Development and validation of an overdose risk prediction tool using prescription drug monitoring program data

Walid F Gellad; Qingnan Yang; Kayleigh M Adamson; Courtney C Kuza; Jeanine M Buchanich; Ashley L Bolton; Stanley M Murzynski; Carrie Thomas Goetz; Terri Washington; Michael F Lann; Chung-Chou H Chang; Katie J Suda; Lu Tang

doi:10.1016/j.drugalcdep.2023.109856

Development and validation of an overdose risk prediction tool using prescription drug monitoring program data

Drug Alcohol Depend. 2023 May 1:246:109856. doi: 10.1016/j.drugalcdep.2023.109856. Epub 2023 Mar 27.

Affiliations

¹ Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA; Center for Pharmaceutical Policy and Prescribing, Health Policy Institute, University of Pittsburgh, Pittsburgh, PA, USA; Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh PA, USA. Electronic address: walid.gellad@va.gov.
² Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA; Center for Pharmaceutical Policy and Prescribing, Health Policy Institute, University of Pittsburgh, Pittsburgh, PA, USA.
³ Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
⁴ Center for Pharmaceutical Policy and Prescribing, Health Policy Institute, University of Pittsburgh, Pittsburgh, PA, USA.
⁵ Drug Surveillance and Misuse Prevention Office, Pennsylvania Department of Health, Harrisburg, PA, USA.
⁶ Drug Surveillance and Misuse Prevention Office, Pennsylvania Department of Health, Harrisburg, PA, USA; Bureau of Epidemiology, Pennsylvania Department of Health, Harrisburg, PA, USA.
⁷ Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA; Center for Pharmaceutical Policy and Prescribing, Health Policy Institute, University of Pittsburgh, Pittsburgh, PA, USA; Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh PA, USA.

PMID: 37001323
DOI: 10.1016/j.drugalcdep.2023.109856

Abstract

Objectives: To develop and validate a machine-learning algorithm to predict fatal overdose using Pennsylvania Prescription Drug Monitoring Program (PDMP) data.

Methods: The training/testing (n = 3020,748) and validation (n = 2237,701) cohorts included Pennsylvania residents with a prescription dispensing from February 2018-September 2021. Potential predictors (n = 222) were measured in the 6 months prior to a random index date. Using a gradient boosting machine, we developed a 20-variable model to predict risk of fatal drug overdose in the 6 months after the index date.

Results: Beneficiaries in the training (n = 1,812,448), testing (n = 1,208,300), and validation (n = 2,237,701) samples had similar age, with low rates of fatal overdose during 6-month follow up (0.12%, 0.12%, 0.04%, respectively). The validation c-statistic was 0.86 for predicting fatal overdose using 20 PDMP variables. When ranking individuals based on risk score, the prediction model more accurately identified fatal overdose at 6 months compared to using opioid dosage or opioid/benzodiazepine overlap, although the percentage of individuals in the highest risk percentile who died at 6 months was less than 1%.

Conclusions and policy implications: A gradient boosting machine algorithm predicting fatal overdose derived from twenty variables performed well in discriminating risk across testing and validation samples, improving on single factor risk measures like opioid dosage.

Keywords: Overdose prevention; Risk prediction; State policy; Surveillance.

Publication types

Research Support, U.S. Gov't, P.H.S.

MeSH terms

Analgesics, Opioid
Drug Overdose* / diagnosis
Humans
Prescription Drug Monitoring Programs*
Prescriptions
Tool Use Behavior*

Substances

Analgesics, Opioid