Evidence map›Paper›PMID 39306938›Full record

Trial reportBehaviour research and therapy2024

Initial evaluation of a personalized advantage index to determine which individuals may benefit from mindfulness-based cognitive therapy for suicide prevention.

Catherine E Myers, Chintan V Dave, Megan S Chesin, Brian P Marx, Lauren M St Hill, Vibha Reddy, Rachael B Miller, Arlene King, Alejandro Interian

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Behaviour research and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Trial
  2. 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

9 authors.

Catherine E MyersResearch and Development Service, VA New Jersey Health Care System, East Orange, NJ, USA; Department of Pharmacology, Physiology & Neuroscience, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, USA.
Chintan V DaveCenter for Pharmacoepidemiology and Treatment Science, Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, USA.
Megan S ChesinDepartment of Psychology, William Paterson University, USA.
Brian P MarxNational Center for PTSD, Behavioral Sciences Division at the VA Boston Health Care System, Boston, MA, USA; Boston University School of Medicine, Boston, MA, USA.
Lauren M St HillMental Health and Behavioral Sciences, VA New Jersey Health Care System, Lyons, NJ, USA.
Vibha ReddyResearch and Development Service, VA New Jersey Health Care System, East Orange, NJ, USA.
Rachael B MillerMental Health and Behavioral Sciences, VA New Jersey Health Care System, Lyons, NJ, USA.
Arlene KingMental Health and Behavioral Sciences, VA New Jersey Health Care System, Lyons, NJ, USA.
Alejandro InterianMental Health and Behavioral Sciences, VA New Jersey Health Care System, Lyons, NJ, USA; Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, USA. Electronic address: Alejandro.Interian@va.gov.

Funding

Neurocognitive markers of short-term risk for suicidal behavior in high-risk VeteransI01CX001826 · VA · VA NEW JERSEY HEALTH CARE SYSTEM · PI MYERS, CATHERINE E · 2019 to 2024
–
Center for Harmonizing and Improving Interventions to Prevent Suicide (CHIIPS)I01CX002621 · VA · VA BOSTON HEALTH CARE SYSTEM · PI GOODMAN, MARIANNE, INTERIAN, ALEJANDRO · 2023 to 2025
–
CSRD VA I01 CX001826CSRD VA I01 CX002621
6 · The paper itself

Abstract

objectiveDevelop and evaluate a treatment matching algorithm to predict differential treatment response to Mindfulness-Based Cognitive Therapy for suicide prevention (MBCT-S) versus enhanced treatment-as-usual (eTAU).

methodsAnalyses used data from Veterans at high-risk for suicide assigned to either MBCT-S (n = 71) or eTAU (n = 69) in a randomized clinical trial. Potential predictors (n = 55) included available demographic, clinical, and neurocognitive variables. Random forest models were used to predict risk of suicidal event (suicidal behaviors, or ideation resulting in hospitalization or emergency department visit) within 12 months following randomization, characterize the prediction, and develop a Personalized Advantage Index (PAI).

resultsA slightly better prediction model emerged for MBCT-S (AUC = 0.70) than eTAU (AUC = 0.63). Important outcome predictors for participants in the MBCT-S arm included PTSD diagnosis, decisional efficiency on a neurocognitive task (Go/No-Go), prior-year mental health residential treatment, and non-suicidal self-injury. Significant predictors for participants in the eTAU arm included past-year acute psychiatric hospitalizations, past-year outpatient psychotherapy visits, past-year suicidal ideation severity, and attentional control (indexed by Stroop task). A moderation analysis showed that fewer suicidal events occurred among those randomized to their PAI-indicated optimal treatment.

conclusionsPAI-guided treatment assignment may enhance suicide prevention outcomes. However, prior to real-world application, additional research is required to improve model accuracy and evaluate model generalization.

Indexed as

Cognitive Behavioral TherapyMindfulnessSuicide PreventionVeteransAdultAlgorithmsFemaleHumansMaleMiddle AgedStress Disorders, Post-TraumaticSuicidal IdeationTreatment OutcomeMachine learningMindfulnessPrecision medicineSuicide preventionTreatment prediction

Identifiers

PMID39306938
PMCPMC11620942

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.