Gerald Cochran1, Jennifer Brown2, Ziji Yu3, Stacey Frede4, M Aryana Bryan5, Andrew Ferguson6, Nadia Bayyari7, Brooke Taylor8, Margie E Snyder9, Elizabeth Charron10, Omolola A Adeoye-Olatunde11, Udi E Ghitza12, T Winhusen13. 1. University of Utah, Department of Internal Medicine, 295 Chipeta Way Salt Lake City, UT 84132, USA. Electronic address: jerry.cochran@hsc.utah.edu. 2. University of Cincinnati, Department of Psychiatry and Behavioral Neuroscience, 260 Stetson Street Cincinnati, OH 45267-0559, USA; Center for Addiction Research, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH 45267, USA. Electronic address: brown5j8@ucmail.uc.edu. 3. University of Utah, Department of Internal Medicine, 295 Chipeta Way Salt Lake City, UT 84132, USA. Electronic address: ziji.yu@hsc.utah.edu. 4. Kroger Pharmacy, 1014 Vine Street, Cincinnati, OH 45202, USA. Electronic address: stacey.frede@kroger.com. 5. University of Utah, Department of Internal Medicine, 295 Chipeta Way Salt Lake City, UT 84132, USA. Electronic address: aryana.bryan@utah.edu. 6. University of Cincinnati, Department of Psychiatry and Behavioral Neuroscience, 260 Stetson Street Cincinnati, OH 45267-0559, USA. Electronic address: fergusam@ucmail.uc.edu. 7. University of Cincinnati, Department of Psychiatry and Behavioral Neuroscience, 260 Stetson Street Cincinnati, OH 45267-0559, USA. Electronic address: bayyarnm@ucmail.uc.edu. 8. Kroger Pharmacy, 1014 Vine Street, Cincinnati, OH 45202, USA. Electronic address: b-taylor.2@onu.edu. 9. Purdue University, College of Pharmacy, 575 Stadium Mall Drive West Lafayette, IN 47907, USA. Electronic address: snyderme@purdue.edu. 10. University of Utah, Department of Internal Medicine, 295 Chipeta Way Salt Lake City, UT 84132, USA. Electronic address: betsy.charron@utah.edu. 11. Purdue University, College of Pharmacy, 575 Stadium Mall Drive West Lafayette, IN 47907, USA. Electronic address: adeoyeo@purdue.edu. 12. National Institute on Drug Abuse, Center for Clinical Trials Network, 3 White Flint North MSC 6022, 301 North Stonestreet Avenue, North Bethesda, MD 20852, USA. Electronic address: ghitzau@nida.nih.gov. 13. University of Cincinnati, Department of Psychiatry and Behavioral Neuroscience, 260 Stetson Street Cincinnati, OH 45267-0559, USA; Center for Addiction Research, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH 45267, USA. Electronic address: winhust@ucmail.uc.edu.
Abstract
BACKGROUND: Prescription drug monitoring programs (PDMPs) are critical for pharmacists to identify risky opioid medication use. We performed an independent evaluation of the PDMP-based Narcotic Score (NS) metric. METHODS: This study was a one-time, cross-sectional health assessment within 19 pharmacies from a national chain among adults picking-up opioid medications. The NS metric is a 3-digit composite indicator. The WHO Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) was the gold-standard to which the NS metric was compared. Machine learning determined optimal risk thresholds; Receiver Operating Characteristic curves and Spearman (P) and Kappa (K) coefficients analyzed concurrent validity. Regression analyses evaluated participant characteristics associated with misclassification. RESULTS: The NS metric showed fair concurrent validity (area under the curve≥0.70; K=0.35; P = 0.37, p < 0.001). The ASSIST and NS metric categorized 37% of participants as low-risk (i.e., not needing screening/intervention) and 32.3% as moderate/high-risk (i.e., needing screening/intervention). Further, 17.2% were categorized as low ASSIST risk but moderate/high NS metric risk, termed false positives. These reported disability (OR=3.12), poor general health (OR=0.66), and/or greater pain severity/interference (OR=1.12/1.09; all p < 0.05; i.e., needing unmanaged-pain screening/intervention). A total of 13.4% were categorized as moderate/high ASSIST risk but low NS metric risk, termed false negatives. These reported greater overdose history (OR=1.24) and/or substance use (OR=1.81-12.66; all p < 0.05). CONCLUSIONS: The NS metric could serve as a useful initial universal prescription opioid-risk screener given its: 1) low-burden (i.e., no direct assessment); 2) high accuracy (86.5%) of actionable data identifying low-risk patients and those needing opioid use/unmanaged pain screening/intervention; and 3) broad availability.
BACKGROUND: Prescription drug monitoring programs (PDMPs) are critical for pharmacists to identify risky opioid medication use. We performed an independent evaluation of the PDMP-based Narcotic Score (NS) metric. METHODS: This study was a one-time, cross-sectional health assessment within 19 pharmacies from a national chain among adults picking-up opioid medications. The NS metric is a 3-digit composite indicator. The WHO Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) was the gold-standard to which the NS metric was compared. Machine learning determined optimal risk thresholds; Receiver Operating Characteristic curves and Spearman (P) and Kappa (K) coefficients analyzed concurrent validity. Regression analyses evaluated participant characteristics associated with misclassification. RESULTS: The NS metric showed fair concurrent validity (area under the curve≥0.70; K=0.35; P = 0.37, p < 0.001). The ASSIST and NS metric categorized 37% of participants as low-risk (i.e., not needing screening/intervention) and 32.3% as moderate/high-risk (i.e., needing screening/intervention). Further, 17.2% were categorized as low ASSIST risk but moderate/high NS metric risk, termed false positives. These reported disability (OR=3.12), poor general health (OR=0.66), and/or greater pain severity/interference (OR=1.12/1.09; all p < 0.05; i.e., needing unmanaged-pain screening/intervention). A total of 13.4% were categorized as moderate/high ASSIST risk but low NS metric risk, termed false negatives. These reported greater overdose history (OR=1.24) and/or substance use (OR=1.81-12.66; all p < 0.05). CONCLUSIONS: The NS metric could serve as a useful initial universal prescription opioid-risk screener given its: 1) low-burden (i.e., no direct assessment); 2) high accuracy (86.5%) of actionable data identifying low-risk patients and those needing opioid use/unmanaged pain screening/intervention; and 3) broad availability.
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