Background: Although undergoing antidepressant treatments, many patients continue to struggle with chronic depression episode. Seeking the potential biomarkers and establishing a predictive model of clinical improvements is vital to optimize personalized management of depression. Mounting evidence showed thyroid hormones changes are central to leading paradigms of depression.
Methods: Here, we conducted a real-world based retrospective study using clinical and biochemical data of 2086 depressive inpatients during period of 2014-2020. We first performed regression analyses to evaluate the contributing effect of free triiodothyronine (FT3), free thyroxine (FT4) and thyroid stimulating hormone (TSH) in predicting the clinical outcomes of depression. Then we established 7 predictive models using different combination of such hormones by supervised learning methods and tested the actual prediction efficacy on clinical outcomes, in order to select the one with the best predictive power.
Results: The results showed that lower values of FT3 and FT4 can both predict a poor clinical outcome in depression. Further, a model with the best performance was selected (sensitivity=0.91, specificity=0.79, and ROC-AUC=0.86), including the values of FT3 and FT4, and the scores of Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA) as features.
Limitations: The predictive model requires further external validation, and multi-center researches to confirm its clinical applicability.
Conclusions: Our findings present a crucial role of thyroid measurements in predicting clinical outcomes of depression. Assessment of thyroid hormone should be extended to routine practice settings to determine which patients should be most in need of earlier or intensive interventions for preventing continued dysfunction.
Keywords: clinical outcomes; depression; machine learning; predictive model; thyroid hormone.
Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.