Novel Scoring System May Better Identify Patients at Risk for Inflammatory Arthritis

In a new study published in Annals of Internal Medicine, various biomarkers were used to assess a patient’s risk for developing inflammatory arthritis (IA) with a simple risk score. The results showed promise for the identification of patients vulnerable to IA.

IA is an immune-related condition that presents clinical synovitis (joint swelling) and is commonly seen in the form of rheumatoid arthritis (RA). Many previous studies have explored effective methodologies, such as the detection of anticitrullinated protein antibodies (anti-CCP), to predict the development of IA and RA—especially since RA, if untreated, destroys joints over time.

Duquenne et al. note the “great interest in defining a population ‘at risk’ for IA,” particularly in cases potentially leading to the “development of clinical disease [where] pharmacologic treatment could be justified.” Considering the benefits of predicting patient outcomes, the researchers sought to develop a scoring system capable of accurately measuring the risk of IA-development for patients presenting with anti-CCP.

Between June 2008 and November 2021, patients were recruited from primary care centers scattered throughout the United Kingdom, as well as rheumatology secondary care clinics in Leeds. Patients were considered if they had new musculoskeletal (MSK) symptoms, a positive test for anti-CCP, and no initial signs of clinical synovitis. Clinicians would later identify IA in participants if their clinical synovitis could not be better explained by another diagnosis. A total of 455 patients were analyzed with median a follow-up of 223 weeks.

A simple score and comprehensive score were developed for patient analysis. Simple scores only included data on available variables in primary care (anti-CCP type 2 [anti-CCP2], rheumatoid factor [RF], early morning stiffness [EMS], and erythrocyte sedimentation rate [ESR]) and informed whether a patient was referred to secondary care. Comprehensive scores included a larger set of variables (smoking history, anti-CCP2, RF, EMS, ESR, Health Assessment Questionnaire, visual analogue scale global pain, shared epitope, and all ultrasound abnormalities) available in secondary care and informed when intervention with a patient was considered. Patients were labeled as low risk (10% risk of IA within 1 year) or high risk (50% risk of IA within 5 years).

Simple scores registered a net benefit of 0.09, which means that for every 100 patients, 9 who would develop IA would receive treatment while the treatment of those who would not develop IA was avoided. The simple scoring identified 249 patients as low risk with a 5% rate of false negatives (meaning those patients ended up developing IA in the following year). The remaining 206 patients were identified as high risk but exhibited a large false-positive rate of 72%.

Comprehensive scoring identified 119 high-risk patients and demonstrated a false-positive rate of 29%. There were 336 low-risk patients identified here with a false-negative rate of 19%. Of all the high-risk patients, 40% went on to develop IA in the first year and this number escalated to 71% within 5 years. Researchers observed a net benefit of 0.11 in these cases.

The authors listed several limitations to their study, among which included the long, 13-year time period their recruitment took place, the varying access some areas might have had to anti-CCP testing, ultrasound, and referrals, as well as the use of anti-CCP2 tests that excluded other variations of anti-CCP. However, they believe the large population of anti-CCP-positive individuals, the range of tested biomarkers, and the follow-up duration were factors that really strengthened their study.

Anti-CCP positivity was a particular focus because, as the researchers note, more erosive and aggressive arthritis phenotypes appear in the disease development of individuals who have this protein. Past research was also cited that demonstrates how high-risk people with positive anti-CCP tests are the most responsive to disease-modifying antirheumatic drugs before the development of IA.

Considering their findings and successes in predicting patient outcomes, Duquenne et al. call for future studies to further validate their results and dig deeper into the impact analyzing biomarkers has for the future of patients at risk for IA. Each scoring system—and its ability to detect high- and low-risk individuals—can provide valuable insights for the future of patient treatment, prognosis, and clinical trials.

Reference

Duquenne L, Hensor E, Wilson M, et al. Predicting inflammatory arthritis in at-risk persons: development of scores for risk stratification. Ann Intern Med. Published online August 1, 2023;176(8):1027-1036. doi: 10.7326/M23-0272