent the slow fibroadipose evolution of the disease,” says Devauchelle-Pensec. “To me, this means that Sjögren’s disease starts long before we find it, so it is important to treat patients early.”

She adds that the study highlights the importance of adding ultrasound findings to the classification criteria for Sjögren’s syndrome and the need for a better understanding of when the disease begins.

New research at ACR Convergence 2023, the annual meeting of the American College of Rheumatology (ACR), describes the expansion of a new program to train primary care physicians (PCPs) in the diagnosis and treatment of rheumatoid arthritis (RA) in Native American communities that little or no access to rheumatology care (Abstract #2455).

Despite the high prevalence of RA and other rheumatologic diseases among Native Americans, many Native American communities lack adequate access to subspecialty care. As a result, the responsibility for treatment has shifted to primary care providers, who often lack confidence in prescribing RA medications or managing the disease. To help offset the critical shortage of rheumatologists, the Rheumatology Access Expansion (RAE) Initiative launched RA ECHO (Extension for Community Healthcare Outcomes) in 2021, a 12-week training program to teach Navajo Nation PCPs how to diagnose and treat RA. The goal was to improve outcomes and reduce entrenched disparities in healthcare. This year, the project expanded to tribes in 15 states.

We successfully offered the RA ECHO curriculum three times on the Navajo Nation from 2021-2022. For our fourth cohort – Spring 2023 – we have dramatically expanded our target audience and invited healthcare professionals serving Native American communities across the country to participate.”

Jennifer Mandal, MD, assistant professor at the University of California, San Francisco (UCSF) and director of the RAE Initiative

Mandal says she and her team worked with an organization called Indian Country ECHO to recruit participants for cohort four.

“We knew that Indian Country ECHO’s established lines of communication with tribes across the country would allow us to reach a much broader audience for our RA ECHO program. And indeed, when Indian Country ECHO put out a call for interested health care providers the response was overwhelming in attending our program. Over 100 providers responded that they would like to participate, and after the final dates and times were selected, 50 providers registered,” says Mandal.

Most participants were PCPs, but pharmacists, community health care representatives, and providers from non-primary care settings such as emergency medicine, ophthalmology, and orthopedics also enrolled.

The Spring 2023 program followed the same format as the first three: 12 weekly interactive classes held virtually, with approximately 30 minutes of high-level didactics on key aspects of RA diagnosis and management, followed by case-based discussion. Participants were encouraged to bring their own anonymized patient cases to class. In addition to the weekly sessions, there were also biweekly virtual ‘office hours’, where participants could interact with a panel of rheumatologists.

To measure how successful the training actually was, the RAE Initiative team collected data on PCP responses to tests and surveys. Before and after each of the four programs (cohort five is currently underway), participants completed a medical knowledge test about RA and surveys about their confidence in the diagnosis and treatment of RA on a five-point Likert scale. Starting in cohort three, participants were also asked to rate changes in their own clinical behavior, such as how often they performed joint screening exams or blood tests before starting immunosuppressants.

Pre- and post-intervention scores were available for more than one-third of participants. When data across cohorts were pooled, test scores increased by 26% and PCP confidence increased by more than one point on the Likert scale. Nearly 80% of participants reported that they performed important clinical behaviors related to the diagnosis and treatment of RA “more often” or “much more often” after taking the course.

While the results are encouraging, Mandal says one limitation is that they did not look directly at patient outcomes.

“While the RAE Initiative team hopes to eventually measure patient health data, it is critical to recognize that, due to centuries of exploitation, there is widespread mistrust of requests for access to private medical records in the Navajo community. Before seeking out personal health information, we strive to prioritize respectful and considerate handling of sensitive information, while still striving to achieve our educational and empowerment goals.”

In the meantime, she lists other next steps, including:

• Creating educational materials for RA patients that are culturally and linguistically tailored to the Navajo community
• Organizing in-person training for community health representatives in Navajo Nation to increase awareness about joint health and different types of arthritis
• Creating online training resources for PCPs who want to learn more about common rheumatologic diseases
• Creating a new ECHO training program for spondyloarthritis

Mandal hopes that the RA ECHO program can serve as a model for creating similar rheumatology training programs for other communities with limited access to rheumatologists, saying, “We are eager to collaborate with others who are interested in joining this important mission to expand access to rheumatology care.”

This work was funded by a grant from the Bristol Myers Squibb Foundation.

New research at ACR Convergence 2023, the annual meeting of the American College of Rheumatology (ACR), shows that a deep learning system can accurately identify and predict joint space narrowing and erosions in hand x-rays of patients with rheumatoid arthritis (RA) (Abstract #0745) .

X-rays are the most commonly used imaging technique for detecting and monitoring RA in the hand. Radiologists often use the well-validated Sharp/van der Heidje (SvH) method to evaluate joint space narrowing and erosions by assessing specific locations in each hand and wrist. However, scoring SvH is time-consuming and requires expertise that is not always available. This has led to increased use of deep learning (also called machine learning) to analyze hand X-ray data in RA.

According to Carol Hitchon, MD, FRCPC, MSc, associate professor at the University of Manitoba and clinical scientist in rheumatology and lead co-author of the study: “Machine learning offers a powerful and complementary approach to traditional RA detection and diagnosis methods. It improves the accuracy, efficiency and objectivity of RA radiograph assessment, while providing the opportunity for early detection of damage and valuable insights into the disease.”

For the current study, Hitchon and colleagues aimed to develop and validate a deep learning system for the automated detection of joints and prediction of SvH scores on hand radiographs of patients with RA.

They used a convolutional neural network (CNN)-based algorithm called You Only Look Once (YOLO). CNN is a deep learning neural network commonly used in computer vision and recognition tasks and has been successfully used in medical image classification. YOLO is a type of CNN model specifically designed for real-time object detection in images and videos and known for its speed and efficiency in image processing. Hitchon and colleagues used a recent version of YOLOv516, which they showed to be more than 90% accurate in detecting hand joints.

The YOLO model was trained to detect joints in 240 training and evaluation pediatric hand radiographs from the Radiologic Society of North America database.

The researchers boxed and labeled the different joints of interest: proximal interphalangeal, metacarpophalangeal, wrist, distal radius, and distal ulna. The joint detection model was validated with 54 clinician-labeled radiographs from four adult RA patients followed for more than ten years.

Researchers then applied a vision transformer model (VTM) to predict the erosion and joint space narrowing score of each joint. Hitchon explains that a VTM is a deep learning architecture designed to efficiently process and understand sets of data.

It works by splitting an image into small chunks, transforming or flattening the chunks into a sequence, creating low-dimensional linear embeddings from the flattened spots, adding the positional embedding, and then running the encoded sequence into a standard transformer encoder for the remaining prediction task. “

Carol Hitchon, MD, FRCPC, MSc, Associate Professor, University of Manitoba

The VTM was validated using more than 2,200 hand radiographs from 381 RA patients to whom the physician assigned SvH scores. Patients were from the Canadian Early Arthritis Cohort, a multicenter Canadian study. These scored radiographs were used as the gold standard for this study.

The joint detection model was trained to detect the entire wrist, but the researchers had SvH scores for individual wrist joints, so they trained a separate model to detect joint space narrowing and erosion in each joint.

When they evaluated the accuracy of their models, they found:

• The joint detection model accurately identified target joints. The F1 score for children was 0.991 and the F1 score for adults was 0.812. (In machine learning, the F1 score is a metric that measures the accuracy of a model).
• VTM predictions for joint space narrowing and erosion were very accurate. The principal square error, which evaluates the accuracy of predictions, was 0.91 and 0.93, respectively.
• The multitask models predicted SvH erosion and joint space narrowing scores of individual wrist joints with moderate accuracy (0.6 to 0.91).

Hitchon says they weren’t surprised by the performance of their model.

“The AI ​​technologies we applied in this study have been successfully and widely used in other domains, some of which have been commercialized. Compared to the model’s performance in other domains, our performance is relatively low in predicting X-ray scores for some joint types, such as the wrist. [This] may be due to the relatively small sample size in our study or to the complexity of the anatomy of the wrist joint,” she notes.

Hitchon also says the model’s performance does not match that of human radiologists for joints such as the wrist.

“The AI ​​models cannot replace human radiologists at this stage, but they will be excellent complementary tools that can improve the overall quality and efficiency of radiograph scoring analysis when used in conjunction with the radiologist’s judgment. [these models] may be applicable to the interpretation of large volumes of radiographs in clinical trials.”

The study has two major limitations: X-rays were obtained from cohorts composed almost entirely of white women, and the findings may not apply to races and ethnicities traditionally underrepresented in research studies. Hitchon acknowledges that the findings need to be replicated in other groups. The model also lacks the ability to learn and become more accurate with subsequent images, although Hitchon says they are developing a new deep learning framework so that the model continuously learns as new data is available.

This study received local funding from the Health Science Center Foundation, a hospital charity in Winnipeg, Manitoba, Canada. One of the co-authors, Pingzhao Hu, is supported by the Canada Research Chair Program. The Canadian Early Arthritis Cohort, which provided one set of radiographs, is funded by multiple sources.