A Longitudinal Study on Predicting Treatment Response in Depression Using Self-Reported Symptoms.

BACKGROUND

Depression causes serious disabilities, and response to treatment is difficult to predict. This study aimed to examine whether self-reported initial symptoms can predict treatment response in depression.

METHODS

One hundred fifty-seven patients who were diagnosed with mood disorders (major depressive disorder or bipolar disorder) and in a state of moderate or severe depression were recruited from six psychiatric hospitals between 2018 and 2022. Participants were classified into treatment responders (n = 89) and non-responders (n = 68) based on the Montgomery-Åsberg Depression Rating Scale, and changes in a wide range of symptoms, including anxiety and depression, were assessed using both clinician-administered scales and self-reports. Logistic regression analysis was conducted to identify factors associated with treatment response, and the performance of several prediction models using demographic information and symptom scores was compared.

RESULTS

There were no significant baseline differences between responders and non-responders in diagnoses, medications, clinician-rated scales, medication adherence, or objective depressive and anxiety scores. However, non-responders initially reported higher levels of obsessive-compulsive symptoms, interpersonal sensitivity, phobic anxiety, and paranoid ideation, along with lower satisfaction in social relationships. The comparison of predictive models showed that those incorporating subjective symptoms outperformed models based solely on demographic covariates (area under the receiver operating characteristic curve [AUROC] = 0.725, 95% confidence interval [CI], 0.643-0.807 vs. 0.666, 95% CI, 0.578-0.754). Notably, phobic anxiety was identified as a strong independent predictor of treatment response (odds ratio [OR], 1.93, 95% CI, 1.30-2.85).

CONCLUSION

Subjective symptoms other than depression or anxiety are important factors for predicting treatment response in depression. Therefore, incorporating patients' self-reported symptoms is crucial for predicting treatment response in depression.