Medical Policy

Policy Num:       11.001.017
Policy Name:     Pharmacogenetic Testing for Pain Management
Policy ID:          [11.001.017]  [Ac / B / M- / P-]  [2.04.131]

Last Review:      February 20, 2025
Next Review:      February 20, 2026

Related Policies:

11.003.049 - Genetic Testing for Diagnosis and Management of Mental Health Conditions
11.003.008 - Cytochrome P450 Genotype-Guided Treatment Strategy

Pharmacogenetic Testing for Pain Management

Summary

Description

While multiple pharmacologic therapies are available for the management of acute and chronic pain, there is a high degree of heterogeneity in pain response, particularly in the management of chronic pain, and in adverse events. Testing for genetic variants that are relevant to pharmacokinetics or pharmacodynamics of analgesics may assist in selecting and dosing drugs affected by these genetic variants.

Summary of Evidence

For individuals who have a need for pharmacologic pain management who receive pharmacogenetic testing to target therapy, the evidence includes a hybrid implementation-effectiveness randomized trial, a single-blind randomized trial, a prospective cohort study with historical controls that assessed genotype-guided management of postoperative pain, and a prospective non-randomized pragmatic trial that evaluated chronic pain control when treatment occurred via a CYP2D6-guided approach to opioid prescribing versus standard management. Relevant outcomes are symptoms, health status measures, medication use, and treatment-related morbidity. The hybrid randomized trial concluded that preemptive CYP2D6-guided opioid selection is feasible in an elective surgery setting and that this approach may decrease postoperative opioid utilization with similar pain control compared to usual care; however, these results were only exploratory in nature. The single-blind randomized trial similarly concluded that postoperative opioid prescription guided by genetic results may improve pain control and reduce opioid consumption compared to usual care. The prospective cohort study reported on the use of genetic panel test results to guide the selection of analgesics in a postoperative setting and reported statistically significant improvement in total scores of a composite endpoint that measured analgesia, patient satisfaction, and the impact of drug-associated side effects versus historical controls. However, methodologic limitations precluded assessment of the effects on outcomes. The prospective non-randomized pragmatic trial evaluated a CYP2D6-guided approach and found a statistically significant but modest improvement in chronic pain control in the intermediate and poor metabolizers. The effect of pharmacogenetic testing alone cannot be determined from this trial. The evidence is insufficient to determine that the technology results in an improvement in the net health outcome.

For individuals who have a need for pharmacologic pain management who receive pharmacogenetic testing to assess the risk of developing opioid use disorder (OUD), the evidence includes nonrandomized studies. Relevant outcomes are symptoms, health status measures, medication use, and treatment-related morbidity. One nonrandomized study has demonstrated the clinical validity of a pharmacogenetic test to assess the risk of developing OUD. From this study the classifier demonstrated a sensitivity of 82.5% (95% CI: 76.1% to 87.8%) and specificity of 79.9% (95% CI: 73.7% to 85.2%), with no significant differences in performance based on gender, age, follow-up length, race, or ethnicity. The positive likelihood ratio was 3.98 (95% CI: 3.26 to 6.87) and the negative likelihood ratio was 0.22 (95% CI: 0.17 to 0.33). However, the study had several limitations, including recall bias due to self-reported opioid use, selection bias due to the study's enrichment strategy, and a lack of diversity. One case-control study was identified that investigated the clinical utility of this technology. An ensemble machine learning model was run using the 15 genetic variants in the FDA-approved algorithm. The model correctly classified 52.83% (95% CI: 52.07% to 53.59%) of individuals and had a sensitivity of 50.72% and a specificity of 54.95%. While the sample was ancestrally diverse, the study population was mostly male and, compared to the general population, was older and had higher rates of OUD and pain. Also electronic health record data was used, which is susceptible to bias. Prospective studies investigating the clinical utility are needed. The evidence is insufficient to determine that the technology results in an improvement in the net health outcome.

Additional Information

Not applicable.

Objective

The objective of this evidence review is to determine whether the use of genetic testing to manage patients with acute or chronic pain improves the net health outcome.

Policy Statements

Genetic testing for pain management is considered investigational for all indications (see Policy Guidelines section).

Genetic testing for acute pain management to assess the risk of developing opioid use disorder is considered investigational for all indications (see Policy Guidelines section).

Policy Guidelines

This policy does not address testing limited to cytochrome p450 genotyping, which is addressed in evidence review 2.04.38. This policy also does not address testing for congenital insensitivity to pain.

Commercially available genetic tests for pain management consist of panels of single-nucleotide variants (SNVs) or (less commonly) individual SNV testing. SNVs implicated in pain management include the following (see also Table 1):

• 5HT2C (serotonin receptor gene)

• 5HT2A (serotonin receptor gene)

• SLC6A4 (serotonin transporter gene)

• DRD1 (dopamine receptor gene)

• DRD2 (dopamine receptor gene)

• DRD4 (dopamine receptor gene)

• DAT1 or SLC6A3 (dopamine transporter gene)

• DBH (dopamine beta-hydroxylase gene)

• COMT (catechol O-methyltransferase gene)

• MTHFR (methylenetetrahydrofolate reductase gene)

• γ-aminobutyric acid (GABA) A receptor gene

• OPRM1(μ-opioid receptor gene)

• OPRK1 (κ-opioid receptor gene)

• UGT2B15 (uridine diphosphate glycosyltransferase 2 family, member 15)

• Cytochrome p450 genes: CYP2D6, CYP2C19, CYP2C9, CYP3A4, CYP2B6, CYP1A2.

A commercially available genetic test (AvertD™, AutoGenomics, Inc.) to assess the risk of developing opioid use disorder consists of a panel that detects single nucleotide polymorphisms (SNPs) involved in the brain reward pathway. SNPs include the following (see also Table 2):

• 5-HTR2A C>T (serotonin 2A receptor)

• COMT G>A (catechol-o-methyltransferase)

• DRD1 A>G (dopamine D1 receptor)

• DRD2 G>A (dopamine D2 receptor)

• DRD4 T>C (dopamine D4 receptor)

• DAT1 A>G (dopamine transporter)

• DBH C>T (dopamine beta hydroxylase)

• MTHFR C>T (methylene tetrahydrofolate reductase)

• OPRKI G>T (kappa Opioid Receptor)

• GABA C>A (gamma-Aminobutyric Acid [GABA])

• OPRM1 A>G (mu Opioid Receptor)

• MUOR G>A (mu Opioid Receptor)

• GAL T>C (galanin)

• DOR G>A (delta Opioid Receptor)

• ABCB1 C>T (ATP binding cassette transporter I [ABCB1])

Coding

See the Codes table for details.

Benefit Application

BlueCard/National Account Issues

Some plans may have contract or benefit exclusions for genetic testing.

Benefits are determined by the group contract, member benefit booklet, and/or individual subscriber certificate in effect at the time services were rendered.  Benefit products or negotiated coverages may have all or some of the services discussed in this medical policy excluded from their coverage.

Background

Pain

During 2021, an estimated 20.9% (51.6 million) of U.S adults experienced chronic pain and 6.9% (17.1 million) had high-impact chronic pain (i.e., chronic pain that limits daily activities).^1,Chronic pain may be related to cancer, or be what is termed chronic noncancer pain, which may be secondary to a wide range of conditions, such as migraines, low back pain, or fibromyalgia. Multiple therapeutic options exist to manage pain, including pharmacotherapies, behavioral modifications, physical and occupational therapy, and complementary/alternative therapies. Nonetheless, the Institute of Medicine has reported that many individuals receive inadequate pain prevention, assessment, and treatment.^2, Given that pain is an individual and subjective experience, assessing and predicting response to pain interventions, including pain medications, is challenging.

Pharmacologic Treatment

A variety of medication classes are available to manage pain: nonopioid analgesics, including acetaminophen and nonsteroidal anti-inflammatory drugs (NSAIDs), opioid analgesics, which target central nervous system pain perception, and classes of adjuvants, including antiepileptic drugs (eg, gabapentin, pregabalin), antidepressants (eg, tricyclic antidepressants, serotonin-norepinephrine reuptake inhibitors), and topical analgesics. The management of chronic pain has been driven, in part, by the World Health Organization’s analgesic ladder for pain management, which was developed to manage cancer-related pain, but has been applied to other forms of pain. The ladder outlines a stepwise approach to pain management, beginning with nonopioid analgesia and proceeding to a weak opioid (eg, codeine), with or without an adjuvant for persisting pain, and subsequently to a strong opioid (eg, fentanyl, morphine), with or without an adjuvant for persisting or worsening pain. Various opioids are available in short- and long-acting preparations and administered through different routes, including oral, intravenous, intramuscular, subcutaneous, sublingual, and transdermal.

For acute pain management, particularly postoperative pain, systemic opioids and nonopioid analgesics remain a mainstay of therapy. However, there has been growing interest in using alternative, nonsystemic treatments in addition to, or as an alternative to, systemic opioids. These options include neuraxial anesthesia, including intraoperative epidural or intrathecal opioid injection, which can provide pain relief for up to 24 hours postoperatively, and postoperative indwelling epidural anesthesia with opioids and local anesthetics, which may be controlled with a patient-controlled anesthesia pump. Postoperative peripheral nerve blocks may also be used.

While available pharmacologic therapies are effective for many patients, there is a high degree of heterogeneity in pain response, particularly for chronic pain. In addition, many opioids are associated with a significant risk of adverse events, ranging from mild (eg, constipation) to severe (eg, respiratory depression), and a risk of dependence, addiction, and abuse. Limitations in currently available pain management techniques have led to an interest in the use of pharmacogenetics to improve the targeting of therapies and prediction and avoidance of adverse events.

Genetics of Pain Management

Genetic factors may contribute to a range of aspects of pain and pain control, including predisposition to conditions that lead to pain, pain perception, and the development of comorbid conditions that may affect pain perception. Currently available genetic tests relevant to pain management assess single-nucleotide variants (SNVs) in single genes potentially relevant to pharmacokinetic or pharmacodynamic processes.

Genes related to these clinical scenarios include, broadly speaking, those involved in neurotransmitter uptake, clearance, and reception; opioid reception; and hepatic drug metabolism. Panels of genetic tests have been developed and proposed for use in the management of pain. Genes identified as being relevant to pain management are summarized in Table 1.

Table 1. Genes Relevant to Pain Management

  CNS: central nervous system; CYP: cytochrome P450;GABA: g-aminobutyric acid; UDP: uridine diphosphate glycosyltransferase.

Opioid Use Disorder Risk

Opioid use disorder (OUD) is a chronic disorder in which individuals have a pattern of opioid misuse. Currently, the standard of care for OUD risk prediction includes structured clinician interviews. Pharmacogenetic testing has recently become commercially available in the United States to assess the risk of developing opioid use disorders in individuals with a need for pharmacologic management of acute pain.

Regulatory Status

Clinical laboratories may develop and validate tests in-house and market them as a laboratory service; laboratory-developed tests must meet the general regulatory standards of the Clinical Laboratory Improvement Amendments (CLIA). The OmeCare OmePainMeds panel, the Millennium PGT (Pain Management) panel, and YouScript Analgesic panel are available under the auspices of the CLIA. Laboratories that offer laboratory-developed tests must be licensed by the CLIA for high-complexity testing. To date, the U.S. Food and Drug Administration (FDA) has chosen not to require any regulatory review of these tests.

No genetic tests approved by the FDA for pain management were identified.

Of note, in February 2020, the FDA expressed "concerns with firms offering genetic tests making claims about how to use the genetic test results to manage medication treatment that are not supported by recommendations in the FDA-approved drug labeling or other scientific evidence".^3, Due to these concerns, the FDA announced a collaboration between the FDA’s Center for Devices and Radiological Health and Center for Drug Evaluation and Research intended to provide the agency’s view of the state of the current science in pharmacogenetics. This collaborative effort includes a web resource^4, that describes "some of the gene-drug interactions for which the FDA believes there is sufficient scientific evidence to support the described associations between certain genetic variants, or genetic variant-inferred phenotypes, and altered drug metabolism, and in certain cases, differential therapeutic effects, including differences in risks of adverse events."

In December 2023, AvertD™ (AutoGenomics, Inc.) received approval from the FDA for their premarket approval application (PMA) (PMA Number: P230032; Product Code: QZH). The device "is a prescription, qualitative genotyping test used to detect and identify 15 genetic polymorphisms in genomic DNA isolated from buccal samples collected from individuals 18 years of age and older. The test may be used as part of a clinical evaluation and risk assessment to identify patients who may be at elevated risk for developing opioid use disorder (OUD). The test is indicated for use only in patients prior to receiving a first prescription of oral opioids for 4-30 days for acute pain, such as in patients scheduled to undergo a planned surgical procedure and who consent to having the test performed." Of note, in October 2022, the FDA voted strongly against AvertD in an Advisory Committee Meeting.^5, The Advisory Committee panel described mitigation strategies to address the risks of the device, including:

• "Presentation of the device results along a continuum rather than as a binary result.

• Strong and plain language that makes clear the test is not intended to be used alone but instead with other tools to evaluate risk.

• Clear labeling that opioid sparing techniques should be used in all patients regardless of the results of the test.

• Additional studies to better understand test performance in subpopulations that were not included in the clinical study population."

Rationale

This evidence review was created in January 2015 and has been updated regularly with searches of the PubMed database. The most recent literature update was performed through January 9, 2025.

Evidence reviews assess the clinical evidence to determine whether the use of technology improves the net health outcome. Broadly defined, health outcomes are the length of life, quality of life, and ability to function, including benefits and harms. Every clinical condition has specific outcomes that are important to patients and managing the course of that condition. Validated outcome measures are necessary to ascertain whether a condition improves or worsens; and whether the magnitude of that change is clinically significant. The net health outcome is a balance of benefits and harms.

To assess whether the evidence is sufficient to draw conclusions about the net health outcome of technology, 2 domains are examined: the relevance, and quality and credibility. To be relevant, studies must represent 1 or more intended clinical use of the technology in the intended population and compare an effective and appropriate alternative at a comparable intensity. For some conditions, the alternative will be supportive care or surveillance. The quality and credibility of the evidence depend on study design and conduct, minimizing bias and confounding that can generate incorrect findings. The randomized controlled trial (RCT) is preferred to assess efficacy; however, in some circumstances, nonrandomized studies may be adequate. Randomized controlled trials are rarely large or long enough to capture less common adverse events and long-term effects. Other types of studies can be used for these purposes and to assess generalizability to broader clinical populations and settings of clinical practice.

Promotion of greater diversity and inclusion in clinical research of historically marginalized groups (e.g., People of Color [African-American, Asian, Black, Latino and Native American]; LGBTQIA (Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, Asexual); Women; and People with Disabilities [Physical and Invisible]) allows policy populations to be more reflective of and findings more applicable to our diverse members. While we also strive to use inclusive language related to these groups in our policies, use of gender-specific nouns (e.g., women, men, sisters, etc.) will continue when reflective of language used in publications describing study populations.

The primary goal of pharmacogenomics testing and personalized medicine is to achieve better clinical outcomes compared to the standard of care. Drug response varies greatly between individuals, and genetic factors are known to play a role. However, in most cases, the genetic variation only explains a modest portion of the variance in the individual response because clinical outcomes are also affected by a wide variety of factors including alternate pathways of metabolism and patient- and disease-related factors that may affect absorption, distribution, and elimination of the drug. Therefore, assessment of clinical utility cannot be made by a chain of evidence from clinical validity data alone. In such cases, evidence evaluation requires studies that directly demonstrate that the pharmacogenomic test alters clinical outcomes; it is not sufficient to demonstrate that the test predicts a disorder or a phenotype.

Population Reference No. 1 

Testing-Guided Treatment for Managing Acute and Chronic Pain

Clinical Context and Therapy Purpose

The purpose of pharmacogenetic testing-guided treatment for the management of acute and chronic pain is to:

• Select appropriate pain medications or avoid the use of inappropriate pain medications, including:

  • To identify individuals likely or unlikely to respond to a specific medication.

  • To identify individuals at high-risk of adverse drug reactions.

  • To identify individuals at high-risk of opioid addiction or abuse.

• Optimize the dose selection or frequency by:

  • Identifying individuals who are likely to require higher or lower doses of a drug.

The following PICO was used to select literature to inform this review.

Populations

The relevant population of interest are individuals with chronic and acute pain, including conditions such as cancer, migraine, low back pain, and fibromyalgia.

Interventions

Testing for individual genes is available for most, or all, of the genes listed in Table 2, as well as for a wider range of genes. Because of a large number of potential genes, panel testing is available from a number of genetic companies. These panels include a variable number of genes that broadly test potential response to relevant medication classes such as opioids, nonsteroidal anti-inflammatory drugs (NSAIDs), selective serotonin reuptake inhibitors, and tricyclic antidepressants. Several test labs market panel or individual tests designed to address one or more aspects of pain management, including but not limited to drug selection, drug dosing, or prediction of adverse events.

OmePainMeds (OmeCare) is a panel test that provides analysis and recommendations regarding how a patient's body is likely to respond to 13 pain relief medications. The results report includes information about the patient's genetic variables and detailed breakdowns of each core aspect of the patient's genetic markers with recommendations. The product generally informs patients about how a patient's body metabolizes a pain medication, relative risks of taking the drug, and appropriate dosages.

Millennium PGT^SM (Pain Management) (Millennium Health) is a genetic panel test intended to help physicians select pain medication. The panel analyzes 11 genes related to pain management; results are provided with a proprietary Millennium Analysis of Patient Phenotype report that provides decision support for medications that may be affected by the patient’s genotype.

Genelex offers several pharmacogenomic panels, one of which (the YouScript® Analgesic Panel) focuses on genes relevant to pain management.^6,

AltheaDx offers IDgenetix pain tests that analyze the genes and genetic variants involved in the metabolism of opioids, NSAIDs, and other pain drugs as well as variations in pharmacodynamic genes, such as the μ-opioid receptor gene (OPRM1).

Other laboratories, including CompanionDx, Kashi Labs, Inagene Diagnostics, Quest Diagnostics, ARUP Laboratories, and AIBioTech, which markets the PersonaGene Genetic Panel, offer panels of cytochrome P450 (CYP) genes. Panels that are restricted to CYP genes are discussed in evidence review 2.04.38 (Cytochrome P450 testing).

In addition to the available panel tests, several labs offer genetic testing for individual genes that are included in some of the panels, including the MTFHR, CYP, and OPRM1 genes (Table 2).

Table 2. Genes Included in Commercially Available Genetic Panels for Pain Management

  CYP: cytochrome P450; GABA: g-aminobutyric acid; UGT: uridine diphosphate glycosyltransferase.

Comparators

The following practice is currently being used to treat chronic and acute pain: standard pain management without genetic testing. For chronic pain management, a multimodal, multidisciplinary approach that is individualized to the individual is recommended.^7, A multimodal approach to pain management consists of using treatments (ie, nonpharmacologic and pharmacologic) from one or more clinical disciplines incorporated into an overall treatment plan. This allows for different avenues to address the pain condition, often enabling a synergistic approach that impacts various aspects of pain, including functionality. The efficacy of such a coordinated, integrated approach has been documented to reduce pain severity, improve mood and overall quality of life, and increase function.

Outcomes

Specific outcomes of interest for individuals with acute or chronic pain are listed in Table 3. The potential beneficial outcomes of primary interest would be improvements in pain, functioning, and quality of life. The potential harmful outcomes are those resulting from a false test result. False-positive or -negative test results can lead to the initiation of unnecessary treatment and associated adverse events or under-treatment.

Table 3. Outcomes of Interest for Individuals with Chronic or Acute Pain

The Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) recommends that chronic pain trials should consider assessing outcomes representing 6 core domains: pain, physical functioning, emotional functioning, participant ratings of improvement and satisfaction with treatment, symptoms and adverse events, and participant disposition.^8, Table 4 summarizes provisional benchmarks for interpreting changes in chronic pain clinical trial outcome measures per IMMPACT.^9,

Table 4. Benchmarks for Interpreting Changes in Chronic Pain Outcome Measures.

Regarding optimal timing of outcome assessment, this varies with pain setting.^10, Per IMMPACT, recommended assessment timing includes at 3, 6, and 12 months in patients with chronic low back pain, 3 to 4 months after rash onset in postherpetic neuralgia, 3 and 6 months in patients with painful chemotherapy-induced peripheral neuropathy, and at various time points in the chronic post-surgical pain setting (ie, 24 to 48 hours after surgery; 3, 6, and 12 months; or surgery-specific times based on the natural history of acute to chronic pain transition).

Study Selection Criteria

Direct evidence of clinical utility is provided by studies that compare health outcomes for patients managed with or without the test. Because these are intervention studies, the preferred evidence is from RCTs.

• We sought RCTs that reported the outcomes of pharmacogenetics testing to diagnose, assess the risk of developing, or to manage pain.

• We sought evidence on outcomes, with emphasis on efficacy outcomes, as the main purpose of genetic testing in pain conditions is to achieve clinically meaningful improvement compared with standard of care.

• We also included studies that reported only on adverse events, although for medications where adverse events tend to be mild, efficacy outcomes are of greater importance.

• The specific patient indications, interventions, comparators, and outcome measures of interest for each indication are described in the clinical context section.

Review of Evidence

Randomized Studies

Hamilton et al (2022) conducted a randomized trial of genotype-guided postoperative pain control compared to usual care in 107 patients who underwent hip or knee arthroplasty.^11, All patients underwent preoperative genetic testing using a 16-gene panel, then patients were randomized in a single-blind manner to genotype-guided opioid therapy or usual care (oxycodone, tramadol, celecoxib, acetaminophen). Self-reported pain scores and opioid usage were recorded for 10 days after surgery. Table 5 summarizes the key characteristics of the trial. The gene panel showed that 22.4% of patients had relevant genetic variations. Among the patients with genetic variants, patients in the genotype-guided group consumed 86.7 mg morphine equivalents during the 10-day study period versus 162.6 mg morphine equivalents (p=.126). Ten-day average pain levels in both groups were 3.1 versus 4.2, respectively (p=.026). Table 6 summarizes the key clinical outcomes of the study.

Thomas et al (2021) completed a hybrid implementation-effectiveness randomized trial of CYP2D6-guided postoperative pain management versus usual care in 260 adults undergoing joint arthroplasty.^12, In this open-label trial, the authors evaluated the feasibility of clinically implementing CYP2D6-guided post-surgical pain management via the collection of feasibility metrics and pain control through measures of opioid consumption and pain intensity. Table 5 summarizes the key characteristics of the trial. In the genotype-guided arm, 20% had a high-risk phenotype (intermediate, poor, or ultrarapid metabolizer). Of these, 72% were administered an alternative opioid versus 0% of usual care participants (p<.001). Effectiveness outcomes were collected 2 weeks postsurgery and results of the exploratory analysis revealed reduced opioid consumption and similar pain intensity between the 2 groups. Table 6 summarizes the key clinical outcomes of the study.

Table 5. Summary of Key Randomized Controlled Trial Characteristics

  CYP: cytochrome P450 NR: not reported.  

Table 6. Summary of Key Randomized Controlled Trial Results

  IQR; interquartile range; MME: morphine milligram equivalents; SD: standard deviation.

Tables 7 and 8 display notable limitations identified in each study. Although Thomas et al (2021) reported a reduction in opioid consumption and similar pain control between the genotype-guided and usual care groups at 2 weeks postsurgical intervention, the evaluation of the clinical outcomes was exploratory in nature.

Table 7. Study Relevance Limitations

  CYP: cytochrome P450; MME: morphine milligram equivalents. The study limitations stated in this table are those notable in the current review; this is not a comprehensive gaps assessment.  a Population key: 1. Intended use population unclear; 2. Clinical context is unclear; 3. Study population is unclear; 4. Study population not representative of intended use. b Intervention key: 1. Not clearly defined; 2. Version used unclear; 3. Delivery not similar intensity as comparator; 4.Not the intervention of interest. c Comparator key: 1. Not clearly defined; 2. Not standard or optimal; 3. Delivery not similar intensity as intervention; 4. Not delivered effectively. d Outcomes key: 1. Key health outcomes not addressed; 2. Physiologic measures, not validated surrogates; 3. No CONSORT reporting of harms; 4. Not establish and validated measurements; 5. Clinical significant difference not prespecified; 6. Clinical significant difference not supported. e Follow-Up key: 1. Not sufficient duration for benefit; 2. Not sufficient duration for harms.

Table 8. Study Design and Conduct Limitations

  MME: morphine milligram equivalents. The study limitations stated in this table are those notable in the current review; this is not a comprehensive gaps assessment. a Allocation key: 1. Participants not randomly allocated; 2. Allocation not concealed; 3. Allocation concealment unclear; 4. Inadequate control for selection bias. b Blinding key: 1. Not blinded to treatment assignment; 2. Not blinded outcome assessment; 3. Outcome assessed by treating physician. c Selective Reporting key: 1. Not registered; 2. Evidence of selective reporting; 3. Evidence of selective publication. d Data Completeness key: 1. High loss to follow-up or missing data; 2. Inadequate handling of missing data; 3. High number of crossovers; 4. Inadequate handling of crossovers; 5. Inappropriate exclusions; 6. Not intent to treat analysis (per protocol for noninferiority trials). e Power key: 1. Power calculations not reported; 2. Power not calculated for primary outcome; 3. Power not based on clinically important difference. f Statistical key: 1. Analysis is not appropriate for outcome type: (a) continuous; (b) binary; (c) time to event; 2. Analysis is not appropriate for multiple observations per patient; 3. Confidence intervals and/or p values not reported; 4. Comparative treatment effects not calculated.

Nonrandomized Studies

One prospective cohort study using historical controls and 1 prospective non-randomized pragmatic trial have assessed genotype-guided management of pain; these studies are summarized in Tables 9 and 10 and discussed next.

Senagore et al (2017) reported on the results of a prospective cohort study of 63 consecutive patients undergoing open or laparoscopic colorectal and major ventral hernia surgery.^13, The authors compared the findings with a historical cohort of 47 patients who underwent similar surgeries but were managed with a standard enhanced recovery protocol. Results showed that the overall benefit of analgesia score was statistically significantly lower in patients in whom the analgesia protocol was initiated based on results of genotype testing versus the historical control on postoperative days 1 and day 5 (all p<.05). The need for narcotic-equivalent analgesics was also statistically significantly lower in the genotype-tested group versus historical controls.

Smith et al (2019) reported a prospective non-randomized pragmatic trial of 375 patients who either underwent a CYP2D6-guided approach to opioid prescribing for pain control at 4 primary care clinics or standard of care pain management at 3 clinics without assessment of CYP2D6.^14, Based on genotyping alone, 10% of the CYP2D6-guided group were considered intermediate or poor metabolizers (IM/PM). The percentage of patients who were considered IM or PM increased to 35% after drug interactions were considered. In the CYP2D6-guided IM/PM group, there was a more frequent change to a nonopioid therapy. The reduction in pain was statistically significant, though modest, compared to the standard of care group (Table 10).

Table 9. Summary of Key Nonrandomized Trials

  CYP: cytochrome P450. a NeuroIDgenetix pain panel analyzes 9 genes, including CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, ABCB1, COMT, and OPRM1.

Table 10. Summary of Key Nonrandomized Trials

  IM: intermediate metabolizer; PM; poor metabolizer; OBAS: Overall Benefit of Analgesia Score; SEM: standard error of the mean. a The primary outcome measure was OBAS, which assesses the combined impact on analgesia, patient satisfaction, and the impact of drug-associated side effects. The lower the score, the better is overall analgesia. b Measured in narcotic equivalent analgesics. c Only includes participants with complete follow-up.

The purpose of the limitations tables (Tables 11 and 12) is to display notable limitations identified in each study. This information is synthesized as a summary of the body of evidence following each table and provides the conclusions on the sufficiency of the evidence supporting the position statement. Although Senagore reported that the 2 groups were similar in terms of patient characteristics, details of disease status and other known prognostic factors were lacking in the published paper. The authors did not report how the historical cohort was selected nor did they describe efforts to control for known confounders using statistical adjustments. These methodologic limitations do not permit conclusions from this study. In the non-randomized study by Smith et al (2019), there were different baseline characteristics between the 2 groups, and possible differences in pain management between the clinics were not controlled. Most importantly for the present evidence review, the effect of gene variants was not distinguished from the drug inhibitors.

Table 11. Study Relevance Limitations

  The study limitations stated in this table are those notable in the current review; this is not a comprehensive gaps assessment. a Population key: 1. Intended use population unclear; 2. Clinical context is unclear; 3. Study population is unclear; 4. Study population not representative of intended use. b Intervention key: 1. Not clearly defined; 2. Version used unclear; 3. Delivery not similar intensity as comparator; 4.Not the intervention of interest. c Comparator key: 1. Not clearly defined; 2. Not standard or optimal; 3. Delivery not similar intensity as intervention; 4. Not delivered effectively. d Outcomes key: 1. Key health outcomes not addressed; 2. Physiologic measures, not validated surrogates; 3. No CONSORT reporting of harms; 4. Not establish and validated measurements; 5. Clinical significant difference not prespecified; 6. Clinical significant difference not supported. e Follow-Up key: 1. Not sufficient duration for benefit; 2. Not sufficient duration for harms..

Table 12. Study Design and Conduct Limitations

  The study limitations stated in this table are those notable in the current review; this is not a comprehensive gaps assessment. a Allocation key: 1. Participants not randomly allocated; 2. Allocation not concealed; 3. Allocation concealment unclear; 4. Inadequate control for selection bias. b Blinding key: 1. Not blinded to treatment assignment; 2. Not blinded outcome assessment; 3. Outcome assessed by treating physicians. c Selective Reporting key: 1. Not registered; 2. Evidence of selective reporting; 3. Evidence of selective publication. d Data Completeness key: 1. High loss to follow-up or missing data; 2. Inadequate handling of missing data; 3. High number of crossovers; 4. Inadequate handling of crossovers; 5. Inappropriate exclusions; 6. Not intent to treat analysis (per protocol for noninferiority trials). e Power key: 1. Power calculations not reported; 2. Power not calculated for primary outcome; 3. Power not based on clinically important differences. f Statistical key: 1. Analysis is not appropriate for outcome type: (a) continuous; (b) binary; (c) time to event; 2. Analysis is not appropriate for multiple observations per patient; 3. Confidence intervals and/or p values not reported; 4.Comparative treatment effects not calculated.

Section Summary: Testing-Guided Treatment for Managing Acute and Chronic Pain

Both randomized and nonrandomized studies have demonstrated that opioid prescribing guided by genetic results may improve pain control and reduce opioid consumption compared to usual care, however limited samples sizes, exploratory nature of results, and methodological limitations preclude assessment on the effects of pharmacogenetic testing alone on pain management.

Summary of Evidence

For individuals who have a need for pharmacologic pain management who receive pharmacogenetic testing to target therapy, the evidence includes a hybrid implementation-effectiveness randomized trial, a single-blind randomized trial, a prospective cohort study with historical controls that assessed genotype-guided management of postoperative pain, and a prospective non-randomized pragmatic trial that evaluated chronic pain control when treatment occurred via a CYP2D6-guided approach to opioid prescribing versus standard management. Relevant outcomes are symptoms, health status measures, medication use, and treatment-related morbidity. The hybrid randomized trial concluded that preemptive CYP2D6-guided opioid selection is feasible in an elective surgery setting and that this approach may decrease postoperative opioid utilization with similar pain control compared to usual care; however, these results were only exploratory in nature. The single-blind randomized trial similarly concluded that postoperative opioid prescription guided by genetic results may improve pain control and reduce opioid consumption compared to usual care. The prospective cohort study reported on the use of genetic panel test results to guide the selection of analgesics in a postoperative setting and reported statistically significant improvement in total scores of a composite endpoint that measured analgesia, patient satisfaction, and the impact of drug-associated side effects versus historical controls. However, methodologic limitations precluded assessment of the effects on outcomes. The prospective non-randomized pragmatic trial evaluated a CYP2D6-guided approach and found a statistically significant but modest improvement in chronic pain control in the intermediate and poor metabolizers. The effect of pharmacogenetic testing alone cannot be determined from this trial. The evidence is insufficient to determine that the technology results in an improvement in the net health outcome.

Population Reference No. 2

Pharmacogenetic Testing to Assess Risk of Developing Opioid Use Disorder

Clinical Context and Test Purpose

The purpose of pharmacogenetic testing in the management of acute pain is to identify treatment-naive individuals at elevated risk of developing opioid use disorder (OUD).

The following PICO was used to select literature to inform this review.

Populations

The relevant population of interest are individuals with acute pain, including for pain management after a procedure (eg, surgery) or event (eg, accident). The intended use population does not include those with chronic pain conditions.

Interventions

The test being considered is pharmacogenetic testing with the AvertD test (AutoGenomics, Inc.) to assess the risk of developing OUD. Genes included in the panel are listed in Table 13.

Table 13. Genes Included in AvertD Genetic Panel for Risk of Developing Opioid Use Disorder

Comparators

The following practice is currently being used to treat acute pain: standard pain management without genetic testing. For pain management, a multimodal, multidisciplinary approach that is individualized to the individual is recommended.^7, A multimodal approach to pain management consists of using treatments (ie, nonpharmacologic and pharmacologic) from one or more clinical disciplines incorporated into an overall treatment plan. This allows for different avenues to address the pain condition, often enabling a synergistic approach that impacts various aspects of pain, including functionality. The efficacy of such a coordinated, integrated approach has been documented to reduce pain severity, improve mood and overall quality of life, and increase function.

Outcomes

The potential beneficial outcomes of primary interest would be improvements in pain, functioning, and quality of life. Benefits of a true-negative result would include improvements in pain, functioning, and quality of life. Benefits of a true-positive result would be the avoidance of treatment-related morbidity. Risks of a false-positive result include preventing an individual from receiving opioid therapy that could relieve pain and may have significant psychological implications and potentially lead to stigmatization of the individual. Risks of a false-negative include not appropriately identify individuals at elevated risk of OUD and result in exposure to opioids leading to adverse events, as well as individuals participating in risky behavior due to a false sense of security of not being at risk of developing OUD.

Study Selection Criteria

For the evaluation of clinical validity of the pharmacogenetic test, studies that meet the following eligibility criteria were considered:

• Reported on the accuracy of the marketed version of the technology (including any algorithms used to calculate scores).

• Included a suitable reference standard such as a clinical evaluation using the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 criteria.

• Patient/sample clinical characteristics were described.

• Patient/sample selection criteria were described.

Clinically Valid

A test must detect the presence or absence of a condition, the risk of developing a condition in the future, or treatment response (beneficial or adverse).

Nonrandomized Studies

Donaldson et al (2021) conducted a multicenter, observational cohort study of adults exposed to prescription oral opioids for 4-30 days to evaluate the performance of an OUD classifier derived from machine learning (ML) (N=385).^15,5, Participants who were 18 years or older were enrolled from 10 sites across the United States. Genotyping was performed using a qualitative SNP microarray on DNA from buccal samples collected after enrollment. The classifier demonstrated 82.5% sensitivity (95% CI: 76.1% to 87.8%) and 79.9% specificity (95% CI: 73.7% to 85.2%), with no significant differences in performance based on gender, age, follow-up length, race, or ethnicity. There was a positive likelihood ratio of 3.98 (95% CI: 3.26 to 6.87) and a negative likelihood ratio of 0.22 (95% CI: 0.17 to 0.33). This suggests that a positive result with AvertD is 18 times more likely (3.98/0.22) to occur in a patient who will develop OUD than it would in a patient who will not develop OUD. The authors concluded that the machine-learning (ML) classifier could provide additional objective information to help healthcare providers and patients make more informed decisions regarding the use of oral opioids. There were several limitations of the study. Participants were evaluated who had experienced a 4-30 day exposure to prescription oral opioids 1 to 51 years prior to study enrollment. Opioid use was self-reported which could have introduced recall bias. There was a lack of diversity in this sample with the race of 92.2% of participants being White. The study evaluated participants' history of comorbidities, including alcohol use disorder, anxiety, bipolar disorder, cannabis use disorder, depression, schizophrenia, and substance use disorder other than opioids, alcohol, or cannabis. A higher percentage of subjects who were OUD-positive had a comorbidity compared to those who were OUD-negative (67.00% versus 22.59%) at any time. Several study sites had at least one prescribers who holds a waiver to prescribe buprenorphine. This was part of the study's enrichment strategy to ensure an adequate number of OUD-positive patients were enrolled, because patients at these sites are more likely to be OUD-positive. This could have led to selection bias. Study relevance limitations and design and conduct limitations are described in Tables 14 and 15.

Donaldson et al (2017) conducted a study on the genetic risk of opioid addiction.^16, However, the genetic panel evaluated in this study was not the same as the panel in the commercially available test.

Hatoum et al (2021) published a study demonstrating that ancestry may confound genetic machine learning models used in candidate-gene prediction of opioid use disorder.^17, They demonstrated that when ancestry was accounted for, their machine learning models did not predict OUD greater than chance. The authors conclude that researchers and clinicians should be skeptical of machine learning-derived genetic algorithms for polygenic traits such as addiction.

Table 14. Study Relevance Limitations

  The study limitations stated in this table are those notable in the current review; this is not a comprehensive gaps assessment. a Population key: 1. Intended use population unclear; 2. Clinical context is unclear; 3. Study population is unclear; 4. Study population not representative of intended use; 5, Enrolled populations do not reflect relevant diversity; 6. Other. b Intervention key: 1. Classification thresholds not defined; 2. Version used unclear; 3. Not intervention of interest; 4. Other. c Comparator key: 1. Classification thresholds not defined; 2. Not compared to credible reference standard; 3. Not compared to other tests in use for same purpose, 4. Other. d Outcomes key: 1. Study does not directly assess a key health outcome; 2. Evidence chain or decision model not explicated; 3. Key clinical validity outcomes not reported (sensitivity, specificity and predictive values); 4. Reclassification of diagnostic or risk categories not reported; 5. Adverse events of the test not described (excluding minor discomforts and inconvenience of venipuncture or noninvasive tests); 6. Other. e Follow-Up key: 1. Follow-up duration not sufficient with respect to natural history of disease (true positives, true negatives, false positives, false negatives cannot be determined); 2. Other.

Table 15. Study Design and Conduct Limitations

  The study limitations stated in this table are those notable in the current review; this is not a comprehensive gaps assessment. a Selection key: 1. Selection not described; 2. Selection not random or consecutive (ie, convenience); 3. Other. b Blinding key: 1. Not blinded to results of reference or other comparator tests; 2. Other. c Test Delivery key: 1. Timing of delivery of index or reference test not described; 2. Timing of index and comparator tests not same; 3. Procedure for interpreting tests not described; 4. Expertise of evaluators not described; 5. Other. d Selective Reporting key: 1. Not registered; 2. Evidence of selective reporting; 3. Evidence of selective publication; 4. Other. e Data Completeness key: 1. Inadequate description of indeterminate and missing samples; 2. High number of samples excluded; 3. High loss to follow-up or missing data; 4. Other. f Statistical key: 1. Confidence intervals and/or p values not reported; 2. Comparison to other tests not reported; 3. Other.

Clinically Useful

A test is clinically useful if the use of the results informs management decisions that improve the net health outcome of care. The net health outcome can be improved if patients receive correct therapy, or more effective therapy, or avoid unnecessary therapy, or avoid unnecessary testing.

Direct Evidence

Direct evidence of clinical utility is provided by studies that have compared health outcomes for patients managed with and without the test. Because these are intervention studies, the preferred evidence would be from randomized controlled trials.

One nonrandomized study was identified that studied the clinical utility of pharmacogenetic testing to assess the risk of developing OUD.

Nonrandomized Studies

Davis et al (2025) conducted a case-control study to assess the clinical utility of the candidate genetic variants included in the FDA-approved algorithm used to identify individuals at risk of OUD.^18, The study used electronic health record data of individuals from the Million Veteran Program (MVP) with opioid exposure from 1992 to 2022 (N=452,664). Of these individuals 33,669 had OUD. 90.46% of the study population were male and the sample was ancestrally diverse. The performance of the 15 genetic variants for identifying OUD risk was assessed using logistic regression and machine learning models. In the logistic regression, the 15 candidate genes accounted for 0.40% of variation in OUD risk. The ensemble machine learning model correctly classified 52.83% (95% CI: 52.07% to 53.59%) of individuals and had a sensitivity of 50.72% and a specificity of 54.95%. The authors noted several limitations of the study. Electronic health record diagnosis codes were used, which can be susceptible to bias. The study population was also mostly male and, compared to the general population, was older and had higher rates of OUD and pain. The authors concluded these results do not demonstrate the clinical utility of this test. Study relevance limitations and design and conduct limitations are described in Tables 16 and 17.

Table 16. Study Relevance Limitations

  The study limitations stated in this table are those notable in the current review; this is not a comprehensive gaps assessment. a Population key: 1. Intended use population unclear; 2. Clinical context is unclear; 3. Study population is unclear; 4. Study population not representative of intended use; 5, Enrolled populations do not reflect relevant diversity; 6. Other. b Intervention key: 1. Classification thresholds not defined; 2. Version used unclear; 3. Not intervention of interest; 4. Other. c Comparator key: 1. Classification thresholds not defined; 2. Not compared to credible reference standard; 3. Not compared to other tests in use for same purpose, 4. Other. d Outcomes key: 1. Study does not directly assess a key health outcome; 2. Evidence chain or decision model not explicated; 3. Key clinical validity outcomes not reported (sensitivity, specificity and predictive values); 4. Reclassification of diagnostic or risk categories not reported; 5. Adverse events of the test not described (excluding minor discomforts and inconvenience of venipuncture or noninvasive tests); 6. Other. e Follow-Up key: 1. Follow-up duration not sufficient with respect to natural history of disease (true positives, true negatives, false positives, false negatives cannot be determined); 2. Other.

Table 17. Study Design and Conduct Limitations

  The study limitations stated in this table are those notable in the current review; this is not a comprehensive gaps assessment. a Selection key: 1. Selection not described; 2. Selection not random or consecutive (ie, convenience); 3. Other. b Blinding key: 1. Not blinded to results of reference or other comparator tests; 2. Other. c Test Delivery key: 1. Timing of delivery of index or reference test not described; 2. Timing of index and comparator tests not same; 3. Procedure for interpreting tests not described; 4. Expertise of evaluators not described; 5. Other. d Selective Reporting key: 1. Not registered; 2. Evidence of selective reporting; 3. Evidence of selective publication; 4. Other. e Data Completeness key: 1. Inadequate description of indeterminate and missing samples; 2. High number of samples excluded; 3. High loss to follow-up or missing data; 4. Other. f Statistical key: 1. Confidence intervals and/or p values not reported; 2. Comparison to other tests not reported; 3. Other.

Chain of Evidence

Indirect evidence on clinical utility rests on clinical validity. If the evidence is insufficient to demonstrate test performance, no inferences can be made about clinical utility.

Section Summary: Pharmacogenetic Testing to Assess Risk of Developing Opioid Use Disorder

One nonrandomized study has demonstrated the clinical validity of a pharmacogenetic test to assess the risk of developing OUD. From this study the classifier demonstrated a sensitivity of 82.5% (95% CI: 76.1% to 87.8%) and specificity of 79.9% (95% CI: 73.7% to 85.2%), with no significant differences in performance based on gender, age, follow-up length, race, or ethnicity. The positive likelihood ratio was 3.98 (95% CI: 3.26 to 6.87) and the negative likelihood ratio was 0.22 (95% CI: 0.17 to 0.33). However, the study had several limitations, including recall bias due to self-reported opioid use, selection bias due to the study's enrichment strategy, and a lack of diversity. One case-control study was identified that investigated the clinical utility of this technology. An ensemble machine learning model was run using the 15 genetic variants in the FDA-approved algorithm. The model correctly classified 52.83% (95% CI: 52.07% to 53.59%) of individuals and had a sensitivity of 50.72% and a specificity of 54.95%. While the sample was ancestrally diverse, the study population was mostly male and, compared to the general population, was older and had higher rates of OUD and pain. Also electronic health record data was used, which is susceptible to bias. More prospective studies with diverse sample populations are needed to assess the clinical utility of this test.

Summary of Evidence

For individuals who have a need for pharmacologic pain management who receive pharmacogenetic testing to assess the risk of developing opioid use disorder (OUD), the evidence includes nonrandomized studies. Relevant outcomes are symptoms, health status measures, medication use, and treatment-related morbidity. One nonrandomized study has demonstrated the clinical validity of a pharmacogenetic test to assess the risk of developing OUD. From this study the classifier demonstrated a sensitivity of 82.5% (95% CI: 76.1% to 87.8%) and specificity of 79.9% (95% CI: 73.7% to 85.2%), with no significant differences in performance based on gender, age, follow-up length, race, or ethnicity. The positive likelihood ratio was 3.98 (95% CI: 3.26 to 6.87) and the negative likelihood ratio was 0.22 (95% CI: 0.17 to 0.33). However, the study had several limitations, including recall bias due to self-reported opioid use, selection bias due to the study's enrichment strategy, and a lack of diversity. One case-control study was identified that investigated the clinical utility of this technology. An ensemble machine learning model was run using the 15 genetic variants in the FDA-approved algorithm. The model correctly classified 52.83% (95% CI: 52.07% to 53.59%) of individuals and had a sensitivity of 50.72% and a specificity of 54.95%. While the sample was ancestrally diverse, the study population was mostly male and, compared to the general population, was older and had higher rates of OUD and pain. Also electronic health record data was used, which is susceptible to bias. Prospective studies investigating the clinical utility are needed. The evidence is insufficient to determine that the technology results in an improvement in the net health outcome.

Supplemental Information

The purpose of the following information is to provide reference material. Inclusion does not imply endorsement or alignment with the evidence review conclusions.

Practice Guidelines and Position Statements

Guidelines or position statements will be considered for inclusion in ‘Supplemental Information’ if they were issued by, or jointly by, a US professional society, an international society with US representation, or National Institute for Health and Care Excellence (NICE). Priority will be given to guidelines that are informed by a systematic review, include strength of evidence ratings, and include a description of management of conflict of interest.

American Academy of Neurology

In 2014, the American Academy of Neurology published a position paper on the use of opioids for chronic noncancer pain.^19, Regarding pharmacogenetic testing, the guidelines stated that genotyping to determine whether the response to opioid therapy can or should be more individualized is an emerging issue that will “require critical original research to determine effectiveness and appropriateness of use.”

Clinical Pharmacogenomics Implementation Consortium

The Clinical Pharmacogenomics Implementation Consortium (2020) published a guideline for cytochrome P450 (CYP) 2C9 and nonsteroidal anti-inflammatory drugs (NSAIDs), which was developed to provide interpretation of CYP2C9 genotype tests so that the results could potentially guide dosing and/or appropriate NSAID use.^20, The guideline notes that CYP2C9 genotyping information may provide an opportunity "to prescribe NSAIDs for acute or chronic pain conditions at genetically-informed doses to limit long-term drug exposure and secondary adverse events for patients who may be at increased risk." However, the authors also acknowledge that "while traditional pharmacogenetic studies have provided evidence associating common CYP2C9 genetic variation with NSAID pharmacokinetics, there is sparse prospective evidence showing that genetically-guided NSAID prescribing improves clinical outcomes."

In 2021, the Consortium published an updated guideline for CYP2D6, μ-opioid receptor gene 1 (OPRM1), and catechol O-methyl-transferase (COMT) genotypes and select opioid therapy.^21, These recommendations state that codeine and tramadol should be avoided in CYP2D6 poor metabolizers due to diminished efficacy and in ultra-rapid metabolizers due to toxicity potential. In both situations, if opioid use is warranted, a non-codeine opioid should be considered. Regarding hydrocodone, there is insufficient evidence and confidence to provide a recommendation to guide clinical practice for CYP2D6 ultra-rapid metabolizers. For CYP2D6 poor metabolizers, the use of hydrocodone labeled age- or weight-specific dosing is recommended; however, if no response is observed and opioid use is warranted, a non-codeine and non-tramadol opioid can be used. There is insufficient evidence and confidence to provide a recommendation to guide clinical practice at this time for oxycodone or methadone based on CYP2D6 genotype. Additionally, there are no therapeutic recommendations for dosing opioids based on either OPRM1or COMTgenotype.

U.S. Preventive Services Task Force Recommendations

Not applicable.

Medicare National Coverage

There is no national coverage determination. In the absence of a national coverage determination, coverage decisions are left to the discretion of local Medicare carriers.

Ongoing and Unpublished Clinical Trials

Some currently ongoing and unpublished trials that might influence this review are listed in Table 18.

Table 18. Summary of Key Trials

  NCT: national clinical trial. a Denotes industry-sponsored or cosponsored trial.

References

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2. Institute of Medicine, Committee on Advancing Pain Research Care and Education. Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research. Washington (DC): National Academies Press; 2011.
3. U.S Food and Drug Administration. FDA announces collaborative review of scientific evidence to support associations between genetic information and specific medications. February 20, 2020. https://www.fda.gov/news-events/press-announcements/fda-announces-collaborative-review-scientific-evidence-support-associations-between-genetic. Accessed December 17, 2024.
4. U.S. Food and Drug Administration. Table of pharmacogenetics associations. October 26, 2022. https://www.fda.gov/medical-devices/precision-medicine/table-pharmacogenetic-associations. Accessed December 17, 2024.
5. Food and Drug Administration (FDA). Summary of Safety and Effectiveness Data (SSED): AvertD Opioid Use Disorder Genetic Risk Assessment Tool (PMA P230032). 2023; https://www.accessdata.fda.gov/cdrh_docs/pdf23/P230032B.pdf. Accessed December 12, 2024.
6. Genelex. Genelex announces new genetic testing options. n.d.; https://youscript.com/genelex-announces-new-genetic-testing-options/. Accessed December 17, 2024.
7. U.S. Department of Health and Human Services. Pain management best practices. May 2019. https://www.hhs.gov/sites/default/files/pain-mgmt-best-practices-draft-final-report-05062019.pdf. Accessed December 17, 2024.
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11. Hamilton WG, Gargiulo JM, Reynolds TR, et al. Prospective Randomized Study Using Pharmacogenetics to Customize Postoperative Pain Medication Following Hip and Knee Arthroplasty. J Arthroplasty. Jun 2022; 37(6S): S76-S81. PMID 35279338
12. Thomas CD, Parvataneni HK, Gray CF, et al. A hybrid implementation-effectiveness randomized trial of CYP2D6-guided postoperative pain management. Genet Med. Apr 2021; 23(4): 621-628. PMID 33420349
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15. Donaldson K, Cardamone D, Genovese M, et al. Clinical Performance of a Gene-Based Machine Learning Classifier in Assessing Risk of Developing OUD in Subjects Taking Oral Opioids: A Prospective Observational Study. Ann Clin Lab Sci. Jul 2021; 51(4): 451-460. PMID 34452883
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