Effective factors on Sharp Score in patients with rheumatoid arthritis: a retrospective study

An engineered glove to follow finger function in rheumatoid arthritis: an observational prospective study

The engineered Hand Test System (HTS) glove has shown high reliability in assessing the baseline functional status of rheumatoid arthritis (RA) hand. Starting from this achievement, the aim of the present observational prospective study was to assess the functionality of the single fingers of rheumatoid hand at follow-up. Eighty RA patients performed HTS glove tests at baseline and among these fifty-six patients were re-tested after 7 months. The HTS glove parameters [Touch Duration (TD), Movement Rate (MR), Inter Tapping Interval (ITI)] were correlated with disease activity and disability clinimetric indexes [Disease Activity Score 28 joint count-C-reactive protein (DAS28-CRP), Clinical Disease Activity Index (CDAI), Simplified Disease Activity Index (SDAI), Health Assessment Questionnaire-Disability Index (HAQ-DI), grip strength, visual analogue scale of pain (VAS), patient global assessment (PGA)], and with laboratory values. HTS glove parameters (TD, ITI, and MR) showed statistically significant correlations with clinimetric and clinical indexes at both time points (p < 0.05). During follow-up, a statistically significant variation of all HTS glove parameters for the fingers that have performed both the worst or best HTS test at baseline was detected (p < 0.05), while the mean HTS glove parameter values by considering all fingers did not show a statistically significant variation over time, as well as the traditional clinimetric indexes. Besides the objective role in assessing the RA hand function by integrating the traditional clinimetric indexes, the HTS glove seems a useful tool for evaluating worst or best finger function during time by measuring the movement speed.

Deep learning-based automatic-bone-destruction-evaluation system using contextual information from other joints

BACKGROUND

X-ray images are commonly used to assess the bone destruction of rheumatoid arthritis. The purpose of this study is to propose an automatic-bone-destruction-evaluation system fully utilizing deep neural networks (DNN). This system detects all target joints of the modified Sharp/van der Heijde score (SHS) from a hand X-ray image. It then classifies every target joint as intact (SHS = 0) or non-intact (SHS ≥ 1).

METHODS

We used 226 hand X-ray images of 40 rheumatoid arthritis patients. As for detection, we used a DNN model called DeepLabCut. As for classification, we built four classification models that classify the detected joint as intact or non-intact. The first model classifies each joint independently, whereas the second model does it while comparing the same contralateral joint. The third model compares the same joint group (e.g., the proximal interphalangeal joints) of one hand and the fourth model compares the same joint group of both hands. We evaluated DeepLabCut's detection performance and classification models' performances. The classification models' performances were compared to three orthopedic surgeons.

RESULTS

Detection rates for all the target joints were 98.0% and 97.3% for erosion and joint space narrowing (JSN). Among the four classification models, the model that compares the same contralateral joint showed the best F-measure (0.70, 0.81) and area under the curve of the precision-recall curve (PR-AUC) (0.73, 0.85) regarding erosion and JSN. As for erosion, the F-measure and PR-AUC of this model were better than the best of the orthopedic surgeons.

CONCLUSIONS

The proposed system was useful. All the target joints were detected with high accuracy. The classification model that compared the same contralateral joint showed better performance than the orthopedic surgeons regarding erosion.

Factors influencing the Sharp score of 1057 patients with rheumatoid arthritis and anemia: a retrospective study

Objective

This study examined the relationship of the Sharp score with demographic factors and clinical immune-inflammatory markers in patients with anemia in rheumatoid arthritis (RA).

Methods

The clinical data of 1057 patients with RA and anemia and 1006 patients with RA without anemia were retrospectively analyzed. Spearman’s correlation coefficient analysis, association rule analysis, and logistic regression were used to study the relationships between the Sharp score and influencing factors in patients with RA and anemia.

Results

The incidence of anemia was 51.24% (1057/2063), and mild anemia accounted for 81.93% (866/1057) of cases. Spearman’s correlation coefficient and association rule analyses revealed that the Sharp score of patients with RA and anemia was correlated with immune-inflammatory response and anemia. Logistic regression analysis illustrated that advanced age (>60 years), female, low serum iron levels, C-reactive protein positivity, and immunoglobulin A positivity were risk factors for a high Sharp score (>28.25) in patients with RA and anemia.

Conclusion

The Sharp score is closely related to clinical disease activity and anemia, and it should be considered in the treatment strategy of patients with RA and anemia.