ABSTRACT
Islet autoimmune syndrome (IAS) is an autoimmune disease caused by high concentrations of insulin autoantibodies (IAA) in the blood. It is characterized by hyperinsulinemia and spontaneous hypoglycemia. The incidence of IAS is low, and the hypoglycemia symptom is usually mild. Hence, the severe manifestations (up to seizures and coma) are rarely reported. Here, we reported two cases of Graves' disease who developed insulin autoimmune syndrome after methimazole treatment. The patients exhibited sudden hypoglycemic coma after receiving methimazole treatment for approximately 2 or 6 months. The patients' serum glucose levels were below 2.8 mmol/L, and laboratory tests showed high levels of serum insulin and high titers of insulin autoantibodies. Patient 1 discontinued methimazole treatment and the hypoglycemic symptoms disappeared after 7 days. However, patient 2 experienced severe hypoglycemia after discontinuation of methimazole, and the patient condition improved after glucocorticoid therapy. He developed thyroid storm during the treatment, and his condition improved after receiving standard treatment procedures for thyroid storm. To the best of our knowledge, this is the first case report of IAS in Graves' disease with thyroid storm.
ABSTRACT
INTRODUCTION: This study aims to explore the predictive value of CT radiomics and clinical characteristics for treatment response in COVID-19 patients. METHODS: Data were collected from clinical/auxiliary examinations and follow-ups of COVID-19 patients. Whole lung radiomics feature extraction was performed at baseline chest CT. Radiomics, clinical, and combined features (nomogram) were evaluated for predicting treatment response. RESULTS: Among 36 COVID-19 patients, mild, common, severe, and critical disease symptoms were found in 1, 21, 13, and 1 of them, respectively. Twenty-five (1 mild, 18 common, and 6 severe) patients showed a good response to treatment and 11 poor/fair responses. The clinical classification (p = 0.025) and serum creatinine (p = 0.010) on admission and small area emphasis (p = 0.036) from radiomics analysis significantly differed between the two groups. Predictive models were constructed based on the radiomics, clinical features, and nomogram showing an area under the curve of 0.651, 0.836, and 0.869, respectively. The nomogram achieved good calibration. CONCLUSION: This new, non-invasive, and low-cost prediction model that combines the radiomics and clinical features is useful for identifying COVID-19 patients who may not respond well to treatment.
