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Background: Depressive disorders are a significant burden to patients and society, possibly leading to catastrophic damage to one's life. Unfortunately, many of these patients become resistant to treatment. Therefore, identifying possible aspects that can influence treatment responsiveness and return to life activities has become essential. Illness perceptions have been associated with many different conditions, including depression, treatment adherence, functionality, and coping behaviours. Objectives: The objectives of this study were to describe illness perceptions in a sample of patients with treatment-resistant depression (TRD) undergoing repetitive transcranial magnetic stimulation (rTMS) treatment; to evaluate its correlation with changes in the level of disability and changes in depression symptoms after treatment; to identify the possible influence of treatment on illness perceptions changes over time. Methods: Participants with a history of treatment-resistant depression were referred from primary and secondary care to receive treatment with rTMS. Measurements were done at baseline and after treatment using BIPQ, HRSD-17, and Sheehan Disability Scale (SDS). Patients were followed for a total of 16 to 18 weeks. Results: The sample consisted of 62 participants. The majority were female with severe depression. Identity, consequences, concern, and emotional representations were very high before treatment and strongly associated with one another. Life stressors, genetics, and trauma were the most perceived causes of depression. There was an indication that identity and other dimensions could explain some of the variances in HRSD-17 scores after rTMS, and perceived identity could also explain the variance in work/school, social, and family/home scores. rTMS appeared to be correlated with changes in illness dimensions after treatment. Conclusions: Depression takes over a patient's perception and life experience affecting social, professional, and personal life aspects. Most illness perceptions in TRD patients are severe and can mildly explain changes in symptoms and functioning over time. Changes in illness perception are part of the common-sense model's dynamic feedback and could partially be attributed to treatment in this sample.
Major depressive disorder (MDD) is a highly prevalent psychiatric disorder that affects millions of people. Repetitive transcranial magnetic stimulation (rTMS) has been recommended as a safe, reliable, non-invasive, neurostimulation therapy option for treatment-resistant depression (TRD). The effectiveness of rTMS treatment varies among individuals; thus, predicting the responsiveness to rTMS treatment can reduce unnecessary expenses and improve treatment capacity. In this study, we combined machine learning models with depression rating scales, clinical variables, and demographic data to predict the outcomes and effectiveness of rTMS treatment for TRD patients. Using the clinical data of 356 TRD patients who each received 20 to 30 sessions of rTMS treatment over a 4-6-week period, we examined the predictive value of different depression rating scales and models for various prediction outcomes, at multiple time points. Our optimal baseline models achieved area under the curve (AUC) values of 0.634 and 0.735 for treatment response and remission prediction, respectively, using the Elastic Net. In the longitudinal analysis, using baseline data and early treatment outcomes for 1–3 weeks, all predictive values improved compared with baseline models. In addition, predicting the percentage of symptom improvement was also feasible using longitudinal treatment outcomes, achieving coefficients of determination of 0.277, at the end of week 1, and 0.464, at the end of week 3. We found that the use of depression rating subscales, combined with clinical and demographic data, including anxiety severity, employment status, age, gender, and education level, may produce higher accuracy at baseline. In the longitudinal analysis, the total scores of depression rating scales were the most significant predictors, allowing prediction models to be built using only the total scores, which resulted in high predictive value and interpretability. This work presented a convenient and economical approach for the prediction of rTMS treatment outcomes in TRD patients, using pre-treatment clinical and demographic data alone, without requiring expensive biomarker data. The predictive value was further enhanced by adding longitudinal treatment outcomes. This method is a plausible approach that could be utilized in clinical practice for individualized treatment selection, leading to better treatment outcomes for rTMS in TRD patients.