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

The Problem With Checklists.....

I have critiqued the checklist approach to psychiatry in many posts on this blog.  Several like-mind psychiatrists have also added many comments in this area. I had recent experience with surgical checklists that leave a lot to be desired.  So much so that if I was not an MD - I might not be sitting here and typing this post right now.  For now, I will just post the bare bones sequence of events for illustrative purposes.  On April 14, I had an operative procedure that required antibiotic prophylaxis that consisted of a single intravenous dose of antibiotics given right before surgery. On July 31, I had a second operative procedure to address complications of the first procedure. Both procedures were done under general anesthesia (fentanyl and midazolam or Versed).  A laryngeal mask airway (LMA) was used instead of intubation.  The general sequence of events went like this.

1.  Preop physical exam - good for 1 month prior to surgery.  The exam is done by a primary care MD.  The surgery will not occur without it.  The goal is to identify and complicating or potentially contraindicating conditions.  Specific instructions are given to the surgeon and anesthesiologist based on this assessment.  Specific instructions are given to the patient about if they need to change up their medications at all prior to the surgery.  For example, it is common advice to hold aspirin and other anti-inflammatory medications (NSAIDs), and certain vitamin supplements for 1-2 weeks prior to the surgery.

2.  Hospital intake - over the hour or two before surgery there are intense meetings with a number of disciplines:

Pre-Op RN:  Reviews the medication list and confirms that all of the recommended medications and pre-op instructions were followed.  Assesses functional capacity as well as presence of eyeglasses, hearing aids, implants, pacemakers, CPAP mcahines and artificial joints.

Pharmacist:  Reviews the detailed list of medications and looks for potential drug-drug interactions as well as drug-anesthesia interactions.

Anesthesiologist:  Reviews the detailed list of medications and rationale.  Takes a detailed cardiovascular history. Examines heart and lungs.  Asks detailed family history and personal history for anesthesia interactions particularly malignant hyperthermia. In both cases this hospital trained nurse anesthetists who asked the same questions and administered the pre-op midazolam before leaving the pre-op area.

 OR Nurse:  Also interviews patient about concerns over the surgery and assures that all intravenous lines and devices that will be in the operating room (OR) are present and working.

That is the overall sequence of events.  Each of these team members has specific jobs and checklists that were entered into an EHR.  The primary care physician handed me a copy of my pre-op exam to take with me in case the faxed version was lost.  It was printed out from a well known enterprise wide EHR.

I have a condition called lone atrial fibrillation that is commonly seen in middle-aged (and now old) men who exercise too much.  It was originally thought to be associated with high levels of dynamic exercise like cycling and running, but epidemiological studies suggest it may also be associated with jobs that require a lot of heavy lifting - like furniture and piano moving.  I have also talked to power lifters in the gym who developed it when they continued the lifting into their 50s and 60s.  I take flecainide and it keeps me in a steady sinus rhythm and that has worked well for the past 8 years.  The problem with flecainide is that it is a fairly toxic medication if you have the wrong biological substrate or if you mix it with the wrong medications. A trial of flecainide in ventricular tachycardia was halted because of increased fatalities in the treatment group compared to placebo.  The last electrophysiologist I talked with suggested that I needed to get an exercise stress test done every year to make sure that the QRS interval was not widening due to the drug.  For the purpose of these surgeries my primary concern was not getting a medication that would interact with flecainide and result in a fatal arrhythmia.  I knew that this surgical specialty used fluoroquinolones preoperatively and if you search that interaction in any database this is a typical result.           

"Moderate risk - can cause QTc prolongation and should be avoided when possible. Increased risk for torsades de pointes and other significant ventricular arrhythmias.  Other factors (old age, female sex, bradycardia, hypokalemia, hypomagnesemia, and higher concentrations of the interacting drugs can increase risk for potential life-threatening arrhythmias."

I naturally wanted to avoid the fatal arrhythmia.

At every step in the above chain, I explained this drug interaction and advised the team members that I can safely take cephalosporins.  And here is what happened.

In both cases I had the same primary care MD doing the pre-op physical exam.  He was very focused on the pre-op checklist and in fact the rooming medical assistance reviewed the med list, vital signs, and review of systems that was entered into the EHR checklist before I saw the physician.  When he was done he asked me if I had any concern and I told him "Any antibiotic or anesthesia agent that interacts with flecainide - I do not want to take. I know that I can take fentanyl and Versed for general anesthesia so those are the preferred agents if they can use them for this surgery."  The first time he pulled up the interaction in the EHR, agreed and said - "I will flag this in my assessment so they see it."  The second time he said the same thing but reviewing the H & P he handed me it was not present.  It is possible it was transmitted on another form.

And so it went with every members of the preop team.  They all seemed surprised every time I brought it up.  They thought I was talking about an allergy as opposed to a drug-drug interaction. One of the pharmacists looked it up on her Smartphone app and confirmed.  There was a lot of confusion about the preop antibiotic right up until the time of administration.  Was there another drug that could be used? Would the doctor change the standard orders to administer another drug?  For the past surgery - I had to tell them to look up the April record and confirm that I was given 2 grams of IV cefazolin and not levofloxacin.

When it was finally clarified, it took two nurses to figure out how the levofloxacin could be discontinued from the standard order in the EHR so that the cefazolin could be given.  I was finally given the cefazolin, operated on and so far (barring another complication) things are going well.

The lessons:

1.  Almost everything you hear about the EHR and checklists increasing safety is a myth -  

In this case all of the professionals were using state of the art (and extremely expensive) EHRs containing checklists and forms that were dutifully completely and the ultimate check here was the patient who happened to be a physician who compulsively studies drug interactions and cardiac complications.  That is not a level of safety that I want to see for any of my family members or patients who are undergoing surgery.

2.  Patients or competent family members are the best safeguards for safety at this point -     

I have worked with very bright and insightful nurses who told me that they have a rule that they accompany hospitalized colleagues and check everything that is going to happen to them as well as what medications are administered to them.  On the other hand I have asked patients what medications they take and been told: "You tell me doc.  I just put them in a pill container.  I don't know the names, doses or what they do."

There is a lot of talk about patient empowerment, but it has to be built on a solid foundation of patient literacy.  I certainly realize that a lot of people do not want to know, but I have also talked with many people having less than a high school education who could tell me every drug they took during a complicated course of cancer treatment that included a bone marrow transplant.  Reading and understanding the pharmacy printout given with a medication is a basic prerequisite for the literacy I am talking about. 

3.  This is a systems problem and not a personnel problem -  

Let's face it - all of the personnel in the system are highly competent licensed professionals. They are all focused on their tasks and they do a good job of it.  The problem is that all of these very competent individual assessments are not synthesized into a useful safety plan.  Experts have been writing about the importance of checklists in industry (like the airline and automotive industries) for decades but medical information is individualized complex, and not redundant.  Any adverse outcome of the sequence of events that I described is likely to be something like this:

"Well Dr./Nurse X - did you fail to read all of the narratives in the chart and the patient stating that he had concerns about drug interactions with flecainide?"

Any response to the effect that the EHR is difficult to read and in some cases incoherent and should have flagged this concern automatically is likely to be met with:

"Well that's our EHR and we have to live with it. Our focus groups with the nurses and the manufacturer have been working on fixes for the past 5 years."

Translation:  somebody has to take the blame and it won't be the EHR.

4.  Why doesn't the EHR/checklist approach work? 

It failed miserably - not just once but twice in my case and I was advocating at every level to flag flecainide and not give me any interacting drug.  Having worked with EHRs now for nearly 20 years I can speculate on a few things.  First, there is very little intelligence built into EHRs.  In this case the EHR will do a comprehensive drug interaction search on the current list.  But there is probably not an automatic search on the standard preop antibiotic.  If there is - physicians are numbed to dismissing so many false positive drug interactions that could have happened as well. Second, any discussion of the patients concern or doctors advice is buried in documentation that is prioritized for billing, rarely read, and not translated into any rational action. An intelligent EHR would convert the concern about flecainide interactions into what is called a hard stop. That means the potentially offending drug could not be ordered until some further action was taken - like a discussion between the physician, pharmacist, and patient.  In this case, my discussion with 10 people was not beneficial and the only reason I did not get levofloxacin was that I was in a hospital bed about 6 feet away from where the nurse was working and I was a physician who has worked for years to prevent these kinds of problems.

It is hard to believe that such extremely expensive and heavily lobbied systems can't provide a basic level of safety.  I was not surprised to read that having the same primary care physician for years is probably a better assurance of longevity.

For the non-medical person reading this - know your medications, what they do, and what the potential safety concerns are when you are in a situation where those medications are being changed. Ask your pharmacist and physician to do a drug interaction search to make sure these transitions can be safely made. Refuse any medication unless a sound rationale can be provided to you about why you are taking the drug and how safe it is to take with your current prescriptions.

George Dawson, MD, DFAPA

     
Graphics Credit - the graphic at the top is from Shutterstock per their standard licensing agreement.

Is AHRQ's National Guideline Clearinghouse disappearing for good tomorrow?

The AHRQ was started 30 years ago in 1999 when it was renamed from the Agency for Health Care Policy and Research (AHCPR) to the Agency for Healthcare Research and Quality (AHRQ) by legislative action.  I have referenced their guidelines on this blog for ADHD and depression.  The post on the depression guideline illustrated that AHCPR guidelines were generally of higher quality than the current managed care guidelines and screening guidelines.

Even looking at the web site today before it is taken down illustrates the depth of research and recommendation on the site.  A search for psychiatry yields 600 references including research and policy recommendations.  Interesting the guidelines at guidelines.gov has 74 psychiatric guidelines ranging from depression in children and adolescents to a guideline for CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants.  A wide number of physician and nonphysician organizations have produced the guidelines.  These are unique sites with few comparable sites in the world.  Only the National Institute for Health and Care Excellence (NICE) in the UK seems similar.  The NICE guidelines are produced by a more uniform methodology rather than disparate organizations.

When the current administration announced it was defunding AHRQ guidelines, there was some hope that someone else would take it over - at least the existing databases.  Some physician professional organizations were suggested.  Given the government's shaky history of ancient information technology and dubious failed upgrades, I am speculating that would be the reason why nobody else would want to take that on.  Clearly nobody in the administration is interested in a smooth transition.  The smoothest transition I can think of would be to make the data available through the National Library of Medicine and their collection of databases.  But as I type this there are about 8 hours to make that transition.

There are several serious questions for the Trump administration.  Some are speculative, but when people question how doctors are influenced by a slice of pizza, I think it is reasonable to ask about health care corporations that are influenced by tens to hundreds of billions of dollars and how they influence politicians.

1.  How does it make sense to take this data and these initiatives offline when the costs are trivial compared to other government projects?

Cost analyses have been done showing not much of a price increase corrected for inflation.  Various analyses have been suggested such as this one pointed out the agency's role in reducing hospital infections resulting in 124,000 fewer fatalities per year a cost saving of about $28 billion.


2.  And possibly even more important - what are the conflicts of interest involved?

The most significant one that I can see is that industry guidelines and standards go unchecked.  There are any number of groups that are primarily comprised of health care executives that are producing standards of care that have nothing to do with medical practice or standards.  Review practices by pharmaceutical benefit managers come under the same category.  These physician intimidation strategies have nothing to do the scientific evidence or quality of care. In this regard the wholesale suspension of guidelines that counter industry practices are suddenly gone.  It is far easier to do than reverse Environmental Protection Agency (EPA) regulations - but the zeitgeist is the same.

Taking down AHRQ means there is one less place in government healthcare sites with the word quality.  I don't think that is an accident either.  Today's healthcare industry would rather advertise how they are the best without using the quality word or any scientifically valid metrics.

3.  As a corollary to the above - what about the professional guidelines that are collated and listed on the site?

I don't have the time to follow other physician professional organizations but the American Psychiatric Association has fallen off greatly over the years.  Critical issues have not been addressed in some cases for decades.  The commonest cause for this problem is cited as the expense it takes to collect all of the experts and data, but in the information age it would seem to be easier than ever.  I speculate the the real reason is that these guidelines are just ignored.  Why produce a hundred page guideline on all the aspects of the treatment of depression when the dominant managed care standard is a 2 minute screening exam and an antidepressant prescription?  Why produce that document when it affects only 5% of the work force for mental disorders?  Why produce that document when the psychiatrists involved have so little political leverage against the industry and the government that they can never use it. 

AHRQ at least provided a broader forum for discussion.

4.  Why the minimal notification and lack of feedback?  

There are so many guidelines and so much information available on this site, it is impossible to know who is using it all and for what purpose.  Unilaterally taking down a resource like this with 4 months notice has to be considered nothing more than a political decision at this point.  If the number of people and organizations accessing this site was published somewhere - I have never seen it.

5.  What about the Centers for Medicare and Medicaid services, the CMS web site?

Since CMS is essentially the billing and regulatory web site for Medicare - I don't think it is any danger of being shut down.  But it does promote and spread a lot of unscientific information that is biased toward running the business side of health care at the expense of the medical side.  It is a massive bureaucracy that is responsible for the bulk of physicians paperwork burden every day. Some clear evidence for the lack of science is psychiatric diagnosis related groups and how they don't accurately reflect diagnoses or the expected course of treatment for hospitalized psychiatric patients.  The most recent post on this blog looks at the rationing of inpatient psychiatric services and how a lot of that has resulted from CMS regulation.  Just a few years ago, I wrote a blog piece about a 55 page CMS document about what psychiatrists would have to do to document the diagnosis and treatment of depression.  That was subsequently taken down.     

6.  Finally what does this imply for other federally funded information programs?

My biggest concern in this era of massive profits for publishers is the National Library of Medicine (NLM) - commonly used by physicians offices on a daily basis.  It is a major resource for researchers, but it is also becoming a competitor for profitable online publishers.  If research is publicly funded - a copy is accessible without charge on the PubMed web site.  Will the day come when for profit medical publishers have enough leverage to put the NLM out of business?  Stranger things have happened.  

It is easy to blame that President Trump.  He is heading the first blatantly anti-science and pro-business administration that I can recall in my decades of existence.  But the reality is that the American healthcare system has been designed by an endless stream of bad decisions for the past 30 years all occurring in the confluence of special interest politics and massive special interest money with a little medical science (and a few doctors) sprinkled in. The press seems to focus on the influence of pharmaceutical companies, but the bulk of those bad decisions have been rationing decisions by the managed care industry.

George Dawson, MD, DFAPA

References:

1:  Heslin KC (AHRQ), Weiss AJ (Truven Health Analytics). Hospital Readmissions Involving Psychiatric Disorders, 2012. HCUP Statistical Brief #189. May 2015. Agency for Healthcare Research and Quality, Rockville, MD. http://www.hcup-us.ahrq.gov/reports/statbriefs/sb189-Hospital-Readmissions-Psychiatric-Disorders-2012.pdf.

Supplementary:

I pulled the following figures on lengths of stay for mood disorders and schizophrenia out of the above article.  If the site goes down at midnight this may be the only place that you can find it and any paper referencing it may lead to a dead end.

Updates:

07/16/2018: 3:30 PM  AHRQ.gov web site is up and running at this point but guidelines.gov is not found.

Governments and Psychiatric Beds

I read a paper yesterday (1) on psychiatric bed policy with a focus on OECD (Organisation for Economic Cooperation and Development) nations.  The OECD has extensive data collection on their member nations and one of the metrics they collect is the number of psychiatric beds per 100,000 inhabitants.  I have demonstrated some of this data before.  For the purpose of this post I downloaded it to create the two graphs above that were used in the paper. One of the authors main points was transinstitutionalization - in this case sending people with serious mental illnesses to jails rather than psychiatric hospitals.  They demonstrate the rough inverse correlation between psychiatric beds and the rate of incarceration.  Throughout my career available psychiatric beds has always been a problem.  It has been a favorite topic on this blog.  I was interested in whether or not this group of authors had anything new to say.

In their introductory section, they provide the back drop with the numbers.  The American state hospital psychiatric beds fell 97% from 558,922 in 1955 to 37,679 in 2016.  In Minnesota, the drop was about 98.5% from 11,449 in 1955 to 175 currently.  Using the OECD data, the average was about 99 beds per 100,000 population in 1998 to 71 per 100,000 in 2015.  Only Germany trended in the other direction by increasing the number of beds.

They do a fairly good job of analyzing the risks of the bed shortage.  They cite rehospitalizations, prolonged stay in emergency departments, pressure to discharge patients from inpatient setting, more frequent involuntary treatment, and associated staff burnout.  They make the argument that higher rates of suicide are noted in community treatment compared to hospitals where suicide is less likely.  They believe acute inpatient care is less available to the acutely suicidal patient and that may account for some increase in the suicide rate. Scandinavian registry studies are cited as providing some confirmatory data with one group of authors stating that the reduction in beds was the "most probable explanation for the rising mortality."  A similar study in Finland where more community resources were available and the beds were at OECD averages described fewer suicides.

Community treatment is typically cited as a reason for the bed reduction.  In the USA, rationing is more clearly the reason since the community resources are rarely developed to compensate for the bed loss.  It is also unstated that the two treatments are not equivalent.  They cite the UK as having extensive community resources that were not enough to overcome the drop in beds leading to higher rates of suicide, transfers out of the area where the patient lives, and involuntary treatment. From the graph, the UK has more beds than the OECD average.

The history of transinstitutionalization is briefly discussed.  The Penrose Hypothesis was developed by Lionel Penrose who pointed out the inverse relationship between mental hospital and prison populations in 1939.  Other authors like Harcourt look at historical data and note the same relationship but discuss it from the perspective of the institutionalized population.  At one point in his book Harcourt suggests that people in the military and in nursing homes may need to be counted as being institutionalized.  Inspection of the bar graphs at the top of this page does illustrate some clear trends but it also illustrates that the relationship is complex and not all of the variables have been studied.  They include a third graph of the Gini coefficient that I did not include.  The Gini coefficient is a measure of income disparity (approaching 0 means less disparity).  The 10/17 countries with Gini coefficients  > 0.3 had the lowest number of psychiatric beds. In other words, more income disparity translates to fewer psychiatric beds.

The statistics about the incarcerated mentally ill in the USA are reviewed and the numbers are significant.  Twenty percent of the incarcerated population or 350,000 people per day are estimated to have serious mental illness.

The problems that I have written about on this blog for years are highlighted including the declining length of stay and what the authors called revolving door admissions.  They point out that schizophrenia has the second highest readmission rate at 1 month compared with any other diagnosis (congestive heart failure is first).  The lengths of stay are not generally long enough to allow for adequate stabilization of severe psychiatric disorders and they provide the references.  I see this population of people as a steady state group that goes from jail to homelessness to a short stay in the hospital.  Substance use disorders are generally not addressed or treated in a cursory manner. 

The paper's strength is that they provide an estimate of what a reasonable number of psychiatric beds is for a given populations.  The Royal College of Psychiatrists established a standard that would give psychiatric patients the same access to high quality medical care as medical and surgical patients.  That includes 4 hour maximum time to wait for admission.  They also said that bed occupancy should not exceed 85% to allow for emergency admissions and the length of stay figure should be 2-4 weeks to allow for real improvement.  Using those parameters a US expert consensus group estimated that 50-60 publicly funded beds per 100,000 population were necessary. In case there is any difficulty reading the above graph, the point plotted was 25 beds per 100,000 US inhabitants - well below the estimated number.  In my home state of Minnesota, that number falls off the precipice to 3 publicly funded beds per 100,000!

A closing example is given of the situation in South Australia.  Hospital beds were closed to a level of 32 per 100,000.  Acute care occupancy exceeded 100%, emergency departments waits went up, acuity increased with increasing risk of the need for physical restraint, and the burden of care was often transferred to relatives and friends.  Reforms were enacted that led to an increase to 35 beds per 100,000 with associated 2 week lengths of stay and decreased rates of suicide.

This is an excellent paper for psychiatric societies and psychiatrists to read.  It documents the problems that we all see on a daily basis and provides some clear answers. The answer does not lie with continued or more perfect rationing.  Unfortunately the people who run these systems - largely bureaucrats in large state human services departments, the politicians who influence those bureaucrats, and administrators of most health care systems all see rationing as their only solution to the problem.  They are incentivized to ration and we (and our patients) are left picking up the pieces.

We finally have a paper that is making a stand against all of this rationing.     
     

George Dawson, MD, DFAPA




Supplementary 1: Data for the top graph was downloaded directly from the OECD and accessed today (July 12, 2018).

Supplementary 2: Data on incarceration rates was taken from the Prison Policy Initiative and accessed today (July 12, 2018).

For both graphs click on them for expanded and improved resolution.




References:

1:  Allison S, Bastiampillai T, Licinio J, Fuller DA, Bidargaddi N, Sharfstein SS. When should governments increase the supply of psychiatric beds? Mol Psychiatry. 2018 Apr;23(4):796-800. doi: 10.1038/mp.2017.139. Epub 2017 Jul 11. PubMed PMID: 28696434.

2:  Osby U, Correia N, Brandt L, Ekbom A, Sparén P. Mortality and causes of death in schizophrenia in Stockholm county, Sweden. Schizophr Res. 2000 Sep 29;45(1-2):21-8. PubMed PMID: 10978869.

3: Bernard E. Harcourt, "From the Asylum to the Prison: Rethinking the Incarceration Revolution," 84 Texas Law Review 1751 (2005). Link

4:  Royal College of Psychiatrists. The Commission to review the provision of acute inpatient psychiatric care for adults.  OLD PROBLEMS, NEW SOLUTIONS: Improving acute psychiatric care for adults in England.  February 2016.  Link  This is a detailed look at bed capacity including current estimates and what can be done to improve it.