Lindsey M Ferris1, Brendan Saloner2, Noa Krawczyk3, Kristin E Schneider3, Molly P Jarman4, Kate Jackson5, B Casey Lyons5, Matthew D Eisenberg6, Tom M Richards7, Klaus W Lemke7, Jonathan P Weiner7. 1. Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Chesapeake Regional Information System for our Patients, Baltimore, Maryland. 2. Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. Electronic address: bsaloner@jhu.edu. 3. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 4. Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Center for Surgery and Public Health, Brigham and Women's Hospital, Boston, Massachusetts. 5. Maryland Department of Health, Behavioral Health Administration, Office of PDMP and Overdose Prevention Applied Data Programs, Baltimore, Maryland. 6. Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 7. Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Johns Hopkins Center for Population Health Information Technology, Baltimore, Maryland.
Abstract
INTRODUCTION: Prescription Drug Monitoring Program data can provide insights into a patient's likelihood of an opioid overdose, yet clinicians and public health officials lack indicators to identify individuals at highest risk accurately. A predictive model was developed and validated using Prescription Drug Monitoring Program prescription histories to identify those at risk for fatal overdose because of any opioid or illicit opioids. METHODS: From December 2018 to July 2019, a retrospective cohort analysis was performed on Maryland residents aged 18-80 years with a filled opioid prescription (n=565,175) from January to June 2016. Fatal opioid overdoses were identified from the Office of the Chief Medical Examiner and were linked at the person-level with Prescription Drug Monitoring Program data. Split-half technique was used to develop and validate a multivariate logistic regression with a 6-month lookback period and assessed model calibration and discrimination. RESULTS: Predictors of any opioid-related fatal overdose included male sex, age 65-80 years, Medicaid, Medicare, 1 or more long-acting opioid fills, 1 or more buprenorphine fills, 2 to 3 and 4 or more short-acting schedule II opioid fills, opioid days' supply ≥91 days, average morphine milligram equivalent daily dose, 2 or more benzodiazepine fills, and 1 or more muscle relaxant fills. Model discrimination for the validation cohort was good (area under the curve: any, 0.81; illicit, 0.77). CONCLUSIONS: A model for predicting fatal opioid overdoses was developed using Prescription Drug Monitoring Program data. Given the recent national epidemic of deaths involving heroin and fentanyl, it is noteworthy that the model performed equally well in identifying those at risk for overdose deaths from both illicit and prescription opioids.
INTRODUCTION: Prescription Drug Monitoring Program data can provide insights into a patient's likelihood of an opioid overdose, yet clinicians and public health officials lack indicators to identify individuals at highest risk accurately. A predictive model was developed and validated using Prescription Drug Monitoring Program prescription histories to identify those at risk for fatal overdose because of any opioid or illicit opioids. METHODS: From December 2018 to July 2019, a retrospective cohort analysis was performed on Maryland residents aged 18-80 years with a filled opioid prescription (n=565,175) from January to June 2016. Fatal opioid overdoses were identified from the Office of the Chief Medical Examiner and were linked at the person-level with Prescription Drug Monitoring Program data. Split-half technique was used to develop and validate a multivariate logistic regression with a 6-month lookback period and assessed model calibration and discrimination. RESULTS: Predictors of any opioid-related fatal overdose included male sex, age 65-80 years, Medicaid, Medicare, 1 or more long-acting opioid fills, 1 or more buprenorphine fills, 2 to 3 and 4 or more short-acting schedule II opioid fills, opioid days' supply ≥91 days, average morphine milligram equivalent daily dose, 2 or more benzodiazepine fills, and 1 or more muscle relaxant fills. Model discrimination for the validation cohort was good (area under the curve: any, 0.81; illicit, 0.77). CONCLUSIONS: A model for predicting fatal opioid overdoses was developed using Prescription Drug Monitoring Program data. Given the recent national epidemic of deaths involving heroin and fentanyl, it is noteworthy that the model performed equally well in identifying those at risk for overdose deaths from both illicit and prescription opioids.
Authors: Noa Krawczyk; Ramin Mojtabai; Elizabeth A Stuart; Michael Fingerhood; Deborah Agus; B Casey Lyons; Jonathan P Weiner; Brendan Saloner Journal: Addiction Date: 2020-02-24 Impact factor: 6.526
Authors: Wei-Hsuan Lo-Ciganic; Julie M Donohue; Eric G Hulsey; Susan Barnes; Yuan Li; Courtney C Kuza; Qingnan Yang; Jeanine Buchanich; James L Huang; Christina Mair; Debbie L Wilson; Walid F Gellad Journal: PLoS One Date: 2021-03-18 Impact factor: 3.240
Authors: Michael Ripperger; Sarah C Lotspeich; Drew Wilimitis; Carrie E Fry; Allison Roberts; Matthew Lenert; Charlotte Cherry; Sanura Latham; Katelyn Robinson; Qingxia Chen; Melissa L McPheeters; Ben Tyndall; Colin G Walsh Journal: J Am Med Inform Assoc Date: 2021-12-28 Impact factor: 7.942
Authors: Chris Delcher; Daniel R Harris; Changwe Park; Gail K Strickler; Jeffery Talbert; Patricia R Freeman Journal: Drug Alcohol Depend Date: 2021-02-15 Impact factor: 4.492