Clinical Note–Extracted Psychosocial Factors for Predicting Suicide Attempt Among ED Patients With Suicidal Ideation

TL;DR

Adding psychosocial factors like chronic stress to clinical data improves the accuracy of predicting suicide attempts in emergency department patients with suicidal ideation.

Contribution

This study demonstrates that integrating psychosocial factors extracted from clinical notes enhances suicide risk prediction models in ED patients.

Findings

Incorporating psychosocial factors significantly improved model performance metrics like AUROC and AUPRC.

Chronic stress was identified as the strongest predictor of suicide attempt.

The enhanced model maintained high specificity while improving predictive accuracy.

Abstract

Is the addition of psychosocial factors to a clinical data–based suicide risk prediction model associated with better performance in predicting suicide attempts among patients presenting to the emergency department (ED) for suicidal ideation?

In this electronic health record–based prognostic study of 4661 patients discharged from the ED after presentation for suicidal ideation, incorporating psychosocial factors was associated with significantly higher performance in predicting suicide attempt, with chronic stress as the strongest predictor.

This study suggests that, for ED patients with suicidal ideation, identifying and using psychosocial factors may be key to accurate risk stratification and may help guide targeted interventions such as therapies addressing chronic stress.

The Joint Commission recommends universal suicide screening in emergency departments (EDs), which emphasizes the need to identify at-risk individuals. Existing suicide risk prediction models rely primarily on clinical data and demonstrate limited performance. The potential of incorporating psychosocial information to enhance predictive performance remains understudied.

To evaluate whether augmenting clinical data–based risk scores with psychosocial factors improves the prediction of suicide attempt (SA).

This retrospective prognostic study based on electronic health record data included 4661 ED patients discharged after presentation for suicidal ideation (SI) from middle Tennessee hospitals between June 1, 2018, and February 27, 2024.

The primary outcome was SA within 90 days of ED admission and time-to-event in days. Clinical data–based Vanderbilt Suicide Attempt and Ideation Likelihood (VSAIL) score and 6 psychosocial factors (homelessness, financial insecurity, chronic stress, social isolation, loneliness, and adverse childhood experiences) derived from clinical notes were integrated using a Cox proportional hazards regression model. Performance metrics included area under the receiver operating curve (AUROC), area under the precision-recall curve (AUPRC), positive predictive value (PPV), negative predictive value, sensitivity, and specificity. Performance was evaluated for models trained on (1) VSAIL, (2) psychosocial factors, and (3) VSAIL plus psychosocial factors.

This study included 3382 Vanderbilt University Hospital (VUH) (mean [SD] age, 26.1 [15.6] years; 1751 males [51.8%]) and 1279 Regional Health Systems (RHS) (mean [SD] age, 34.5 [18.0] years; 715 males [55.9%]) ED visits for SI. Within 90 days, SAs were reported in 160 (4.7%) VUH and 34 (2.7%) RHS ED visits for SI. Compared with VSAIL alone, VSAIL plus psychosocial factors was associated with significantly increased median AUROC (VUH: 0.645 [IQR, 0.645-0.645] vs 0.734 [IQR, 0.719-0.747]; P < .001; RHS: 0.547 [IQR, 0.547-0.547] vs 0.680 [IQR, 0.672-0.687]; P < .001), AUPRC (VUH: 0.083 [IQR, 0.083-0.083] vs 0.122 [IQR, 0.111-0.137]; P < .001; RHS: 0.029 [IQR, 0.029-0.029] vs 0.054 [IQR, 0.052-0.058]; P < .001), and PPV (VUH: 0.093 [IQR, 0.082-0.094] vs 0.143 [IQR, 0.123-0.161]; P < .001; RHS: 0.042 [IQR, 0.040-0.043] vs 0.112 [IQR, 0.096-0.129]; P < .001) while maintaining specificities above 0.90. Chronic stress emerged as the strongest predictor of SA (β = 0.643 [95% CI, 0.427-0.859]; P < .001).

In this prognostic study of patients discharged from the ED after presentation for SI, augmenting a clinical data–based suicide risk prediction model with clinical note–extracted psychosocial factors was associated with significantly higher predictive performance. These findings suggest that psychosocial factors can enhance risk stratification and support targeted interventions, such as therapies addressing chronic stress.

This prognostic study evaluates whether augmenting clinical database risk scores with psychosocial factors was associated with improved prediction of suicide attempt among emergency department patients.

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