Exploration of acute gout diagnosis based on ultrasound viscoelastic imaging: quantitative parameter analysis and clinical validation

基于超声粘弹性成像的急性痛风诊断探索:定量参数分析和临床验证

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Abstract

OBJECTIVES: To explore the optimal quantitative parameters and clinical application value of ultrasound viscoelastic imaging in the differential diagnosis of acute gout (AG) and non-AG. METHODS: This study enrolled 102 patients presenting with acute joint swelling and pain, and divided them into the AG group and non-AG group. Differences in viscoelastic quantitative parameters, including the shear wave velocity (C), viscosity coefficient (V) and dispersion coefficient (D), between the two groups were compared. Parameters with significant differences were included in generalized estimating equations (GEE) analysis to screen out the optimal parameters for predicting AG, and receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic efficacy of each parameter alone and in combination. RESULTS: The AG group included 45 patients, and the non-AG group included 57 patients. There were statistically significant differences in C(mean), C(max), C(min), C(SD), V(mean), V(max), V(SD), D(mean), D(max), and D(SD) between the two groups (all P < 0.05). GEE analysis showed that the parameters finally included in the equation were C(mean) and D(max), and the equation was Logit(P) = (-11.186) + (3.456 × C(mean)) + (0.191 × D(max)). ROC curve analysis revealed that among single parameters, C(mean) had the highest AUC of 0.836 (95% CI 0.774∼0.897), with an optimal cutoff value of 2.36 m/s. The AUC of the GEE model was 0.885 (95% CI 0.833∼0.938), with an optimal cutoff value of 0.41. The AUC of the GEE model combining multiple parameters was superior to that of single-parameter diagnosis (P = 0.033). CONCLUSION: Among ultrasound viscoelastic parameters, C(mean) and D(max) are effective non-invasive quantitative indicators for diagnosing AG, and the diagnostic efficacy of combined multi-parameter diagnosis is superior to that of single-parameter diagnosis.

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