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Modifiable predictors of nonresponse to psychotherapies for late-life depression with executive dysfunction: a machine learning approach

Molecular Psychiatry volume 26, pages 5190–5198 (2021)Cite this article

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Abstract

The study aimed to: (1) Identify distinct trajectories of change in depressive symptoms by mid-treatment during psychotherapy for late-life depression with executive dysfunction; (2) examine if nonresponse by mid-treatment predicted poor response at treatment end; and (3) identify baseline characteristics predicting an early nonresponse trajectory by mid-treatment. A sample of 221 adults 60 years and older with major depression and executive dysfunction were randomized to 12 weeks of either problem-solving therapy or supportive therapy. We used Latent Growth Mixture Models (LGMM) to detect subgroups with distinct trajectories of change in depression by mid-treatment (6th week). We conducted regression analyses with LGMM subgroups as predictors of response at treatment end. We used random forest machine learning algorithms to identify baseline predictors of LGMM trajectories. We found that ~77.5% of participants had a declining trajectory of depression in weeks 0–6, while the remaining 22.5% had a persisting depression trajectory, with no treatment differences. The LGMM trajectories predicted remission and response at treatment end. A random forests model with high prediction accuracy (80%) showed that the strongest modifiable predictors of the persisting depression trajectory were low perceived social support, followed by high neuroticism, low treatment expectancy, and low perception of the therapist as accepting. Our results suggest that modifiable risk factors of early nonresponse to psychotherapy can be identified at the outset of treatment and addressed with targeted personalized interventions. Therapists may focus on increasing meaningful social interactions, addressing concerns related to treatment benefits, and creating a positive working relationship.

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Fig. 1: Latent Growth Mixture Model (LGMM) of estimated growth curves of depression severity from baseline to Week 6.
Fig. 2: Variable importance in predicting membership in growth curves of depression severity (from baseline to week 6) estimated by random forests.
Fig. 3: Single interpretable classification tree.

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Notes

  1. The missing pattern of predictors at baseline was sporadic across the sample with no consistent patterns. That is, no specific subset of predictors had more missing data than other sets. In addition, there were no significant concurrent missing patterns among any pair of variables.

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Funding

This research was funded by National Institute of Mental Health grants P50 MH113838, R01 MH064099, R01 MH063982, T32 MH019132, K23 MH123864, and the Sanchez Foundation.

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Authors and Affiliations

  1. Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, USA

    Nili Solomonov, Jihui Lee, Samprit Banerjee, Dora Kanellopoulos, Faith M. Gunning, Jo Anne Sirey & George S. Alexopoulos

  2. Department of Healthcare Policy & Research, Weill Cornell Medicine, New York, NY, USA

    Jihui Lee & Samprit Banerjee

  3. Psychologisches Institut, University of Zürich, Zürich, Switzerland

    Christoph Flückiger

  4. Feil Family Brain Mind Research Institute, Weill Cornell Medicine, New York, NY, USA

    Conor Liston

  5. Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA

    Patrick J. Raue & Patricia A. Areán

  6. Teachers College, Columbia University, New York, NY, USA

    Thomas D. Hull

  7. TalkSpace, New York, NY, USA

    Thomas D. Hull

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Correspondence to George S. Alexopoulos.

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Conflict of interest

GSA serves on the Eisai Advisory Board and Otsuka Speakers Bureau. He also served on the Speakers Bureaus of Allergan and Takeda-Lundbeck and Janssen Advisory Board. TDH is an employee of “Talk Space.” All other authors report no conflict of interest.

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Solomonov, N., Lee, J., Banerjee, S. et al. Modifiable predictors of nonresponse to psychotherapies for late-life depression with executive dysfunction: a machine learning approach. Mol Psychiatry 26, 5190–5198 (2021). https://doi.org/10.1038/s41380-020-0836-z

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