Predicting multigland disease in primary hyperparathyroidism using ultrasound and clinical features

BackgroundThe identification of multigland disease (MGD) in primary hyperparathyroidism (PHPT) patients is essential for minimally invasive surgical decision-making. ObjectiveTo develop a nomogram based on ultrasound (US) findings and clinical factors to predict MGD in PHPT patients. Materials and methodsPatients with PHPT who had surgery between March 2021 and January 2022 were consecutively enrolled to this study. Biochemical and clinicopathological data were recorded. US images were analyzed to extract US features for prediction. Logistic regression analyses were used to identify MGD risk factors. A nomogram was constructed based on these factors and its performance evaluated by area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow tests, and decision curve analysis (DCA). ResultsA total of 102 PHPT patients were included; 82 (80.4%) had single-gland disease (SGD) and 20 (19.6%) had MGD. Using multivariate analyses, MGD was positively correlated with age (odds ratio (OR) = 1.033, 95% confidence interval (CI): 0.190-4.047), PTH levels (OR = 1.001, 95% CI: 1.000-1.002), multiple endocrine neoplasia type 1 (MEN1) (OR = 29.730, 95% CI: 3.089-836.785), US size (OR = 1.198, 95% CI: 0.647-2.088), and US texture (cystic-solid) (OR = 5.357, 95% CI: 0.499-62.912). MGD was negatively correlated with gender (OR = 0.985, 95% CI: 0.190-4.047), calcium levels (OR = 0.453, 95% CI: 0.070-2.448), and symptoms (yes) (OR = 0.935, 95% CI: 0.257-13.365). The nomogram showed good discrimination with an AUC = 0.77 (0.68-0.85) and good agreement in predicting MGD in PHPT patients. Also, 65 points was recommended as a cut-off value, with specificity = 0.94 and sensitivity = 0.50. ConclusionUS was useful in evaluating MGD. Combining US and clinical features in a nomogram showed good diagnostic performance for predicting MGD.