Activated coagulation is associated with the disease activity of axial spondyloarthritis

Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression

BACKGROUND

HLA-B27 positivity is normal in patients undergoing rheumatic diseases. The diagnosis of many diseases requires an HLA-B27 examination.

METHODS

This study screened totally 1503 patients who underwent HLA-B27 examination, liver/kidney function tests, and complete blood routine examination in First Affiliated Hospital of Guangxi Medical University. The training cohort included 509 cases with HLA-B27 positivity whereas 611 with HLA-B27 negativity. In addition, validation cohort included 147 cases with HLA-B27 positivity whereas 236 with HLA-B27 negativity. In this study, 3 ML approaches, namely, LASSO, support vector machine (SVM) recursive feature elimination and random forest, were adopted for screening feature variables. Subsequently, to acquire the prediction model, the intersection was selected. Finally, differences among 148 cases with HLA-B27 positivity and negativity suffering from ankylosing spondylitis (AS) were investigated.

RESULTS

Six factors, namely red blood cell count, human major compatibility complex, mean platelet volume, albumin/globulin ratio (ALB/GLB), prealbumin, and bicarbonate radical, were chosen with the aim of constructing the diagnostic nomogram using ML methods. For training queue, nomogram curve exhibited the value of area under the curve (AUC) of 0.8254496, and C-value of the model was 0.825. Moreover, nomogram C-value of the validation queue was 0.853, and the AUC value was 0.852675. Furthermore, a significant decrease in the ALB/GLB was noted among cases with HLA-B27 positivity and AS cases.

CONCLUSION

To conclude, the proposed ML model can effectively predict HLA-B27 and help doctors in the diagnosis of various immune diseases.

Elevated Serum D-Dimer May Reflect the Presence of Gut Inflammation in Spondyloarthritis

Background

To investigate the association of D-dimer with gut inflammation in spondyloarthritis (SpA).

Methods

Sixty-five patients with SpA and 70 healthy controls were included. Demographic, clinical, and laboratory parameters were collected. The differences of clinical and laboratory parameters were compared between patients with SpA and healthy controls, and between patients with SpA, with and without gut inflammation. The associations of D-dimer with laboratory data were analyzed. The predictive value of D-dimer was obtained by a receiver operator characteristic (ROC) curve analysis. The independent risk factors for gut inflammation in SpA were investigated by binary logistic regression analysis.

Results

Patients with SpA had higher D-dimer than healthy controls (P = 0.016). D-dimer was positively correlated with platelet (PLT), erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP), and negatively correlated with hemoglobin (Hb). Besides, significant differences were observed in D-dimer between SpA patients with and without gut inflammation (P < 0.001). Furthermore, SpA patients with gut inflammation were more likely to have peripheral joint involvement than those without gut inflammation (P < 0.001). The AUC of D-dimer was 0.865 at cut-off value of 0.29 mg/L, with a sensitivity of 82.6%, and a specificity of 81%. Elevated D-dimer (OR = 15.451, 95% CI: 3.030–78.780, P = 0.001) was independently associated with gut inflammation in SpA.

Conclusion

D-dimer may be a potential biomarker for identifying SpA patients with gut inflammation.