Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression.

Journal: Translational psychiatry
PMID: 40289155

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.

Authors

  • Lindsay L Benster
    Department of Psychiatry, University of California, San Diego, CA, USA.
  • Cory R Weissman
    Department of Psychiatry, University of California, San Diego, CA, USA.
  • Federico Suprani
    Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.
  • Kamryn Toney
    Morehouse School of Medicine, Atlanta, GA, USA.
  • Houtan Afshar
    Department of Psychiatry, University of California, San Diego, CA, USA.
  • Noah Stapper
    Department of Psychiatry, University of California, San Diego, CA, USA.
  • Vanessa Tello
    Department of Psychiatry, University of California, San Diego, CA, USA.
  • Louise Stolz
    Department of Psychiatry, University of California, San Diego, CA, USA.
  • Mohsen Poorganji
    Department of Psychiatry, University of California, San Diego, CA, USA.
  • Zafiris J Daskalakis
    Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
  • Lawrence G Appelbaum
    Department of Psychiatry, University of California, San Diego, CA, USA.
  • Jordan N Kohn
    Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, CA, USA. jokohn@health.ucsd.edu.

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