Therapeutic Advances in Musculoskeletal Disease最新文献

Background: The sequence and temporal relationship between Raynaud's phenomenon (RP) and the first non-Raynaud's sign/symptom (NRP) in systemic sclerosis (SSc) have been partially investigated.

Objectives: To evaluate whether the mode and ages of clinical onset are associated with disease endotype and survival in SSc.

Design: We included SSc patients from the Systemic sclerosis Progression INvestiGation registry of the Italian Society of Rheumatology (SPRING-SIR) registry in a cohort study, with post hoc cross-sectional and longitudinal analysis.

Methods: Patients were grouped based on age-RP and age-NRP quartiles. Additionally, categories were defined based on mode of onset: RP group-RP onset at least 1 year before NRP; Simultaneous group-RP onset within the same year of NRP; NRP group-RP onset after at least 1 year after NRP. Comparisons were made using Chi-square and ANOVA tests. Logistic, linear, and multinomial regression models were applied to assess associations, while Kaplan-Meier curves and Cox regression were used to assess mortality.

Results: A total of 1748 patients were eligible: 682 (39.0%) in the RP group, 1026 (58.8%) in the simultaneous group, and 39 (2.2%) in the NRP group. A higher prevalence of anti-centromere antibodies was found In the RP group, while the simultaneous group had more diffuse cutaneous SSc (dcSSc), anti-topoisomerase-I antibodies, and higher Rodnan's skin score (mRSS). The NRP group presented higher prevalence of pulmonary arterial hypertension. On logistic regression, the simultaneous group was associated with a higher prevalence of dcSSc compared to the RP group (odds ratio, 1.491, 95% confidence interval (CI): 1.032-2.154). Younger age at RP onset was associated with lower systolic pulmonary artery pressure and mRSS. In 943 patients with available follow-up (median 24 months), the simultaneous group had higher mortality compared to the RP group (hazard ratio, 1.975, 95% CI: 1.002-3.893).

Conclusion: The timing of RP and NRP onset may help define SSc endotype and survival. Patients with simultaneous RP-NRP onset have more severe disease features and higher mortality risk, emphasizing the relevance of onset timing in disease stratification.

Background: Evidence shows that gout attack is closely related to immune and inflammatory levels. However, there are limited evidence-based results for immune-inflammatory indices to predict gout attacks.

Objectives: This study aimed to explore the predictive value of immune-inflammation index for acute gout.

Design: Systematic review and meta-analysis.

Data sources and methods: A systematic search was conducted based on PubMed, Web of Science, Embase, Cochrane, CNKI, and Wanfang. Literature screening and data extraction were performed independently by two investigators, and standardized mean difference (SMD) and 95% confidence interval (95% CI) were used to evaluate the differences in inflammatory indexes between gout patients and individuals in the healthy control group. A random-effects model was used to analyze the predictive value of different inflammatory indexes for acute gout patients. In addition, sensitivity analysis and subgroup analysis were also conducted to explore the sources of heterogeneity.

Results: A total of 8 articles involving 2655 participants were included in this meta-analysis. The findings showed that both neutrophil-to-lymphocyte ratio (NLR; SMD: 0.75; 95% CI: 0.48-1.03) and platelet-lymphocyte ratio (PLR; SMD: 0.52; 95% CI: 0.24-0.79) showed significant value in predicting acute gout attacks. The sensitivity analysis results showed that both NLR and PLR were significantly stable in predicting acute gout attacks. Meanwhile, subgroup analyses were performed based on region, sample size, and age. The results showed that the predictive value of NLR and PLR for acute gout was significant in all subgroups, and age and sample size were potential sources of heterogeneity in PLR. However, no sources of heterogeneity were found in NLR.

Conclusion: NLR and PLR are well-performed in predicting acute gout attacks. Given a lack of prospective studies, a limited number of related references, and significant heterogeneity, large-scale prospective studies are needed to verify the relationship between immune-inflammation index and gout attacks.

Background: Cartilage damage in rheumatoid arthritis (RA) is commonly evaluated by joint space narrowing (JSN) on conventional radiograph (CR). Cross-sectional studies suggest that ultrasound (US) can directly assess finger-joint cartilage damage; however, whether it can sufficiently evaluate cartilage changes over time remains unclear.

Objectives: This study aimed to investigate temporal changes in cartilage damage in patients with RA using US.

Design: A single-center, retrospective observational study.

Methods: Fifty-three patients with RA underwent US scans of finger joints and CR of both hands at baseline and 1 year later. Cartilage thickness was measured at the bilateral second to fifth metacarpophalangeal and proximal interphalangeal joints, and cartilage damage was semiquantitatively scored using recorded US images. JSN was scored on CR using the van der Heijde-modified Sharp method. Patients were grouped by disease activity over 1 year, assessed by Disease Activity Score of 28 joints with C-reactive protein. Continuous variables were analyzed using the Mann-Whitney U or Wilcoxon signed-rank test. Correlations were assessed using Spearman's rank correlation coefficients.

Results: Median patient age and disease duration were 68 and 6.3 years, respectively. Baseline total cartilage thicknesses of 16 joints ranged from 3.1 to 9.1 mm (median, 6.5 mm). Total semiquantitative scores were 0-22 (median, 5). Cartilage thickness (rho, -0.63; p < 0.001) and semiquantitative (rho, 0.67; p < 0.001) scores were significantly correlated with JSN scores. Patients with sustained moderate-to-high disease activity showed greater cartilage thickness reduction (median, -6.2%) than others (median, -1.2%; p = 0.004), although semiquantitative (21.6% vs 16.7%, p = 0.74) and JSN (0% vs 0%, p = 0.68) scores remained unchanged. The two groups differed significantly in the amount of change (-0.4 vs -0.1 mm, p = 0.006).

Conclusion: US detected cartilage damage progression in RA over 1 year, supporting its usefulness for longitudinal assessment.

Background: Bone fractures present a significant diagnostic challenge in medical imaging, necessitating accurate and automated classification methods. Recent advancements in deep learning have greatly enhanced the diagnostic precision while reducing human error.

Objectives: This study proposes an ensemble deep learning model, EnsembleAttenBoneNet, that integrates fine-tuned ResNet50 and EfficientNetB3 models augmented with a Squeeze-and-Excitation (SE) attention mechanism, for robust classification of bone fractures in X-ray images.

Design: The dataset consists of ten distinct fracture categories, such as avulsion, comminuted, greenstick, and pathological fractures.

Methods: Preprocessing techniques, including resizing, normalization, and augmentation, have been applied to improve generalization. Features extracted from both networks were concatenated and refined using the SE attention module to enhance feature representation.

Results: The proposed model achieved a classification accuracy of 99.48%, outperforming the individual models (EfficientNetB3: 98.56%, ResNet50: 97.86%).

Conclusion: Experimental results affirm that integrating deep learning models with attention mechanisms significantly improve diagnostic accuracy, rendering the model a valuable tool for clinical fracture detection. Future research will investigate dataset extension and conduct real-world validation to enhance its usability in medical imaging.

Background: Pulmonary hypertension associated with systemic sclerosis (SSc-PH) carries a significant risk of mortality. Although interstitial lung disease (ILD) often coexists with SSc-PH, its impact on prognosis varies considerably depending on disease extension. The prognostic implications of different degrees of functional impairment secondary to ILD in patients with SSc-PH remain poorly understood.

Objectives: This study aimed to stratify patients with SSc-PH according to their functional parameters and to identify potential prognostic factors in different patterns of ILD extension.

Design: A retrospective study was conducted on consecutive patients with SSc-PH diagnosed according to the European Society of Cardiology/European Respiratory Society guidelines for PH since 2015, from two Italian Scleroderma Centers.

Methods: Patients were classified according to high-resolution computed tomography (HRCT) findings and forced vital capacity (FVC) values as pulmonary arterial hypertension (PAH) without ILD, PH-ILD with FVC ⩾70%, or FVC <70%. Survival analysis, clinical outcomes, and prognostic factors were assessed by Kaplan-Meier curves, multivariate logistic regression, and receiver operating characteristic (ROC) analysis.

Results: Fifty-three patients with SSc-PH were enrolled, and their 5-year overall survival rate was 66.7%. The 29 patients with PH-ILD demonstrated significantly lower survival than the 24 patients with PAH (48% vs 87%, p = 0.005). FVC <70% was associated with markedly reduced survival (29% vs 80% for FVC ⩾70%, p = 0.003). ROC analysis confirmed FVC as a significant prognostic marker (area under the curve = 0.713, p = 0.005). Multivariate analysis identified the FVC <70% at PH diagnosis as the only significant predictor of mortality. Moreover, PH-ILD patients with FVC <70% showed the most significant functional deterioration during follow-up, with a 19% decline in FVC and a greater deterioration in World Health Organization functional class.

Conclusion: An FVC <70% at baseline represents an important negative prognostic factor in patients with SSc-PH, independently of the extension of ILD on HRCT scan. Functional assessment may complement radiological evaluation and emphasize the importance of routine monitoring of lung function for risk stratification in patients with SSc-PH.

Knee osteoarthritis (KOA) presents heterogeneous phenotypes, motivating a need for clinicians to deliver targeted therapies. There is a plethora of options that can be encompassed in KOA treatment regimes. Clinical decision support (CDS) tools that incorporate individual patient data have the capacity to tailor treatments to meet a patient's individual needs and assist with clinical decision-making. We aim to identify and evaluate CDS tools for individuals with KOA that use individualised prediction models to guide intervention decisions. A scoping review of the literature. A systematic search of six electronic databases, including Ovid Embase, Ovid Medline, Cochrane, CHINAL Ultimate, Scopus and Web of Science, was conducted for articles published between January 1, 2010 and May 17, 2024. Two reviewers independently screened articles and extracted data on study design, tool implementation and underlying prediction models. Eligible studies implemented personalised decision aids, designed to support clinical decisions regarding KOA interventions. The search yielded 5376 publications, of which 2445 were duplicates, leaving 2931 for screening. After title/abstract and full-text reviews, 14 studies were included in the final analysis, with one added through citation searching. Ten distinct decision aids were identified across the included studies. Most studies originated from the United States. Fewer than half of the decision aids included personalised information about non-surgical alternatives. Outcomes such as knee pain and physical function were the most commonly addressed, while psychosocial and financial impacts were rarely reported. Limited details were provided about the development and functionality of the underlying prediction models. Personalised decision aids for KOA show promise in supporting patient-centred decision-making. However, their clinical utility is constrained by limited transparency in model development and implementation. Future studies should emphasise the inclusion of non-surgical treatment options, early-stage KOA patients and personalised outcomes beyond pain and function to enhance their relevance and impact in clinical practice.

Background: Immune cells are involved in rheumatoid arthritis (RA), but the link between other blood cell indices and the disease activity of RA, along with the underlying mechanisms, is unclear.

Objective: This study aimed to develop an interpretable machine learning model based on blood cell parameters to assess RA disease severity and assist in personalized treatment decisions.

Methods: A retrospective case-control study was conducted with blood routine and biochemical detection data from 4401 patients at the First Affiliated Hospital of Guangxi Medical University, spanning from January 1, 2018, to January 1, 2024. The primary outcome was disease severity stratification. Recursive feature elimination was applied to identify key variables, and 10 machine learning algorithms were benchmarked on 55 clinical features with internal validation. Model interpretability was assessed with SHAP, while logistic regression and restricted cubic spline models were used to examine associations between blood cell indices and disease severity. In addition, Mendelian randomization analysis was performed to explore potential causal relationships.

Design: This was a retrospective case-control study.

Results: Blood cell indices were identified as the primary factors associated with RA severity. In model evaluation, the Random Forest achieved the best performance, with test set AUCs of 0.870 and 0.874. Mendelian randomization supported a causal relationship between blood cell indices and RA risk.

Conclusion: These results reinforce the associations between blood cell indices and RA severity. The machine learning model demonstrates good predictive capabilities for RA severity and may assist clinicians in developing personalized treatment strategies.

Imaging has become a cornerstone in the clinical management of giant cell arteritis (GCA), with its role extending beyond diagnosis to include disease monitoring, risk stratification of relapse, and therapeutic decision-making. While imaging modalities have been well-established for diagnostic purposes, growing evidence supports their utility in tracking disease activity and stratifying the risk of relapse. In this narrative review, we discuss the available evidence on the prognostic and therapeutic implications of imaging-guided stratification in GCA, and highlight areas that require further research. Ultrasound assessment of intima-media thickness shows measurable improvement following treatment. While this response is relatively rapid in the cranial arteries, it occurs slowly or very slowly in extracranial vessels. Moreover, ultrasound signs and quantitative indices may be useful for distinguishing remission from relapse. Similarly, arterial inflammation assessed by ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) also improves with treatment. However, persistent large vessel vascular uptake is often observed in patients deemed to be in clinical remission. The clinical relevance of such subclinical imaging abnormalities remains unclear, underscoring the need for further research before imaging can be routinely employed for disease monitoring. Imaging may also provide valuable prognostic information. Evidence remains conflicting as to whether patients with large-vessel involvement detected by imaging may relapse more frequently, require glucocorticoid-sparing agents more often, and have an increased risk of cardiovascular events compared to those with isolated cranial GCA. However, quantifying vascular inflammation by ultrasound at diagnosis could support risk stratification and inform individualized treatment decisions. Moreover, elevated arterial uptake on ¹⁸F-FDG PET at baseline has been associated with a higher likelihood of late vascular complications, such as aneurysm formation.

Background: Cathepsins are pivotal regulators of critical physiological processes implicated in cancer, rheumatic disorders, and inflammatory conditions.

Objectives: This study employed Mendelian randomization (MR) to evaluate causal relationships between cathepsins and ankylosing spondylitis (AS).

Design: A retrospective study.

Methods: Single nucleotide polymorphism data of cathepsins were obtained from the INTERVAL study, and AS data were obtained from the FinnGen database. Inverse variance weighting was used as the primary method to assess the causal relationship described above. Cochran's Q test, MR Egger intercept test, MR-PRESSO, and leave-one-out method were used to analyze study sensitivity, heterogeneity, and pleiotropy.

Results: In the forward MR analysis, inverse variance weighted results indicated that higher cathepsin S might be associated with an increased risk of AS (inverse-variance weighting, odds ratio = 1.08, 95% confidence interval = 1.00-1.16, p = 0.047). MR-Egger intercept test and Cochran's Q test did not detect significant heterogeneity or horizontal pleiotropy of instrumental variables. The leave-one-out method confirmed the reliability of causality. The reverse MR analysis found no significant causal relationship between cathepsins and AS.

Conclusion: Our MR analysis results suggest a potential causal relationship between cathepsin S and AS. Further studies on the pathogenesis of cathepsin-mediated AS may provide new insights into the prevention and treatment of AS.