Evidence map›Paper›PMID 38158050›Full record

ArticleJournal of affective disorders2024

Predicting adolescent suicidal behavior following inpatient discharge using structured and unstructured data.

Nicholas J Carson, Xinyu Yang, Brian Mullin, Elizabeth Stettenbauer, Marin Waddington, Alice Zhang, Peyton Williams, Gabriel E Rios Perez, Benjamin Lê Cook

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of affective disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Article
  6. 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.

Nicholas J CarsonHealth Equity Research Lab, Cambridge Health Alliance, 1035 Cambridge Street, Cambridge, MA 02139, USA. Electronic address: ncarson@cha.harvard.edu.
Xinyu YangParexel, 275 Grove St., Suite 101C, Newton, MA 02466, USA.
Brian MullinHealth Equity Research Lab, Cambridge Health Alliance, 1035 Cambridge Street, Cambridge, MA 02139, USA.
Elizabeth StettenbauerBrown University School of Public Health, Providence RI 02903.
Marin WaddingtonDivision of Gastroenterology at Brigham and Women's Hospital, Resnek Family Center for PSC Research, 75 Francis Street, Boston, MA 02115, USA.
Alice ZhangDepartment of Psychology, New York University, 6 Washington Place, New York, NY 10003, USA.
Peyton WilliamsHealth Equity Research Lab, Cambridge Health Alliance, 1035 Cambridge Street, Cambridge, MA 02139, USA.
Gabriel E Rios PerezHealth Equity Research Lab, Cambridge Health Alliance, 1035 Cambridge Street, Cambridge, MA 02139, USA.
Benjamin Lê CookHealth Equity Research Lab, Cambridge Health Alliance, 1035 Cambridge Street, Cambridge, MA 02139, USA.

Funding

Understanding the Role of Trauma Over the Lifecourse in Order to Improve Trauma-informed TreatmentP50MH126283 · NIMH · CAMBRIDGE HEALTH ALLIANCE · PI COOK, BENJAMIN LE · 2021 to 2024
$6.6M
AHRQ HHS R01 HS021486NIMH NIH HHS P50 MH126283
6 · The paper itself

Abstract

backgroundThe objective was to develop and assess performance of an algorithm predicting suicide-related ICD codes within three months of psychiatric discharge.

methodsThis prognostic study used a retrospective cohort of EHR data from 2789 youth (12 to 20 years old) hospitalized in a safety net institution in the Northeastern United States. The dataset combined structured data with unstructured data obtained through natural language processing of clinical notes. Machine learning approaches compared gradient boosting to random forest analyses.

resultsArea under the ROC and precision-recall curve were 0.88 and 0.17, respectively, for the final Gradient Boosting model. The cutoff point of the model-generated predicted probabilities of suicide that optimally classified the individual as high risk or not was 0.009. When applying the chosen cutoff (0.009) to the hold-out testing set, the model correctly identified 8 positive cases out of 10, and 418 negative cases out 548. The corresponding performance metrics showed 80 % sensitivity, 76 % specificity, 6 % PPV, 99 % NPV, F-1 score of 0.11, and an accuracy of 76 %. LIMITATIONS: The data in this study comes from a single health system, possibly introducing bias in the model's algorithm. Thus, the model may have underestimated the incidence of suicidal behavior in the study population. Further research should include multiple system EHRs.

conclusionsThese performance metrics suggest a benefit to including both unstructured and structured data in design of predictive algorithms for suicidal behavior, which can be integrated into psychiatric services to help assess risk.

Indexed as

Patient DischargeSuicidal IdeationAdolescentAlgorithmsChildHumansInpatientsRetrospective StudiesYoung AdultAdolescenceElectronic health recordsMachine learningPatient dischargeRiskSuicide

Identifiers

PMID38158050
PMCPMC10923087

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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.