Development of a prediction score (ThyroCOVID) for identifying abnormal thyroid function in COVID-19 patients.

Purpose: Thyroid dysfunction in COVID-19 carries clinical and prognostic implications. In this study, we developed a prediction score (ThyroCOVID) for abnormal thyroid function (TFT) on admission amongst COVID-19 patients.
Methods: Consecutive COVID-19 patients admitted to Queen Mary Hospital were prospectively recruited during July 2020-May 2021. Thyroid-stimulating hormone (TSH), free thyroxine (fT4) and free triiodothyronine (fT3) were measured on admission. Multivariable logistic regression analysis was performed to identify independent determinants of abnormal TFTs. ThyroCOVID was developed based on a clinical model with the lowest Akaike information criteria.
Results: Five hundred and forty six COVID-19 patients were recruited (median age 50 years, 45.4% men, 72.9% mild disease on admission). 84 patients (15.4%) had abnormal TFTs on admission. Patients with abnormal TFTs were more likely to be older, have more comorbidities, symptomatic, have worse COVID-19 severity, higher SARS-CoV-2 viral loads and more adverse profile of acute-phase reactants, haematological and biochemical parameters. ThyroCOVID consisted of five parameters: symptoms (malaise), comorbidities (ischaemic heart disease/congestive heart failure) and laboratory parameters (lymphocyte count, C-reactive protein, and SARS-CoV-2 cycle threshold values). It was able to identify abnormal TFT on admission with an AUROC of 0.73 (95% CI 0.67-0.79). The optimal cut-off of 0.15 had a sensitivity of 75.0%, specificity of 65.2%, negative predictive value of 93.5% and positive predictive value of 28.1% in identifying abnormal TFTs on admission amongst COVID-19 patients.
Conclusion: ThyroCOVID, a prediction score to identify COVID-19 patients at risk of having abnormal TFT on admission, was developed based on a cohort of predominantly non-severe COVID-19 patients.
(© 2022. The Author(s), under exclusive licence to Italian Society of Endocrinology (SIE).)