Dataset Information

Using prescription claims to detect aberrant behaviors with opioids: comparison and validation of 5 algorithms.

ABSTRACT: OBJECTIVE:Compare and validate 5 algorithms to detect aberrant behavior with opioids: Opioid Misuse Score, Controlled Substance-Patterns of Utilization Requiring Evaluation (CS-PURE), Overutilization Monitoring System, Katz, and Cepeda algorithms. STUDY DESIGN AND SETTING:We identified new prescription opioid users from 2 insurance databases: Medicaid (2000-2006) and Clinformatics Data Mart (CDM; 2004-2013). Patients were followed 1 year, and aberrant opioid behavior was defined according to each algorithm, using Cohen's kappa to assess agreement. Risk differences were calculated comparing risk of opioid-related adverse events for identified aberrant and nonaberrant users. RESULTS:About 3.8 million Medicaid and 4.3 million CDM patients initiated prescription opioid use. Algorithms flagged potential aberrant behavior in 0.02% to 12.8% of initiators in Medicaid and 0.01% to 7.9% of initiators in CDM. Cohen's kappa values were poor to moderate (0.00 to 0.50 in Medicaid; 0.00 to 0.30 in CDM). Algorithms varied substantially in their ability to predict opioid-related adverse events; the Overutilization Monitoring System had the highest risk differences between aberrant and nonaberrant users (14.0% in Medicaid; 13.4% in CDM), and the Katz algorithm had the lowest (0.96% in Medicaid; 0.47% in CDM). CONCLUSIONS:In 2 large databases, algorithms applied to prescription data had varying accuracy in identifying increased risk of adverse opioid-related events.

SUBMITTER: Rough K

PROVIDER: S-EPMC6200661 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Using prescription claims to detect aberrant behaviors with opioids: comparison and validation of 5 algorithms.

Rough Kathryn K Huybrechts Krista F KF Hernandez-Diaz Sonia S Desai Rishi J RJ Patorno Elisabetta E Bateman Brian T BT

Pharmacoepidemiology and drug safety 20180424 1

<h4>Objective</h4>Compare and validate 5 algorithms to detect aberrant behavior with opioids: Opioid Misuse Score, Controlled Substance-Patterns of Utilization Requiring Evaluation (CS-PURE), Overutilization Monitoring System, Katz, and Cepeda algorithms.<h4>Study design and setting</h4>We identified new prescription opioid users from 2 insurance databases: Medicaid (2000-2006) and Clinformatics Data Mart (CDM; 2004-2013). Patients were followed 1 year, and aberrant opioid behavior was defined a ...[more]

PMID: 29687539

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Project description:Administrative claims studies do not adequately distinguish pulmonary arterial hypertension (PAH) from other forms of pulmonary hypertension (PH). Our aim is to develop and validate a set of algorithms using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and electronic medical records (EMR), to identify patients with PAH. From January 2012 to August 2015, the EMRs of patients with ICD-9-CM codes for PH with an outpatient visit at the University of Texas Medical Branch were reviewed. Patients were divided into PAH or non-PAH groups according to EMR encounter diagnosis. Patient demographics, echocardiography, right heart catheterization (RHC) results, and PAH-specific therapies were assessed. RHC measurements were reviewed to categorize cases as hemodynamically determined PAH or not PAH. Weighted sensitivity, specificity, and positive and negative predictive values were calculated for the developed algorithms. A logistic regression analysis was conducted to determine how well the algorithms performed. External validation was performed at the University of Virginia Health System. The cohort for the development algorithms consisted of 683 patients with PH, PAH group (n?=?191) and non-PAH group (n?=?492). A hemodynamic diagnosis of PAH determined by RHC was recorded in the PAH (26%) and non-PAH (3%) groups. The positive predictive value for the algorithm that included ICD-9-CM and PAH-specific medications was 66.9% and sensitivity was 28.2% with a c-statistic of 0.66. The positive predictive value for the EMR-based algorithm that included ICD-9-CM, EMR encounter diagnosis, echocardiography, RHC, and PAH-specific medication was 69.4% and a c-statistic of 0.87. A validation cohort of 177 patients with PH examined from August 2015 to August 2016 using EMR-based algorithms yielded a similar positive predictive value of 62.5%. In conclusion, claims-based algorithms that included ICD-9-CM codes, EMR encounter diagnosis, echocardiography, RHC, and PAH-specific medications better-identified patients with PAH than ICD-9-CM codes alone.

Project description:BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has led to disruptions of healthcare delivery and may thus have impacted patterns of prescription opioid use, including risk factors for long-term use.ObjectiveWe aimed to describe changes in patterns of prescription opioid use due to the COVID-19 pandemic in community-dwelling adults without a cancer diagnosis.MethodsUsing administrative claims data of the province of Quebec, Canada, a random sample of adults (aged ≥18 years) was selected. These were members of the public drug plan without a cancer diagnosis who initiated a prescription opioid in the outpatient setting between 1 January, 2018 and 28 December, 2020. We assessed the daily dose of initial prescription opioids, the number of days' supply of initial dispensing, and the total duration of opioid use over the first 6 months following initiation. We applied interrupted autoregressive integrated moving average models to examine weekly patterns of prescription opioids before and during the pandemic (starting at the lockdown). Our models included a step intervention function (immediate change) and a ramp intervention function (slope change).ResultsThere were 112,650 and 34,261 patients who initiated opioid therapy, respectively, in the 115-week pre-pandemic period and in the 41-week pandemic period. At the start of the lockdown, there was a significant immediate decrease in opioid treatment initiation (-326; 95% confidence interval [CI] -419 to -234) and initial daily dose (-1.7 morphine milligram equivalents; 95% CI -2.7 to -0.7). Conversely, there was a significant immediate increase in the number of days' supply of initial dispensing (1.4 days; 95% CI 1.0 to 1.8) and the total duration of opioid use over 6 months (5.7 days; 95% CI 4.6 to 6.8). All these weekly measures returned to values close to those of the pre-pandemic period 10 weeks after the start of lockdown.ConclusionsOur findings showed that the COVID-19 lockdown had an impact on initial number of days' supply, which is a risk factor for long-term use and ultimately opioid-related harm. However, over time, prescription practices and use reverted to those observed in the pre-pandemic period.

Dataset Information

Using prescription claims to detect aberrant behaviors with opioids: comparison and validation of 5 algorithms.

Publications

Using prescription claims to detect aberrant behaviors with opioids: comparison and validation of 5 algorithms.

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