Performance of a Predictive Model versus Prescription-Based Thresholds in Identifying Patients at Risk of Fatal Opioid Overdose.

Background: Prescription Drug Monitoring Programs (PDMPs) collect controlled substance prescriptions dispensed within a state. Many PDMP programs perform targeted outreach (i.e., "unsolicited reporting") for patients who exceed numerical thresholds, however, the degree to which patients at highest risk of fatal opioid overdose are identified has not been compared with one another or with a predictive model. Methods: A retrospective analysis was performed using statewide PDMP data for Maryland residents aged 18 to 80 years with an opioid fill between April to June 2015. The outcome was opioid-related overdose death in 2015 or 2016. A multivariable logistic regression model and three PDMP thresholds were evaluated: (1) multiple provider episodes; (2) high daily average morphine milligram equivalents (MME); and (3) overlapping opioid and benzodiazepine prescriptions. Results: The validation cohort consisted of 170,433 individuals and 244 deaths. The predictive model captured more individuals who died (46.3% of total deaths) and had a higher death rate (7.12 per 1000) when the risk score cutoff (0.0030) was selected for a comparable size of high-risk individuals (n = 15,881) than those meeting the overlapping opioid/benzodiazepine prescriptions (n = 17,440; 33.2% of total deaths; 4.64 deaths per 1000) and high MME (n = 14,675; 24.6% of total deaths; 4.09 deaths per 1000) thresholds. Conclusions: The predictive model identified more individuals at risk of fatal opioid overdose as compared with PDMP thresholds commonly used for unsolicited reporting. PDMP programs could improve their targeting of unsolicited reports to reach more individuals at risk of overdose by using predictive models instead of simple threshold-based approaches.

A Novel Approach to PDMP Reporting: Adapting Opioid Quality Measures to PDMP Data.

OBJECTIVE: The SUPPORT Act provided resources for developing prescription drug monitoring programs (PDMPs) capable of reporting on four specific opioid quality measures. Therefore, the objective of this pilot study was to map, test, and adapt these claims-based opioid quality measures specified for health plan performance to PDMP data for state-level performance. MATERIALS AND METHODS: Maryland PDMP and claims from Maryland Medicaid beneficiaries continuously enrolled from April 1, 2019, to March 31, 2020. RESULTS: The measure rates as specified using claims data are closely aligned with the measure rates when mapped and adapted to PDMP data. The Concurrent Use of Opioids and Benzodiazepines measure rates were 14.49% and 15.31%, the OHD rates were 12.44% and 13.54%, the OHDMP rates were 0.01% and 0.40%, and the Use of Opioids from Multiple Providers in Persons Without Cancer rates were 0.12% and 3.03% for the claims-based and adapted measures, respectively. DISCUSSION: This is a novel application that may be replicated in other states to support quality improvement and can have a measurable effect on stemming the drug abuse epidemic. CONCLUSIONS: This will facilitate data sharing of the opioid quality measure reporting within the Maryland PDMP and stakeholders responsible for caring for Maryland Medicaid beneficiaries. Owing to the encouragement by the Centers for Medicare and Medicaid Services, other states' PDMPs may require the adaptation of these measures. This will open the door for innovative state-level policy and practice interventions. The quantification of outcomes related to these measures will inform our learning healthcare system and help support the Quintuple Aim.

Comparing person-level matching algorithms to identify risk across disparate datasets among patients with a controlled substance prescription: retrospective analysis.

Background: The opioid epidemic in the United States has precipitated a need for public health agencies to better understand risk factors associated with fatal overdoses. Matching person-level information stored in public health, medical, and human services datasets can enhance the understanding of opioid overdose risk factors and interventions. Objective: This study compares approximate match versus exact match algorithms to link disparate datasets together for identifying persons at risk from an applied perspective. Methods: This study used statewide prescription drug monitoring program (PDMP), arrest, and mortality data matched at the person-level using an approximate match and 2 exact match algorithms. Impact of matching was assessed by analyzing 3 independent concepts: (1) the prevalence of key risk indicators used by PDMP programs in practice, (2) the prevalence of arrests and fatal opioid overdose, and (3) the performance of a multivariate logistic regression for fatal opioid overdose. The PDMP key risk indicators included (1) multiple provider episodes (MPE), or patients with prescriptions from multiple prescribers and dispensers, (2) high morphine milligram equivalents (MMEs), which represents an opioid's potency relative to morphine, and (3) overlapping opioid and benzodiazepine prescriptions. Results: Prevalence of PDMP-based risk indicators were higher in the approximate match population for MPEs (n = 4893/1 859 445 [0.26%]) and overlapping opioid/benzodiazepines (n = 57 888/1 859 445 [4.71%]), but the exact-basic match population had the highest prevalence of individuals with high MMEs (n = 664/1 910 741 [3.11%]). Prevalence of arrests and deaths were highest for the approximate match population compared with the exact match populations. Model performance was comparable across the 3 matching algorithms (exact-basic validation area under the receiver operating characteristic curve [AUC]: 0.854; approximate validation AUC: 0.847; exact + zip validation AUC: 0.826) but resulted in different cutoff points balancing sensitivity and specificity. Conclusions: Our study illustrates the specific tradeoffs of different matching methods. Further research should be performed to compare matching algorithms and its impact on the prevalence of key risk indicators in an applied setting that can improve understanding of risk within a population.

Predicting Opioid Overdose Deaths Using Prescription Drug Monitoring Program Data.

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.