Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression
Abstract Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (TRD). Leveraging electronic medical records (EMR), this retrospective cohort study applied supervised machine learning (ML) to sociodemograph...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2025-04-01
|
| Series: | Translational Psychiatry |
| Online Access: | https://doi.org/10.1038/s41398-025-03380-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850225738349805568 |
|---|---|
| author | Lindsay L. Benster Cory R. Weissman Federico Suprani Kamryn Toney Houtan Afshar Noah Stapper Vanessa Tello Louise Stolz Mohsen Poorganji Zafiris J. Daskalakis Lawrence G. Appelbaum Jordan N. Kohn |
| author_facet | Lindsay L. Benster Cory R. Weissman Federico Suprani Kamryn Toney Houtan Afshar Noah Stapper Vanessa Tello Louise Stolz Mohsen Poorganji Zafiris J. Daskalakis Lawrence G. Appelbaum Jordan N. Kohn |
| author_sort | Lindsay L. Benster |
| collection | DOAJ |
| description | Abstract Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (TRD). Leveraging electronic medical records (EMR), this retrospective cohort study applied supervised machine learning (ML) to sociodemographic, clinical, and treatment-related data to predict depressive symptom response (>50% reduction on PHQ-9) and remission (PHQ-9 < 5) following rTMS in 232 patients with TRD (mean age: 54.5, 63.4% women) treated at the University of California, San Diego Interventional Psychiatry Program between 2017 and 2023. ML models were internally validated using nested cross-validation and Shapley values were calculated to quantify contributions of each feature to response prediction. The best-fit models proved reasonably accurate at discriminating treatment responders (Area under the curve (AUC): 0.689 [0.638, 0.740], p < 0.01) and remitters (AUC 0.745 [0.692, 0.797], p < 0.01), though only the response model was well-calibrated. Both models were associated with significant net benefits, indicating their potential utility for clinical decision-making. Shapley values revealed that patients with comorbid anxiety, obesity, concurrent benzodiazepine or antipsychotic use, and more chronic TRD were less likely to respond or remit following rTMS. Patients with trauma and former tobacco users were more likely to respond. Furthermore, delivery of intermittent theta burst stimulation and more rTMS sessions were associated with superior outcomes. These findings highlight the potential of ML-guided techniques to guide clinical decision-making for rTMS treatment in patients with TRD to optimize therapeutic outcomes. |
| format | Article |
| id | doaj-art-dbbd7140be5a4dad8b9ae72a9f4c0e22 |
| institution | OA Journals |
| issn | 2158-3188 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Translational Psychiatry |
| spelling | doaj-art-dbbd7140be5a4dad8b9ae72a9f4c0e222025-08-20T02:05:16ZengNature Publishing GroupTranslational Psychiatry2158-31882025-04-011511910.1038/s41398-025-03380-wPredictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depressionLindsay L. Benster0Cory R. Weissman1Federico Suprani2Kamryn Toney3Houtan Afshar4Noah Stapper5Vanessa Tello6Louise Stolz7Mohsen Poorganji8Zafiris J. Daskalakis9Lawrence G. Appelbaum10Jordan N. Kohn11Department of Psychiatry, University of CaliforniaDepartment of Psychiatry, University of CaliforniaSection of Psychiatry, Department of Medical Sciences and Public Health, University of CagliariMorehouse School of MedicineDepartment of Psychiatry, University of CaliforniaDepartment of Psychiatry, University of CaliforniaDepartment of Psychiatry, University of CaliforniaDepartment of Psychiatry, University of CaliforniaDepartment of Psychiatry, University of CaliforniaDepartment of Psychiatry, University of CaliforniaDepartment of Psychiatry, University of CaliforniaHerbert Wertheim School of Public Health and Human Longevity Science, University of CaliforniaAbstract Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (TRD). Leveraging electronic medical records (EMR), this retrospective cohort study applied supervised machine learning (ML) to sociodemographic, clinical, and treatment-related data to predict depressive symptom response (>50% reduction on PHQ-9) and remission (PHQ-9 < 5) following rTMS in 232 patients with TRD (mean age: 54.5, 63.4% women) treated at the University of California, San Diego Interventional Psychiatry Program between 2017 and 2023. ML models were internally validated using nested cross-validation and Shapley values were calculated to quantify contributions of each feature to response prediction. The best-fit models proved reasonably accurate at discriminating treatment responders (Area under the curve (AUC): 0.689 [0.638, 0.740], p < 0.01) and remitters (AUC 0.745 [0.692, 0.797], p < 0.01), though only the response model was well-calibrated. Both models were associated with significant net benefits, indicating their potential utility for clinical decision-making. Shapley values revealed that patients with comorbid anxiety, obesity, concurrent benzodiazepine or antipsychotic use, and more chronic TRD were less likely to respond or remit following rTMS. Patients with trauma and former tobacco users were more likely to respond. Furthermore, delivery of intermittent theta burst stimulation and more rTMS sessions were associated with superior outcomes. These findings highlight the potential of ML-guided techniques to guide clinical decision-making for rTMS treatment in patients with TRD to optimize therapeutic outcomes.https://doi.org/10.1038/s41398-025-03380-w |
| spellingShingle | Lindsay L. Benster Cory R. Weissman Federico Suprani Kamryn Toney Houtan Afshar Noah Stapper Vanessa Tello Louise Stolz Mohsen Poorganji Zafiris J. Daskalakis Lawrence G. Appelbaum Jordan N. Kohn Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression Translational Psychiatry |
| title | Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression |
| title_full | Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression |
| title_fullStr | Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression |
| title_full_unstemmed | Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression |
| title_short | Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression |
| title_sort | predictive modeling of response to repetitive transcranial magnetic stimulation in treatment resistant depression |
| url | https://doi.org/10.1038/s41398-025-03380-w |
| work_keys_str_mv | AT lindsaylbenster predictivemodelingofresponsetorepetitivetranscranialmagneticstimulationintreatmentresistantdepression AT coryrweissman predictivemodelingofresponsetorepetitivetranscranialmagneticstimulationintreatmentresistantdepression AT federicosuprani predictivemodelingofresponsetorepetitivetranscranialmagneticstimulationintreatmentresistantdepression AT kamryntoney predictivemodelingofresponsetorepetitivetranscranialmagneticstimulationintreatmentresistantdepression AT houtanafshar predictivemodelingofresponsetorepetitivetranscranialmagneticstimulationintreatmentresistantdepression AT noahstapper predictivemodelingofresponsetorepetitivetranscranialmagneticstimulationintreatmentresistantdepression AT vanessatello predictivemodelingofresponsetorepetitivetranscranialmagneticstimulationintreatmentresistantdepression AT louisestolz predictivemodelingofresponsetorepetitivetranscranialmagneticstimulationintreatmentresistantdepression AT mohsenpoorganji predictivemodelingofresponsetorepetitivetranscranialmagneticstimulationintreatmentresistantdepression AT zafirisjdaskalakis predictivemodelingofresponsetorepetitivetranscranialmagneticstimulationintreatmentresistantdepression AT lawrencegappelbaum predictivemodelingofresponsetorepetitivetranscranialmagneticstimulationintreatmentresistantdepression AT jordannkohn predictivemodelingofresponsetorepetitivetranscranialmagneticstimulationintreatmentresistantdepression |