Predictive Model for Graves' Ophthalmopathy in Patients with New-Onset Graves' Disease

Background: The most prevalent extrathyroidal manifestation of Graves' disease (GD) is Graves' ophthalmopathy (GO). However, only few methods allow for predictions of GO occurrence or progression in patients with GD. Methods: We retrospectively analyzed 1,074 patients with new-onset GD, and divided them into a derivation and a validation cohort based on the date of their GD diagnosis. We then separately analyzed clinical risk factors affecting the occurrence and progression of GO using multivariable regression analysis and created a predictive model based on the factors we identified as significant. Results: Of the 853 GD patients included in the derivation cohort, 101 (11.8%) developed GO. Those who developed GO were more likely to be smokers (25.7% vs. 8.5%, p < 0.001), were younger at the time of their GD diagnosis (35.0 years vs. 42.0 years, p < 0.001), more commonly had a family history of GD (27.7% vs. 17.2%, p = 0.015), and had higher thyrotropin-binding inhibitor immunoglobulin (TBII) levels at the time of their diagnosis (13.5 IU/L vs. 10.0 IU/L, p = 0.020) than those who did not develop GO. Of the 101 GO patients in the derivation cohort, after excluding 8 who initially had active and moderate-to-severe GO, 11 of the remaining 93 had progressed to more active or severe GO. GO patients with confirmed progression had a higher proportion of those older than 45 years (54.5% vs. 19.8%, p = 0.031), and they had a different initial clinical activity score distribution. The multivariable regression analysis identified age at GD diagnosis, sex, smoking history, family history of GD, total cholesterol level, and TBII level at the time of the diagnosis as significant risk factors of GO occurrence, and a predictive model including these risk factors was built to create a nomogram. Conclusions: The predictors of GO occurrence in patients with new-onset GD were female sex, positive smoking history, young age, family history of GD, high cholesterol level, and high TBII level. The predictive nomogram developed in this study may be useful in patient counseling and facilitating informed treatment decision-making.