Category Archives: Medical Article Review

The Epidemiology of Delirium: A Paper from 2013

Delirium

There is an interesting paper on Delirium from 2013 by Davis et al titled ‘The Epidemiology of Delirium: Challenges and Opportunities for Population Studies’. In the first section of the paper the authors look at the problems faced in epidemiological research. The issues here are particularly relevant to epidemiological researchers but also hold some interest in the interpretation of epidemiological studies or meta-analyses/systematic reviews. A key point in the discussion looked at how results were interpreted – were subjects with complete data included only (with associated bias) or was there an attempt to anticipate data for missing subjects (e.g using random effects modelling).

What I found quite interesting about the paper is the later section which reviews some of the epidemiological studies. The Gerontological Database research study revealed some very useful data about prevalence in people over the age of 85. Age was strongly linked to the prevalence of Delirium in this study which used DSM-IV criteria. The prevalence of Delirium in the 85-89 year age group was 19% compared to 39% in the 95+ age group. The only drawback was that the confidence interval data was missing and it wasn’t clear if this was point or 1-month period prevalence as reported for other data from the study. Regardless, the original study would merit further attention as if this pattern is replicated there is likely to be an important biological link (e.g blood-brain barrier integrity etc) that could be further investigated. The other point is whether there is comorbid Dementia. Nevertheless this does not escape the fact that there is Delirium regardless of whether it is a comorbidity.

The final section looks at the issue of improving epidemiological studies and the methodology here has some overlap with approaches with the potential to improve clinical practice.

Index: There are indices for the TAWOP site here and here Twitter: You can follow ‘The Amazing World of Psychiatry’ Twitter by clicking on this link. Podcast: You can listen to this post on Odiogo by clicking on this link (there may be a small delay between publishing of the blog article and the availability of the podcast). It is available for a limited period. TAWOP Channel: You can follow the TAWOP Channel on YouTube by clicking on this link. Responses: If you have any comments, you can leave them below or alternatively e-mail justinmarley17@yahoo.co.uk. Disclaimer: The comments made here represent the opinions of the author and do not represent the profession or any body/organisation. The comments made here are not meant as a source of medical advice and those seeking medical advice are advised to consult with their own doctor. The author is not responsible for the contents of any external sites that are linked to in this blog. Conflicts of Interest: For potential conflicts of interest please see the About section.

The Origins of Catatonia

KahlbaumThe German psychiatrist Karl Ludwig Kahlbaum first described Catatonia in a lecture in 1868 according to this paper. Kahlbaum later wrote up his description of Catatonia in 1874 in ‘Die Katatonie oder das Spannungsirresein. Eine klinische Form psychischer Krankheit’. Interested readers can find a copy of this book in various formats at the Internet Archive in the ‘Medical Heritage Library’.

While Kahlbaum recognised Catatonia in association with many psychiatric and medical illnesses, it was Kraeplin who later took Catatonia and subsumed it under Dementia Praecox which later became Schizophrenia. The rest is history.

However there are many papers in the research literature that show Catatonia in association with medical as well as mental illnesses. Just as Kahlbaum originally reported.

Index: There are indices for the TAWOP site here and here Twitter: You can follow ‘The Amazing World of Psychiatry’ Twitter by clicking on this link. Podcast: You can listen to this post on Odiogo by clicking on this link (there may be a small delay between publishing of the blog article and the availability of the podcast). It is available for a limited period. TAWOP Channel: You can follow the TAWOP Channel on YouTube by clicking on this link. Responses: If you have any comments, you can leave them below or alternatively e-mail justinmarley17@yahoo.co.uk. Disclaimer: The comments made here represent the opinions of the author and do not represent the profession or any body/organisation. The comments made here are not meant as a source of medical advice and those seeking medical advice are advised to consult with their own doctor. The author is not responsible for the contents of any external sites that are linked to in this blog. Conflicts of Interest: For potential conflicts of interest please see the About section.

 

 

Brodmann Area 51 – The Prepiriform Area

Korbinian_Brodmann

Dr Korbinian Brodmann, German Neurologist, Frontpiece of ‘Localisation in the Cerebral Cortex’, 1909, Public Domain*

In the nineteenth century the eminent German Neurologist Dr Korbinian Brodmann developed a brain map based on the brain’s microscopic properties. This map has been very successful and is used even today. Many of the Brodmann areas have been covered in detail elsewhere on this site (see Appendix). This post continues the series looking at selective Brodmann areas in this instance focusing on Brodmann Area 51. Brodmann does not describe this area in humans. Instead, in his classic 1909 text ‘Localisation in the Cerebral Cortex’ he provides a comparison of neuroanatomy in several distinct species. Brodmann Area 51 is described in the Kinkajou. The Kinkajou which can be seen in this video is an arboreal mammal living in Central and South America. Brodmann Area 51 is referred to by Brodmann as the Prepiriform area within the Olfactory region. Brodmann also describes this area in the Rabbit and Ground Squirrel. Brodmann also describes Brodmann Area 51 in the Hedgehog noting that it is expanded and he further subdivides this into 51a, 51b, 51c and 51d. This perhaps reflects the different significance of the Olfactory apparatus across species.

Appendix

Neuroanatomy Resources

*Public Domain in those countries where the Copyright term of the life of the author (Korbinian Brodmann 1868-1918) plus the additional country specific term has lapsed from Copyright at the time of writing

An index of the site can be found here. The page contains links to all of the articles in the blog in chronological order. Twitter: You can follow ‘The Amazing World of Psychiatry’ Twitter by clicking on this link. Podcast: You can listen to this post on Odiogo by clicking on this link (there may be a small delay between publishing of the blog article and the availability of the podcast). It is available for a limited period. TAWOP Channel: You can follow the TAWOP Channel on YouTube by clicking on this link. Responses: If you have any comments, you can leave them below or alternatively e-mail justinmarley17@yahoo.co.uk. Disclaimer: The comments made here represent the opinions of the author and do not represent the profession or any body/organisation. The comments made here are not meant as a source of medical advice and those seeking medical advice are advised to consult with their own doctor. The author is not responsible for the contents of any external sites that are linked to in this blog.

Doing Science Using Open Data – Part 6: Modelling Populations

In this 6th part of the series on using open data for science I’ve take a slight diversion to look at populations and the issue of sampling. This was prompted by a look at the UK mid-2011 Census data shown in the graph below.

Figure 1: Summation of male and female figures for each age from mid-2011 Census. Red bars represent the age group 45-65 and the blue bars represent the age group 16-44

What were going to do is look at the UK population and build a mathematical population and build a model for the populations we’ve looked at in the previous posts. Just to recap, when we compared two populations there are a number of statistical methods for doing this which are dependent on the characteristics of the population. For a normally distribution population we can define this population by the mean and standard deviation. As discussed in previous posts the populations in this post from the census study in mid 2011 which are not normally distributed. In the first segment aged 16-44 there is a somewhat homogenous group?? whilst in the group 45-65 there is a right skewed distribution that is the numbers for each year get progressively smaller.

In the third part in this series I included some of the data from the mid-2011 Census which I will reproduce here to support the subsequent discussion. Summing the male and female figures we get the following results for ages 16 through to 44

680,979
706,234
711,491
741,667
765,895
757,901
757,295
771,297
756,449
768,415
774,921
759,889
768,860
770,810
778,986
782,510
751,251
700,825
690,775
702,024
716,419
729,013
761,347
794,300
820,805
800,550
821,037
819,650
832,297

For ages 45-65 we get the following results

832,727
838,064
831,041
813,798
797,077
770,066
739,859
723,861
708,371
682,824
659,795
637,073
641,145
634,399
618,132
623,508
638,118
655,668
694,644
754,834
583,734

The total estimated population in England and Wales in Mid-2011 for the age group 16-44 is

21993892

and for the age group 45-65 is

15711035

So if we move firstly to the population aged 45-65. This population has a value that begins with 832,727 people aged 45 and decreases to 583,734 at age 65 . First recall that the x-axis represents age and the y-axis is the number of people in each age group. The population can be approximately described by a line of decreasing slope.  Now if we’re going to model this we’re going to need to understand what the relationship is between x and y. Quite obviously as x increases y decreases and the relationship is described by y = -x. Looking at the graph above this doesn’t seem intuitive. None of the y values are negative. However if the graph began at (0,0) then it would become negative as x increased. The reason that this doesn’t happen in the above graph is that the line y = -x is translated in a positive direction along the y-axis. So in other words (I will take out the negative sign at this stage as it will be dealt with by the coefficient a)

y =  x + c

In addition to this, rather than a straight line with a unit gradient (i.e for every unit increase along the x-axis there is a unit increase along the y-axis) the line has a gradient which we have yet to determine. For the sake of convenience I will refer to this as

y =  a x + c

There is a simple introduction to lines and slopes below.

Our job now is to find out what those two variables a and c are. This is going to be an approximation. Turning first to people aged 45

y =  a x + c

832,727 =  44 a + c

and for the age 65

583,734 =  65 a + c

We have two equations that we have to solve and two sets of values to do this. Since

832727 = 44 a + c

44 a = 832727 – c

a = (832727-c)/44

Now from the original equations we know that

583,734 = 65 a + c

and therefore substituting

a = (832,727-c)/44

we get

583734 = 65/44 (832727-c) + c

Multiplying out we get

583734 =  (1 – 1.477)c + 1230164.89

- 646430.88636 = -0.477c

c = 1354426.6

Substituting back into the original equation

583,734 = 65 a + 1354426.6

Rearranging we get

(583,734 – 1354426.6)/65 = a

a = -11856.81

Substituting the values for a and c into the original equations above, the reader will be see that these values solve the equations. The numbers have been rounded up. Indeed rounding to the nearest number we arrive at the following equation

y = -11857 x + 1354427

This equation approximately describes the UK mid-2011 Census data for the age group 45-65 where y is the total population for each age and x is the age in years within the given range.

Appendix

Doing Science Using Open Data – Part 1

Doing Science Using Open Data – Part 2

Doing Science Using Open Data – Part 3

Doing Science Using Open Data – Part 4

Doing Science Using Open Data – Part 5

Index: There are indices for the TAWOP site here and here Twitter: You can follow ‘The Amazing World of Psychiatry’ Twitter by clicking on this link. Podcast: You can listen to this post on Odiogo by clicking on this link (there may be a small delay between publishing of the blog article and the availability of the podcast). It is available for a limited period. TAWOP Channel: You can follow the TAWOP Channel on YouTube by clicking on this link. Responses: If you have any comments, you can leave them below or alternatively e-mail justinmarley17@yahoo.co.uk. Disclaimer: The comments made here represent the opinions of the author and do not represent the profession or any body/organisation. The comments made here are not meant as a source of medical advice and those seeking medical advice are advised to consult with their own doctor. The author is not responsible for the contents of any external sites that are linked to in this blog.

Working with PubMed – Part 1: Getting Started with a Shortcut

Figure 1 – Preferences Page in NCBI

 

This is the first in a series on using PubMed. PubMed is the gateway for several important biomedical databases including Medline. Being able to work with PubMed is a very useful skill in the life sciences. In the first part the reader will need to set up an account with the National Center for Biotechnology Information. I will assume that the reader has done this. The first lesson is very simple and focuses on preferences. As someone that uses PubMed frequently I find shortcuts really useful. The shortcut i’m going to discuss here is one used in searches. The first step is to go to the preferences page once you’re logged into your NCBI account. Then under PubMed Preferences click on ‘Result Display Settings’. Finally select abstract, 200 and Pub Date under preferences. Every time you log into your NCBI account and use this to access PubMed, these preferences will be used automatically.

So what does all this mean? Well firstly the ‘abstract’ preference simply means that all returned results will be displayed with the abstract. This enables you to get a quick overview of the paper without needing to click on a hypertext link to get to the abstract. The second preference ‘200’ means that each page will feature 200 results per page. The default is 20 which means you have to click 10 times to see all of the results. The only drawback is that you need appropriate resources on your computer to avoid a sluggish response. Finally the ‘Pub Date’ preferences means that the articles will be displayed in chronological order. This is especially useful if your interested in the most recent papers in the field.

So that’s the lesson – brief and simple and you’ll see the benefits after using the saved preferences on just a few occasions. Of course if your needs are different then you can just adjust the preferences accordingly.

Index: There are indices for the TAWOP site here and here Twitter: You can follow ‘The Amazing World of Psychiatry’ Twitter by clicking on this link. Podcast: You can listen to this post on Odiogo by clicking on this link (there may be a small delay between publishing of the blog article and the availability of the podcast). It is available for a limited period. TAWOP Channel: You can follow the TAWOP Channel on YouTube by clicking on this link. Responses: If you have any comments, you can leave them below or alternatively e-mail justinmarley17@yahoo.co.uk. Disclaimer: The comments made here represent the opinions of the author and do not represent the profession or any body/organisation. The comments made here are not meant as a source of medical advice and those seeking medical advice are advised to consult with their own doctor. The author is not responsible for the contents of any external sites that are linked to in this blog.

Doing Science Using Open Data – Part 5: Looking at Populations

In this fifth part of the series on using open data for science I’ve take a slight diversion to look at populations and the issue of sampling. This was prompted by a look at the UK Mid-2011 Census data shown in the graph below.

 

The issue arose after looking at comparing two segments of the population. What was interesting was that rather than samples, the census data is providing information about the population. When we look at samples from a population, a number of assumptions are made about the population. For instance it may be assumed that the population is normally distributed in which case there are some standard measures that would describe that population. However looking above we can see that the population does not resemble that of a normal distribution. Instead it is skewed right (higher values are on the left) but has some additional variation characterised by a number of peaks and troughs and a spike in the mid sixties. In comparing two segments of the population that were examined in the earlier part of the series (16-44 and 45-65) it became clear that there were distinct characteristics for these two segments of the population.

In statistical terms this would be a multimodal population since there are numerous peaks in the data. Is the data discrete or continuous? Of course our ages are continuous. However for the purposes of the census, the ages are presented as discrete values. This is necessary in order to organise the data. The graph above might therefore be better presented as a bar chart although the graph still gives a useful overview of the data. The female and male data follow similar patterns over the ages 16-65 although there is a larger variation towards the earlier part of the age group. If we consider the two groups in turn we get the following

16-44 – this segment of the population has a bimodal distribution with peaks at around the mid-twenties (the summed data would be more useful for visual inspection)

45-65 – this segment of the population has a right skewed distribution with a superimposed peak towards the mid-sixties.

The benefits of dwelling on this are that whenever we are sampling from a population we should bear in mind the original population. Studies usually have strict inclusion criteria which effectively transforms the population and makes generalisations limited to similar populations. Even before the inclusion criteria are applied the population may not be representative of the national population. However even if we look at the population within one geographical location, although it is sampled from the national population, it may differ from that population. For instance the age may be skewed relative to the national population. In this case we would have to think about three populations – the characteristics of the national population, the characteristics of the local population and the characteristics of the population included in the study. We may be able to apply transformations of the study findings to these different populations although the difficulty is that stratifications of study data may result in loss of significance of study findings.

What is also interesting about this is that if we talk about a sample population – we can compare it against the national population in a number of ways. Although it is usual to match against the variable of interest, the population can also differ according to characteristics such as age and years of education. Although these are usually adjusted for in comparisons it would be useful to have a compound metric to describe the sample population. This in turn could be provided in the national census data. This metric would be a simple measure for enabling generalisation of study findings to populations.

To illustrate the above discussion, suppose that in a study there are some interesting findings about a sample of older adults women with an average age of 80 and a range of 65-90 with a normal distribution. A cursory examination of the above census data would reveal that this is not representative of the national profile. We would therefore have to think about how the study findings can be generalised and why the study sample characteristics differs from the national profile as it is still a sample from the national population. This is relevant in understanding the epidemiology of Dementia for example.

Appendix

Doing Science Using Open Data – Part 1

Doing Science Using Open Data – Part 2

Doing Science Using Open Data – Part 3

Doing Science Using Open Data – Part 4

Index: There are indices for the TAWOP site here and here Twitter: You can follow ‘The Amazing World of Psychiatry’ Twitter by clicking on this link. Podcast: You can listen to this post on Odiogo by clicking on this link (there may be a small delay between publishing of the blog article and the availability of the podcast). It is available for a limited period. TAWOP Channel: You can follow the TAWOP Channel on YouTube by clicking on this link. Responses: If you have any comments, you can leave them below or alternatively e-mail justinmarley17@yahoo.co.uk. Disclaimer: The comments made here represent the opinions of the author and do not represent the profession or any body/organisation. The comments made here are not meant as a source of medical advice and those seeking medical advice are advised to consult with their own doctor. The author is not responsible for the contents of any external sites that are linked to in this blog.

An Overview of Frontotemporal Lobar Degeneration

There is a brief albeit comprehensive open-access review of Frontotemporal Lobar Degeneration (FTLD) by Sieben and colleagues in Act Neuropathologica. The authors have assembled findings from the research literature and presented the results in a highly technical but succinct way. The clinical presentation of FTLD in this review is divided into

1. Behavioural variant FTD (or bvFTD)

2. Primary Progressive Aphasia (PPA). PPA is further subdivided according to recent consensus criteria into

  • Nonfluent/agrammatic variant PPA
  • Semantic variant PPA
  • Logopenic variant PPA

The authors also identify the main autosomal dominant genes associated with FTLD

  • MAPT
  • GRN
  • C9orf72
  • VCP
  • CHMP2B
  • TARDBP and FUS (more commonly associated with Amyotrophic Lateral Sclerosis than FTLD)

The authors expand on each gene and identify clinicopathological correlates although these are far from straightforward. There is also a brief section on susceptibility genes. The authors have produced a useful overview of FTLD which incorporates recent findings and elucidates the relationship of FTLD with a number of important gene associations. The review offers a useful starting point for further exploration of the literature.

Index: There are indices for the TAWOP site here and here Twitter: You can follow ‘The Amazing World of Psychiatry’ Twitter by clicking on this link. Podcast: You can listen to this post on Odiogo by clicking on this link (there may be a small delay between publishing of the blog article and the availability of the podcast). It is available for a limited period. TAWOP Channel: You can follow the TAWOP Channel on YouTube by clicking on this link. Responses: If you have any comments, you can leave them below or alternatively e-mail justinmarley17@yahoo.co.uk. Disclaimer: The comments made here represent the opinions of the author and do not represent the profession or any body/organisation. The comments made here are not meant as a source of medical advice and those seeking medical advice are advised to consult with their own doctor. The author is not responsible for the contents of any external sites that are linked to in this blog.