Evidence map›Paper›PMID 41818636›Full record

Observational studyJMIR formative research2026

Identifying Undiagnosed High-Risk Suicidality Cases by Matching Patients With a Similar Comorbidity Burden: Retrospective Observational Study.

Louisa Bode, Rena Xu, Matthew Garber, Kenneth D Mandl, Andrew J Mcmurry

Abstract readObservational Study
In one paragraph

Observational study in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Louisa BodePeter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Hannover, Germany.ORCID 0000-0002-4570-9588
Rena XuComputational Health Informatics Program, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, United States, 1 (617) 355-6000.ORCID 0000-0002-0142-7589
Matthew GarberComputational Health Informatics Program, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, United States, 1 (617) 355-6000.ORCID 0009-0008-9838-8416
Kenneth D MandlComputational Health Informatics Program, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, United States, 1 (617) 355-6000.ORCID 0000-0002-9781-0477
Andrew J McmurryComputational Health Informatics Program, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, United States, 1 (617) 355-6000.ORCID 0000-0001-5604-0704

Funding

Instrumenting the Delivery System for a Genomics Research Information CommonsU01TR002623 · NCATS · BOSTON CHILDREN'S HOSPITAL · PI MANDL, KENNETH D. · 2019 to 2024
$8.6M
NCATS NIH HHS U01 TR002623
6 · The paper itself

Abstract

Background: Suicide is the second leading cause of death for children and adolescents aged 6 to 18 years. Pediatric suicidality is underreported, which poses significant challenges for effective intervention and prevention strategies. Identifying populations at risk of suicidality can provide critical benefits in terms of study cohort selection, prevalence estimation, and clinical resource allocation. Objective: This study sought to (1) measure the prevalence of mental health comorbidities in pediatric suicidality and (2) identify undiagnosed high-risk suicidality cases by matching them with patients with a similar mental health comorbidity burden. Methods: Electronic health record data from a large academic pediatric hospital in Boston, Massachusetts, were analyzed for patients aged 6 to 18 years presenting to the emergency department between June 1, 2016, and June 1, 2022. Suicidality cases were defined using International Classification of Diseases, 10th Revision (ICD-10) codes for 3 suicidality subtypes: suicidal ideation, self-harm, and suicide attempt. Comorbidities of suicidality were calculated as the conditional probability of ICD-10 code pairs. After multiple hypothesis corrections, statistically significant comorbidities and patient encounter demographics were input as covariates into a propensity score matching (PSM) model. The accuracy of the PSM model was validated against chart review by 2 independent subject matter experts. Results: In total, 2.9% (2638/90,980) of emergency department encounters met an ICD-10-based case definition of suicidality during the study period. The prevalence of suicidality by subtype was 2.5% (2275/90,980) for ideation, 1.1% (1030/90,980) for self-harm, and 0.2% (177/90,980) for suicide attempt. Suicidality prevalence was more common for the female sex (1825/43,929, 4.2%) than for the male sex (813/47,045, 1.7%). Comorbidities of suicidality were statistically significant for 55 frequently co-occurring ICD-10 codes. Nearly half of these comorbidities (26/55, 47.3%) were not present in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and nearly a quarter (12/55, 21.8%) consisted of ICD-10 codes for accidental rather than intentional self-harm. Increased probability of suicidality was observed for patients with personality disorder (84/190, 44.2%), gender dysphoria (143/333, 42.9%), bipolar disorder (162/448, 36.2%), depression (1791/5426, 33%), and schizophrenia spectrum disorders (133/411, 32.4%). On the basis of gold-standard chart review, 53.4% of propensity-matched noncases were unrecognized cases of suicidality. Conclusions: PSM using comorbidity profiles is an effective approach for identifying suicidality cases that lack ICD-10 codes for suicidality.

Indexed as

Suicidal IdeationSuicideAdolescentBostonChildComorbidityElectronic Health RecordsFemaleHumansInternational Classification of DiseasesMaleMental DisordersPrevalenceRetrospective StudiesRisk AssessmentSuicide, AttemptedcomorbidityDiagnostic and Statistical Manual of Mental DisordersDSMelectronic health recordsmedical informaticsmental disorderspediatricspropensity scoreself-injurious behaviorsuicidal ideation

Identifiers

PMID41818636
PMCPMC12981544

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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.