Observational studyJAMA network open2021
Prospective Validation of an Electronic Health Record-Based, Real-Time Suicide Risk Model.
Observational study in JAMA network open, 2021. 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 55 papers, 3 of them syntheses that pooled it.
What it found
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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.
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.
A Pragmatic Randomized Controlled Trial of Machine Learning-driven Clinical Decision Support to Improve Suicide Risk Screening in Ambulatory Care Settings
Who cites it
55 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prediction models for self-harm and suicide: a systematic review and critical appraisal.BMC medicine · 2025Pooled it
- Suicidal risk in patients with aggression in schizophrenia: a systematic review.Frontiers in psychiatry · 2025Pooled it
- Applications and challenges of biomarker-based predictive models in proactive health management.Frontiers in public health · 2025Pooled it
- Risk Model-Guided Clinical Decision Support for Suicide Screening: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Gut Microbial Topology and Metabolic Signatures Associated With Colorectal Neoplasia.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Temporal maintenance of an adolescent suicide attempt screening score in national surveys: An observational study.Medicine · 2026Observational
- Factors Influencing Physician Adherence to Venous Thromboembolism Risk Assessment Model Recommendations: A Best-Worst Scaling.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2026Article
- Life events extraction from healthcare notes for veteran acute suicide risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Conceptualization and development of the Maryland suicide data warehouse to improve mortality prediction.JAMIA open · 2026Article
- Enhancing personalized suicide risk prediction for VA patients by integrating discrete natural language processing models.Translational psychiatry · 2026Article
- Clinical Note-Extracted Psychosocial Factors for Predicting Suicide Attempt Among ED Patients With Suicidal Ideation.JAMA network open · 2026Article
- Using transfer learning to improve prediction of suicide risk in acute care hospitals.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Human-centered design of an artificial intelligence monitoring system: the Vanderbilt Algorithmovigilance Monitoring and Operations System.JAMIA open · 2025Article
- Screening and Risk Algorithms for Detecting Pediatric Suicide Risk in the Emergency Department.JAMA network open · 2025Article
- Applying large language models to stratify suicide risk using narrative clinical notes.Journal of mood and anxiety disorders · 2025Article
- Accuracy of ICD-10 Codes for Suicidal Ideation and Action in Pediatric Emergency Department Encounters.Pediatric emergency care · 2025Article
- Identifying patients at risk of suicide using data from health information exchanges.BMC public health · 2025Article
- Considerations and Challenges When Using Clinical and Vital Record Review for Suicide Research.Journal of patient safety · 2025Article
- Continuous time and dynamic suicide attempt risk prediction with neural ordinary differential equations.NPJ digital medicine · 2025Article
- Navigating extreme class imbalance in suicide risk prediction.Frontiers in psychiatry · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Importance: Numerous prognostic models of suicide risk have been published, but few have been implemented outside of integrated managed care systems. Objective: To evaluate performance of a suicide attempt risk prediction model implemented in a vendor-supplied electronic health record to predict subsequent (1) suicidal ideation and (2) suicide attempt. Design, Setting, and Participants: This observational cohort study evaluated implementation of a suicide attempt prediction model in live clinical systems without alerting. The cohort comprised patients seen for any reason in adult inpatient, emergency department, and ambulatory surgery settings at an academic medical center in the mid-South from June 2019 to April 2020. Main Outcomes and Measures: Primary measures assessed external, prospective, and concurrent validity. Manual medical record validation of coded suicide attempts confirmed incident behaviors with intent to die. Subgroup analyses were performed based on demographic characteristics, relevant clinical context/setting, and presence or absence of universal screening. Performance was evaluated using discrimination (number needed to screen, C statistics, positive/negative predictive values) and calibration (Spiegelhalter z statistic). Recalibration was performed with logistic calibration. Results: The system generated 115 905 predictions for 77 973 patients (42 490 [54%] men, 35 404 [45%] women, 60 586 [78%] White, 12 620 [16%] Black). Numbers needed to screen in highest risk quantiles were 23 and 271 for suicidal ideation and attempt, respectively. Performance was maintained across demographic subgroups. Numbers needed to screen for suicide attempt by sex were 256 for men and 323 for women; and by race: 373, 176, and 407 for White, Black, and non-White/non-Black patients, respectively. Model C statistics were, across the health system: 0.836 (95% CI, 0.836-0.837); adult hospital: 0.77 (95% CI, 0.77-0.772); emergency department: 0.778 (95% CI, 0.777-0.778); psychiatry inpatient settings: 0.634 (95% CI, 0.633-0.636). Predictions were initially miscalibrated (Spiegelhalter z = -3.1; P = .001) with improvement after recalibration (Spiegelhalter z = 1.1; P = .26). Conclusions and Relevance: In this study, this real-time predictive model of suicide attempt risk showed reasonable numbers needed to screen in nonpsychiatric specialty settings in a large clinical system. Assuming that research-valid models will translate without performing this type of analysis risks inaccuracy in clinical practice, misclassification of risk, wasted effort, and missed opportunity to correct and prevent such problems. The next step is careful pairing with low-cost, low-harm preventive strategies in a pragmatic trial of effectiveness in preventing future suicidality.
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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.