[The derivation and validation of a prediction rule for differential diagnosis of thyroid nodules]

To set up a prediction rule for the pro-operative differential diagnosis of thyroid nodules and evaluate its clinical value.

All patients of thyroid nodules underwent thyroid operations in Changzheng hospital from June, 1997 to July, 2012 were included in this study. They were randomly divided into the derivation cohort (2/3) and the validation cohort (1/3). A prediction rule was developed based on the logistic regression model and the scoring system was established in accordance with assigning of the value of each variable β in the model. The prediction consistency, discriminatory power and diagnostic accuracy were conducted to evaluate the clinical value of the scoring system.

A total of 13 980 patients were enrolled in the study with 9195 in the derivation cohort and 4785 in the validation cohort. The prediction rule consisted of 18 variables, which were gender, clinical manifestations including fever, neck sore, neck mass, palpitation and sweating, serum level of thyroid stimulating hormone (TSH) , free triiodothyronine (FT3) , thyroid peroxidase antibody (TPOAb) , thyroglobulin antibody (TgAb) , thyroglobulin (Tg) , calcitonin and carcinoembryonic antigen (CEA) , ultrasonography features including nodules number, location, size, boundaries and ethological patterns and the presence and patterns of lymph nodes. The model showed good calibration consistency (P = 0.437) and discrimination power (area under the receiver operating characteristic curve was 0.928) in the derivation cohort. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio of the model were 89.3%, 81.5%, 83.2%, 56.8%, 96.6%, 4.83 and 0.13, respectively.

The prediction rule and its scoring system established in the study are efficacious for the calibration and discrimination of thyroid nodules in Chinese population, which could be a useful tool for the pro-operative risk stratification.