Happy Labor Day 2018!

I have posted Labor Day greetings here since 2013 and did not want to miss this year. The previous posts highlight the problems of being a physician in the USA including being treated like production workers, mismanagement by managed care and their backer in the US government, the electronic health record as a physician burden, maintenance of certification and burnout.  There was continued concern over the past year about burnout and physician suicide. Like my last posts there was very little positive to report. Physicians are still laboring under a ridiculous productivity system that reimburses them a trivial amount with the expectation of physicians who can form their own independent groups and escape the burden of management by health care corporations.  Healthcare corporations in turn seem quite content to hire non physicians to replace the doctors who have left.  The question is:  "Have there been any bright spots?"

On the whole the answer is "No." In one of my previous posts I pointed out the number of young colleagues in Minnesota who were going into private practice and I considered that to be a plus.  Let's take a look at the scorecard:


1.  Physician productivity - physicians directly employed by healthcare companies or those who accept private insurance are still working under a rationed system that expects excessive productivity to make up for both insufficient reimbursement and the fact that physicians have to waste at least half of their time as clerical workers or working to legitimize and insurance or pharmaceutical benefit managers rationing decision.  I am seeing more paperwork rather than less and that is a hot topic on Physician Twitter.  In addition to prior authorizations and denial of care, these companies are now sending out notifications about prescriptions and prescription patterns.  They frequently get the prescribing physician wrong and they issue warning for medication  that are antiquated like: "Doctor did you know that your patient is on two medications form the same class?" Or "Doctor - did you know that your patient did not refill their antidepressant at the expected time?"  The vast majority of these warnings are irrelevant - but they want a return fax upon receipt but warn that all personal health identifiers must be shredded.  A new way to harass physicians with irrelevant faxes and mailings.

2.  EHR -  there have been no breakthroughs in the EHR.  It is still a repetitive stress disorder clickfest that produces unreadable documents.  The major EHR companies continue to have monopoly power and the ability to charge outrageous licensing fees for some of the poorest quality software ever written.  They have no incentives to change anything. At least members of Congress are no longer talking about how the enhanced productivity from this software will result in cancellation of medical inflation.  None of that has happened.  The only potential bright spot is that some regulators are talking about bringing some high tech companies into the area because the existing companies have done as poor a job at interoperability as they have about everything else. 

3.  Pharmaceutical benefit managers -  every physicians nightmare has stayed about as bad as ever with the exception of the forms I mentioned that seem to be a very crude attempt at saying they are engaged in pharmacovigilance.  Of course they are not because quality is a distant memory when you are monitoring a medication that the physician may not have wanted in the first place.  It may be a medication that the PBM got the patient to take because they denied the physician's first choice or erected a steep enough copay that the patient could not afford the physician's first choice.

4.  Managed care/Health Insurance companies - they continue to run the healthcare system in the USA as proxies for the irrational ideas from Congress.  The most irrational of these ideas is that a systems that has led to a 3,000% increase in administrators in the past 30 years can shortchange patients and physicians enough to in some cases turn a profit for shareholders.  The coexisting political myths that this is about "market choice" or "single payer/socialized medicine versus capitalism" don't help anyone but apparently reassure Congress that these proxies are doing what they want them to do. 

5.  Maintenance of certification - The American Board of Medical Specialties and the respective specialty boards continue to have a stranglehold on physicians with this arbitrary expensive and time consuming recertification process.  In combination with the work expectations and inefficiencies, MOC is a significant contributor to burnout and there has been no gain in patient treatment or outcomes related to this process.  Life long learning has been the mainstay of physician education rather than arbitrary exams that seem suitable for prep school rather then working professionals.  There have been some decided bright spots in this area.  The National Board of Physicians and Surgeons (NBPAS) has more visibility as an organization that supports the longstanding tradition of life long medical education as the standard for recertification.  It is gaining support in some states and some physicians in states where it is not formally supported have learned that they can get NBPAS certification and use it nonetheless.  Make no mistake about it - this is a hot political issue and there are many organizations with a clear interest in using MOC to sink physician autonomy once and for all.  To me this is reminiscent of when a manged care company took over a hospital I worded at and the physician department heads were either fired or replaced by administrators. Every politician and bureaucrat out there knows that the best way to squelch physician dissent is to work them to the point they have no time to do anything else. MOC burns bright as the last tool they need to make this happen.

6.  Burnout - number 1 - 5 above directly lead to physician burnout.  The only bright spot in this category is rhetorical.  Articles suggesting that self-management or a yoga deficiency are less likely to be advanced as causes of burnout.  Physicians are not longer accepting this propaganda and I was an early proponent rejecting those arguments.  The only meaningful way to improve on the burnout situation is by improving the work environment.  Now that we have rating scales for burnout, there is a real danger that we will see groups rated from year to year and any random fluctuation on a hardly used scale will be taken as a sign of improvement.  If there have been no concrete improvments in 1-5 above - be assured that burnout is unchanged.

The other bright spots here are the Cardiologists who have stepped up with both NBPAS and the Practicing Physicians of America initiative to go after the organizations behind this MOC movement from an antitrust and fraud perspective.  I never thought Cardiologists were that politically active but these initiatives have clearly changed my mind.  The incongruency in this process is that physicians everywhere belong to professional organizations often more than one.  Physicians in the trenches everywhere support traditional life long medical education and not the MOC appraoch and yet none of our expensive professional organizations will make that stand or for that matter take a stand on any of the above matters.  This is a classic example of what happens when a few special interests get in positions of power in professional organizations. 

That is the summary for this year. I am really hoping that the NBPAS and PPA can make differences and make the lives of physicians everywhere somewhat easier.  I did not touch on the subject of physician suicide.  It is a topic that requires a more detailed discussion and improving the work environment for physicians is likely to have an impact.

Every week I talk with doctors in very stressful circumstances who are trying to solve difficult problems.  None of them should have to work in the present work environment for physicians. 


George Dawson, MD, DFAPA






   

The Importance of Electrocardiography In Psychiatric Practice....

The US Preventive Services Task Force came out with general recommendations for resting and exercise electrocardiography a few days ago in JAMA Cardiology.  I posted the above comments on Twitter to emphasize the importance of the ECG in psychiatric practice.  I was also pleased to hear the word I frequently hear when patients, families, and other staff question the need for electrocardiography and my refusal to start of continue certain medications without seeing that ECG. That word is that the ECG is an expensive test and why do we want to incur the additional expense?  It is not an expensive test and the information it yields for the money spent is high.  The actual guidelines are available free online and if you are a psychiatrist or any other practicing physician I encourage you to read them.

The interesting aspect of this publication is that the accompanying editorial by Joseph S. Alpert, MD is more informative that the USPSTF document.  The conclusion of the USPSTF document is that screening asymptomatic individuals with resting or exercise electrocardiography does not provide useful information for predicting or preventing events due to cardiovascular disease. Dr. Alpert points out that this is a straw man argument and provides a very handy list of 15 clinical indications for resting or exercise electrocardiogram (ECG) recording.

Over the past 30 years I have ordered hundreds of ECGs and in reviewing that list, I found 6 of the common indications.  Admittedly some of these tests were in suboptimal conditions.  I am thinking primarily of the patient on a psychiatric unit with chest pain. In settings where I have practiced, several factors led to situations where I was getting the ECG and doing the early work before the consultant arrived.  In some rare cases, the troponins were being collected while the patient was still on the psychiatric unit.  In those cases the indication was to rule out acute ischemia pending additional biomarkers. 

If you examine patients, I found it was a common occurrence to makes diagnoses of arrhythmias - that were previously unknown to the patient - most commonly atrial fibrillation.  In some cases, ventricular arrhythmias and rhythms due to conduction disturbance are also noted.  An ECG is a very inexpensive test to determine what is happening. It is also a good way to get consultants interested in the problem.

The advent of concern about the QTc interval in psychopharmacology was probably the biggest driver for psychiatrists to order ECGs.  According to Dr. Alpert this is a legitimate indication for ordering the ECG and the bulk of ECGs I have ordered have been focused on matters of cardiac conduction.  The majority of those orders are based on the characteristics of the medication.  The FDA has made this problem a lot harder than I think it needs to be.  It would be useful to have a screening test for rapidly identifying patients who might be at higher risk for this abnormality but that would probably not take into account anatomical abnormalities that can affect the problem.  There is a certain amount of mystery in this area - when I see medications like haloperidol flagged for QTc prolongation and I have consulted on ICU patients taking haloperidol on telemetry and never seen a case - it tells me that some of this screening and concern may not be that well founded.

I also use the ECG (in addition to the cardiac review of systems) in cases of polypharmacy when the patient is taking multiple QTc prolonging medications.  That may involve a baseline ECG and repeat ECGs over the course of treatment.  On the front end of the evaluation, I often show the patient a drug interaction profile with all of the QTc prolongation flags, discuss the plan with them, and advise them to attend to cardiac symptoms suggestive of rhythm problems.  Even though the USPSTF is usually against screening I have picked up many problems with screening ECGs including QTCs of greater that 510 msec and complete heart block.  I think the problem with characterizing ECGs as screening seem to imply that the patient is asymptomatic and has no cardiac risk factors.  Adding a Bayesian term for the patients psychiatrists see would generally taken them out of that low risk category.  That fact and the low cost of the ECG make this an ideal test for the above problems. 

When I consider the ECG issue and the fact that I have ordered more and more of these tests over the years - I also think back to the 1980s and 1990s when it was not uncommon to see a QTc of 500 msec on a person taking thioridazine.  The Cardiology consultants would ask the high risk threshold question: "Does he really need the medication?"  Any affirmative answer usually resulted in leaving the medication in place.

As psychiatry has evolved, I think that psychiatrists are now in a place where they are (or can be) much more proactive about cardiac conduction problems - both in the initial pharmacological approach and in reacting to ECG abnormalities. The only consistent problem I see is the availability of ECG machines and technicians if the clinic is isolated from medical resources.  It may be impossible to get ECGs when you need them for the above purposes.

If you are a resident or an experienced psychiatrist, I think it will pay to get a copy of Dr. Alpert's editorial and take a look at the list.  I think it is also useful to remember there is screening and there is screening.  While you are at it - consider Cardiology Twitter and follow the Cardiologists there who post ECG tracings, how to read them, and what the clinical findings and treatment was. It is an outstanding learning resource in this skill.

Carefully consider all of the cardiac risk factors affecting the patients that you treat and I am sure you will agree with me - the threshold for obtaining ECGs in patients with severe psychiatric disorders should be low. 



George Dawson, MD, DFAPA


Reference:

1: Alpert JS. Does Resting or Exercise Electrocardiography Assist Clinicians in Preventing Cardiovascular Events in Asymptomatic Adults?. JAMA Cardiol. 2018;3(8):678–679. doi:10.1001/jamacardio.2018.1800.

Why The Antipsychiatrists Have It All Wrong

Twitter is an odd place to read about antipsychiatry.  There are apparently some academics in the UK who are keeping it alive and well. I sent this Tweet about the continued mischaracterization of psychiatry by various antipsychiatry factions. Those factions certainly are varied ranging from cults to academics - but they all seem to have an agenda that they are promoting. I certainly don't hope to correct their various rants and obvious conflicts of interest - only to set the record straight from this psychiatrist's perspective.

In a previous post, I pointed out how some of the more famous antipsychiatrists characterize psychiatry as monolithic and fail to appreciate both the diversity in the field and the complexity of the field.  Examples of those errors abound and I included them in previous posts about the monolithic mischaracterization and another rhetorical attack on the DSM-5.

It comes down to power and that argument is a gross distortion of reality. Before I proceed, let me say that I am talking about the time frame that encompasses my training and clinical practice. At this time that is the last 32 years post residency. During that time I have lived and breathed psychiatry and know what really happens in the field.  I came in to this field with my eyes wide open since I had a family member with severe bipolar disorder who was treated for years by primary care physicians with benzodiazepines and antidepressants so that by the time she was able to see psychiatrists - she could be partially stabilized but continued to have significant comorbidity. That family member was my mother.  As her son, I experienced first hand the lack of concern and care by any responsible entity in the community.  When she was extremely agitated and ill to the point that the police were being called repeatedly, I know what it is like when you are a kid and an angry cop says to you: "Do you want us to lock her up like a chicken in a chicken coop?" The cop of course knew nothing about severe mental illness and just wanted to leave and not have to deal with my mother's illness and her 5 young kids (my father was deceased).  In addition to my mother's illness, I witnessed first hand the toll that psychiatric illness had on the neighborhood as I walked to school every day. My point here is that I am not the only kid who had these problems.  In fact, I am certain the general view that psychiatric illnesses and addictions are diseases begins with this experience.

As a clinical psychiatrist with a solid medical orientation, my method has always been one that tries to engage the patient in a detailed analysis and solution to their problem.  Like many physicians, as a resident there is always an emphasis on what you are doing to solve the person's problem, but it was fairly evident that medical interventions themselves were risky and that higher risk interventions should be reserved for high risk conditions. It was also obvious that medical treatment depended on informed consent.  In other words provide the information to the patient and they either consent or don't consent to treatment.  It is really no different than seeing any other physician.

Since antipsychiatrists are a diverse group, they advance diverse rhetoric to advance their agendas.  That typically includes making money or seeking to elevate their status over psychiatry.  I will focus on a single common agenda and that is power.  The last time I actually studied power it was in a physics class.  It certainly never came up in medical school.  Studying psychiatry was an identical process to studying medicine and surgery.  Recognize the problems, diagnose, and treat them.

Somewhere along the line I realized that people were using rhetoric based on Foucault and whatever Szasz adapted from that to suggest that psychiatry had a hidden agenda.  It is so well hidden that it is unknown to psychiatrists.  It is more or less of a conspiracy theory that psychiatry wants to medicalize the treatment of all human behaviors and treat those behaviors as an illness.  Of course along the way, psychiatrists will enrich themselves and inflict untold suffering on the people they misdiagnose and treat.  Take a look at this argument that the DSM-5 was supposed to be a manual about how to live as an example. Their supporting arguments range from the non-existence of all mental illness to the fact that there are no tests that prove there is such a thing as mental illness.  The underlying antipsychiatry theories are predominately from the 1960s and 1970s and they have been classified by philosophers (1).  There has been little change since then - just a long series of repetitive recycled arguments.  The rhetoric can range from the recycled arguments of Szasz to overt threats.  One uniform feature of antipsychiatrists is that they believe they are above any sort of criticism.

The table below contains some of the common rhetoric used by antipsychiatrists. It is not exhaustive, but it is a good example of the rhetoric I referred to in my Tweet.

Here is why their power arguments and all of the associated rhetoric are irrelevant. The reality is that psychiatrists represent only 5% of mental health providers in the US.  Primary care physicians and now nurse practitioners and physician assistants prescribe far more medications than psychiatrists do and they have for some time.  Even though psychiatrists are a little slow in picking up on it – health plans are replacing physicians with non-physician prescribers and that is also true of psychiatry.  In fact, in most cases if you are trying to see a psychiatrist about medications you will end up seeing a nurse practitioner. Does that sound like an all-powerful profession?

The second point that the detractors seem oblivious to is that physicians in general have not run the field of medicine for the past 35 years. Nobody cares what a psychiatrist or for that matter any physician has to say.  Businessmen and politicians determine who patients see, for how long, and what those physicians are paid.  The only exception is specialty groups (Radiology, Orthopedics, Neurology, Urology, Ophthalmology) that can avoid employment relationships with healthcare organizations.  Does that sound like an all powerful profession? Strange that the antipsychiatrists with guild issues don't get that since they are under the same constraints from these monopolies.  

More to the point – if you see any physician in the USA and you don’t like what you are hearing – you are free to walk away and see somebody else.  It is not a question of being a victim of medical or psychiatric treatment.  In fact, psychiatric treatment is just as straightforward as I have portrayed it.  Come in, sit down and we will talk about your problems. My job is to give you the best possible scientifically based advice.  Your job is to decide whether to take it or not.  There is no medical treatment known that does not involve some risk.  Accepting treatment involves risk. If you accept that risk and are injured that does not mean that you were intentionally victimized by that physician or the profession.  In fact, only antipsychiatrists seem to routinely use that argument. 

Consider an example very familiar to me. Let’s say you are diagnosed with a hormone secreting pituitary adenoma.  The neurosurgeon you are seeing recommends removal but also says there is a chance that the carotid artery may be cut and the result would be catastrophic and irreparable.  Your choices are an experimental procedure with an uncertain outcome that may lead to surgery or radiation therapy (gamma knife) or doing nothing and trying to manage symptoms that will lead to your eventual death by congestive heart failure.  The risks are clear and significant, but the majority of people who I have met who have had this conversation decided on surgery. Antipsychiatrists will say it is not the equivalent to a suicidal person deciding to take an antidepressant.  I would say the risk of no treatment is equivalent, but the actual risk of psychiatric treatment is much less.  I have not seen a catastrophic, irreversible event from taking antidepressants as prescribed.  As far as the power dynamic – there is no comparision.  Being unconscious under general anesthesia for hours while an ENT surgeon and a neurosurgeon drill through your sphenoid bone into your pituitary fossa doesn’t compare to consciously talking to a psychiatrist for an hour, picking up a prescription, and then deciding on a day to day basis to keep taking an antidepressant pill.  There is really no comparison at all.

The point of this example is not that patient injuries do not occur during patient care. The point is that they do occur but that is the risk people generally have to take to get well.  The notion that psychiatrists are somehow more likely to cause these injuries and that the entire profession should be blamed as a significant cause of injuries compared with other specialists is a dubious argument at best.   

Antipsychiatry rhetoric has really not changed much over the years.  There is just a question of how much distortion, overt paranoia, or conflict of interest it contains.  In the 50 years that the antipsychiatrists have been hard at work, they have had more than ample time to come up with an alternate way to help people with severe mental illness.  To my knowledge they have not come up with a single treatment for mental illness. Of course that is no problem if you don't believe mental illness exists or that there is any way to diagnose or treat it.

That would also mean that the antipsychiatrists would have to do something positive instead of just blaming psychiatrists.  I am not holding my breath for that day to come.

George Dawson, MD, DFAPA




References:


1:  Fulford KWM, Thornton T, Graham G.  Oxford Textbook of Philosophy and Psychiatry.  Oxford University Press, Oxford, 2006: 17.



Graphic Credit:

Samei Huda contributed 3 points on the graphic.

Genomics And Metabolomics In Psychiatry As A Combined Tool - Predicting SSRI Response

From Reference 1

The last two posts here were an introduction to some recent work that combine aspects of genomics and metabolomics.  The paper that I will briefly discuss here is done by researchers from the same groups focused on studies in genomics and metabolomics.  They present work that is quite exciting because it illustrates the amount of information necessary to analyze the biological complexity present in brain science and they produce results that may prove very useful from a clinical standpoint.  The study involves serotonin metabolism in depression. I have discussed serotonin in many places on this blog as a significant neurotransmitter that refuses to go away despite the various critics.  This work will illustrate why that may be.  The first paper (1) looks at the relationship between baseline serotonin levels and levels after treatment with selective serotonin reuptake inhibitors and what those phenotypes may be associated with at a genetic level.  A second paper (2) that I will discuss in a subsequent post looks at kynurenine metabolism in depression and the association of that metabolite with symptoms severity and the genetic correlates.  Both papers offer a new look at serotonin metabolism in depression and its genetic basis.

In paper 1, the sample included 366 patients in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study (PGRN-AMPS). 918 samples from these patients after 4 and 8 weeks of SSRI therapy were used for the metabolomics part of the study.  Baseline serotonin and changes in these levels were measured.  Patients with higher baseline levels and/or greatest drops in serotonin levels were determined to have the best response to treatment with SSRI medications.

From Reference 1

When a GWAS study was performed looking at the baseline serotonin concentrations as the phenotype and at 4 and 8 weeks of treatment a significant single nucleotide polymorphism (SNP) cluster was noted at the Tetraspanin 5 (TSPAN5) gene on chromosome 4 and a number of SNPs were noted at the Glutamate-rich 3 (ERICH3) gene on chromosome 1.  Both are highlighted in the Manhattan plot at the top of this post. Both of these genes were noted to be novel genes involved in serotonin metabolism and plasma serotonin concentrations.

The SNPs 5' of the TSPAN5 gene were cis regulatory elements for that gene (referred to in the paper as cis-expression quantitative trait loci or eQTLs).  In the ERICH3 cluster SNPs, two were variants that were associated with proteosome mediated degradation of ERICH3.  Changes in the expression of this gene were correlated with plasma serotonin concentrations but the serotonin pathway expression was unaltered.  One of the SNPs was also associated with clinical response in the STAR*D study.

The study is interesting because of the plasma serotonin concentration phenotypes, positive treatment response, and identification of associated genes and SNPs. What has not been determined is the specific mechanism of the drop in serotonin levels and the specific genetic mechanisms.  In the experimental section of their paper they show how the serotonin concentrations in the periphery could also affect levels in the CNS by looking at TSPAN5 and ERICH3 expression in neuroblastoma cells.  The authors were able to demonstrate that both mRNA and protein fold changes of enzymes involved in the synthesis, degradation, and transport of serotonin were affected by knockdown (KD) - (a technique of gene silencing by the introduction of doublestranded interfering RNAs (siRNAs)) or overexpression (OE) (a technique leading to enhanced gene transcription by the introduction of regulatory elements for transcription.) genes.     

The paper is an excellent example of molecular psychiatry and a possible application in the field of precision medicine.  The ultimate goal is to determine the treatment in psychiatry that has a high probability of working as soon as possible and eliminate the long trials that many people have to endure before they find a medication that is effect for (in this case) depression.  Along the way it will be evident that just as clinical psychiatrists have know for some time -  the general categories of psychiatric disorders - just like all polygenic illnesses are really a collection of diverse disorders at the omic levels.

The rapid identification of these many subtypes will not only lead to more rapid and efficient treatment - but also prevent unnecessary exposure to medications that can be both intolerable and ineffective.

In closing, some will question the utility of reading papers that contain a lot of terminology from what we used to call molecular biology.  I like to read these papers because it continues to consolidate what I learned in medical school and add to that knowledge. In my biochemistry seminars back then - there was still a lot of emphasis on enzymatic pathways and protein function. We had to know the basics of nucleic acid structure, function, and analysis - but nothing like the details presented in this paper.  As an example, we knew the synthetic pathways and enzymes for serotonin biosynthesis discussed in this paper - but the idea of analyzing human DNA with a chip encompassing 7.5 million SNPs across hundreds of research subjects would have been mind blowing and in many ways it still is.  Reading papers like this one also assures that you are not stuck in serotonin metabolism and receptor theory from the 1980s.  If that is all you know these days - it is not enough!

I realize this is not for everybody - but for some of us it is very exciting stuff.

 
George Dawson, MD, DFAPA

Graphics Credit:

All of the above figures are from reference 1, per a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License.  The graphics are unaltered and are taken from the paper as they are printed in reference 1 below.  This is a non-commercial and not-for-profit blog.




References:

1:  Gupta M, Neavin D, Liu D, Biernacka J, Hall-Flavin D, Bobo WV, Frye MA, Skime M, Jenkins GD, Batzler A, Kalari K, Matson W, Bhasin SS, Zhu H, Mushiroda T, Nakamura Y, Kubo M, Wang L, Kaddurah-Daouk R, Weinshilboum RM. TSPAN5, ERICH3 and selective serotonin reuptake inhibitors in major depressive disorder: pharmacometabolomics-informed pharmacogenomics. Mol Psychiatry. 2016 Dec;21(12):1717-1725. doi: 10.1038/mp.2016.6. Epub 2016 Feb 23. PubMed PMID: 26903268

2: Liu D, Ray B, Neavin DR, Zhang J, Athreya AP, Biernacka JM, Bobo WV,Hall-Flavin DK, Skime MK, Zhu H, Jenkins GD, Batzler A, Kalari KR, Boakye-Agyeman F, Matson WR, Bhasin SS, Mushiroda T, Nakamura Y, Kubo M, Iyer RK, Wang L, Frye MA, Kaddurah-Daouk R, Weinshilboum RM. Beta-defensin 1, aryl hydrocarbon receptor and plasma kynurenine in major depressive disorder: metabolomics-informed genomics. Transl Psychiatry. 2018 Jan 10;8(1):10. doi: 10.1038/s41398-017-0056-8. PubMed PMID: 29317604.


3: Ji Y, Biernacka JM, Hebbring S, Chai Y, Jenkins GD, Batzler A, Snyder KA, Drews MS, Desta Z, Flockhart D, Mushiroda T, Kubo M, Nakamura Y, Kamatani N, Schaid D, Weinshilboum RM, Mrazek DA. Pharmacogenomics of selective serotonin reuptake inhibitor treatment for major depressive disorder: genome-wide associations and functional genomics. Pharmacogenomics J. 2013 Oct;13(5):456-63. doi: 10.1038/tpj.2012.32. Epub 2012 Aug 21. PubMed PMID: 22907730.

4: Mrazek DA, Biernacka JM, McAlpine DE, Benitez J, Karpyak VM, Williams MD, Hall-Flavin DK, Netzel PJ, Passov V, Rohland BM, Shinozaki G, Hoberg AA, Snyder KA, Drews MS, Skime MK, Sagen JA, Schaid DJ, Weinshilboum R, Katzelnick DJ. Treatment outcomes of depression: the pharmacogenomic research network antidepressant medication pharmacogenomic study. J Clin Psychopharmacol. 2014 Jun;34(3):313-7. doi: 10.1097/JCP.0000000000000099. Erratum in: J Clin Psychopharmacol. 2014 Oct;34(5):558. PubMed PMID: 24743713.

5: Ji Y, Schaid DJ, Desta Z, Kubo M, Batzler AJ, Snyder K, Mushiroda T, Kamatani N, Ogburn E, Hall-Flavin D, Flockhart D, Nakamura Y, Mrazek DA, Weinshilboum RM. Citalopram and escitalopram plasma drug and metabolite concentrations: genome-wide associations. Br J Clin Pharmacol. 2014 Aug;78(2):373-83. doi: 10.1111/bcp.12348. PubMed PMID: 24528284; PubMed Central PMCID: PMC4137829.

6: Athreya A, Iyer R, Neavin D, Wang L, Weinshilboum R, Kaddurah-Daouk R, Rush J, Frye M, Bobo W. Augmentation of Physician Assessments with Multi-Omics Enhances Predictability of Drug Response: A Case Study of Major Depressive Disorder. IEEE Comput Intell Mag. 2018 Aug;13(3):20-31. doi: 10.1109/MCI.2018.2840660. Epub 2018 Jul 20. PubMed PMID: 30467458; PubMed Central PMCID: PMC6241311.

Does Intimate Knowledge of Your Personal Genome - Really Help?

As an offshoot of my previous post - I think this is an obvious question.  I am speaking from a medical context and not from the standpoint of genealogy.  DNA studies of human origins that I posted here in the past are also a valuable use of genomic material.  The latter are common reasons why people send their DNA off for analysis.  Another reason is to learn if they have certain disease susceptibilities and that is where the real problems come in.

The genetics of many major diseases causing significant mortality and morbidity were worked out before the genomic era. They are the typical heritable disorders and inborn errors of metabolism that are flagged in popular sites like 23 & Me.  I can't imagine that there are many surprises when subscribers find out that they do not have a fatal error of metabolism as an adult. Most of the concern is about complex polygenic disorders that may or may not have a significant environmental factor and that also lead to significant morbidity and mortality.  From a philosophical standpoint this is also an interesting group of illnesses because there are clear parallels between psychiatric disorders and what are typically considered usual medical problems like hypertension, coronary artery disease, asthma, and diabetes mellitus.  For the past decade, the standard genomic approach to study these disorders has been to scan large groups of genomes looking for mutations associated with these disorders.  Those analyses are complex.

I thought I would continue with some clear cut examples form my own genome to illustrate the polygenes behind both heritability of complex disorders as well as the polygenes behind my current chronic conditions.

The at risk condition is bipolar disorder.  My mother had severe bipolar 1 disorder.  That is why nobody in my family ever doubts that bipolar disorder or "diagnoses" exist in psychiatry.  Bipolar disorder is not a subtle condition and it is currently fairly easy to diagnosis.  I credit DSM technology with making this an easy to recognize diagnosis. That was not the case 2 generations ago.  The treatment in those days was less clear and there were very few resources to treat people in their communities.  Despite my mother's diagnosis none of my siblings or their offspring has been diagnosed with bipolar disorder or any mood disorder.  One of the inquiries for my genome is whether or not there are any polygenes associated with a bipolar disorder diagnosis.

The methodology I used for this post was to export all of my 23&Me data to Promethease, a search and cataloging software that arranges specific SNPs by disease, medication, genes, and several additional classification parameters.  Before this software was available - I was stuck looking up every rsID in PubMed. The processing time of my entire genome in Promethease was 133 seconds.  All of the correlates posted below are from that data.  The first graphic is for bipolar disorder risk (click to enlarge).

Ten SNPs associated with bipolar disorder were identified.  The risk is modest 1.39-2x. A more interesting feature is the facts that some of the identified SNPs were protective against bipolar disorder.  In some cases, the SNP identified had to be in association with another gene in order to create the risk. Ethnic groups are also noted in association with some of these SNPs to increase and decrease risk.  Standard approaches to this in the literature are to construct equations with risk terms from identified SNPs to determine which of those equations is the best predictor of risk.  A second approach that I will discuss in a subsequent post is to use neural networks to determine associations between the SNPs and quantitative estimates to determine risk.  At a macro level,  the lesson is that a person with a non-bipolar disorder phenotype can carry multiple SNPs that may confer risk for bipolar disorder.

What about actual disease phenotypes?  I am fortunate enough to have several for analysis.  The first and in many cases the most illustrative is asthma.  I have had asthma since childhood with various diagnoses along the way.  The first diagnosis was a misdiagnosis and that it was a psychosomatic condition and not really asthma.  Then it was diagnosed as allergic asthma.  Then it was exercise induced asthma.  I have received just about every conceivable treatment for asthma including some that have been determined to not be effective.  There was also the famous disproven mechanism of action (increased intracellular cAMP) that was used to explain the mechanism of action for theophylline. I had a long quiescent period of about 20 years where I did not require any medical treatment at all.  That ended about 6 years ago when I developed an upper respiratory infection and I have had to take medications ever since.  My experience with available treatments has generally been disappointing.  The variable course of the illness seems to have a more significant effect.  That is probably why most treated asthmatics are symptomatic and the clinical markers of illness are mostly subjective.  I have several posts on asthma on this blog discussing why it is an ideal comparison disease to polygenic psychiatry disorders.  What does my genome say? (click to enlarge)

I have one sibling and one offspring of a sibling with asthma.  In this case the situation is more complicated - 47 SNPs with varying risk and qualifiers based on numerous contexts such as ethnicity, smoking status of the parents, exposure to allergens, medication responsiveness  and others. I highlighted a couple of SNPs that show a very high risk compared to what was seen in the bipolar disorder - specifically an odds ratio of 7.84 and a 3-fold to 39-fold increase in risk.  Like the bipolar disorder case - some of the polygenes decrease risk as well. There is no available level of data integration beyond that and no clear guidance in terms of therapy.

UpToDate  has a brief chapter (1 ) on the genetics of asthma.  The authors point out that it is a complex polygenic illness that in some cases depends on environmental interactions.  Like pre-genomic twin and family studies in psychiatric disorders there is a range of heritability.  The authors recommend genetic testing in patients with asthma only to exclude monogenic obstructive lung diseases that can be misdiagnosed as asthma, such as cystic fibrosis, primary ciliary dyskinesia, and alpha-1 antitrypsin deficiency.  They see other genetic testing as useful at the heuristic but not at a clinical level.   They point out that the study of asthma genetics is complicated by the lack of a gold standard test and the uneven application of clinical diagnostic criteria.  That has led to a study of a number of asthma traits.

From a pharmacogenomics standpoint up to half of asthma patients do not respond well to initial pharmacotherapy and even then the response is quite variable.  They review the strategies used for genetic analyses, including SNPs as mentioned here but do not comment on any specific SNPs.  I had a previous post on genes and GWAS studies of asthma. They do name several genes.  After reading this chapter it is clear that the parallels between asthma and major psychiatric disorders is clearer than ever.  All of the features of complex psychiatric illness including polygenic inheritance, complex heritability, lack of a gold standard medical test, and a lack of or incomplete response to medication that also occurs with severe psychiatric disorders.

 The final chronic condition is atrial fibrillation.  I had an onset about 8 years ago and as long as I take flecainide - I have no atrial fibrillation.  I had one grandparent with atrial fibrillation.  The SNPs identified follow and it is similar to asthma except fewer identified SNPs.  Multiple SNPs with associated conditions (embolic or ischemic stroke) and qualifiers.

Fifteen SNPs noted in my genome for atrial fibrillation.  Some of these genes have bare bones information (GWAS = genome wide association study, OMIM = online Mendelian Inheritance in Man).  There are complementary approaches that involve using other databases like the GWAS database.  Searching atrial fibrillation in that database. identifies a number of genotypes that were not found in my genome (rs247617, rs2129977, rs2220427, and rs6843082) and one that was - rs6843082.

Clearly, the approach I have outlined about is an improvement over searching Medline and my genome for SNP correlates but below any threshold for being able to use this information for precision medicine.  That means it is below any standard required to look at diagnosis, prevention, or treatment.

There is probably a lot of information even at this level that is sitting there under analyzed.  From my own genome, the involvement of the cytokine system (interleukins are cytokines) with multiple SNPs affecting those genes is a case in point.  Many asthmatics have multiple allergic conditions including atopy, eczema, urticaria, and episodic anaphylaxis.  These same individuals will see allergists, get tested and learn that they are allergic to everything. Those associated conditions are currently treated as medical mysteries or symptomatically as they flare up occasionally.  Are there deeper patterns in the immune system that have not been realized at this time?  Give the complexity of this system, I think that there are.

One of the key questions is whether the identified genes are producing identifiable products.  At that level the short answer is that current detailed genomic information is interesting from an academic perspective - but like genomic testing we are years away from clinical applications.  I could see the shadows of some serious family illnesses in my DNA like systemic lupus erythematosus and diabetes mellitus.  The reasons why my relatives developed these diseases and I did not is not clear at this time. I think most people might come to that same conclusion if they compare their personal genome with SNP markers of diseases.

With a few exceptions, it takes more than correlating mutations in your own DNA to what is known about those mutations across a much larger population and coming up with a diagnosis.  It probably takes more than knowing the mutations exist.  Multiple omics approaches might provide better information and I hope to post one one of those experiments soon and the result of that experiment in the case of selective serotonin reuptake inhibitor (SSRI) antidepressants will be shocking.


George Dawson, MD, DFAPA   



References:

1:  Author: Barnes KC.  Section Editors: Barnes, PJ; Raby BA, Deputy Editor: Hollingsworth H.  Genetics of asthma.  In: UpToDate  Accessed on August 14, 2018.

Genetic Testing and Pharmacogenomics in Psychiatry

Ever since I studied Shannon and Weavers's classic paper on information theory and studied entropy as a chemistry major - I have been interested in the flow of information in biological systems.  With the advent of modern techniques it is now possible to study the DNA of entire organisms (genomics), the collection of all RNA gene readouts in a cell (transcriptomics), the resulting protein locations, concentrations, turnover and post translation modifications (proteomics), and all of the small molecules within these systems (metabolomics).  There are a large number of studies in all of these areas across medicine in general but also psychiatry and addiction.  Before I launch into some of these studies I thought I would address a basic question I get from colleagues that looks at basically the genomic approach to drug metabolism.   

One of the most common questions I get from colleagues and online is: "Do you think that genetic testing is necessary?  Do you think it is useful?"  My standard response has been that in some cases it is but most of the time it is unnecessary.  I also point out that I am old school  and that plasma levels of antidepressants seem to be more of an accurate approach to antidepressant therapy.  The debate at the about plasma levels is always whether there is a known therapeutic level or not.  A lot of that debate dates back to the 1980s when we were using tricyclic antidepressants.  Psychiatrists typically used nortriptyline because the therapeutic levels were well defined.  Clinical trials data at the time provided therapeutic levels for all of the major tricyclics (amitriptyline, imipramine, desipramine).  Clinical chemistry companies also provided levels for less commonly used tricyclics like doxepin and trimipramine quoting smaller trials or observational studies for the therapeutic levels.   All of these drugs had toxic levels because of studies done on drug overdoses using these drugs.   A psychiatrist would typically get back a report with a quoted therapeutic level (or proposed levels), active metabolite levels, and toxicity levels.  In the case of a drug with no metabolites like nortriptyline the report would give the level, the range, and a much higher toxicity range.

When we entered the human genomics era there was expanded interest in the genetic bases of the pharmacokinetics (PK) and pharmacodynamics (PD) of psychiatric drugs.  That involved understanding the genetics of the hepatic cytochromes that metabolize most drugs and the genetics of the protein targets (reuptake proteins and receptor sites) where the drugs had their purported effects. Most of the pharmacogenomics of drug metabolism is focused on PK rather than PD.

One of the best independent reviews of the issue of genetic testing can be found in reference 3 below.  The authors do a systematic review looking at the question and come away with 5 clinical trials that look at the efficacy of commercially available pharmacogenomic testing and 5 studies that look at the issue of cost-effectiveness.   

     

Test	Type
GeneSight	Combinatorial
Genecept Assay	Combinatorial
CNSDose	Combinatorial

Combinatorial in this case means testing for multiple genes and it has been shown to currently be more predictive of antidepressant response than any single test.  Each of the assays typically looks at gene loci involved in drug metabolism and flags markers at that site.  For example,  I took my 23&Me data and ran it through a second software site that yielded the following analysis:

The CYP pharmacogenes on the left are associated with drug metabolism.  The rsID in column 2 designates the single nucleotide polymorphism noted in that gene.   Alleles are just different gene forms based on the difference at a single locus.   In this chart nucleotide bases (A.T,G,C) are designated. The result column notes if the genes have mutations (+) or not (-) and are homozygous (+/+, -/-) or heterozygous (-/+).  The red color code is homozygous for the mutation and the yellow color code is heterozygous.

In the above chart. red color codes for deficiencies in drug metabolism.  For example, the
rs1799853 on the pharmacogene CYP2C9*2 C430T (TT) is associated with a 40% reduction in warfarin metabolism and a greater risk for NSAID metabolism.  In fact, I was treated briefly with warfarin - had a difficult time with dose adjustments and was physically ill from the medication.

What about the common drug metabolizing genes?  In the case of common genes like CYP2C19 and CYP2D6 several variants are flagged and none of the variants are detected.  In terms of drug metabolism, the following results could be expected if they were found:

CYP2C19*17 (rs12248560) CC - normal genotype, CT - ultrafast metabolizer, TT - ultrafast metabolizer

CYP2D6 S486T  (rs1135840) CC - normal variant studied in clozapine metabolism

CYP2D6 100C>T  (rs1065852) GG - variant (C;T)(T;T) variants are associated with non functioning CYP2D6

CYP2D6 2850C>T  (rs16947) GG - normal variant - other variants may be associated with ultra-rapid CYP2D6 metabolism

These examples illustrate that SNPs can result in non-functioning CYP enzymes that would lead to an accumulation of the target drug or in some cases ultra-metabolism of the target drug with lower than expected plasma levels or in some cases physical effects from rapidly decreasing plasma levels.  There are a finite number of SNPs studied in this approach and a relevant question is what is the total universe of clinically relevant SNPs for a particular pharmacogene.  For example, in the PharmVar database database tables are available for CYP2D6 that give the frequencies of 113 alleles across all major race and ethnic groups.  A separate table lists 140 haplotypes with 993 variants.  39% of the variants are decreased function or no function alleles (54/140), 20% were normal (28/140), and 34% were unknown (48/140).

A recent study (4) looks at the practical aspects of predicting drug metabolizing phenotype from available CYP2D6 genotypes.  This is essentially the task of consumer pharmacogenomic testing. Thuis study has the additional advantage  of applying uniform methodologies across a large number of samples (N=104,509) instead of the small samples and non-uniform methodologies often used in typical databases.  They looked at both single nucleotide variants (SNVs) and copy number variants (CNVs) like gene duplications, deletions of entire CYP2D6 genes, and gene rearrangements.  CYP2D6 copy numbers of 0, 1, 2, 3 or > 3 were assigned.  Metabolizer status was assigned based on copy number with: Ultrarapid metabolize (UM ≥ 3 normal functioing gene copies); normal metabolizer (NM, 1 or 2 normal functioing alleles), intermediate metabolizer (IM, ≥  2 decreased functioing alleles), and poor metabolizer (PM, ≥  2 no function alleles).

37 CYP2D6 alleles were detected in the sample including 23 structural variants.  13.1% of the sample had copy number variants (CNVs).  The majority of structural variants had no function due to CYP2D6*5 gene deletion.  93% of the alleles were single copy variants with normal function (62%).  Based on the above convention phenotypic predictions were 2.2% Ultrarapid Metabolizers (UM), 81.4% Normal Metabolizers (NM), 10.7% intermediate metabolizers (IM), and 5.7% poor metabolizers (PM).  The authors point out that copy number variants (CNVs) contribute to significant variance in drug metabolism and may be underestimated in a number of studies.  They also point out that there are no current standards to predict genotypes from phenotypes.  The importance of CNVs in phenotypic variation is captured by this graphic from the original reference (4):

Figure 6. Contribution of structural variants and single copy variants to predicted phenotypes. Proportion of individuals in each predicted phenotype that had at least one structural variant is shown (from reference 4).

Getting back to the study in reference 3, the authors looked at what genes were studied in the commercial tests. Genesight looked at 6 genes (CYP2D6, CYP2C19, CYP1A2, CYP2C9, serotonin transporter gene (SLC6A4) and serotonin 2A receptor gene (HTR2A). The authors review 2 unrandomized studies of major depression by the same primary author comparing a group guided by the Genesight to a group that was not. There were increased improvement in depression scores in the Genesight guided group. A separate randomized study of Genesight guidance versus treatment as usual showed greater improvement in depression scores but no statistical significance.

Genecept was another commercially available studied product that was used 2 clinical trials. Genecept looks at CYP2D6, CYP2C19, CYP3A4, SLC6A4, 5HT2C, DRD2 (dopamine-2receptor), CACNA1C (L-type voltage gated calcium channel), ANK-3 (ankyrin g, COMT (catechol-O-methyl transferase, and MTHFR (methylenetetrahydrofolate reductase). The first was a naturalistic study with no control group (N=685) and all subjects got genetic testing. 77% of participants improved - 39% with improved scores and 38% with remitted depression.

CNSDose was the final assay that was studied clinically. This assay looks at CYP2D6, CYP2C19, and ABCC1 and ABCB1 (blood brain barrier transporters). This was a prospective double blind randomized guided versus unguided study (N=148). Guided subjects had a 72% remission rate compared with the 28% remission rate in the unguided group or a 2.52-fold greater chance of recovery. It was unclear how the guidance resulted in such high remission rates and the study has not been replicated.

The authors go on to review similar problems with cost-effectiveness analysis applied to the currently available genetic tests. They also review the methodological limitations of the current studies and conclude that pharmacogenomic testing is generally not ready for prime time at this point for most clinical psychiatrists. That is what I have been saying for years - along with advocating for plasma levels of antidepressants and antipsychotics when they are available. That said - I have done pharmacogenomic testing for patients who do not tolerate antidepressant medications or seem to be experiencing atypical side effects - like discontinuation symptoms the same day from SSRI or SNRI antidepressants that usually do not cause those symptoms.

Striking features from this brief review include the apparent lack of standard rules on interpreting drug metabolizing phenotype from genotypes and the importance of copy number variants in predicting phenotype. If clinicians are getting genotyping for genotyping predictions it is a good idea to make sure that these genotypes are also determined in addition to the single nucleotide variants. Specific genes in reports can all be looked up for specifics based on the rsID located in this post.

Given these constraints, I think that commercially available pharmacogenomics assays need to be very explicit on how they determine drug metabolizing phenotypes, what genetic information they are actually measuring, and what might be missed.  The ultimate clinical trial would be to look at a group of patients who did not tolerate specific antidepressants and see if higher than expected abnormal drug metabolizer frequencies could be detected.



George Dawson, MD, DFAPA




References:

1: Weinshilboum RM, Wang L. Pharmacogenomics: Precision Medicine and Drug Response. Mayo Clin Proc. 2017 Nov;92(11):1711-1722. doi: 10.1016/j.mayocp.2017.09.001. Epub 2017 Nov 1. Review. PubMed PMID: 29101939.

2: Wang L, McLeod HL, Weinshilboum RM. Genomics and drug response. N Engl J Med. 2011 Mar 24;364(12):1144-53. doi: 10.1056/NEJMra1010600. Review. PubMed PMID: 21428770.

3: Rosenblat JD, Lee Y, McIntyre RS. Does Pharmacogenomic Testing Improve Clinical Outcomes for Major Depressive Disorder? A Systematic Review of Clinical Trials and Cost-Effectiveness Studies. J Clin Psychiatry. 2017 Jun;78(6):720-729. doi: 10.4088/JCP.15r10583. Review. PubMed PMID: 28068459.

4: Del Tredici AL, Malhotra A, Dedek M, Espin F, Roach D, Zhu G, Voland J and Moreno TA (2018) Frequency of CYP2D6 Alleles Including Structural Variants in the United States. Front. Pharmacol. 9:305. doi: 10.3389/fphar.2018.00305




Relevant Databases:

1: Transformer Database (formerly Super CYP Database).

Michael F. Hoffmann, Sarah C. Preissner, Janette Nickel, Mathias Dunkel, Robert Preissner and Saskia Preissner. The Transformer database: biotransformation of xenobiotics.  Nucleic Acids Res. 2014 Jan 1;42(1):D1113-7. doi: 10.1093/nar/gkt1246. Epub 2013 Dec 10.

Preissner S, Kroll K, Dunkel M, Senger C, Goldsobel G, Kuzman D, Guenther S, Winnenburg R, Schroeder M, Preissner R. SuperCYP: a comprehensive database on Cytochrome P450 enzymes including a tool for analysis of CYP-drug interactions. Nucleic Acids Res. 2010 Jan;38(Database issue):D237-43. doi: 10.1093/nar/gkp970. Epub 2009 Nov 24. PubMed PMID: 19934256

2: Pharmacogene Variation (PharmVar) Consortium (formerly Human Cytochrome P450 (CYP) Allele Nomenclature Database).

PharmVar Genes


3: FDA:

Table of pharmacogenomic markers in drug labeling.

Other FDA resources related to pharmacogenomics.

FDA Precision Medicine site.


4: CPIC (The Clinical Pharmacogenetics Implementation Consortium) web site.

CPIC Alleles

CPIC Genes-Drugs




Graphics Credit:

Structural Variant versus Predicted Phenotype graphic is from reference 4 above and is unaltered. It is used per the Creative Commons Attribution 4.0 International Public License.

Drug Outbreak Testing Service (DOTS)

I wanted to get this information out as soon as I saw it. It is a service that offers free testing for drug outbreaks that are occurring more frequently as the pendulum swings to more acceptance of using various intoxicants.  This is a valuable service because many of these outbreaks occur so rapidly and with an unknown compound that leads to a sudden burst of morbidity and mortality and the medical systems in some towns are ill equipped to identify the offending agent.  I have posted about an epidemic of synthetic cannabinoids that required an intensive effort to look identify the compound being used.

This service offers rapid testing for up to 240 prescription and primarily nonprescription street drugs.  In their first paper on methodology at the references below they describe the rationale and procedure.  To qualify as a DOTS site, there has to be an identifiable outbreak of intoxicant use that cannot be identified locally.  That site needs to be able to submit 20 identified urine samples for testing.  In the following references the patterns of detected drugs vary considerably by site and level of sophistication necessary to identify the compounds.  For example, the King County DOTS samples yielded bufotenine, cathinine, alpha-PVP, mitragynine/7-hydroxy mitragynine, and U-47700 - compounds that are unlikely to be picked up in standard testing even at most substance use treatment centers.  Their assays also have sensitivity enough to pick up compounds like fentanyl that may be missed with lower sensitivity assays.

A brief discussion of the sample sites is required.  Comparing the toxicology data shows considerable variation across the sites.  The first sample was from an emergency department (ED) at the University of Maryland looking at the question of agitated patients and suspecting the use of cathinones or Bath Salts.  There were only 8 samples submitted.  There was more evidence that fentanyl may have been involved but there were a wide variety of compounds noted including diphenhydramine.  The King County Medical Examiner samples were from 20 people who had died of drug overdoses. Fifteen of the 20 samples contained methamphetamine and 14/20 contained fentanyl or analogues.  None of the 20 sample contained 10 or more compounds showing a high degree of polysubstance use in this sample.  The Montgomery County Maryland site is 20 samples from an outpatient clinic treating uninsured or publicly insured patients. The majority (75%) contained THC with 6/20 containing cocaine and 3/20 containing methamphetamine.  The Recovery Research Network submitted 23 samples from patients who were all in voluntary outpatient treatment.  The samples show a high degree of polypharmacy with 35% containing 10 or more drugs and 91% containing 5 or more drugs.   Aspenti Health submitted 20 samples on outpatients and their interest was in detecting possible fentanyl use.  12/20 samples were positive for fentanyl even though the lab results from the originating facility were b=negative due to a higher detection limit on their assay.  90% were positive for buprenorphine and 53% contained naloxone.

All together there are 91 samples from the 5 sites.  There is a lot of information contained in that data.  There is more detailed toxicology here that is available on most reports that I have reviewed, although data is generally not presented well in electronic health records.  I have included the data form the King County Medical Examiner's Office because it is the most complex and because this appears to be a publicly funded document with no copyright constraints.  Click to enlarge the graphic.

 
This is the way the data is presented from all 5 sites.  Major drug categories are color coded across all of the sites and there are row and column sums.  Interesting observations can be made in the data, but incident and sampling heterogeneity precludes any scientific conclusions.  One of the first things I noticed was the low frequency of psychiatric medications 26/91 were on antidepressants. One sample contained haloperidol and there were no samples containing quetiapine - a medication commonly used in residential treatment centers for insomnia. This could mean that much of the psychiatric comorbidity in the sample was not addressed.  The autopsy samples contained the fewest antidepressants.  Despite the recent concerns about gabapentin it was found in 13 of 91 samples and 4/20 autopsy specimens.

The greatest totals were for THC (50/91) and fentanyl and fentanyl metabolites (46/91).  The fentanyl was overrepresented in the autopsy specimens where 11/20 were positive and the Recovery Research Network sample where 19/20 were positive for fentanyl.  More  concerning 5 or those samples were also positive for buprenorphine indicating that patient may have been on MAT for opioid use disorder (OUD).  A similar pattern exists in the Aspenti Health sample where  11/20 were positive for both fentanyl and buprenorphine.  That is not to say that MAT for OUD is not indicated, but it probably reflects that fact that a significant number of drug users are not risk averse and do not consider MAT as a way to help them avoid the risks of OUD.  It is consistent with a recent story I heard about a number of OUD patients leaving a residential treatment facility because they heard that fentanyl was available in that community.  Many of those patients were on buprenorphine at an appropriate maintenance dose.

In may last look at the DEA schedule of controlled substances it contained about 330 compounds and I am sure that number has grown by now.  DOTS tests for 240.  If you have an outbreak in your community and the sources are not clearly known and little toxicology is available - it might be worthwhile to give them a call.  This is a valuable service to provide insight into what intoxicants may be responsible.

Being an undergrad chemistry major, this project also lead me to think about why every metropolitan area with a university or college having a chemistry major and access to GC/Mass Spectrometry should not have similar testing services.  These departments could be subsidized or reimbursed for the testing, incentivized for quality, and train the next generation of analytical organic chemists all in the same process.



George Dawson, MD, DFAPA


References:

1:  DOTS Bulletin A Pilot Study of the National Drug Early Warning System (NDEWS) University of Maryland, College Park Drug Outbreak Testing Service July 2018. Issue 6 DOTS Web Site