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Prediction of remission in pharmacotherapy of untreated major depression: development and validation of multivariable prediction models

  • Toshi A. Furukawa (a1), Tadashi Kato (a2), Yoshihiro Shinagawa (a3), Kazuhira Miki (a4), Hirokazu Fujita (a5), Naohisa Tsujino (a6), Masaki Kondo (a1), Masatoshi Inagaki (a7) and Mitsuhiko Yamada (a8)...

Abstract

Background

Depression is increasingly recognized as a chronic and relapsing disorder. However, an important minority of patients who start treatment for their major depressive episode recover to euthymia. It is clinically important to be able to predict such individuals.

Methods

The study is a secondary analysis of a recently completed pragmatic megatrial examining first- and second-line treatments for hitherto untreated episodes of non-psychotic unipolar major depression (n = 2011). Using the first half of the cohort as the derivation set, we applied multiply-imputed stepwise logistic regression with backward selection to build a prediction model to predict remission, defined as scoring 4 or less on the Patient Health Quetionnaire-9 at week 9. We used three successively richer sets of predictors at baseline only, up to week 1, and up to week 3. We examined the external validity of the derived prediction models with the second half of the cohort.

Results

In total, 37.0% (95% confidence interval 34.8–39.1%) were in remission at week 9. Only the models using data up to week 1 or 3 showed reasonable performance. Age, education, length of episode and depression severity remained in the multivariable prediction models. In the validation set, the discrimination of the prediction model was satisfactory with the area under the curve of 0.73 (0.70–0.77) and 0.82 (0.79–0.85), while the calibration was excellent with non-significant goodness-of-fit χ2 values (p = 0.41 and p = 0.29), respectively.

Conclusions

Patients and clinicians can use these prediction models to estimate their predicted probability of achieving remission after acute antidepressant therapy.

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Copyright

This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.

Corresponding author

Author for correspondence: Toshi A. Furukawa, E-mail: furukawa@med.nagoya-cu.ac.jp

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Psychological Medicine
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