Drug Testing in Pain Management and Substance Use Disorder Treatment
Number: 0965
Table Of Contents
PolicyApplicable CPT / HCPCS / ICD-10 Codes
Background
References
Policy
Scope of Policy
This Clinical Policy Bulletin addresses drug testing in pain management and substance use disorder treatment. Note: This CPB does not address therapeutic drug monitoring, drug testing in the emergency room, or monitoring of persons prescribed drugs with abuse potential that are prescribed outside of a pain management program or substance use disorder program (e.g., amphetamines for attention-deficit hyperactivity disorder, benzodiazepines for anxiety disorders, certain controlled drugs indicated for seizure disorders).
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Medical Necessity
Aetna considers the following as medically necessary unless otherwise stated:
- Presumptive urine drug testing is considered medically necessary for the following indications for persons in chronic pain programs or substance use disorder program:
- Persons who are initiating treatment in a pain management or substance use disorder program; or
- Persons whose clinical evaluation suggests use of illegal substances or non-prescribed medications with abuse potential; or
- Suspected drug overdose in persons with unexplained coma or altered mental status, severe or unexplained cardiovascular stability, unexplained metabolic or respiratory acidosis, or seizures of undetermined etiology; or
- Monitoring of persons on chronic opioid therapy who are receiving treatment for chronic pain with prescription opioid or other potentially abused medications; or
- Persons on chronic opioid therapy or other potentially abused medications who have a history of substance abuse, exhibit aberrant behavior (e.g., multiple lost prescriptions, multiple requests for early refill, obtained opioids from multiple providers, unauthorized dose escalation, and apparent intoxication), or who are otherwise at high risk for medication abuse (see appendix for validated standardized risk assessment tools); or
- Persons in a pain management or substance abuse program when medical records document testing as part of an active treatment plan;
To be considered medically necessary, drug testing should be individualized to test for substances only specific to the individual member's plan of treatment. Clinical documentation must specify how the test results will be used to guide clinical decision making. The medically necessary frequency of drug testing for any indication should be individualized to the treatment plan.
- Definitive or confirmatory urine drug testing is considered medically necessary for persons who meet medical necessity criteria for presumptive urine drug testing, and have any of the following medically necessary indications for definitive testing:
- A presumptive test for the specific drug is not commercially available; or
- A presumptive test was negative for prescribed medications with abuse potential and the provider was expecting the test to be positive for the prescribed medication, and the member disputes the drug testing results; or
- A presumptive test was positive for a prescription drug with abuse potential that was not prescribed to the member and the member disputes the drug testing results; or
- A presumptive test was inconclusive or inconsistent; or
- A presumptive test was positive for an illegal drug and the member disputes the presumptive drug testing results;
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The following drug tests are considered not medically necessary:
- Standing or blanket orders of drug tests (i.e., routine orders that are not individualized to the member's history and clinical presentation); or
- Simultaneous performance of presumptive and definitive tests for the same drugs or metabolites at the same time (Definitive testing should be guided by the results of presumptive testing); or
- Same-day testing of the same drug or metabolites from two different specimen types (e.g., both a blood and a urine specimen); or
- Broad panels of drug tests (see Appendix) (to be considered medically necessary, the specific drugs being tested should be supported by the person's clinical presentation (e.g., drug abuse history, symptoms, physical findings). An exception may be in an emergency setting for persons in a coma or with altered mental status where a reliable history is not available); or
- Immunoassay (IA) testing to definitively identify or "confirm" a presumptive drug test result (e.g., performance by a clinician of a qualitative point-of-care test and ordering a presumptive test from a reference laboratory for the same drug). Definitive urine drug testing provides specific identification and/or quantification typically by gas chromatography-mass spectrometry (GC-MS) or liquid chromatography - tandem mass spectrometry (LC-MS/MS); or
- Reflex definitive testing of point-of-care presumptive urine drug tests (see Appendix); or
- Performance of definitive tests of excessive frequency not justified by medical necessity (for example, routine weekly ordering of definitive testing to confirm buprenorphine/norbuprenorphine levels without change in member status);
- Testing ordered by or on the behalf of third parties (e.g., courts, school, employment, sports and recreation, community extracurricular activities, residential monitoring, marriage licensure, insurance eligibility) are considered not medically necessary treatment of disease;
- Serum drug testing is considered medically necessary in emergency room settings or when urine testing is not feasible (e.g., persons in renal failure).
Note: Drug testing more frequently than once weekly, should be part of a comprehensive and individualized patient treatment plan that outlines the rationale for more frequent testing. Frequency of testing should be dictated by patient acuity and level of care. Generally, ordering a definitive test should have an individualized patient treatment plan that outlines the rationale for this type of testing on each occasion.
- Presumptive urine drug testing is considered medically necessary for the following indications for persons in chronic pain programs or substance use disorder program:
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Experimental, Investigational, or Unproven
Aetna considers the use of PrecisView CNS / SyncViewPain / SyncViewPainPlus experimental, investigational, or unproven for therapeutic drug monitoring of pain medications and other drugs including psychotropics, neurologic agents, opioids, and benzodiazepine medications because there is a lack of evidence regarding the clinical values of these drug-monitoring programs/products.
Aetna considers pharmaco-genotyping experimental, investigational, or unproven for management of chronic pain because of insufficient evidence regarding the clinical value of this approach.
Aetna considers the use of SafeDrugs experimental, investigational, or unproven for the management of polypharmacy and medication safety because of a lack of evidence regarding its clinical value.
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Policy Limitations and Exclusions
Note: Specimen verification (e.g., the use of the ToxLok Test, and the ToxProtect Test) is considered part of a laboratory's quality assurance process and is not separately reimbursed.
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Related Policies
- CPB 0608 - Salivary Tests - for drug testing by oral fluid analysis
- CPB 0300 - Hair Analysis - for drug testing by hair analysis
Background
Urine Drug Testing is an important tool in the care of patients with substance use disorder, chronic pain and other medical conditions. The challenge for clinicians who order these tests is making sure that the test they order for each individual patient is the right test, done in the right order and right frequency in a manner consistent with clinical practice guidelines.
A presumptive urine drug test uses an immunoassay to qualitatively identify the presence or absence of one or more drugs or drug classes (ASAM, 2017).
Definitive urine drug testing is a quantitative test that identifies a specific drug or metabolite by a specific test such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography tandem mass spectrometry (LC-MS/MS). Definitive urine drug testing is typically used to confirm a presumptive urine drug test (ASAM, 2017).
A white paper by the American Society of Addiction Medicine (ASAM, 2017) stated that, in general, a presumptive immunoassay test result need only be subjected to definitive testing when the results conflict with patients’ account of their drug use or when drug specificity is needed in class-specific assays (i.e. amphetamines, benzodiazepines, opiates). The ASAM also stated that random testing schedules are preferred to fixed testing schedules.
The ASAM appropriate use criteria for drug testing in clinical addiction medicine (Jarvis, et al., 2017) state that presumptive testing provides immediate, albeit less accurate, results and should be a routine part of patient assessment. The ASAM stated that urine testing is the best specimen type for presumptive testing, as well as for testing at the point of care. The ASAM states that definitive testing should be used where highly accurate results are needed, when necessary to quantify substance levels, and where necessary to detect specific substances not identified by presumptive methods. The ASAM stated that definitive testing should be used when the results will inform decisions that have major implications for the patient, such as changes in medications, transitions in treatment, and where test results have legal implications. They also stated that definitive testing should be done when the patient disputes the results of a presumptive test.
The ASAM appropriate use criteria for drug testing in addiction (Jarvis, et al., 2017) stated that the frequency of testing should be dictated by patient acuity and level of care. Clinicians should consider the tests' detection capabilities, including the window of detection, in determining the appropriate frequency of testing. Drug testing should be scheduled more frequently at the beginning of treatment, and less frequently as recovery progresses. They state that drug testing should occur on a random schedule, and recommend testing at least weekly during the initial phase of substance abuse treatment. They recommend at least monthly random drug testing once a patient is stable, with consideration of less frequent testing for patients in stable recovery. The appropriate use criteria noted that, although increasing the frequency of drug testing increases the likelihood of detection, there is insufficient evidence that increasing the frequency of drug testing affects the substance abuse itself.
An ASAM public policy statement on the ethical use of drug testing in addiction medicine (ASAM, 2019) states that drug tests should be selected based on an individualized clinical assessment of the patient. The scope of the analyte panel and the frequency of testing should be justified by the patient’s clinical status and the ordering clinician’s need for information. They state that clinicians should document the rationale for the drug tests they order and the decisions they make based on the test results. They state that panels that test for multiple drugs may be useful for new patients in addiction treatment programs, but follow-up testing should be individualized to the patient's history, needs, initial test results, and drugs commonly used in the patient’s geographic location and peer group. They noted that it is not appropriate to use drug testing panels for every patient at every testing time regardless of the patient’s individual clinical history and needs. The public policy statement said that it is inappropriate to repeatedly order definitive testing for all analytes in every drug test, without regard to the results from previous tests or the patient’s overall response to addiction treatment interventions.
American Pain Society (APS) and American Academy of Pain and Medicine (AAPM) joint clinical practice guidelines on the use of opioid therapy in chronic noncancer pain (Chou, et al., 2009) state that most urine drug screening tests utilize immunoassays, but cross-reactivity between various drugs and chemicals can cause false positive results. The guidelines state that urine tests based on gas chromatography-mass spectrometry are considered the most specific for identifying individual drugs and metabolites and are often used to confirm positive immunoassay results.
The Centers for Disease Control and Prevention (CDC) guidelines on opioids for chronic pain (Dowell, et al., 2016) recommends: “When prescribing opioids for chronic pain, clinicians should use urine drug testing before starting opioid therapy and consider urine drug testing at least annually to assess for prescribed medications as well as other controlled prescription drugs and illicit drugs."
The Washington State Agency Medical Directors' Group published an Interagency Guideline on opioid dosing for chronic non-cancer pain (AMDG, 2010). This guideline recommends that low risk individuals have urine drug testing up to once per year, moderate risk up to 2 per year, high risk individuals up to 3-4 tests per year, and individuals exhibiting aberrant behaviors should be tested at the time of the office visit.
Prediction of Opioid Misuse in Individuals Receiving Opioid Therapy for Cancer Pain
Yennurajalingam et al (2018) noted that opioid misuse is a growing crisis; and patients with cancer who are at risk of aberrant drug behaviors (ADB) are frequently under-diagnosed. These researchers examined the frequency and factors predicting a risk for ADB among patients who received an outpatient supportive care consultation at a comprehensive cancer center. Furthermore, the screening performance of the Cut Down-Annoyed-Guilty-Eye Opener (CAGE) questionnaire adapted to include drug use (CAGE-AID) was compared with that of the 14-item Screener and Opioid Assessment for Patients with Pain (SOAPP-14) tool as instruments for identifying patients at risk for ADB. A total of 751 consecutive patients with cancer who were referred to a supportive care clinic were reviewed. Patients were eligible if they had diagnosis of cancer and had received opioids for pain for at least 1 week. All patients were evaluated using the Edmonton Symptom Assessment Scale (ESAS), the SOAPP-14, and the CAGE-AID. SOAPP scores of 7 or higher (SOAPP-positive) were used to identify patients who were at risk of ADB. Among the 729 of 751 (97 %) evaluable consults, 143 (19.6 %) were SOAPP-positive, and 73 (10.5 %) were CAGE-AID-positive. Multi-variate analysis revealed that the odds ratio (OR) of a positive SOAPP score was 2.3 for patients who had positive CAGE-AID scores (p < 0.0001), 2.08 for men (p = 0.0013), 1.10 per point for ESAS pain (p = 0.014), 1.13 per point for ESAS-anxiety (p = 0.0015), and 1.09 per point for ESAS-financial distress (p = 0.012). A CAGE-AID cut-off score of 1 in 4 had 43.3 % sensitivity and 90.93 % specificity for screening patients with a high risk of ADB. The authors concluded that these findings indicated a high frequency of an elevated risk of ADB among patients with cancer. Men and patients who exhibited anxiety, financial distress, and a prior history of alcoholism/illicit drug use were at increased risk of ADB. These researchers stated that further investigation is needed to establish effective management for these patients.
The authors stated that one of the drawbacks of this trial was that these investigators were unable to evaluate opioid use following the supportive care clinic consultation or to obtain real data on non-medical opioid use, such as urine drug screening (UDS). These researchers stated that further studies are needed, because this information will be very valuable in understanding how much aberrant opioid use behaviors will influence treatment and response in real practice.
Arthur et al (2021) stated that there is limited information on the true frequency of non-medical opioid use (NMOU) among individuals receiving opioid therapy for cancer pain. Data to guide patient selection for urine drug testing (UDT) as well as the timing and frequency of ordering UDT are insufficient. In a retrospective study, these researchers examined the frequency of abnormal UDT among patients with cancer who underwent random UDT and their characteristics. Demographic and clinical information for patients with cancer who underwent random UDT were reviewed and compared with a historical cohort that underwent targeted UDT. Random UDT was ordered regardless of a patient's risk potential for NMOU. Targeted UDT was ordered on the basis of a physician's estimation of a patient's risk for NMOU. A total of 552 of 573 eligible patients (96 %) underwent random UDT. Among these patients, 130 (24 %) had 1 or more abnormal results; 38 of the 88 patients (43 %) who underwent targeted UDT had 1 or more abnormal results. When marijuana was excluded, 15 % of the random group and 37 % of the targeted group had abnormal UDT findings (p < 0.001). It took a shorter time from the initial consultation to detect 1 or more abnormalities with the random test than the targeted test (median, 130 versus 274 days; p = 0.02). Abnormal random UDT was independently associated with younger age (p < 0.0001), male sex (p = 0.03), CAGE-AID positivity (p = 0.001), and higher ESAS-anxiety (p = 0.01). The authors concluded that approximately 25 % of patients receiving opioids for cancer pain at a supportive care clinic who underwent random UDT had 1 or more abnormalities. Random UDT detected abnormalities earlier than the targeted test. These researchers stated that these findings suggested that random UDT was justified among patients with cancer pain. Moreover, these investigators stated that further studies are needed to ascertain these observations in different cohorts and clinical settings to better characterize its use in cancer pain management.
The authors stated that one drawback of this trial was its retrospective design. Furthermore, the study was carried out among patients with cancer who had a relatively high level of symptom burden and distress and a potentially higher level of NMOU; thus, these findings may not be generally applicable to other cancer patient populations receiving opioid therapy. Lastly, a normal UDT result does not always rule out NMOU. One of the most common forms of NMOU is taking prescribed opioids more frequently than directed. Unfortunately, such behavior could not be detected by UDT; therefore, such patients may have normal UDT but still be using the opioid in an excessive or maladaptive manner. It was possible that the frequency of NMOU was higher than what the authors found in this trial. The therapeutic decision-making process surrounding opioid therapy should not be based solely on UDT, and more research is needed.
Keall et al (2022) noted that cancer prevalence is increasing, with many patients requiring opioid analgesia. Clinicians need to ensure patients receive adequate pain relief; however, opioid misuse is widespread, and cancer patients are at risk. In a systematic review, these researchers identified screening approaches that have been used to evaluate and monitor risk of opioid misuse in patients with cancer; compared the prevalence of risk estimated by each of these screening approaches; and compared risk factors among demographic and clinical variables associated with a positive screen on each of the approaches. Medline, Cochrane Controlled Trial Register, PubMed, PsycINFO, and Embase databases were searched for articles reporting opioid misuse screening in cancer patients, along with hand-searching the reference list of included articles. Bias was assessed using tools from the Joanna Briggs Suite. A total of 18 studies met the eligibility criteria, evaluating 7 approaches: UDT (n = 8); SOAPP and 2 variants, Revised and Short Form (n = 6); the CAGE tool and 1 variant, AID (n = 6); the Opioid Risk Tool (ORT) (n = 4); Prescription Monitoring Program (PMP) (n = 3); the Screen for Opioid-ABR (SOABR) (n = 1); and structured/specialist interviews (n = 1); 8 studies compared 2 or more approaches. The rates of risk of opioid misuse in the studied populations ranged from 6 % to 65 %, acknowledging that estimates were likely to have varied partly because of how specific to opioids the screening approaches were and whether a single or multi-step approach was used. UDT prompted by an intervention or observation of aberrant opioid behaviors (AOB) were conclusive of actual opioid misuse found to be 6.5 % to 24 %. Younger age, found in 8/10 studies; personal or family history of anxiety or other mental ill health, found in 6/8 studies; and history of illicit drug use, found in 4/6 studies, showed an increased risk of misuse. The authors concluded that younger age, personal or familial mental health history, and history of illicit drug use consistently showed an increased risk of opioid misuse. Clinical suspicion of opioid misuse may be raised by data from PMP or any of the standardized list of AOBs. Clinicians may use UDT to confirm suspicion of opioid misuse or monitor adherence; however, UDT failed to identify those at risk. There is no research to understand the psychosocial effects of screening and management of opioid misuse; there remains an urgent need for further research in this area given the increasing rates of opioid prescription. The authors stated that this systematic review had several drawbacks including the narrow search criteria to include only studies with active cancer diagnoses published in a peer-reviewed English language journal. The heterogeneity of the studies made comparative analysis challenging including detailing the reliability of some of the tools.
Preux et al (2022) stated that the opioid use disorder (OUD) is an international public health problem; and in the last 2 decades it has been the subject of numerous publications concerning patients treated for chronic pain other than cancer-related pain. Patients with cancer-related pain are also at risk of OUD. In a systematic review, these investigators examined the prevalence of OUD in patients with cancer-related chronic pain. Its secondary objective was to identify the characteristics of these opioid users. These researchers carried out a literature review of studies published over the past 2 decades, from January 1, 2000 to December 31, 2020 identified by searching the 3 main medical databases: PubMed, Cochrane, and Embase. A meta-analysis took account of between and within-study variability with the use of random-effects models estimated by the DerSimonian and Laird method. The prevalence of OUD was 8 % (1 % to 20 %) and of the risk of use disorder was 23.5 % (19.5 % to 27.8 %) with I2 values of 97.8 % and 88.7 %, respectively. The authors concluded that further studies are needed on the prevalence of OUD in patients treated for cancer-related chronic pain. These researchers stated that a screening scale adapted to this patient population is urgently needed.
Racial Disparities in Urine Drug Testing
Perlman et al (2022) noted that despite illicit substance use in pregnancy occurring across all demographic groups, minority pregnant and delivering patients with a low income tend to undergo testing at a higher rate than their counterparts. National guidelines for indications do not exist and ordering of toxicology testing may be applied inequitably. In a retrospective, cohort study, these researchers examined if any documented indications in a large cohort of patients were associated with a positive toxicology test; and whether indications for urine toxicology testing were applied consistently to different demographic groups. They reviewed pregnant and delivering patients who underwent toxicology testing on obstetrical units at 1 institution from May 30, 2015, to December 31, 2018. Age, race, marital status, median income of residential ZIP code, indications for testing, and test results were collected for each patient by individual chart review. Indications included pre-term complications (pre-term pre-labor rupture of membranes or pre-term labor), abruption or hypertension, reported substance use, fetal complications, maternal complications, and none. Multi-variate logistic regression models were analyzed for the association between indication and test result and the likelihood of marijuana as the sole positive test result. Logistic regression was employed to examine the relationship of indication for testing with maternal race. Among 20,274 births, 551 patients underwent toxicology testing during the study period. No indication for drug toxicology testing was associated with a positive result, except reported current or previous substance use. Compared with White patients, Black and Hispanic women were 4.26 times (95 % confidence interval [CI]: 2.55 to 7.09) and 5.75 times (95 % CI: 2.89 to 11.43) more likely to have toxicology testing for an indication other than reported substance use, respectively. Of all patients with positive test results (n = 194), 48 % tested positive for marijuana only. The authors concluded that compared with their White counterparts, Black and Hispanic pregnant and delivering patients may be more frequently toxicology tested for indications less clearly associated with illicit substance use. The absence of evidence-based guidelines for toxicology testing on obstetrical units risks inequitable care and stigmatization of patient groups.
Peterson et al (2023) stated that drug use during pregnancy can have implications for maternal and fetal morbidity and mortality and legal ramifications for patients. The American College of Obstetricians and Gynecologists (ACOG) guideline states that drug screening policies during pregnancy should be applied equally to all individuals and notes that biological screening is not necessary, stating that verbal screening is adequate. Despite this guidance, institutions do not consistently implement urine drug screening policies that reduce biased testing and mitigate legal risks to the patient. In a retrospective, cohort study, these investigators examined the effects of a standardized urine drug testing policy in labor and delivery on the number of drug tests performed, self-reported racial makeup of those tested, provider-reported testing indications, and neonatal outcomes. A urine drug screening and testing policy was introduced in December 2019. The electronic medical record was queried for the number of urine drug tests carried out on patients admitted to the labor and delivery unit from January 1, 2019, to April 30, 2019. The number of urine drug tests performed between January 1, 2019, and April 30, 2019, was compared with the number of urine drug tests performed between January 1, 2020, and April 30, 2020. The primary outcome was the proportion of urine drug tests conducted based on race before and after the implementation of a drug testing policy. The secondary outcomes included total number of drug tests, Finnegan scores (a proxy for the neonatal abstinence syndrome), and testing indications. To understand perceived testing indications, pre- and post-intervention provider surveys were administered. Chi-square and Fisher exact tests were used to compare categorical variables. The Wilcoxon rank-sum test was used to compare non-parametric data. The Student t-test and 1-way analysis of variance were employed to compare means. Multi-variable logistic regression was used to construct an adjusted model that included co-variates. In 2019, Black patients were more likely to undergo urine drug testing than White patients, even after adjusting for insurance status (adjusted OR of 3.4; CI: 1.55 to 7.32). In 2020, there was no difference in testing based on race after adjusting for insurance status (adjusted OR of 1.3; CI: 0.55 to 2.95). There was a reduction in the number of drug tests conducted between January 2019 and April 2019 compared with between January 2020 and April 2020 (137 versus 71; p < 0.001). This was not accompanied by a statistically significant change in the incidence of neonatal abstinence syndrome measured by mean Finnegan scores (p = 0.4). Before the implementation of a drug testing policy, 68 % of providers requested patient consent for testing; after the implementation of a drug testing policy, 93 % requested patient consent for testing (p = 0.002). The authors concluded that the implementation of a urine drug testing policy improved consent for testing and reduced disparities in testing based on race and the overall rate of drug testing without affecting neonatal outcomes.
The ToxLok Test
ToxLok (InSource Diagnostics) is a genetic test used to confirm the identity of laboratory specimens. With ToxLok, a buccal swab is collected with the urine sample, and DNA markers from the swab and the urine are matched. The test can identify synthetic urine as well as samples that might be obtained from a friend or relative. In addition to revealing falsified sampling, ToxLok is a safety measure that guards against accidental mis-matching of sample and patient name. ToxLok employs 52 genetic markers to identify each patient and urine sample. Analysis is performed using qPCR and matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS).
The ToxProtect Test
The ToxProtect (Genotox Laboratories) is a DNA-verified urine drug test designed to authenticate samples, reveal accidental mis-labeling errors, and detect urine sample substitutions. By adding a one-time cheek swab to the urine collection process, the ToxProtect employs genomic cross-verification to match a urine sample to its donor. Along with detecting over 100 controlled substances, ToxProtect also reveals the presence of synthetic or substitute human urine.
PrecisView CNS / SyncViewPain / SyncViewPainPlus (Phenomics HealthTM, Inc.) for Therapeutic Monitoring of Pain Medications and Other Drugs
Phenomics Health is the 1st precision medicine company to personalize medication treatment by combining advanced methods to predict the right drug and right dose for multiple medical conditions with direct measurement of medications in a patient’s blood to accurately determine patient poly-pharmacy, drug-drug interactions, as well as patient drug adherence. PrecisView CNS quantitates medications to enable dose optimization of therapy related to anxiety, chronic pain (anatomical or idiopathic), depression (major depressive disorder, and treatment-resistant depression), and mental illness. It identifies 87 medications and over the counter (OTCs) sand includes severe and major drug interactions and adverse drug reaction alerts with a simple point-of-care (POC) finger-stick.
Phenomics Health patented PrecisMed pharmaco-metabolomics products can identify and quantify more than 230 drugs in a patient’s circulation to monitor drug adherence, provide a reconciliation of actual medications taken, guide dose-tailoring, identify harmful drug-drug interactions, and avoid adverse drug reactions. PrecisMed includes 4 comprehensive medication management products that can measure actual medications in a patient’s circulation, including: First -- SyncViewRx, which can identify 211 prescription and over-the-counter (OTC) medications; Second -- PrecisViewCNS, which can quantify 87 anti-depressant, anti-psychotic, anxiety, and chronic pain medications; Third -- SyncViewPain, which can identify 90 or more psychotropics, neurologic agents, opioids, and benzodiazepine medications; and SyncViewPainPlus, which can identify 110 or more drugs or substances, specific to pain, depression, and anxiety. PainPlus addresses chronic pain management by recognizing the co-morbidities of depression, anxiety, and addiction; and Fourth -- PrecisMed PMP, which can quantitate 232 medications and analytes in a patient’s circulation across multiple therapeutic categories.
Pennazio et al (2022) stated that therapeutic drug monitoring (TDM) receives growing interest in different psychiatric clinical settings (emergency, in-patient, as well as out-patient services). Despite its usefulness, TDM remains under-utilized in mental health. This is partly due to the need for evidence regarding the relationship between drug serum concentration and effectiveness and tolerability, both in the general population and even more in sub-populations with atypical pharmacokinetics. These investigators the available evidence published after 2017, when the most recent guidelines on the use of TDM in mental health were written. They identified 164 pertinent studies that could be included in the review. Some promising studies highlighted the possibility of correlating early drug serum concentration and clinical effectiveness and safety, especially for anti-psychotics, potentially enabling clinicians to make decisions on early laboratory findings and not proceeding by trial and error. About populations with pharmacokinetic peculiarities, the latest studies confirmed very common alterations in drug blood levels in pregnant women, generally with a progressive decrease over pregnancy and a very relevant dose-adjusted concentration increase in the elderly. For adolescents, several drugs result in having different dose-related concentration values compared to adults. These findings emphasize the recommendation to use TDM in these populations to ensure a safe and effective treatment. Moreover, the integration of TDM with pharmacogenetic analyses may allow clinicians to adopt precise treatments, addressing therapy on an individual pharmaco-metabolic basis. Mini-invasive TDM procedures that may be easily carried out at home or in a POC setting are very promising and may represent a turning point toward an extensive real-world TDM application. The authors concluded that although the highlighted recent evidence, further investigations have to be performed on: further studies, especially prospective and fixed-dose, are needed to replicate present findings and provide clearer knowledge on relationships between dose, serum concentration, and safety/effectiveness. Furthermore, these researchers stated that a larger sample size of studies is needed to obtain more accurate scientific evidence with a good balance of statistical power and significance. These objectives could be attained via consortia aimed at designing multi-center studies with a shared methodology designed to expand knowledge about TDM in psychopharmacology.
Biso et al (2024) noted that psychiatric disorders often require pharmacotherapies to alleviate symptoms and improve quality of life (QOL); however, achieving an optimal therapeutic outcome is challenging due to several factors, including variability in the individual response, inter-individual differences in drug metabolism, as well as drug interactions in poly-therapy. TDM represents a valuable tool to address these challenges by tailoring medication regimens to each individual. These researchers examined the available evidence on TDM in psychiatric practice, highlighting its significance in optimizing drug dosages, minimizing adverse effects, and improving therapeutic effectiveness. The metabolism of psychiatric medications (i.e., mood stabilizers, anti-psychotics, anti-depressants) often exhibits significant inter-patient variability. TDM can aid in addressing this variability by enhancing treatment personalization, facilitating early suboptimal- or toxic-level detection, and allowing for timely interventions to prevent treatment failure or adverse effects. In addition, these investigators discussed technological advancements and analytical methods supporting the implementation of TDM in psychiatric settings. These innovations enable quick and cost-effective drug concentration measurements, fostering the widespread adoption of TDM as a routine practice in psychiatric care. The authors concluded that they analyzed the role of TDM in the main pharmacological classes of psychiatric medications and how it should be implemented in clinical practice. These researchers did not include the use of TDM for anxiolytic medications, for drugs such as methylphenidate or atomoxetine, or for drugs used to treat substance use disorder because the usefulness of monitoring these particular drugs is still uncertain, as these medications have level 3 or 4 recommendations in the Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP)-TDM consensus guidelines; thus, more research is needed to study the utility and the possibility of implementing TDM in these drug categories. In addition, these investigators stated that future research should also focus on implementing cost-effective and less-invasive methods to carry out TDM, such as salivary sampling, to increase patients’ adherence to treatment and monitoring.
De Donatis et al (2024) stated that the usefulness of TDM has recently been reported for some 1st-line anti-depressants; however, few studies have been reported on the relationship between blood levels of mirtazapine and its anti-depressant effects. These investigators examined the association between blood concentration of mirtazapine and anti-depressant response. A total of 59 out-patients treated with mirtazapine for depression were recruited and followed-up for 3 months in a naturalistic setting. Hamilton Depression Rating Scale-21 (HAMD-21) was administered at baseline, month 1, and month 3 to examine anti-depressant response. Mirtazapine serum concentration was measured at steady state. Linear regression analysis and non-linear least-squares regression were used to estimate association between serum concentration of mirtazapine and anti-depressant response. The findings of this study showed no overall association between serum concentration of mirtazapine and symptom improvement at month 1 and month 3. A marginally significantly higher serum concentration of mirtazapine was found in responders versus non-responders at month 3. The authors concluded that the findings of this study suggested that serum concentration of mirtazapine was not strongly associated with the anti-depressant effectiveness of mirtazapine, which was probably attributed to its pharmacodynamic profile, even though higher blood levels appeared to be marginally more effective.
Parmar and Pal (2024) noted that in 2019 India published its 1st edition of the National Essential Diagnostics List (NEDL), which depicted the list of diagnostic tests that could ensure affordable and quality healthcare delivery by removing barriers toward accessibility and reducing out-of-pocket expenditure. In 2024, the Indian Council of Medical Research has invited suggestions for revision of the list. These investigators stated that TDM has been a promising modality and has been useful for a range of indications like monitoring medication adherence, diagnosing suboptimal treatment, detecting drug interactions, as well as guiding initiation or withdrawal of therapy. The authors made a case for inclusion of TDM for certain psychotropic drugs like lithium, sodium valproate, carbamazepine, and clozapine at the district hospital level; these researchers tried to justify the inclusion backed by recent evolving evidence. These investigators stated that TDM has been a promising approach in the effort of providing safe and effective pharmacotherapy to patients. Its benefits have been highlighted in all recent publications on the topic; however, its practical use in clinical settings has been rudimentary and largely sporadic. The NEDL revision, which is scheduled to take place soon, provides a golden opportunity to revise their outlook toward this promising modality. The authors recommended that it should be included in the next amendment of the NEDL. Initially, the TDM services ought to be initiated for lithium, sodium valproate, carbamazepine, and clozapine as all these drugs are also included in the National List of Essential Medicines (NLEM).
In a retrospective, real-world data (RWD) sourced from a single center's clinical data warehouse, Shin et al (2024) examined the usage patterns and impact of TDM for risperidone and paliperidone in patients diagnosed with schizophrenia. This study cohort comprised patients diagnosed with schizophrenia undergoing treatment with either risperidone or paliperidone. Data on demographic characteristics, co-morbidities, medication use, and clinical outcomes were collected. Patients were categorized into 2 groups: those undergoing TDM; and those not undergoing TDM. Furthermore, within the TDM group, patients were further stratified based on their risperidone and paliperidone concentrations relative to the reference range. The findings showed that patients in the TDM group received higher risperidone and paliperidone doses (320 mg/day and 252 mg/day, p = 0.0045) compared to their non-TDM counterparts. Nevertheless, no significant disparities were observed in hospitalization rates, duration of hospital stays, or compliance between the 2 groups (p = 0.9082, 0.5861, 0.7516, respectively). Subgroup analysis within the TDM cohort exhibited no notable distinctions in clinical outcomes between patients with concentrations within or surpassing the reference range. The authors concluded that despite the possibility of a selection bias in assigning patients to the groups, this study provided a comprehensive analysis of TDM utilization and its ramifications on schizophrenia treatment outcomes.
These researchers stated that a drawback of this study was that TDM was carried out based on clinical practice rather than randomization, which may have resulted in selection bias, as patients with more severe symptoms or poorer treatment responses were more likely to be in the TDM group. Nonetheless, this retrospective study could help in understanding the characteristics of TDM and non-TDM groups, and the generated data could serve as reference for planning future prospective, randomized studies. Although this real-world-data-based study was limited in its effectiveness compared with a randomized controlled design, it was significant because it was able to identify differences in the characteristics, such as anti-psychotic doses for patients who use TDM in clinical practice versus those who do not; thus, important insights could be obtained when planning RWD for further studies. Furthermore, it is anticipated that these drawbacks were unlikely to substantially influence the outcomes given that the researchers’ involvement did not extend to the selection of anti-psychotics for individual patients. It was noteworthy that the TDM group did not differ significantly from the non-TDM group in hospitalization and complaints, despite the fact that the TDM group may have had poorer symptom severity or treatment response. This finding alone has significant medical value. Second, the database provided limited data concerning the severity of symptoms or the occurrence of side effects associated with anti-psychotics, such as tardive dyskinesia. Consequently, this information was not used as a clinical outcome in this study. In real-world clinical settings, especially in countries such as Korea, where physicians often handle larger numbers of patients, consistent documentation of patient treatment responses as well as side effects in Electronic Medical Records (EMRs) is exceedingly challenging. In this study, this limitation was addressed by assessing the treatment response of patients with schizophrenia based on anti-psychotic dosage, hospitalization, and compliance; side effects were evaluated based on the frequency of therapeutic medication utilization when side effects occurred. Thirdly, this study utilized a CDW based on EMRs from a single hospital. Hence, the complete medical histories of patients spanning their entire lives, such as those contained the National Insurance Claims Database, could not be accessed. Therefore, the medical records of patients who received treatment at different hospitals could not be verified. However, as most institutions tend to record treatments for the same condition during disease management in the EMRs, the EMRs of all patients were reviewed to mitigate this bias. Records of patients receiving schizophrenia treatment at other medical centers during the treatment period were incorporated into the dataset. Fourth, this study used 2 anti-psychotics, risperidone and paliperidone. Some patients took only 1 drug, whereas others used both drugs. During the observation period, the drug regimen was changed for each patient. Additionally, risperidone has numerous similarities and disparities from paliperidone; thus, the possibility that these factors influenced the results of this study could not be ruled out. In the future, a large-scale study that controls for these variables is needed. Fifth, this study employed a relatively small sample of approximately 200 patients, and the data-set did not include pharmacogenetic information related to drug metabolism and action, such as CYP2D6 enzyme measurements. A larger sample combined with pharmacogenetic information would allow for a more precise study. These researchers stated that these drawbacks can be overcome by further studies that employ corporate data warehouse (CDW) where multiple healthcare organizations share EMRs and collect pharmacogenetic information.
Furthermore, there is a lack of evidence regarding the clinical value of these drug-monitoring programs/products.
Pharmaco-Genotyping in Chronic Pain Management
Bollinger et al (2025) stated that chronic pain is a complex condition affecting patients' health-related QOL (HR-QOL). Pharmaco-genetic (PGx) testing offers an approach to personalize pain management by optimizing medication regimens; however, the impact of this approach on measurable patient reported outcomes (PROs) remains unexplored. In an exploratory, observational, pre-post study, these researchers examined the association of PGx testing on PROs in chronic pain patients and investigated differences between those who received PGx-guided therapy and those who did not, focusing on changes in HR-QOL and pain intensity from pre-to-post PGx. This trial was carried out as part of an observational case-series study examining the influence of PGx testing and subsequent PGx-guided therapy on PROs in chronic pain patients with drug-related problems under their analgesic regimen. PROs were assessed in 29 patients pre-PGx (baseline) and post-PGx (follow-up, 4 to 6 weeks later). HR-QOL was measured using the EQ-5D-5L. The EQ index was calculated using the German value set. Pain intensity was determined with the Numeric Rating Scale (NRS); and minimal important difference (MID) threshold was applied for both outcomes. Statistical analyses included Wilcoxon signed-rank tests, Chi-square tests, and effect size calculations. The mean EQ index score improved from pre-to-post PGx (0.379 ± 0.420 to 0.697 ± 0.307, p < 0.001, d = -0.84). Stratification showed that the PGx-guided therapy group showed significantly greater improvements in HR-QOL and NRS compared to the non-PGx guided therapy group (p < 0.01). Among 19 patients who met the MID for the EQ index, 18 had undergone PGx-guided therapy. For NRS, MID was reached in 3 pain intensity categories in the PGx-guided therapy group. The authors concluded that HR-QOL and pain intensity significantly improved after PGx testing, with potentially clinically relevant results in the PGx-guided therapy group. Moreover, these researchers stated that as a consequence of the observational nature of the study, further controlled studies with randomized designs and long-term outcomes are needed to examine the clinical impact and economic feasibility of PGx-guided therapy.
The authors stated that this study had several drawbacks. First, while efforts were made to standardize pre-to-post assessment of PROs, differences in data collection methods (pre-PGx in-person versus post-PGx as telephone-based interviews) may have introduced a response bias. However, existing literature suggested that telephone respondents are more likely to express dissatisfaction, which may result in more honest responses rather than favoring more favorable responses in the post-PGx assessment. Second, from a methodological perspective, this trial evaluated health state utilities through PRO assessment, showing potentially clinically relevant trends toward outcome improvements in the PGx-guided therapy group. The non-PGx-guided therapy group was not designed as a control group and should rather be seen as an opportunity for observational comparison that emerged during the study. This meant these researchers could not reliably examine if the observed pre-to-post changes were solely due to PGx-based medication changes. Furthermore, the sample size was small and limited the statistical power of the study. Due to the exploratory nature of the study, no power analysis was carried out. Statistical findings regarding pre-to-post differences might differ in a larger study population, especially given the unequal group sizes between patients who received PGx-guided therapy and those who did not. Moreover, the study was exploratory with multiple statistical tests, without a correction for multiple testing, which increased the risk of type I errors. In this context, it was also important to note that the small sample size in the non-PGx-guided therapy group (n = 6) limited the ability to detect statistically significant effects. While this may have contributed to the non-significant findings observed in that group, these investigators chose not to perform sample size calculations for future studies, as such estimates based on observed effects of small samples may be unreliable. Instead, these limitations highlighted the need for prospective, adequately powered studies to confirm these exploratory findings. Third, due to the absence of a randomized control group, no causal conclusions can be drawn from these findings. The observed associations should be interpreted as exploratory and hypothesis-generating. Fourth, the study did not incorporate cost calculations; thus, it was not a pharmaco-economic study. However, these findings could serve as a foundation for future pharmaco-economic studies examining if PGx interventions are clinically relevant and financially viable in the long-term. Such studies are crucial in the overall implementation process and will be critical in informing policy discussion regarding PGx reimbursement.
SafeDrugs
SafeDrugs is an artificial intelligence (AI)-powered polypharmacy reporting platform developed by Astraeus Lab, LLC, in partnership with Quantlio Technologies. It is intended for use by medical laboratories to aid healthcare providers in managing complex medication regimens for high-risk patients. The platform is designed to provide advanced analytics on drug interactions, contraindications, as well as patient medication adherence patterns. By identifying potential problems in medication combinations, SafeDrugs is intended to prevent patient harm and the consequent increased costs associated with adverse drug events. Currently, there are no published data on SafeDrugs in the available literature.
Appendix
Documentation Requirements
Drugs or drug classes for which screening is performed should only reflect those likely to be present based on the member's medical history or current clinical presentation. Each drug or drug class being tested for must be ordered by the clinician and documented in the member's medical record. Additionally, the clinician’s documentation must be specific to the member and accurately reflect the need for each test.
If definitive testing for an individual drug or drugs (qualitative or quantitative) is required based on the member's specific history and treatment plan and the indications above, a targeted and limited number of tests defined by codes in the CPT range 80320 - 80377 is generally medically necessary; the rationale for each test ordered should be included in the medical record.
If definitive testing for substances of abuse are medically necessary based on the member's specific history and treatment plan and the indications above, HCPCS G0480 (1 - 7 drug classes) or G0481 (8 - 14 drug classes) should be used. When choosing between G0480 and G0481, the clinician should consider which drug classes are pertinent to the care of each member based on the medical indications listed above; the target drug classes should be documented on the order for the test and in the medical record.
Definitive tests G0482 (15 – 21 drug classes) and G0483 (22 or more drug classes) are rarely medically necessary for routine testing in the outpatient setting. In the rare instances where these tests may be medically necessary, the medical record must include a specific rationale, based on the history and other relevant details (including a detailed list of all drug classes in question), for such expansive definitive testing.
Examples of Validated Risk Assessment Tools
The following are links to standard validated tools for assessing the risk for abuse:
Note on Medical Necessity of Reflex Testing
Reflex definitive testing is not considered medically necessary when presumptive testing is performed at point of care because the clinician should have sufficient information to determine if confirmation of a presumptive test is needed, such as when the member admits to using a particular drug, or the immunoassay cut-off is sufficiently low that the clinician is satisfied with the presumptive test . If the clinician is not satisfied, he can then order specific subsequent definitive testing.
Because reference laboratories do not have access to patient-specific data, it is considered medically necessary for a reference lab to reflex to a definitive test before reporting a positive presumptive result to the clinician. It is also considered medically necessary for a reference lab to reflex to a definitive test to confirm the absence of prescribed medications when a negative presumptive result is obtained for a prescribed medication listed by the ordering physician.
References
The above policy is based on the following references:
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