Evidence map›Paper›PMID 38562678›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Randomized Controlled Comparative Effectiveness Trial of Risk Model-Guided Clinical Decision Support for Suicide Screening.

Colin G Walsh, Michael A Ripperger, Laurie Novak, Carrie Reale, Shilo Anders, Ashley Spann, Jhansi Kolli, Katelyn Robinson, Qingxia Chen, David Isaacs and 11 more

Registry-linked trialAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05312437 (A Pragmatic Randomized Controlled Trial of Machine Learning-driven Clinical Decision Support to Improve Suicide Risk Screening in Ambulatory Care Settings), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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.

NCT05312437 nacompletednot on this map

A Pragmatic Randomized Controlled Trial of Machine Learning-driven Clinical Decision Support to Improve Suicide Risk Screening in Ambulatory Care Settings

TypeinterventionalSponsorVanderbilt University Medical CenterRan2022 to 2023Enrolled596ConditionsSuicide, Attempted, Suicidal IdeationArmsInterruptive Alert, CDS, Passive Prompt, CDS
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Observational
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

21 authors.

Colin G Walsh
Michael A Ripperger
Laurie Novak
Carrie Reale
Shilo Anders
Ashley Spann
Jhansi Kolli
Katelyn Robinson
Qingxia Chen
David Isaacs
Lealani Mae Y Acosta
Fenna Phibbs
Elliot Fielstein
Drew Wilimitis
Katherine Musacchio Schafer
Rachel Hilton
Dan Albert
Jill Shelton
Jessica Stroh
William W Stead
Kevin B Johnson

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Suicide prevention requires risk identification, appropriate intervention, and follow-up. Traditional risk identification relies on patient self-reporting, support network reporting, or face-to-face screening with validated instruments or history and physical exam. In the last decade, statistical risk models have been studied and more recently deployed to augment clinical judgment. Models have generally been found to be low precision or problematic at scale due to low incidence. Few have been tested in clinical practice, and none have been tested in clinical trials to our knowledge. Methods: We report the results of a pragmatic randomized controlled trial (RCT) in three outpatient adult Neurology clinic settings. This two-arm trial compared the effectiveness of Interruptive and Non-Interruptive Clinical Decision Support (CDS) to prompt further screening of suicidal ideation for those predicted to be high risk using a real-time, validated statistical risk model of suicide attempt risk, with the decision to screen as the primary end point. Secondary outcomes included rates of suicidal ideation and attempts in both arms. Manual chart review of every trial encounter was used to determine if suicide risk assessment was subsequently documented. Results: From August 16, 2022, through February 16, 2023, our study randomized 596 patient encounters across 561 patients for providers to receive either Interruptive or Non-Interruptive CDS in a 1:1 ratio. Adjusting for provider cluster effects, Interruptive CDS led to significantly higher numbers of decisions to screen (42%=121/289 encounters) compared to Non-Interruptive CDS (4%=12/307) (odds ratio=17.7, p-value <0.001). Secondarily, no documented episodes of suicidal ideation or attempts occurred in either arm. While the proportion of documented assessments among those noting the decision to screen was higher for providers in the Non-Interruptive arm (92%=11/12) than in the Interruptive arm (52%=63/121), the interruptive CDS was associated with more frequent documentation of suicide risk assessment (63/289 encounters compared to 11/307, p-value<0.001). Conclusions: In this pragmatic RCT of real-time predictive CDS to guide suicide risk assessment, Interruptive CDS led to higher numbers of decisions to screen and documented suicide risk assessments. Well-powered large-scale trials randomizing this type of CDS compared to standard of care are indicated to measure effectiveness in reducing suicidal self-harm. ClinicalTrials.gov Identifier: NCT05312437.

Identifiers

PMID38562678
PMCPMC10984050

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Registered trials

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.