Evidence map›Paper›PMID 34610516›Full record

ArticleDrug and alcohol dependence2021

Validation and threshold identification of a prescription drug monitoring program clinical opioid risk metric with the WHO alcohol, smoking, and substance involvement screening test.

Gerald Cochran, Jennifer Brown, Ziji Yu, Stacey Frede, M Aryana Bryan, Andrew Ferguson, Nadia Bayyari, Brooke Taylor, Margie E Snyder, Elizabeth Charron and 3 more

Abstract read
In one paragraph

Article in Drug and alcohol dependence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. CDC Clinical Practice Guideline for Prescribing Opioids for Pain - United States, 2022.MMWR. Recommendations and reports : Morbidity and mortality weekly report. Recommendations and reports · 2022
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  12. A call for better validation of opioid overdose risk algorithms.Journal of the American Medical Informatics Association : JAMIA · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Gerald CochranUniversity of Utah, Department of Internal Medicine, 295 Chipeta Way Salt Lake City, UT 84132, USA. Electronic address: jerry.cochran@hsc.utah.edu.
Jennifer BrownUniversity 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.
Ziji YuUniversity of Utah, Department of Internal Medicine, 295 Chipeta Way Salt Lake City, UT 84132, USA. Electronic address: ziji.yu@hsc.utah.edu.
Stacey FredeKroger Pharmacy, 1014 Vine Street, Cincinnati, OH 45202, USA. Electronic address: stacey.frede@kroger.com.
M Aryana BryanUniversity of Utah, Department of Internal Medicine, 295 Chipeta Way Salt Lake City, UT 84132, USA. Electronic address: aryana.bryan@utah.edu.
Andrew FergusonUniversity of Cincinnati, Department of Psychiatry and Behavioral Neuroscience, 260 Stetson Street Cincinnati, OH 45267-0559, USA. Electronic address: fergusam@ucmail.uc.edu.
Nadia BayyariUniversity of Cincinnati, Department of Psychiatry and Behavioral Neuroscience, 260 Stetson Street Cincinnati, OH 45267-0559, USA. Electronic address: bayyarnm@ucmail.uc.edu.
Brooke TaylorKroger Pharmacy, 1014 Vine Street, Cincinnati, OH 45202, USA. Electronic address: b-taylor.2@onu.edu.
Margie E SnyderPurdue University, College of Pharmacy, 575 Stadium Mall Drive West Lafayette, IN 47907, USA. Electronic address: snyderme@purdue.edu.
Elizabeth CharronUniversity of Utah, Department of Internal Medicine, 295 Chipeta Way Salt Lake City, UT 84132, USA. Electronic address: betsy.charron@utah.edu.
Omolola A Adeoye-OlatundePurdue University, College of Pharmacy, 575 Stadium Mall Drive West Lafayette, IN 47907, USA. Electronic address: adeoyeo@purdue.edu.
Udi E GhitzaNational 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.
T WinhusenUniversity 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.

Funding

The Ohio Valley Node of the Clinical Trials Network (CTN-0153)UG1DA013732 · NIDA · UNIVERSITY OF CINCINNATI · PI T John WINHUSEN · 2015 to 2026
$42.5M
Greater Intermountain Node (CTN-0151)UG1DA049444 · NIDA · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Gerald T. Cochran, Gerald T. Cochran · 2019 to 2026
$16.8M
NIDA NIH HHS UG1 DA013732NIDA NIH HHS UG1 DA049444
6 · The paper itself

Abstract

backgroundPrescription 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.

methodsThis 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.

resultsThe 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).

conclusionsThe 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.

Indexed as

Opioid-Related DisordersPrescription Drug Monitoring ProgramsAdultAnalgesics, OpioidCross-Sectional StudiesHumansSmokingWorld Health OrganizationAnalgesics, OpioidAnd risky opioid useCommunity pharmacyPrescription drug monitoring program

Identifiers

PMID34610516
PMCPMC8612015

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.