- Developed and validated a machine learning SPM to predict 6-month suicide risk among adult PTSD patients prescribed opioids using OneFlorida+ data.
- Random forest SPM showed C-statistic 83.6%, accuracy ~86-87%, sensitivity ~65%, specificity ~87-88%, precision 22-27% across cohorts.
- SPM can prioritise preventive interventions and allocate follow-up resources by identifying high risk deciles, capturing ~79% of suicide intervals in top three deciles.
Depress Anxiety. 2026 Jul 30;2026:5305506. doi: 10.1155/da/5305506. eCollection 2026.
ABSTRACT
BACKGROUND: The cooccurrence of posttraumatic stress disorder (PTSD) and opioid use heightens suicide risk. We aimed to develop and validate a machine learning-based suicide prediction model (SPM) to identify PTSD patients prescribed opioids who are at risk of suicide within 6-month prediction intervals.
METHODS: Using 2016-21 OneFlorida+ data, we compared the predictive performance of multiple models, including least absolute shrinkage and selection operator (LASSO) regression, gradient boosting machines (GBMs), random forest (RF), and deep neural networks (DNNs). The best-performing SPM was selected to predict 6-month suicide risks among adult PTSD patients who received opioids. The index date was the first day when both an opioid prescription and a PTSD diagnosis occurred within 180 days. We divided the 2016-18 cohort into training and internal validation datasets (2:1 ratio), applying machine learning models to the training dataset to predict suicide-related outcomes. We evaluated model prediction performance using various metrics on internal (2016-18) and external (2019-21) validation cohorts.
RESULTS: Among 5578 patients (age = 38.1 ± 11.1, female = 83.2%) in the 2016-18 cohort, 709 (12.7%) patients had at least one suicide-related outcome during follow-up. The final RF model selected 271/299 covariates, with C-statistics, accuracy, sensitivity, specificity, and precision being 83.6% (81.7%-85.9%), 86.2% (85.2%-87.1%), 65.0% (59.8%-70.1%), 87.6% (86.7%-88.5%), and 26.6% (24.6%-28.8%), respectively. We classified intervals into 10 suicide-risk subgroups based on the predicted probabilities, with 79.2% of suicide intervals captured in the top 3 decile subgroups. We observed similar findings in the 2019-21 cohort (n = 4849), with C-statistics, accuracy, sensitivity, specificity, and precision being 83.6% (82.2%-84.9%), 87.0% (86.5%-87.6%), 65.3% (61.7%-68.7%), 88.2% (87.6%-88.7%), and 22.3% (21.1%-23.5%), respectively.
CONCLUSIONS: Our SPM performed well in internal and external validations. It may serve as a feasible tool to identify patients at risk of suicide, helping prioritize preventive interventions, and enabling providers to allocate time and resources more efficiently to patients who may benefit from closer follow-up.
PMID:42534247 | PMC:PMC13420204 | DOI:10.1155/da/5305506
Share Evidence Blueprint
Save to Google Notes
Search Google Scholar
Save as PDF
⭐ My Revision List
