Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Cost-utility analysis of single nucleotide polymorphism panel-based machine learning algorithm to predict risk of opioid use disorder.

Journal of comparative effectiveness research - 1 Dec 2021

Mallow Peter J, Belk Kathy W

Abstract excerpt

Aim: To conduct a cost-utility analysis of a novel genetic diagnostic test (OUDTEST) for risk of developing opioid use disorder for elective orthopedic surgery patients. Materials & Methods: A simulation model assessed cost-effectiveness and quality-adjusted life-years (QALYs) for OUDTEST from private insurer and self-insured employer perspectives over a 5-year time horizon for a hypothetical patient population....

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Cost-Benefit Analysis

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Predisposition to Disease

Share to Machine Learning

Share to Opioid-Related Disorders

Share to Polymorphism, Single Nucleotide

Share to Quality-Adjusted Life Years

Source

Identifiers

PMID: 34672212
DOI: 10.2217/cer-2021-0115

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Cost-utility analysis of single nucleotide polymorphism panel-based machine learning algorithm to predict risk of opioid use disorder.PMID 34672212Read ↗

Multi-variant Genetic Panel for Genetic Risk of Opioid Addiction.Semantic proximity Read ↗

Clinical Performance of a Gene-Based Machine Learning Classifier in Assessing Risk of Developing OUD in Subjects Taking Oral Opioids: A Prospective Observational Study.Semantic proximity Read ↗

Identification of a sex-stratified genetic algorithm for opioid addiction risk.Semantic proximity Read ↗

Candidate Genes from an FDA-Approved Algorithm Fail to Predict Opioid Use Disorder Risk in Over 450,000 VeteransSemantic proximity Read ↗

Machine Learning Prediction of Pharmacogenetic Test Uptake Among Opioid-Prescribed Patients Using Electronic Health Records: A Retrospective Cohort StudySemantic proximity Read ↗

A systematic review of pharmacogenetic guided opioid therapy for pain.Semantic proximity Read ↗

High Genetic Addiction Risk Score (GARS) in Chronically Prescribed Severe Chronic Opioid Probands Attending Multi-pain Clinics: an Open Clinical Pilot Trial.Semantic proximity Read ↗

Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses.Semantic proximity Read ↗

Genetic Predictors of the Clinical Response to Opioid AnalgesicsSemantic proximity Read ↗

Clinical prediction of opioid use disorder in chronic pain patients: a cohort-retrospective study with a pharmacogenetic approach.Semantic proximity Read ↗

Cost-effectiveness of pharmacogenetic testing to tailor smoking-cessation treatment.Semantic proximity Read ↗

Retrospective Analysis of the Utility of Genetic Testing in Predicting Drug Response in Chronic PainSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.