Prediction model of Graves' disease in general clinical practice based on complete blood count and biochemistry profile.

Abstract

Although untreated Graves' disease (GD) is associated with a higher risk of cardiac complications and mortality, there is no well-established way to predict the onset of thyrotoxicosis in clinical practice. The aim of this study was to identify important variables that will make it possible to predict GD and thyrotoxicosis (GD + painless thyroiditis (PT)) by using a machine-learning-based model based on complete blood count and standard biochemistry profile data. We identified 19,335 newly diagnosed GD patients, 3,267 PT patients, and 4,159 subjects without any thyroid disease. We built a GD prediction model based on information obtained from subjects regarding sex, age, a complete blood count, and a standard biochemistry profile. We built the model in the training set and evaluated the performance of the model in the test set by using the artificial intelligence software Prediction One. Our machine learning-based model showed high discriminative ability to predict GD in the test set (area under the curve [AUC] 0.99). The main contributing factors to predict GD included age and serum creatinine, total cholesterol, alkaline phosphatase, and total protein levels. We still found high discriminative ability even when we restricted the variables to these five most contributory factors in our prediction model (AUC 0.97) built by using artificial intelligence software showed high GD prediction ability based on information regarding only five factors.