Carotid plaque vulnerability is considered a key predictor of cerebrovascular events, including stroke, according to prior studies. In a retrospective study of 292 patients with newly diagnosed gout, researchers evaluated whether musculoskeletal ultrasound findings could predict carotid plaque instability, as classified by the Plaque Reporting and Data System (Plaque-RADS).

The study identified 3 gout-related variables as independent risk factors for plaque vulnerability: presence of tophi, power Doppler (PD) signal intensity, and number of gout flares in the preceding year. Tophi were present in 50.7% of patients and were associated with a 76% increase in the odds of higher-risk plaque classification (odds ratio [OR], 1.76; P = .009). PD grade 2 and grade 3 signals were associated with 54% (OR, 1.54; P = .002) and 89% (OR, 1.89; P = .001) increases in odds, respectively, compared with patients without Doppler signal. Each additional gout flare in the previous year was linked to a 52% increase in odds (OR, 1.52; P = .001).

A machine learning model using random forest analysis was developed to predict plaque vulnerability, integrating both gout-specific indicators and traditional cardiovascular risk factors (age, diabetes, renal impairment, cholesterol level, and antihypertensive therapy). The model demonstrated high performance, with a concordance index (C-index) of 0.997, accuracy of 92.5%, precision of 96.0%, and recall of 90.5%.

Compared with a conventional model excluding gout-specific features, the integrated model achieved significantly better discriminative power (area under the curve [AUC], 0.912 vs 0.765).

During follow-up, 57.2% of patients had detectable carotid plaques. These were classified according to the Plaque-RADS system. Although no plaques were rated as grade 4 (highest risk), a substantial proportion were assigned intermediate grades (eg, 3a–3c).

Musculoskeletal ultrasound also documented joint-level findings, including the double contour sign, hyperechoic aggregates, and bone erosion. However, these were not statistically associated with plaque vulnerability and were excluded from the final predictive model.

The authors concluded that ultrasound features and clinical indicators of gout may assist in cardiovascular risk stratification, particularly in identifying patients at elevated risk for stroke. Limitations of the study included its retrospective design and lack of detailed lifestyle data such as smoking and alcohol use.

The authors reported no conflicts of interest.

Source: Frontiers