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Kathryn Duensing,1 Robert Twillman,1– 3 Stephen Ziegler,4,5 M Soledad Cepeda,6 David Kern,6 Maribel Salas,7,8 Gregory Wedin9 1RKT Consulting, LLC, Lenexa, KS, USA; 2Behavioral Health Section, Saint Luke’s Health System, Kansas City, MO, USA; 3Department of Psychiatry and Behavioral Sciences, University of Kansas School of Medicine, Kansas City, KS, USA; 4Center for Effective Regulatory Policy and Safe Access, Denver, CO, USA; 5College of Professional Studies, Purdue University, Fort Wayne, IN, USA; 6Department of Epidemiology, Janssen Research & Development, Titusville, NJ, USA; 7Department of Epidemiology, Clinical Safety and Pharmacovigilance, Daiichi Sankyo, Basking Ridge, NJ, USA; 8University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; 9Upsher-Smith Laboratories, LLC, Pharmacovigilance and Risk Management, Maple Grove, MN, USACorrespondence: Robert TwillmanSaint Luke’s Health System, Kansas City, MO, USATel +1 913-205-3746Email Bob.Twillman@gmail.comIntroduction: Opioid overdose deaths in the United States have climbed sharply over the past two decades. Simultaneously, increased awareness of inadequately treated chronic pain has resulted in increased opioid analgesic prescribing. The correlation between these two phenomena has led policymakers to posit that they are causally linked, and to implement policy changes supporting safe opioid prescribing.Purpose: To evaluate the impact of its Opioid Analgesic Risk Evaluation and Mitigation Strategy (REMS) program, the US Food and Drug Administration (FDA) requested the opioid manufacturers responsible for implementing that program provide information regarding opioid policy changes from 2016 to 2018. FDA also requested a survey of state requirements for pain and opioid prescribing continuing education (CE), the number of prescribers affected by those requirements, the extent to which a REMS-compliant CE program would meet each state’s requirements, and the number of relevant CE programs available.Results: Results indicate that 527 federal and state opioid-related policies (statutes, rules/regulations, and guidelines) were approved during the 2016– 2018 study period. While the largest number of these policies focused on prescription drug monitoring programs, 170 specifically imposed limits on opioid prescribing and an additional 35 specifically referred to, or incorporated, the Centers for Disease Control and Prevention opioid prescribing guideline. We also found that 46 states and the District of Columbia mandated some amount of pain or opioid prescribing CE for prescribers renewing their licenses. These mandates potentially affected as many as 1.7 million prescribers. In 69% of cases, a REMS-compliant CE program would fully meet the state mandates for various types of prescribers.Conclusion: The severity and complexity of the problems of pain management and opioid overdose have led to large-scale intervention by policymakers. Assessing the impact of these changes is difficult, at best, but will be necessary if interventions are to be refined to increase their effectiveness.Keywords: opioids, FDA blueprint, prescribing, law, policy, CDC, REMS

Purpose: According to the World Health Organization classification for tumors of the central nervous system, mutation status of the isocitrate dehydrogenase (IDH) genes has become a major diagnostic discriminator for gliomas. Therefore, imaging-based prediction of IDH mutation status is of high interest for individual patient management. We compared and evaluated the diagnostic value of radiomics derived from dual positron emission tomography (PET) and magnetic resonance imaging (MRI) data to predict the IDH mutation status non-invasively. Methods: Eighty-seven glioma patients at initial diagnosis who underwent PET targeting the translocator protein (TSPO) using [18F]GE-180, dynamic amino acid PET using [18F]FET, and T1-/T2-weighted MRI scans were examined. In addition to calculating tumor-to-background ratio (TBR) images for all modalities, parametric images quantifying dynamic [18F]FET PET information were generated. Radiomic features were extracted from TBR and parametric images. The area under the receiver operating characteristic curve (AUC) was employed to assess the performance of logistic regression (LR) classifiers. To report robust estimates, nested cross-validation with five folds and 50 repeats was applied. Results: TBRGE-180 features extracted from TSPO-positive volumes had the highest predictive power among TBR images (AUC 0.88, with age as co-factor 0.94). Dynamic [18F]FET PET reached a similarly high performance (0.94, with age 0.96). The highest LR coefficients in multimodal analyses included TBRGE-180 features, parameters from kinetic and early static [18F]FET PET images, age, and the features from TBRT2 images such as the kurtosis (0.97). Conclusion: The findings suggest that incorporating TBRGE-180 features along with kinetic information from dynamic [18F]FET PET, kurtosis from TBRT2, and age can yield very high predictability of IDH mutation status, thus potentially improving early patient management. [ABSTRACT FROM AUTHOR]

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