Evidence map›Paper›PMID 39407131›Full record

ArticleBMC psychiatry2024

Towards accurate screening and prevention for PTSD (2-ASAP): protocol of a longitudinal prospective cohort study.

Jeanet F Karchoud, Chris M Hoeboer, Greta Piwanski, Juanita A Haagsma, Miranda Olff, Rens van de Schoot, Mirjam van Zuiden

Abstract read
In one paragraph

Article in BMC psychiatry, 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. Article
  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

7 authors.

Jeanet F KarchoudAmsterdam UMC, University of Amsterdam, Psychiatry, Amsterdam Public Health, Amsterdam, The Netherlands.
Chris M HoeboerAmsterdam UMC, University of Amsterdam, Psychiatry, Amsterdam Public Health, Amsterdam, The Netherlands.
Greta PiwanskiAmsterdam UMC, University of Amsterdam, Psychiatry, Amsterdam Public Health, Amsterdam, The Netherlands.
Juanita A HaagsmaDepartment of Public Health, Erasmus MC, Rotterdam, The Netherlands.
Miranda OlffAmsterdam UMC, University of Amsterdam, Psychiatry, Amsterdam Public Health, Amsterdam, The Netherlands.
Rens van de SchootDepartment of Methods and Statistics, Utrecht University, Utrecht, The Netherlands.
Mirjam van ZuidenAmsterdam UMC, University of Amsterdam, Psychiatry, Amsterdam Public Health, Amsterdam, The Netherlands. m.vanzuiden@uu.nl.

Funding

ZonMw 636340004
6 · The paper itself

Abstract

backgroundEffective preventive interventions for PTSD rely on early identification of individuals at risk for developing PTSD. To establish early post-trauma who are at risk, there is a need for accurate prognostic risk screening instruments for PTSD that can be widely implemented in recently trauma-exposed adults. Achieving such accuracy and generalizability requires external validation of machine learning classification models. The current 2-ASAP cohort study will perform external validation on both full and minimal feature sets of supervised machine learning classification models assessing individual risk to follow an adverse PTSD symptom trajectory over the course of 1 year. We will derive these models from the TraumaTIPS cohort, separately for men and women.

methodThe 2-ASAP longitudinal cohort will include N = 863 adults (N = 436 females, N = 427 males) who were recently exposed to acute civilian trauma. We will include civilian victims of accidents, crime and calamities at Victim Support Netherlands; and who were presented for medical evaluation of (suspected) traumatic injuries by emergency transportation to the emergency department. The baseline assessment within 2 months post-trauma will include self-report questionnaires on demographic, medical and traumatic event characteristics; potential risk and protective factors for PTSD; PTSD symptom severity and other adverse outcomes; and current best-practice PTSD screening instruments. Participants will be followed at 3, 6, 9, and 12 months post-trauma, assessing PTSD symptom severity and other adverse outcomes via self-report questionnaires. DISCUSSION: The ultimate goal of our study is to improve accurate screening and prevention for PTSD in recently trauma-exposed civilians. To enable future large-scale implementation, we will use self-report data to inform the prognostic models; and we will derive a minimal feature set of the classification models. This can be transformed into a short online screening instrument that is user-friendly for recently trauma-exposed adults to fill in. The eventual short online screening instrument will classify early post-trauma which adults are at risk for developing PTSD. Those at risk can be targeted and may subsequently benefit from preventive interventions, aiming to reduce PTSD and relatedly improve psychological, functional and economic outcomes.

Indexed as

Stress Disorders, Post-TraumaticAdultFemaleHumansLongitudinal StudiesMachine LearningMaleMass ScreeningNetherlandsProspective StudiesGenderLongitudinalPTSDSexSupervised machine learningTrauma

Identifiers

PMID39407131
PMCPMC11476939

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Registered trials

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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.