Ever looked at the Archimedes section and thought “I wonder what I could write about?” or “I wish they’d look at this?” Here’s the space you were looking for.

Well, here’s the first one. Feel free to add some more:

Q: Encouraging adolescents to take their medication
Question- In adolescents with malignant disease on chronic medication (>3 months duration) [patients] does text messaging [intervention] or emailing [intervention] or phone contact [intervention] or anything [desperate intervention] compared with just keeping telling them how important it is [comparison] improve adherence to prescriptions and survival/morbidity [outcomes]

Context - So my primary interest is oncology, but it could be adapted to any area, I guess. How do you help adolescents keep taking the medicines that might save their lives?
Feel free to register your interest and intention to answer the question as a comment on this post. We can then turn it into it’s own little blog-stream and remove it from the pool. You’d have 3 months to come back with something (see here for hints) or we’d pop it back into the pot for answering.

(Remember if you’re posting, follow this format)

Topic Area
Question- structured format
Context - 100 words max
[ Contact details - optional, if the poster wishes]

Brave? Stupid? Evidence based? Drs Ray & Rylance from Newcastle give us this provocative scenario.

Can a relatively normal CSF - indeed can a completely white-cell free CSF - be relied on to rule out menigitis?

And if it can’t, do we need to change how we practice?

Acknowledgement:

Photo from http://flickr.com/photos/26837176@N00/481711124/ under creativecommons 2.0

As an oncologist, I really haven’t the faintest idea what to do with this finding. (I’d pop into EBM-free mode and call the nephrologist who I know has a good track record in EBM and hope they knew the answer.) However, Jon Kim and colleagues are taking this by the horns and determining the answer. Well, trying anyway.

What do you do?

There’s an answer now available … look here

Report by

Somnath Banerjee Associate Specialist in Community Paediatrics, School & Child Health, Ramsgate, Kent.

Hani F Ayyash Consultant Paediatrician, Doncaster Royal Infirmary, Doncaster, South
Yorkshire.

Ahmed Shakir Mohammed F2, Queen Elizabeth the Queen Mother Hospital Margate Kent.

The basics of health economic analysis are simple. If you are comparing treatment Adrug and Bepill, and they both work well, you want the one that is cheapest. (A ‘cost minimisation’ process.) But if Adrug works better than Bepill, then the element of costs becomes more important. (Obviously, if the Adrug is cheaper, it’s a no-brainer!) The question becomes “How much is society/your insurance company/your patient prepared to pay for the extra benefit?”.

If you have two treatments which have the same outcome, this can be a difficult question to answer (it’s a cost-effectiveness analysis). Imagine the difficulty that is added by asking the same question, but needing the answer to compare different conditions, for example asthma and neurodevelopmental disability (a cost-utility analysis). What you need is a common metric to compare the ‘benefit’ across these conditions: that is what the Quality Adjusted Life Year (QALY) is intended to be. There are lots of things to think about with QALYs - you can read about some of them here and more intelligently here - but QALY’s are a good starting point and may help avoid the divisive judgement issues which would come out of comparing surgery for chronic constipation, chemotherapy for relapsed-refractory leukaemia and treatment for alcohol and drug dependence in teenagers. (To answer the question of ‘How much?’ in the UK there’s been a rough guide of £20,000-£30,000 per QALY fixed by NICE - but this is a guide.)

The advanced bits of health economics are not simple. They compare the presumptions about how effective and costly things are, perform analysis on how sensitive the results are to variations in the ‘truths’ that the model has supposed, and add factors that balance for the fact that we value health now much greater than health in the future. (You know this is true - what happens when you’re offered another piece of fruit cake?) These all lead to clouds of data, odd looking graphs, but usually a relatively straightforward answer — along the lines of ‘It probably costs £10,000 or so per QALY, and the truth might be between £5,000 and £42,000′.

Which then puts you back to the beginning - it works, but at what cost?

Oh dear, I think I’m turning into a radiologist. “And what …” my mind is crying “will you do differently after I scan your child?” Perhaps I had to visit too many small dark rooms when I was a House Officer (*).

[Edit - May 2008]

Well, there is an answer of sorts … The summary of the literature surrounding this (by Dr Ambika Karthikeyan) found only three papers, all of poor quality, and none of them suggested any useful yield of diagnostic information. Unless we have a specific indication for scanning (or a study is conceived and open in your centre) then “no” to MRI seems like the right answer.

Acknowledgement
Image from Wikipedia

* - House Officer. {Archaic} Pre 2000 AD, an almost-qualified doctor who assessed patients, initiated investigations and formulated clinical management plans.

Is it reasonable to leave someone with an appendix just ready to become inflamed again? Or should the poor little thing be left alone, rather than expose the peritoneum to the risks of the open air and a surgeon’s gloves?

Mark Fisher and Maud Meates-Dennis from University of Otago, Christchurch have decided to look for an answer…

… and they found one - it’s online here

You can always wonder about testing for things, I think. As I mentioned elsewhere, you also need to think if the test is accurate and useful.

What Dr Kelly & pals at the Mary Sheridan Centre for Child Health in London have wondered is should they be undertaking FRAX testing. It’s certainly possible, but costs money - and what benefits can it produce?

What does the winder world think - do you test? Don’t you test? Why (both ways!)?

Dr Max Nathan of Morriston Hospital, Swansea, UK has had this happen … has it happened to you? And what did you do?

(I think it raises some interesting questions - how much do we need to worry vs how much do we need to know about ’something’ before we act? If you already advise to take, what would it take to stop you? If you advise to avoid, how much data would you need to change your mind?)

[Edit - 27 March 2008]

Since first posting,  in February, the BNF committee has produced new guidance highlighting the ineffectiveness of antibacterial prophylaxis for preventing bacterial endocarditis. This accords with the findings of Dr Nathan and colleagues.

Take an imagined example: using leucocyte urine dipsticks to diagnose UTIs. If the original study is undertaken in the acute assessment department of a paediatric hospital, then the sticks will be used to differentiate between children with similar alternative diagnoses and conditions (e.g. viral infections, UTIs and avoiding school) . This is the disease spectrum. It might be that in the referred population of the assessment unit, 10% of febrile children have UTIs (that’s the prevalence).

Now the sensitivity and specificity that his study describes are likely to be true when used in a paediatric ED, or when seeing kids in primary care. Why? Because although the prevalence of UTI may vary (maybe 1% in primary care and 5% in ED’s), the other competing diagnoses are similar. The prevalence of the condition has changed, but the spectrum of illnesses seen hasn’t.

Now imagine using the sticks in the paediatric haematology-oncology ward, or outpatients. The proportion of kids with UTI may be similar to that in the ED, but here the spectrum is not the same. The competing diagnoses (e.g. neutropenic sepsis, chemotherapy-induced cystitis) will be very different. In this case, the spectrum change alters the expected sensitivity and specificity of the dipsticks.

So - take away sensitivity, specificity (or LRs) from diagnostic studies when the spectrum is similar regardless of the prevalence in your patients. If the spectrum differs - beware.

* Just a reminder: if the population has a lower prevalence of disease, then the positive predictive value will be lower (ie a positive test will be wrong more often) but the negative predictive value will be higher (ie a negative test will be right more often). The reverse is true if the prevalence of the disease is higher.