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New Technology Evaluation
Name of New Technology: A QEEG Database Method for Predicting Pharmacotherapeutic Outcomes: Referenced-EEG (rEEG)
Date Review Completed: December, 2000
Date Review Approved by the Clinical Technology Assessment Committee: December, 2000
Date Re-Review Completed: December, 2006
Date Re-Review Approved by the Clinical Technology Assessment Committee: January 9, 2007
Reason for Re-Review: The 2000 Evaluation of Reference EEG was done in response to a request for this technology. The 2007 Re-review was done also in
response to a request from a network provider for re-examination, and in response to a pilot project proposed for United Behavioral Health, as well as the span of
time that has elapsed since the original review.
Date Re-Review Approved by the Clinical Technology Assessment Committee: March 29, 2010
Reason for Re-Review: Technology potentially relevant to a priority OptumHealth Behavioral Solutions population - treatment resistant depression
Reason for Re-Review (July 22, 2011): rEEG Tech Assessment to be posted externally on UBHonline
Date Re-Review Approved by the Clinical Technology Assessment Committee: July 22, 2011
INSTRUCTIONS FOR USE
This technology evaluation provides assistance in interpreting the enrollee’s benefit plans. When deciding coverage, the enrollee specific document must be
referenced. The terms of an enrollee's document (e.g., Certificate of Coverage (COC) or Summary Plan Description (SPD)) may differ greatly. In the event of a
conflict, the enrollee's specific benefit document supersedes this medical policy. All reviewers must first identify enrollee eligibility, any federal or state
regulatory requirements and the plan benefit coverage prior to use of this technology evaluation. Other Policies and Coverage Determination Guidelines may
apply. OptumHealth reserves the right, in its sole discretion, to modify its Policies and Guidelines as necessary. This technology evaluation is provided for
informational purposes. It does not constitute medical advice.
This document is for reference only and the information is not guaranteed or warranted as accurate in that the information herein may change from time to time
without notice. Any changes to this document will become effective, regardless of any amendment hereto, as of its effective date in accordance with applicable
law, OptumHealth policies or procedures, whichever occurs first. Any coverage of these services is subject to the emerging technology policy. A technology rated
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emerging means the technology is still not considered a proven, covered technology, indicating that it has not yet risen to the level of services backed by credible
research soundly demonstrating that the services or supplies will have a measureable and beneficial health outcome. The level of evidence for an emerging
technology can move to a proven status with the collection of additional, obtainable data.
What population
is the technology
used with?
Referenced-EEG (rEEG) is used for patients:
Age: Between 6 and 90 years of age
Group: Treatment resistant neurobehavioral disorders. According to CNS Response, treatment resistant is defined by the following
criteria:
Two or more failed medication trials
Patient has not been diagnosed with, and has no history of psychotic disorder (e.g. schizophrenia)
Gender: Male or Female (Gender non-specific)
What diagnosis
or clinical
condition is the
technology used
for?
Referenced-EEG (rEEG) is a biomarker system used by physicians to improve pharmacotherapy outcomes based on the unique
neurophysiology of their patients. Since the referenced-EEG tool is used exclusively to predict medication response based on
physiology data, it is agnostic with respect to diagnosis or symptomotology. Nevertheless, clinical trials have been performed using
Referenced-EEG (rEEG) for a wide range of neurobehavioral disorders, including:
Depression
Anxiety
ADD/ADHD
Eating Disorders
Substance Abuse
For purposes of this review, rEEG is being reviewed only as it relates to depression (treatment-resistant)
What aspect(s) of
treatment is the
For purposes of this review, the use of referenced-EEG (rEEG) is a decision support tool intended to be used by a physician to
acquire information that can reliably be used as a guide in selecting appropriate medication(s) based on a patient’s unique
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neurophysiology. A physician determines initial patient assessment and diagnosis prior to ordering a Referenced-EEG (rEEG)
and must utilize their professional clinical judgment in interpreting referenced-EEG results and in developing treatment plans.
referenced-EEG was NOT reviewed with intention of it being a diagnostic test.
technology used
for?
For the purpose of this review, Referenced-EEG (rEEG) is NOT reviewed as a diagnostic test and does NOT dictate therapy.
Referenced-EEG (rEEG) is not considered a prevention or wellness technology
Description of
the technology
Referenced-EEG (rEEG) is a biomarker system that is intended to be used by physicians as a decision support tool based on the unique
neurophysiology of their patient that is intended to improve the pharmacotherapy selection process and therefore potentially improving
treatment outcomes. The technology involves three processes:
1.
Neurophysiology data is obtained from patients using standard 21-lead digital EEG equipment over a 30-40 minute period. The
EEG data is then uploaded to CNS Response, which maintains secure, HIPAA-compliant hosted test utility.
2.
Digital EEGs are transformed into Quantitative EEG data and compared with FDA-approved normative databases to identify
physiological abnormalities. These databases are routinely used by Neurologists for evaluation of neurological conditions such
as brain-based disorders. Two software systems may be used by CNS Response to perform this transformation (a third, from
Brain Resource Corporation, is currently under review):
Neurometric Analysis System from NxLink Ltd. – the first large-scale neurometric database
Neuroguide software from Applied Neurosciences – a neurometric database composed of normative EEG data from
over 10,000 patients.
3.
The process of generating the referenced-EEG report involves comparing the deviations from normal of the QEEG described
above to a CNSR proprietary database of similar deviations for known responders to certain medication. The 2-page report
shows the probability that a patient will be responsive or resistant to a library of medication used in the treatment of similar
patients.
This process has been correlated to treatment outcomes in a database of over 1,800 patients and 17,000 medication trials, with an
average outcome follow-up duration of 405 days. To date, according to CNS Response, over 6,000 tests have been performed.
How is it applied
in a clinical
setting?
Physicians identify patients suitable for Referenced-EEG (rEEG) based on the following criteria:
Patient has failed two or more unsuccessful adequate medication trials
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Patient has not been diagnosed with and has no history of psychotic disorder (e.g. schizophrenia)
Patient is medication free or can safely become medication free for the duration of the trial
Depending on the selected patient’s current therapy, the patient is carefully removed from psychotropic medications for no less
than 5 half lives for each medication (running concurrently)
Upon completing medication washout, the patient undergoes a standard digital EEG test (painless, non-invasive) for 30-40
minutes in a resting but awake state.
The recorded EEG is uploaded to CNS Response and artifacted (30 minutes of EEG data is distilled to 2 minutes of useful
data).
A Referenced-EEG (rEEG) result is provided to the physician in less than 24 hours (via fax or online physician portal)
The physician then reads the suggested information and decides on appropriate pharmacotherapy based on the patient’s medical
history, medication tolerances, other lab tests, referenced-EEG report, and the provider’s own clinical judgment.
Training
required to
implement the
technology?
According to CNS Response:
Clinician must be a physician to order rEEG and make the final clinical decision on which medication to prescribe.
Physicians receive a combination of online and live (one-on-one, physician administered) Referenced-EEG (rEEG) training.
Online training consists of web-based SCORM-based training modules. Online training is administered prior to ordering the
first Referenced-EEG (rEEG) and takes 90 minutes to complete.
Physicians are provided one-on-one training to assist in the proper interpretation of the initial five Referenced-EEG (rEEG)
tests processed.
Professional online and telephone support is provided for ongoing Referenced-EEG (rEEG) interpretation questions.
Operational / referenced-EEG ordering/processing training is conducted by clinical (Physician) personnel.
Referenced-EEG (rEEG) interpretation training is conducted by CNS Response’s network of Regional Medical Directors.
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Discipline or
educational level
required
implementing the
technology?
What is the
training process
to assure fidelity?
Does it include:
Only licensed physicians are able to order an referenced-EEG test
Only qualified EEG Technical professionals are permitted to collect digital EEG data at the point of care
All tests are reviewed by a Board-certified Neurologist
CNS Response is responsible for and conducts all Referenced-EEG (rEEG) user training.
Manualized training
Manualized training is delivered through online SCORM-based training modules.
Supervision by experienced clinician included in training
All training is supervised / administered by CNS Response clinical leadership. Non-clinical training is provided for
operational processing. Ongoing support by a clinician is provided online and telephonically for Referenced-EEG (rEEG)
interpretation.
Written standard demonstration of knowledge competencies
SCORM-based training modules measure all individuals trained
Monitoring of clinician’s implementation of the technology with demonstration of success
SCORM-based training modules measure all individuals trained.
Certification process by the vendor or by a credible independent specialty organization
CNS Response does not currently credential or “certify” the physicians who use its test. The company does, however, provide
structured training and online QA of physician users
Process to maintain competency post-training
CNS Response assigns clinical and non-clinical support staff to each physician who has successfully completed ReferencedEEG (rEEG) training. Ongoing support includes customized online (physician portal), telephonic and on-site in-service
training.
8) Safety of the
technology:
Referenced-EEG was developed to reduce Trial & Error prescribing, the dominant prescribing method for treatment resistant patients
who have failed in previous treatment trials. Given the general lack of evidence for pharmacotherapy in TR patients, the test does not
and cannot claim to be error free. Like most other decision support guides, the referenced-EEG test is positioned as an adjunct to the
physician’s clinical judgment and all available medical information (medical history, lab tests, medication history, and other chart data),
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and is provided solely for use by qualified medical professionals. The test does not introduce new or novel risks to patient care beyond
the risks posed by the prescribed medication themselves, which the physician must consider every time he/she prescribes and any risk
associated with a medication wash-out procedure. Both procedures are conducted under the judgment and supervision of the attending
physician. CNS Response has conducted numerous clinical studies under IRB (Institutional Review Board) supervision to ensure trial
safety for human subjects. Digital EEG equipment is used as an example of a “safe device” in FDA guidelines on clinical trial safety.
a) Potential areas
of harm
Side effects
EEG testing introduces no side effects to the patient being tested. Referenced-EEG (rEEG) utilizes standard FDA-approved
digital EEG recording technology which passively records the electrical activity of the brain. As prescribers, physicians using
Referenced-EEG (rEEG) make all final medication selection decisions for patient treatment plans based on their clinical
judgment. Referenced-EEG (rEEG) only provides clinical decision support data to the physician.
Potential results from malfunctioning of the technology
Malfunctioning of the Referenced-EEG (rEEG) technology could produce an inaccurate Referenced-EEG (rEEG) report.
Further, as outlined above, physicians are instructed at each stage of training that the report is not error-free, but a stochastic
process designed to reduce error by informing physicians of patient outcomes for patients with similar pathophysiology. In
addition to CNS Response quality control processes (see below), physician users are expected to perform basic ReferencedEEG (rEEG) identification and quality checks. As with all diagnostic and treatment guides, physicians using Referenced-EEG
(rEEG) make all final medication selection decisions based on their clinical judgment.
Other: Medication washout
Medication wash-out effects could include exacerbation of depressive symptoms and withdrawal symptoms associated with
some antidepressant medications.
b) Risk
management
processes
According to CNS Response, experience with medication tapering has been generally positive for physicians using referencedEEG. A 2008 Medication Washout study of 2,009 patients undergoing medication tapering evaluated for drug discontinuation
problems. Only 20 patients (<1%) experienced discontinuation problems, and only five patients (.25% of the total) reported
sufficient discomfort from medication tapering to discontinue washout. There were no serious adverse events.
According to CNS Response, all References-EEG (rEEG) tests are delivered under a structured quality control process pursuant to
the FDA’s Quality System Regulation 21CFR Part 820 final rule for medical devices. If a quality issue is identified at any stage (for
example, a corrupted EEG file or a misspelling on a patient form) CNS Response personnel utilize standard operating procedures
(SOP) for its resolution, and physician users are trained on customer support processes to report and correct any quality issue.
For treatment resistant depressed patients, there are also known risks (side effects) of keeping patients on medications when they
are ineffective. Continuation on medications that are not working is a known clinical quality problem, as evidenced by a growing
body of literature on medication errors.
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According to CNS Response, there have been no adverse events reported by physicians utilizing referenced-EEG (rEEG) – either
commercially or in clinical trial settings – beyond the medication side effects which are consistent with medication labels. CNS
Response does not have a formal adverse event reporting system.
According to CNS Response, physician users of Referenced-EEG (rEEG) have reported no change in their professional liability
experience or premiums, but have reported general improvement in patient outcomes.
Technology
Description
Checklist
Key Issues to be
answered by the
evidence
Is the technology reproducible or can it be delivered with a high degree of fidelity?
Y
Is it relevant to the needs of the targeted population?
Y
Has a protocol/manual for the intervention been established? (indications, course, etc)
Y
Is there specific training needed to implement the intervention?
Y
Is the technology considered relatively safe given to risk associated with usual care?
Y
How does medication choice guided by referenced-EEG (rEEG) compare to standard of care algorithms in treatment-resistant or
treatment-naïve patients in terms of efficacy and safety across different neurobehavioral indications?
Is the use of referenced-EEG better than the outcome from trial and error prescribing?
For what conditions is there the necessary threshold of evidence?
What is the reliability of the medication prediction of referenced-EEG in real practice patients and in test subjects?
rEEG_Appendix A
rEEG_Appendix B
rEEG_PPT
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Summary of Strength of Evidence Classification for referenced-EEG: (Proven vs. Emerging vs. Unproven)
1) Quantity of Evidence: (3+ published papers on identified
outcomes for proven, at least 2 for emerging)
Two published, peer-reviewed studies focusing on areas of
technology as determined by CTAC discussion
2) SMRS Score: (need average of at least 4.0 for proven, average
of at least 3.0 for emerging)
Average score of 3.45 overall (average of 4.05 for the two
“critical studies”) – see rEEG_Appendix B
3) Treatment Effects: (published studies must demonstrate
statistical significance and reasonable effect size; must
demonstrate no evidence of harm)
Statistical significance and reasonable effect size
Evidence Review Checklist:
1. Is there demonstration of clinically significant improvement in the measured outcomes?
2. Is the technology as beneficial as established alternatives or any comparative effectiveness studies?
3. Has a naturalized study been conducted on the technology?
Y
Y
Y
Summary of Research Evidence Strengths (relevant posters included):
Each of the published papers focused on subjects with depression and some with treatment-resistant depression. All but two of the papers reviewed characterized
the relationship between quantitative EEG and the presence of depression or clinical response. Study 1, Suffin et al 2007, and Study 10, Battista et al, are the
most relevant papers as they demonstrate the performance of referenced-EEG in guiding medication selection which is the current proposed use of this
technology. Study 10 compares clinical efficacy of treatment following referenced-EEG reports to the best medications determined from the NIMH-sponsored
trial STAR*D and is more compelling than Study 1 because of the larger sample size and a wider group of outcome measures.
Seven posters were provided as additional supporting evidence for this technology, one was a poster of Study 10 and thus not included in the review. Two of the
seven posters dealt with dually diagnosed subjects who had a substance abuse and another Axis I, nonpsychotic psychiatric disorder. As these were from the
same treatment center, the poster with the larger N was reviewed. One poster appeared to combine the results from several of the other posters and was not
included in the review. Of the 4 other posters included in this review, one described the use of referenced-EEG compared with the Texas Medication Algorithm
Project or TMAP in patients with TRD (N=18), two described the use of referenced-EEG in patients with eating disorders (N=16, 94), and one described the use
of referenced-EEG in dually diagnosed subjects (N=77). The most compelling evidence was in subjects with eating disorders – among 94 subjects with anorexia,
bulimia, or eating disorder NOS choosing to participate in an unblinded use of the referenced-EEG medication guidance, 81 completed at least 8 weeks of the
recommended referenced-EEG medication and 83% demonstrated clinical improvement as assessed by CGI. The median follow-up over which these benefits
were sustained was 8 to 10 months. An examination of 16 subjects with comorbid depression and eating disorders from Walden Behavioral Care found that of
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the 13 subjects who elected to participate in referenced-EEG guided medication, 100% showed a substantial improvement in HAMD at 8 weeks and 6 months.
For a third of the subjects data was available at 2 years and the improvement in depression scores was maintained. Hospitalizations were substantially decreased
following adoption of referenced-EEG medications compared with the 24 months prior to participation. The case series of 77 dually-diagnosed subjects found
that almost 80% of subjects were much to very much improved following at least 6 weeks of referenced-EEG guided pharmacotherapy; among the more ill
subset (N=65), 77% demonstrated response.
In summary, medication selection guided by referenced-EEG appears beneficial in subjects with treatment-resistant depression and possibly eating disorders. A
compelling question from the available literature is whether referenced-EEG is better than the best of the STAR*D medications in subjects with resistant MDD.
Per Study 10, the answer is affirmative for some subjects. For patients whose EEG is artifact-free, who have not failed the medications recommended by
referenced-EEG, for whom referenced-EEG recommends a medication combination different than the best of STAR*D recommendations, and who are willing to
comply with referenced-EEG medications, there appears to be clinical benefit (Response rates on MADRS referenced-EEG =57.5%, STAR*D=44.9%,
p=0.0456, remission rates on QIDS-SR16, referenced-EEG=35.0% vs. STAR*D=26.5%, p=0.0154, both findings in the per-protocol analysis group).
Summary of Research Evidence Weaknesses (relevant posters included):
Nearly all of the studies show short-term results from open trials and are centered on the relationship between certain aspects of QEEG and either the presence of
depression or clinical response in depression. With regard to QEEG markers of clinical response, the particular measure from QEEG varies across studies and it
appears that the Assessment of Treatment Response Index has been refined with subsequent research. This does not relate to the use of referenced-EEG but
highlights the difficulty in replicating findings from small studies. That principle is relevant in the database matching step for referenced-EEG. It will be
imperative for the medication experience database to grow as new medications enter the market to ensure accuracy in the reports. The most compelling study,
Study 10, is limited in several important ways. The raters were aware of treatment group assignment, and thus unblinded, which could lead to bias in the ratings.
The findings are limited to subjects where referenced-EEG can be used; 50 (11%) subjects had an artifact that precluded use of referenced-EEG or had failed
medications that referenced-EEG suggested and were ineligible for the trial. For 54 (12%) subjects, the referenced-EEG did not recommend treatment that was
different from the STAR*D algorithm and were thus excluded from the trial. This trial had two groups of treatment-resistant subjects, those who had failed an
SSRI (stratum A = 44%) and those who met the more accepted definition of failing more than 1 class of antidepressants (stratum B=55%) in the current episode.
No analysis of the relative benefit of referenced-EEG for stratum B was provided although this was requested from the sponsor. This is notable as physicians
treating stratum B subjects might be a better use for this technology. The majority of subjects that were excluded from the analysis set (per-protocol group) were
excluded due to failure to comply with medication instructions (11 subjects). Most of these subjects were in the referenced-EEG group; it is unknown whether
this rather substantial rate of non-compliance would be found in other trials or in clinical practice. For most efficacy measures, significance between treatment
approaches was noted in the per-protocol group, but not in the larger modified intent-to-treat group. The current goal of depression treatment is remission, not
response; the lack of significance in remission rates between medication selection arms (except for self-rated QIDS-SR), especially in an open-label trial, limits
the results. Finally, the referenced-EEG approach does not dictate which medication class is used first; this ambiguity limits the generalizability of the results in
other physician settings. Blinded, controlled studies of longer duration are needed to determine whether the use of referenced-EEG results in greater compliance
with medication and sustained symptom improvement.
In the comparative studies, Study 10 and TMAP poster, the focus has been on results when referenced-EEG suggests alternative medications than would be
recommended by the comparator (TMAP or STAR*D). In these trials, referenced-EEG and the comparator recommended the same course of treatment in 27% of
subjects in the TMAP study and at least 10% of subjects in the STAR*D study. As the STAR*D trial excluded subjects for whom referenced-EEG suggested the
same medication as STAR*D, the findings are limited to subjects whose EEG would suggest alternate treatment than the best of the STAR*D treatments. With
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only 2 published studies of referenced-EEG, there is insufficient evidence to gauge the reliability of this technology in predicting response to treatment in test
subjects and real patients.
Although the technology itself is safe, it is very difficult to evaluate the safety of referenced-EEG as it will be a function of the class selected by the physician
and doses used, neither of which are prescribed by the technology. As referenced-EEG medication combinations that have not been extensively studied which
could lead to interactions, the potential risk may be greater than usual care. It is inherent to the referenced-EEG that the physician evaluates the risk associated
with any medication dosage level and combination selected.
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Technology Assessment from Cochrane or Hayes and Any Additional Information:
There is a Hayes brief for QEEG dated 2007. At the time of its publication, the literature primarily consisted of studies illustrating QEEG’s ability to distinguish
between subjects with psychiatric conditions and healthy controls or the association with QEEG outcomes (frequently cordance) with clinical response. There
were no papers at that time describing the performance of QEEG measures in predicting response or non-response to antidepressant medications. This review,
due to its timing, did not include Studies 1, 3, 4, 6, 9 and 10. The vendor, CNS Response, provided additional information in response to questions from the
reviewer about the database and Study 10, the STAR*D comparison trial. A summary of their responses are as follows (complete response provided as
attachment):
Database: The medication response database is comprised of over 1,800 patients and 17,000 medication trials, with an average outcome follow-up
duration of 405 days. The medication trials encompass 30 psychotropic drugs, weighted according to prevalence of use in the marketplace; atypical
antipsychotics are not included in the database and it will be 1-2 years until they are incorporated. Historically the company relied on internal scientists
to generate the data as well as any completed trial with the tool; beginning in 2010 they are also incorporating data from 50-60 physicians who use the
tool frequently. An iPad application has been developed to capture this data. Each database entry includes medication type, date, and dose; demographic
information; primary and secondary diagnoses; results of the EEG in an unmedicated state; CGI-S; CGI-I. To be included in the correlational modelbuilding, there must be at least 45 days of follow-up after the subject achieved stability or maximum medical improvement. The range of follow-up for
these subjects is 45 days to 405 days.
Study 10 (Battista et al 2010): Subjects in both medication groups were allowed to switch treatments during the trial; this occurred more frequently in
the STAR*D arm, but rather infrequently in either arm. No analysis was performed within strata. The strata defined the severity of the subjects –
subjects in stratum A had failed 1 or more SSRIs while subjects in stratum B failed at least 2 antidepressants of different classes. Per protocol, subjects
in stratum A randomized to STAR*D would receive venlafaxine while subjects in stratum B would sequence through the latter steps of the STAR*D
which involve medication augmentations. The treatment arms were balanced by strata – similar rates of stratum A subjects in each arm; no efficacy
information within the strata is available. The first FDA-approved medication for treatment-resistant depression is Symbyax which is a combination of
an antidepressant, fluoxetine, and an antipsychotic, olanzapine. Vagus nerve stimulation and ECT are also used in treatment-resistant depression.
Antipsychotics and these techniques were not allowed in this study so it is unclear how referenced-EEG would compare to these treatments often used in
this population.
Summary of any Federal and State regulatory decisions such as those by the FDA:
FDA currently regulates and has approved the two key products which precede rEEG: Digital EEG equipment, and Quantitative EEG (QEEG) software. The
rEEG product is a reference database which currently has no regulatory predicate or FDA guidelines. Currently, AHRQ has provided detailed guidelines for
clinical outcome registries, which the rEEG database is fully compliant with. It is unclear what jurisdiction the FDA will have in the future, if any, over
electronic health databases under the HITECH Act. rEEG has been in ongoing discussions with the FDA to assist them in their endeavor to understand the
application of databases like rEEG to clinical decision support.
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Conclusions:
Referenced-EEG (rEEG) is a biomarker system that is focused on predicting medication response based on physiological data, agnostic with respect to diagnosis
or symptomotology, and based on comparing the deviations from normal of the QEEG described above to a CNSR proprietary database of similar deviations for
known responders to certain medication. The rEEG is based on well established EEG technology and can be implemented with the same degree of fidelity as
standard EEGs. Adverse events are no more than those existing for EEGs, medication wash-out and prescribing a psychotropic medication. Referenced-EEG
(rEEG) is considered to have a level of relative risk no higher than existing care practices and may be lower due to positive results with medication choices.
Physicians using the technology receive training on the technology but only need to include the resulting information as a part of their clinical decision making.
The technology appears to be implementable with fidelity and not dependent on extensive training of the prescribing physician.
The evidence review revealed the major studies had a Scientific Merit Rating Scale scores ranging from 3, 4, or 5 (Studies 1, 3-10), with an average score of
3.45, and a score of 4.05 for the two major peer-reviewed studies. Against the criteria requirement of 3 peer-reviewed studies, there were only two credible peerreviewed focused on the efficacy of referenced-EEG to predict medication responsiveness for treatment resistant depression as opposed to another EEG outcome.
Studies 1 and 10 both are peer-reviewed, have Scientific Merit Rating Scale scores averaging 4.05, and reported greater response to medication for subjects with
treatment-resistant depression who followed the referenced-EEG suggested medications as opposed to other treatment algorithms. In the pilot study with 13
subjects described in Study 1, the referenced-EEG group demonstrated a statistically significantly better improvement in the Beck Depression Inventory and
Hamilton Depression Scale over six weeks than subjects receiving traditional care, referenced-EEG = 50% and 60% versus usual care = 11% and 25%. Study 10
demonstrated effects of the technology in a randomized clinical trial of 114 subjects with treatment-resistant depression (89 in per protocol group) through
greater response (MADRS, referenced-EEG =57.5%, STAR*D=44.9%, p=0.0456) and remission (QIDS-SR16, referenced-EEG=35.0% vs. STAR*D=26.5%,
p=0.0154) rates in the group whose medication was guided by referenced-EEG than by the STAR*D algorithm.
The SMRS score average for all studies was below 4, however the strongest studies averaged a SMRS score of 4.05, which is in the proven range. However, the
quantity of peer-reviewed studies focused on the treatment resistant depression population (2 studies as opposed to the required 3 studies) falls in the unproven
category. The committee felt that given the SMRS scores and other factors, this technology does demonstrate a higher level of evidence, and therefore is
considered an emerging technology.*
* Emerging technologies are still in the unproven category, but are elevated above other unproven technologies and will be subject to special consideration
outlined in future policies applicable to this designation.
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References
PAPERS:
Bares, Brunovsky, Kopecek, Stopkova, Novak, Kozeny & Hoschl (2007). Changes in QEEG prefrontal cordance as a predictor of response to antidepressants in
patients with treatment resistant depressive disorder: A pilot study. Journal of Psychiatric Research, 41, 319-325.
Blackhart, Minnix, & Kline (2006). Can EEG asymmetry patterns predict future development of anxiety and depression? A preliminary study. Biological
Psychology, 72, 46-50.
Cook, Leuchter, Morgan, Stubbeman, Siegman, Abrams (2005). Changes in prefrontal activity characterize clinical response in SSRI nonresponders: A pilot
study. Journal of Psychiatric Research, 39, 461-466.
Cook, Leuchter, Morgan, Witte, Stubbeman, Abrams, & Rosenberg (2002). Early changes in prefrontal activity characterize clinical responders to
antidepressants. Neuropsychopharmacology, 27(1), 120-131.
DeBattista, Kinrys, Hoffman, Goldstein, Zajecka, Kocsis, et al (2010). Efficacy of Referenced-EEG (rEEG) in guiding medication selection for the treatment of
depression. Journal of Psychiatric Research (Publication date pending)
Iosifescu, Greenwald, Devlin et al (2009). Frontal EEG predictors of treatment outcome in major depressive disorder. European Psychopharmacology, 19, 772777.
Leuchter, Cook, Marangell, Gilmer, Burgoyne, Howland, et al (2009). Comparative effectiveness of biomarkers and clinical indicators for predicting outcomes
of SSRI treatment in MDD: Results of the BRITE-MD study. Psychiatry Research, 169, 124-131.
Leuchter, Cook, Gilmer, Marangell, Burgoyne, Howland, et al (2009). Effectiveness of a quantitative electroencephalographic biomarker for predicting
differential response or remission with escitalopram and bupropion in major depressive disorder. Psychiatry Research, 169, 132-138.
Morgan, Witte, Cook, Leuchter, Abrams, & Siegman (2005). Influence of age, gender, health status, and depression on quantitative EEG. Neuropsychobiology,
52, 71-76.
Suffin, Emory, Gutierrez, Arora, Schiller, & Kling (2007). A QEEG database method for predicting pharmacotherapeutic outcome in refractory major depressive
disorders. Journal of American Physicians and Surgeons, 12(4), 104-108.
POSTERS:
DeBattista, Hoffman, Schiller, Iosifescu. Referenced-EEG (rEEG) guidance of medications for treatment resistant depressed patients – A pilot study.
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DeBattista, Kinrys, Hoffman, Schiller. Referenced-EEG (rEEG) efficacy compared to STAR*D for patients with depression treatment failure.
Greenblatt, Sussman, Jameson. EEG guided medication predictions in treatment refractory eating disorder patients with comorbid depression: Opportunities for
personalized medicine in managed healthcare.
Schiller, Emory, Shaffer, Hamilton, Hoffman, Davis, Suffin. EEG guidance of psychopharmacologic treatment: Multi-site experience.
Schiller, Emory, Suffin. Referenced-EEG in the treatment of eating disorders.
Schiller, Shaffer, Milner. Referenced-EEG-guided pharmacotherapy of dual diagnosis patients.
Shaffer, Milner, Schiller. Referenced-EEG-guided pharmacotherapy for severely ill, dually diagnosed patients.
OTHER:
FDA Approval Documents (Digital EEG; Quantitative EEG) http://www.accessdata.fda.gov/cdrh_docs/pdf/k012079.pdf
http://www.accessdata.fda.gov/cdrh_docs/pdf9/K092039.pdf http://www.accessdata.fda.gov/cdrh_docs/pdf8/K082886.pdf
Proprietary Information of OptumHealth.
Copyright 2011 OptumHealth Behavioral Solutions by United Behavioral Health