Predicting overdose among individuals prescribed opioids using routinely collected healthcare utilization data

Jenny W Sun, Jessica M Franklin, Kathryn Rough, Rishi J Desai, Sonia Hernández-Díaz, Krista F Huybrechts and Brian T Bateman

PLOS ONE, 2020, vol. 15, issue 10, 1-17

Abstract: Introduction: With increasing rates of opioid overdoses in the US, a surveillance tool to identify high-risk patients may help facilitate early intervention. Objective: To develop an algorithm to predict overdose using routinely-collected healthcare databases. Methods: Within a US commercial claims database (2011–2015), patients with ≥1 opioid prescription were identified. Patients were randomly allocated into the training (50%), validation (25%), or test set (25%). For each month of follow-up, pooled logistic regression was used to predict the odds of incident overdose in the next month based on patient history from the preceding 3–6 months (time-updated), using elastic net for variable selection. As secondary analyses, we explored whether using simpler models (few predictors, baseline only) or different analytic methods (random forest, traditional regression) influenced performance. Results: We identified 5,293,880 individuals prescribed opioids; 2,682 patients (0.05%) had an overdose during follow-up (mean: 17.1 months). On average, patients who overdosed were younger and had more diagnoses and prescriptions. The elastic net model achieved good performance (c-statistic 0.887, 95% CI 0.872–0.902; sensitivity 80.2, specificity 80.1, PPV 0.21, NPV 99.9 at optimal cutpoint). It outperformed simpler models based on few predictors (c-statistic 0.825, 95% CI 0.808–0.843) and baseline predictors only (c-statistic 0.806, 95% CI 0.787–0.26). Different analytic techniques did not substantially influence performance. In the final algorithm based on elastic net, the strongest predictors were age 18–25 years (OR: 2.21), prior suicide attempt (OR: 3.68), opioid dependence (OR: 3.14). Conclusions: We demonstrate that sophisticated algorithms using healthcare databases can be predictive of overdose, creating opportunities for active monitoring and early intervention.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241083 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 41083&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0241083

DOI: 10.1371/journal.pone.0241083

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().