Evidence map›Paper›PMID 32651477›Full record

Trial reportMolecular psychiatry2021

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

Nili Solomonov, Jihui Lee, Samprit Banerjee, Christoph Flückiger, Dora Kanellopoulos, Faith M Gunning, Jo Anne Sirey, Conor Liston, Patrick J Raue, Thomas D Hull and 2 more

Open access · greenAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Molecular psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 3 pooled it
3.0field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 37 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Perceived Social Support and Interpersonal Functioning as Predictors of Treatment Response Among Depressed Older Adults.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2021
    Trial
  6. Targeting mechanisms of change as a path to precision psychotherapy for community settings.Psychotherapy research : journal of the Society for Psychotherapy Research · 2026
    Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024
    Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Observational
  19. Improving Interventions for Loneliness and Social Isolation in Late Life: The Potential Role of Positive Affect.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 2 countries.

Nili SolomonovWeill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, USA.ORCID http://orcid.org/0000-0003-1573-5715
Jihui LeeWeill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, USA.
Samprit BanerjeeWeill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, USA.
Christoph FlückigerPsychologisches Institut, University of Zürich, Zürich, Switzerland.
Dora KanellopoulosWeill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, USA.
Faith M GunningWeill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, USA.
Jo Anne SireyWeill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, USA.
Conor ListonFeil Family Brain Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
Patrick J RaueDepartment of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA.
Thomas D HullTeachers College, Columbia University, New York, NY, USA.
Patricia A AreánDepartment of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA.
George S AlexopoulosWeill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, USA. gsalexop@med.cornell.edu.ORCID http://orcid.org/0000-0001-5677-7001
Cornell University · USUniversity of Washington · USPace University · USUniversity of Zurich · CH

Funding

Efficacy of biomarker-guided rTMS for treatment resistant depressionR01MH118388 · NIMH · WEILL MEDICAL COLL OF CORNELL UNIV · PI BHATI, MAHENDRA T, GUNNING, FAITH M · 2019 to 2025
$6.2M
Relief: A Behavioral Intervention for Depression and Chronic Pain for Primary Care PracticesP50MH113838 · NIMH · WEILL MEDICAL COLL OF CORNELL UNIV · PI ALEXOPOULOS, GEORGE S · 2017 to 2020
$5.7M
RESEARCH TRAINING IN GERIATRIC MOOD DISORDERST32MH019132 · NIMH · WEILL MEDICAL COLL OF CORNELL UNIV · PI GUNNING, FAITH M · 1990 to 2025
$3.4M
PST in Geriatric Depression with Executive DysfunctionR01MH063982 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI AREAN, PATRICIA A. · 2002 to 2006
$1.9M
PST in Geriatric Depression with Executive DysfunctionR01MH064099 · NIMH · WEILL MEDICAL COLL OF CORNELL UNIV · PI SCHULBERG, HERBERT CHARLES · 2002 to 2006
$1.8M
Positive valence systems engagement during social reward exposure psychotherapy for mid- and late-life depressionK23MH123864 · NIMH · WEILL MEDICAL COLL OF CORNELL UNIV · PI SOLOMONOV, NILI · 2020 to 2024
$954k
NIMH NIH HHS K23 MH123864NIMH NIH HHS P50 MH113838NIMH NIH HHS R01 MH063982NIMH NIH HHS R01 MH064099NIMH NIH HHS R01 MH118388NIMH NIH HHS T32 MH019132
6 · The paper itself

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.

Indexed as

Cognitive DysfunctionMajor Depressive DisorderAdultDepressionHumansMachine LearningPsychotherapy

Identifiers

PMID32651477
PMCPMC8120667
OpenAlexW3041054623

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.