Performance of the Centers for Medicare & Medicaid Services’ Opioid Overutilization Criteria for Classifying Opioid Use Disorder or Overdose

Abstract

Author Contributions: Drs Wei and Winterstein had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Trends in prescription opioid use and dose trajectories before opioid use disorder or overdose in US adults from 2006 to 2016: A cross-sectional study

“…To monitor inappropriate prescribing, almost all states (excluding Missouri) now have a prescription drug monitoring program to capture controlled substance prescriptions paid by third-party payers or in cash [14]. Yet, these policies and programs, which depend on prescription-dispensing data and do not capture opioids traded in street markets, have been criticized for their low sensitivity in identifying high-risk patients, especially in an era of increasing transition to illicit opioid use [15].…”

Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study

“…Identifing such risk groups can be a valuable prospect for policy makers and payers who currently target interventions based on less accurate risk measures. [14] We identified eight prior published opioid prediction models, each focusing on predicting a different aspect of OUD: six-month risk of diagnosis-based OUD using private insurance claims; [30] 12-month risk of having aberrant behaviors of opioid use after an initial pain clinic visit; [15] 12-month risk of diagnosis-based OUD using private insurance claims [19,23] or claims data from a pharmacy benefit manager [29]; two-year risk of clinical-documented problematic opioid use in electronic medical records (EMR) in a primary care setting; [24] and five-year risk of diagnosis-based OUD using EMR from a medical center [27] and using Rhode Island Medicaid data; [28] These studies had several key limitations, including measuring predictors at baseline rather than over time, using case-control designs that might not be able to calibrate well to population-level data with the true incidence rate of OUD, and having a C-statistic of up to 0.85 in non-case-control designs. [15,24,28,29] Our study overcomes these limitations by using a population-based sample and is the first study, to our knowledge, that predicts more immediate OUD risk (in the subsequent 3-month period) as opposed to a year or longer time period.…”

“…Our predicted model and risk stratification strategies can be used to more efficiently determine whether a patient is at high risk of incident OUD compared to recent CMS measures. [14] The EN model predicting OUD and the model predicting a composite outcome of OUD and overdose could first exclude a large segment of the population with minimal risk of the outcome. While the CMS opioid safety measures use only prescription data, over 70% of incident OUD cases occurred among those not viewed as high risk.…”

“…[13] However, a recent study indicated that the Centers for Medicare & Medicaid Services (CMS) opioid highrisk measures miss over 90% of individuals with an actual OUD diagnosis or overdose. [14] Several studies have developed automated algorithms to identify nonmedical opioid use and OUD using claims or electronic health records. [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] These algorithms mainly use traditional statistical methods to identify risk factors but do not focus on predicting an individual's risk.…”

“…[31] Recent studies have highlighted the shortcomings of current OUD prediction tools and call for developing more advanced models to improve identification of individuals at risk (or no risk) of OUD. [14,26,[32][33][34] In particular, use of machine-learning techniques may enhance the ability to handle numerous variables and complex interactions in large data and generate predictions that can be acted upon in clinical settings. [35][36][37][38][39][40][41] We previously successfully developed a machine-learning algorithm in Medicare to predict risk of overdose that attained a C-statistic over 0.90.…”