In:
Psychological Medicine, Cambridge University Press (CUP), Vol. 49, No. 14 ( 2019-10), p. 2405-2413
Abstract:
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 χ 2 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.
Type of Medium:
Online Resource
ISSN:
0033-2917
,
1469-8978
DOI:
10.1017/S0033291718003331
Language:
English
Publisher:
Cambridge University Press (CUP)
Publication Date:
2019
detail.hit.zdb_id:
1470300-2
SSG:
5,2
