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

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

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.

Suggested Citation

  • Jenny W Sun & Jessica M Franklin & Kathryn Rough & Rishi J Desai & Sonia Hernández-Díaz & Krista F Huybrechts & Brian T Bateman, 2020. "Predicting overdose among individuals prescribed opioids using routinely collected healthcare utilization data," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-17, October.
  • Handle: RePEc:plo:pone00:0241083
    DOI: 10.1371/journal.pone.0241083
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    References listed on IDEAS

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    6. Jenna Marie Reps & M Soledad Cepeda & Patrick B Ryan, 2020. "Wisdom of the CROUD: Development and validation of a patient-level prediction model for opioid use disorder using population-level claims data," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-12, February.
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    1. Jenna Wong & Daniel Prieto-Alhambra & Peter R. Rijnbeek & Rishi J. Desai & Jenna M. Reps & Sengwee Toh, 2022. "Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations," Drug Safety, Springer, vol. 45(5), pages 493-510, May.

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