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.

Use of Medicare Administrative Claims to Identify a Population at High Risk for Adverse Drug Events and Hospital Use for Quality Improvement.

BACKGROUND: A system using administrative claims to monitor medication use patterns and associated adverse events is not currently available. Establishment of a standardized method to identify Medicare beneficiaries at high risk for adverse events, by assessing Medicare Part D medication claim patterns and associated outcomes, including outpatient adverse drug events (ADEs) and hospital use, enhances prevention efforts and monitoring for quality improvement efforts. OBJECTIVES: To (a) demonstrate that Medicare claims data can be used to identify a population of beneficiaries at high risk for adverse events for quality improvement and (b) define trends associated with adverse health outcomes in identified high-risk beneficiaries for quality improvement opportunities. METHODS: We used Medicare fee-for-service Part D claims data to identify a population at high risk for adverse events by evaluating medication use patterns. This population was taking at least 3 medications, 1 of which was an anticoagulant, an opioid, or an antidiabetic agent. Next, we used associated Part A claims to calculate rates of outpatient ADEs, looking for specific ICD-9-CM or ICD-10-CM codes in the principal diagnosis code position. Rates of hospital use (inpatient hospitalization, observation stays, emergency department visits, and 30-day rehospitalizations) were also evaluated for the identified high-risk population. The data were then shared for targeted quality improvement. RESULTS: We identified 8,178,753 beneficiaries at high risk for adverse events, or 20.7% of the total eligible fee-for-service population (time frame of October 2016-September 2017). The overall rate of outpatient ADEs for beneficiaries at high risk was 46.28 per 1,000, with anticoagulant users demonstrating the highest rate of ADEs (68.52/1,000), followed by opioid users (42.11/1,000) and diabetic medication users (20.72/1,000). As expected, the primary setting for beneficiaries at high risk to seek care for outpatient ADEs was the emergency department, followed by inpatient hospitalizations and observation stays. CONCLUSIONS: Medicare claims are an accessible source of data, which can be used to establish for quality improvement a population at high risk for ADEs and increased hospital use. Using medication use patterns to attribute risk and associated outcomes, such as outpatient ADEs and hospital use, is a simple process that can be readily implemented. The described method has the potential to be further validated and used as a foundation to monitor population-based quality improvement efforts for medication safety. DISCLOSURES: This work was performed under contract HHSM-500-2014-QINNCC, Modification No. 000004, funded by Centers for Medicare & Medicaid Services (CMS), an agency of the U.S. Department of Health and Human Services. CMS did not have a role in the analysis. At the time of this analysis, Digmann, Peppercorn, Zhang, Irby, and Brock were employees of Telligen, which was awarded the National Coordinating Center-Quality Improvement Organization contract from CMS, which supported the work. Ryan was an employee at Qsource, which was awarded the Quality Innovation Network-Quality Improvement Organization contract from CMS, which supported the work. Thomas was employed by CMS. The content is solely the responsibility of the authors and does not necessarily represent the official views or policies of the CMS. This work is posted on the QIOprogram.org website, as recommended in the Common Rule ( https://www.hhs.gov/ohrp/regulations-and-policy/regulations/common-rule/index.html ).

Hepatitis associated with low-dose venlafaxine for postmenopausal vasomotor symptoms.

OBJECTIVE: To report a case of drug-induced hepatitis associated with low-dose venlafaxine. CASE SUMMARY: A 60-year-old white woman receiving venlafaxine 75 mg daily for vasomotor symptoms presented after one month of therapy with nonspecific complaints, including abdominal pain. A series of diagnostic and laboratory tests revealed an enlarged liver and elevated alanine aminotransferase (ALT) up to 372 U/L, aspartate aminotransferase (AST) up to 99 U/L, gamma-glutamyltransferase (GGT) up to 962 U/L, and alkaline phosphatase up to 758 U/L. All potential hepatotoxic medications were discontinued. Within one week after stopping venlafaxine, her liver function test results showed marked improvement. Almost 4 weeks after discontinuing therapy, venlafaxine 37.5 mg was reinitiated. Her ALT, AST, GGT, and alkaline phosphatase again increased to 269, 49, 256, and 263 U/L, respectively, 6 days after resuming therapy. Upon discontinuation of venlafaxine, her liver function abnormalities resolved. DISCUSSION: This case is significant due to the severity of symptoms and consequent liver function test results involved in diagnosing drug-induced hepatitis. It is also remarkable because of the hepatotoxicity that occurred initially and on rechallenge with low-dose venlafaxine. The hepatotoxic effects of venlafaxine have been characterized as rare and idiosyncratic. The Naranjo probability scale revealed that the adverse drug event was probable. CONCLUSIONS: Venlafaxine therapy can lead to drug-induced hepatitis, even when used at low doses. Clinicians should be aware of this possible adverse effect of venlafaxine therapy and monitor patients closely after initiation of therapy.