Magnetic resonance imaging in individuals at risk of rheumatoid arthritis

Predicting Inflammatory Arthritis in At-Risk Persons: Development of Scores for Risk Stratification

BACKGROUND

Inflammatory arthritis (IA) is an immune-related condition defined by the presence of clinical synovitis. Its most common form is rheumatoid arthritis.

OBJECTIVE

To develop scores for predicting IA in at-risk persons using multidimensional biomarkers.

DESIGN

Prospective observational cohort study.

SETTING

Single-center, Leeds, United Kingdom.

PARTICIPANTS

Persons with new musculoskeletal symptoms, a positive test result for anticitrullinated protein antibodies, and no clinical synovitis and followed for 48 weeks or more or until IA occurred.

MEASUREMENTS

A simple score was developed using logistic regression, and a comprehensive score was developed using the least absolute shrinkage and selection operator Cox proportional hazards regression. Internal validation with bootstrapping was estimated, and a decision curve analysis was done.

RESULTS

Of 455 participants, 32.5% (148 of 455) developed IA, and 15.4% (70 of 455) developed it within 1 year. The simple score identified 249 low-risk participants with a false negative rate of 5% (and 206 high-risk participants with a false-positive rate of 72%). The comprehensive score identified 119 high-risk participants with a false-positive rate of 29% (and 336 low-risk participants with a false-negative rate of 19%); 40% of high-risk participants developed IA within 1 year and 71% within 5 years.

LIMITATIONS

External validation is required. Recruitment occurred over 13 years, with lower rates of IA in later years. There was geographic variation in laboratory testing and recruitment availability.

CONCLUSION

The simple score identified persons at low risk for IA who were less likely to need secondary care. The comprehensive score identified high-risk persons who could benefit from risk stratification and preventive measures. Both scores may be useful in clinical care and should also be useful in clinical trials.

PRIMARY FUNDING SOURCE

National Institute for Health and Care Research Leeds Biomedical Research Centre.

Added value of multiple autoantibody testing for predicting progression to inflammatory arthritis in at-risk individuals

BACKGROUND

Predicting progression to clinical arthritis in individuals at-risk of developing rheumatoid arthritis is a prerequisite to developing stratification groups for prevention strategies. Selecting accurate predictive criteria is the critical step to define the population at-risk. While positivity for anti-citrullinated protein antibodies (ACPA) remains the main recruitment biomarker, positivity for other autoantibodies (AutoAbs) identified before the onset of symptoms, may provide additional predictive accuracy for stratification.

OBJECTIVE

To perform a multiple AutoAbs analysis for both the prediction and the time of progression to inflammatory arthritis (IA).

METHODS

392 individuals were recruited based on a new musculoskeletal complaint and positivity for ACPA or rheumatoid factor (RF). ELISAs were performed for ACPA, RF, anti-nuclear Ab, anti-carbamylated protein (anti-CarP) and anti-collagen AutoAbs. Logistic and COX regression were used for analysis.

RESULTS

Progression to IA was observed in 125/392 (32%) of cases, of which 78 progressed within 12 months. The AutoAbs ACPA, RF, anti-CarP were individually associated with progression (p<0.0001) and improved prediction when combined with demographic/clinical data (Accuracy >77%; area under the curve (AUC) >0.789), compared with prediction using only demographic/clinical data (72.9%, AUC=0.760). Multiple AutoAbs testing provided added value, with +6.4% accuracy for number of positive AutoAbs (AUC=0.852); +5.4% accuracy for AutoAbs levels (ACPA/anti-CarP, AUC=0.832); and +6.2% accuracy for risk-groups based on high/low levels (ACPA/RF/anti-CarP, AUC=0.837). Time to imminent progression was best predicted using ACPA/anti-CarP levels (AUC=0.779), while the number of positive AutoAbs was/status/risk were as good (AUC=0.778).

CONCLUSION

We confirm added value of multiple AutoAbs testing for identifying progressors to clinical disease, allowing more specific stratification for intervention studies.

Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis

Rheumatoid arthritis (RA), an autoimmune disease of unknown etiology, is a serious threat to the health of middle-aged and elderly people. Although western medicine, traditional medicine such as traditional Chinese medicine, Tibetan medicine and other ethnic medicine have shown certain advantages in the diagnosis and treatment of RA, there are still some practical shortcomings, such as delayed diagnosis, improper treatment scheme and unclear drug mechanism. At present, the applications of artificial intelligence (AI)-based deep learning and cloud computing has aroused wide attention in the medical and health field, especially in screening potential active ingredients, targets and action pathways of single drugs or prescriptions in traditional medicine and optimizing disease diagnosis and treatment models. Integrated information and analysis of RA patients based on AI and medical big data will unquestionably benefit more RA patients worldwide. In this review, we mainly elaborated the application status and prospect of AI-assisted deep learning and cloud computation-oriented western medicine and traditional medicine on the diagnosis and treatment of RA in different stages. It can be predicted that with the help of AI, more pharmacological mechanisms of effective ethnic drugs against RA will be elucidated and more accurate solutions will be provided for the treatment and diagnosis of RA in the future.

Ultrasound subclinical synovitis in anti-CCP+ at-risk individuals with MSK symptoms: an important and predictable stage in the RA continuum

Objectives

To investigate whether anti-CCP2-positive at-risk individuals with musculoskeletal (MSK) symptoms but without clinical synovitis (CCP2⁺ at-risk) develop US subclinical synovitis before inflammatory arthritis and if US subclinical synovitis can be predicted.

Methods

First, US scans of CCP2⁺ at-risk individuals who developed inflammatory arthritis (‘progressors’) were reviewed for subclinical synovitis prior to inflammatory arthritis development. Patients in whom the pre-progression US scan was negative but the scan was conducted >6 months before progression were excluded. Subsequently, regression analyses were performed to identify predictors of US synovitis in CCP2⁺ at-risk individuals without baseline US abnormalities who had one or more longitudinal US scan and a complete dataset.

Results

US subclinical synovitis was detected in one or more scan in 75 of 97 progressors (77.3%) {median time to inflammatory arthritis development from first evidence of US synovitis 26.5 weeks [interquartile range (IQR) 7–60]}, in whom one or more scan was available, excluding those with a negative scan >6 months from inflammatory arthritis development (n = 38). In 220 CCP2⁺ at-risk individuals with normal baseline US scans, who had one or more longitudinal US scan and a complete dataset, US synovitis was detected in 69/220 (31.4%) [median time to first developing US synovitis 56.4 weeks (IQR 33.0–112.0)]. In the multivariable analysis, only anti-CCP3 antibodies were predictive for the development of US synovitis [odds ratio 4.75 (95% CI 1.97, 11.46); P < 0.01].

Conclusions

In anti-CCP2⁺ at-risk individuals, a stage of subclinical synovitis usually precedes the development of inflammatory arthritis. Anti-CCP2⁺/CCP3⁺ individuals without clinical or US subclinical synovitis may represent the optimal window of opportunity for intervention to prevent joint disease.

In anti-CCP+ at-risk individuals, radiographic bone erosions are uncommon and are not associated with the development of clinical arthritis

OBJECTIVES

To investigate the prevalence, distribution and predictive value for the development of inflammatory arthritis (IA) of conventional radiography (CR) bone erosions (BE) in anti-CCP positive (CCP+) at-risk individuals with musculoskeletal (MSK) symptoms but without clinical synovitis.

METHODS

Baseline CR of the hands and feet of 418 CCP+ at-risk individuals were analysed. The presence of US-BE was explored in the anatomical areas in which CR-BE were reported. Hands and feet CR at the time of progression were analysed in a subset of individuals who developed IA (73/123, 59.3%). Logistic regression analyses were performed to calculate the predictive value of baseline CR-BE for the development of IA in 394 CCP+ individuals with ≥1 follow-up visit.

RESULTS

BE were detected in 17/418 (4.1%) CCP+ at-risk individuals (median Simple Erosions Narrowing Score-BE = 2.0, IQR: 1.0-2.0; median Sharp van der Heijde score-BE = 4.0, IQR: 3.0-8.5), most frequently in the foot joints (11/17, 64.7% individuals). A total of 123/394 (31.2%) CCP+ at-risk individuals developed IA; 7/17 (41.2%) with, and 116/377 (30.8%) without BE on CR (P = 0.37). US-BE were found in 4/7 (57.1%) individuals with CR-BE who developed IA, but only in 1/10 (10.0%) who did not. At the time of progression, new BE were detected in 4/73 (5.5%) CCP+ individuals on repeated CR. In the regression analyses, baseline CR-BE were not predictive for the development of IA.

CONCLUSIONS

In CCP+ at-risk individuals with MSK symptoms, CR-detected BE are uncommon and do not predict the development of IA.

What Is the Value of Ultrasound in Individuals 'At-Risk' of Rheumatoid Arthritis Who Do Not Have Clinical Synovitis?

The identification of biomarkers that help identify individuals at imminent risk of progression to rheumatoid arthritis (RA) is of crucial importance for disease prevention. In recent years, several studies have highlighted the value of musculoskeletal (MSK) ultrasound (US) in predicting progression to inflammatory arthritis (IA) in individuals ‘at-risk’ of RA. These studies have highlighted the following main aspects: first, in RA-related autoantibody-positive individuals, MSK symptoms seem to develop before ‘sub-clinical’ joint inflammation occurs on US. Second, the detection of ‘sub-clinical’ synovitis (and/or bone erosions) greatly increases the risk of IA development in these ‘at-risk’ individuals. US has a potential key role for better understanding the ‘pre-clinical’ stages in individuals ‘at-risk’ of RA, and for the early identification of those individuals at high risk of developing IA. Further research is needed to address questions on image analysis and standardization. In this review, we provide an overview of the most relevant studies which have investigated the value of US in the prediction of RA development in individuals ‘at-risk’ of RA who have MSK symptoms, but no clinical evidence of IA. We highlight recent insights, limitations, and future perspectives of US use in this important population.

The Bone Marrow Edema Links to an Osteoclastic Environment and Precedes Synovitis During the Development of Collagen Induced Arthritis

Objectives: To determine the relationship between bone marrow edema (BME), synovitis, and bone erosion longitudinally using a collagen induced arthritis mice (CIA) model and to explore the potential pathogenic role of BME in bone erosion. Methods: CIA was induced in DBA/1J mice. BME and corresponding clinical symptoms of arthritis and synovitis during the different time points of CIA development were assayed by magnetic resonance imaging (MRI), arthritis sore, and histologic analyses. The expression of osteoclasts (OCs), OCs-related cytokines, and immune cells in bone marrow were determined by flow cytometry, immunohistochemistry, immunofluorescence staining, and real-time PCR. The OCs formation was estimated using in vitro assays. Results: MRI detected BME could emerge at day 25 in 70% mice after the first immunization (n = 10), when there were not any arthritic symptoms, histological or MRI synovitis. At day 28, BME occurred in 90% mice whereas the arthritic symptom and histological synovitis were only presented in 30 and 20% CIA mice at that time (n = 10). The emergence of BME was associated with an increased bone marrow OCs number and an altered distribution of OCs adherent to subchondral bone surface, which resulted in increased subchondral erosion and decreased trabecular bone number during the CIA process. Obvious marrow environment changes were identified after BME emergence, consisting of multiple OCs related signals, including highly expressed RANKL, increased proinflammatory cytokines and chemokines, and highly activated T cells and monocytes. Conclusions: BME reflects a unique marrow "osteoclastic environment," preceding the arthritic symptoms and synovitis during the development of CIA.

Analysis of pulmonary sounds for the diagnosis of interstitial lung diseases secondary to rheumatoid arthritis

The diagnosis of interstitial lung diseases in patients affected by rheumatoid arthritis is fundamental to improving their survival rate. In particular, the average survival time of patients affected by rheumatoid arthritis with pulmonary implications is approximately 3 years. The gold standard for confirming the diagnosis of this disease is computer tomography. However, it is very difficult to raise diagnosis suspicion because the symptoms of the disease are extremely common in elderly people. The detection of the so-called velcro crackle in lung sounds can effectively raise the suspicion of an interstitial disease and speed up diagnosis. However, this task largely relies on the experience of physicians and has not yet been standardized in clinical practice. The diagnosis of interstitial lung diseases based on thorax auscultation still represents an underexplored field in the study of rheumatoid arthritis. In this study, we investigate the problem of the automatic detection of velcro crackle in lung sounds. In practice, the patient is auscultated using a digital stethoscope and the lung sounds are saved to a file. The acquired digital data are then analysed using a suitably developed algorithm. In particular, the proposed solution relies on the empirical observation that the audio bandwidth associated with velcro crackle is larger than that associated with healthy breath sounds. Experimental results from a database of 70 patients affected by rheumatoid arthritis demonstrate that the developed tool can outperform specialized physicians in terms of diagnosing pulmonary disorders. The overall accuracy of the proposed solution is 90.0%, with negative and positive predictive values of 95.0% and 83.3%, respectively, whereas the reliability of physician diagnosis is in the range of 60-70%. The devised algorithm represents an enabling technology for a novel approach to the diagnosis of interstitial lung diseases in patients affected by rheumatoid arthritis.