Comments on: Middlekoop et al chapter three – what do the numbers mean? http://bodyinmind.org/middlekoop-et-al-chapter-three-what-do-the-numbers-mean/ Research into the role of the brain in chronic pain Tue, 07 Feb 2012 09:36:16 +0000 hourly 1 http://wordpress.org/?v=3.2.1 By: Anne Smith http://bodyinmind.org/middlekoop-et-al-chapter-three-what-do-the-numbers-mean/#comment-6695 Anne Smith Wed, 19 May 2010 04:14:25 +0000 http://bodyinmind.com.au/?p=3575#comment-6695 Some great points raised in this discussion. The comment by Neil expressing doubt that we will ever get data from longitudinal trials homogenous and convincing is totally understandable. And it is also true that we probably consider way too much information and therefore struggle to find the true keys to focus on. Goldmans algorithm uses a technique called 'recursive partitioning' which is a really nice way to try and get a handle on all the complex interactions on your data and simplify things down to a few key variables. When we are trying to nut out the pathway to chronicity I think the big problems we have to deal with is the presence of pathways and interactions. So when we have a great big dataset with lots of variables we can use to try and predict something, we have a problem trying to break it down into a few 'things' that predict our outcome using traditional stats techniques. That is because some things influence other things which in turn influence our outcome, but also some things may negate or inflate the influence of some other thing on our outcome (consider the effect of external stressors on a persons anxiety levels at a critical point in their pain history). We can't possibly hypothesise all these complex relationships and model them under a traditional statistics framework; these frameworks work much better for confirming or refuting particular, relatively simple hypotheses. A paper that really gets me thinking about this is one Peter O'Sullivan has already referenced, Mazes, Conflict, and Paradox: Tools for Understanding Chronic Pain Cary A. Brown, Pain Practice, Volume 9, Issue 3, 2009 235–243. I think chronic back pain research is still in the exciting time of sorting out the key things that may be important for transition from acute to chronic, rather like Goldman did with his algorithm. Our clues are coming from quality data from a combination of study types, not only large epidemiological studies but also in depth qualitative and laboratory based studies. Some great points raised in this discussion.
The comment by Neil expressing doubt that we will ever get data from longitudinal trials homogenous and convincing is totally understandable.
And it is also true that we probably consider way too much information and therefore struggle to find the true keys to focus on. Goldmans algorithm uses a technique called ‘recursive partitioning’ which is a really nice way to try and get a handle on all the complex interactions on your data and simplify things down to a few key variables. When we are trying to nut out the pathway to chronicity I think the big problems we have to deal with is the presence of pathways and interactions. So when we have a great big dataset with lots of variables we can use to try and predict something, we have a problem trying to break it down into a few ‘things’ that predict our outcome using traditional stats techniques. That is because some things influence other things which in turn influence our outcome, but also some things may negate or inflate the influence of some other thing on our outcome (consider the effect of external stressors on a persons anxiety levels at a critical point in their pain history). We can’t possibly hypothesise all these complex relationships and model them under a traditional statistics framework; these frameworks work much better for confirming or refuting particular, relatively simple hypotheses. A paper that really gets me thinking about this is one Peter O’Sullivan has already referenced, Mazes, Conflict, and Paradox: Tools for Understanding Chronic Pain Cary A. Brown, Pain Practice, Volume 9, Issue 3, 2009 235–243. I think chronic back pain research is still in the exciting time of sorting out the key things that may be important for transition from acute to chronic, rather like Goldman did with his algorithm. Our clues are coming from quality data from a combination of study types, not only large epidemiological studies but also in depth qualitative and laboratory based studies.

]]> By: SnippetPhysTher http://bodyinmind.org/middlekoop-et-al-chapter-three-what-do-the-numbers-mean/#comment-6683 SnippetPhysTher Tue, 18 May 2010 22:08:05 +0000 http://bodyinmind.com.au/?p=3575#comment-6683 Just throwing some questions out there... Do we as humans make the issue more difficult than it really is? For example, Lee Goldman worked with some mathematicians interested in creating statistical rules. Goldman was interested in knowing what factors predicted a heart attack, so he dumped boatloads of patient data into a system and came up with an algorithm. Mathematically, he came up with an algorithm in how to treat chest pain. Obviously, chest pain can be much more deadly than back pain (unless of course one drinks wine in moderation which does have a positive effect on the heart... but that's another topic). Goldman defined combinations of risk factors and created decision trees for treatment options. Goldman's algorithm guessed right something like 95% of the time. Goldman made the process simple and basically took out a ton of individualistic factors because those factors were just "noise." Humans thought a lot of factors were relevant and important, but reality they weren't. Treatment decisions were based on ECG results and 3 other factors - that was it. (Now we won't have a discussion on how cardiologists felt about using mathematics to make clinical decisions... that's a whole different topic.) We have a boatload of back pain virgins (first-timers/first episode of back pain) all around the world. Neil mentioned some factors that need to be collected and dumped into some system... there are other factors... and mix in some examination findings too. I doubt there will be a true homogeneous group, but I'd be willing to bet, that mathematically, certain factors could shake out to help determine effective treatment interventions. In other words, there might be a way to create a decision-tree for low back pain but NOT when the person is in the chronic state... when the patient first has an episode. I am under the assumption if the right intervention is provided at the right time, there is a better chance at reducing the frequency of chronic back pain. Would it even be possible to create a list of factors mathematically derived to help highlight what might be common for people with chronic low back pain (we have a boatload of that population too)? When we see what the factors in the chronic back pain situation are and the strength of those factors is there a way to change the course to prevent those factors by implementing them in the decision-making tree during the first episode of back pain? In other words, I think we need to know if factors in the chronic back pain situation are also potentially present in the acute back pain situation. And then, to really add confusion... if factors in the acute back pain situation are different than in the chronic back pain situation - when and why do factors change? Just throwing some questions out there… Do we as humans make the issue more difficult than it really is? For example, Lee Goldman worked with some mathematicians interested in creating statistical rules. Goldman was interested in knowing what factors predicted a heart attack, so he dumped boatloads of patient data into a system and came up with an algorithm. Mathematically, he came up with an algorithm in how to treat chest pain. Obviously, chest pain can be much more deadly than back pain (unless of course one drinks wine in moderation which does have a positive effect on the heart… but that’s another topic). Goldman defined combinations of risk factors and created decision trees for treatment options. Goldman’s algorithm guessed right something like 95% of the time. Goldman made the process simple and basically took out a ton of individualistic factors because those factors were just “noise.” Humans thought a lot of factors were relevant and important, but reality they weren’t. Treatment decisions were based on ECG results and 3 other factors – that was it. (Now we won’t have a discussion on how cardiologists felt about using mathematics to make clinical decisions… that’s a whole different topic.)

We have a boatload of back pain virgins (first-timers/first episode of back pain) all around the world. Neil mentioned some factors that need to be collected and dumped into some system… there are other factors… and mix in some examination findings too. I doubt there will be a true homogeneous group, but I’d be willing to bet, that mathematically, certain factors could shake out to help determine effective treatment interventions. In other words, there might be a way to create a decision-tree for low back pain but NOT when the person is in the chronic state… when the patient first has an episode. I am under the assumption if the right intervention is provided at the right time, there is a better chance at reducing the frequency of chronic back pain.

Would it even be possible to create a list of factors mathematically derived to help highlight what might be common for people with chronic low back pain (we have a boatload of that population too)?

When we see what the factors in the chronic back pain situation are and the strength of those factors is there a way to change the course to prevent those factors by implementing them in the decision-making tree during the first episode of back pain?

In other words, I think we need to know if factors in the chronic back pain situation are also potentially present in the acute back pain situation. And then, to really add confusion… if factors in the acute back pain situation are different than in the chronic back pain situation – when and why do factors change?

]]> By: Lorimer http://bodyinmind.org/middlekoop-et-al-chapter-three-what-do-the-numbers-mean/#comment-6682 Lorimer Tue, 18 May 2010 20:56:42 +0000 http://bodyinmind.com.au/?p=3575#comment-6682 Why am I not surprised that one can bring exercise in back pain round to wine? Nice work John. Why am I not surprised that one can bring exercise in back pain round to wine? Nice work John.

]]> By: Johnbarb1 http://bodyinmind.org/middlekoop-et-al-chapter-three-what-do-the-numbers-mean/#comment-6676 Johnbarb1 Tue, 18 May 2010 10:54:12 +0000 http://bodyinmind.com.au/?p=3575#comment-6676 Since Butler, Moseley, and the like have filled my head with all of their strange thoughts, I am less surprised that the spinal pain research has been less than stellar. All of the interventional techniques have focused on changing nociception, but the measurement tools have been instruments to measure pain, function, etc. Why wouldn't we expect a poor correlation? They are not the same. One would expect better grapes to produce better wine, but that is not true. A lot can go on between the vine and the bottle on my table that can confound that correlation. Since Butler, Moseley, and the like have filled my head with all of their strange thoughts, I am less surprised that the spinal pain research has been less than stellar. All of the interventional techniques have focused on changing nociception, but the measurement tools have been instruments to measure pain, function, etc. Why wouldn’t we expect a poor correlation? They are not the same. One would expect better grapes to produce better wine, but that is not true. A lot can go on between the vine and the bottle on my table that can confound that correlation.

]]> By: Neil O'Connell http://bodyinmind.org/middlekoop-et-al-chapter-three-what-do-the-numbers-mean/#comment-6675 Neil O'Connell Tue, 18 May 2010 06:26:08 +0000 http://bodyinmind.com.au/?p=3575#comment-6675 Hi Ann, I totally agree that calculating pooled effect sizes from such a range of studies is questionable and the bias problems are very real but even accepting such imprecision exercise doesn’t look great across the trials particularly as the weight of bias tends to favour the “active” arm. I love the idea of looking closely at the pathway to chronicity but what amazes me across the existing big prospective observational studies in back pain is the lack of decent clues. Again methodological issues get in the way of clarity but for non-specific back pain the predictors of outcome are not great apart from things like initial levels of pain and disability. Psychosocial factors like distress, depression, self efficacy come in and out of focus despite no shortage of data. I wonder if we could ever get this data homogenous and convincing enough to drive better clinical research/therapy provision? Hi Ann,

I totally agree that calculating pooled effect sizes from such a range of studies is questionable and the bias problems are very real but even accepting such imprecision exercise doesn’t look great across the trials particularly as the weight of bias tends to favour the “active” arm.

I love the idea of looking closely at the pathway to chronicity but what amazes me across the existing big prospective observational studies in back pain is the lack of decent clues. Again methodological issues get in the way of clarity but for non-specific back pain the predictors of outcome are not great apart from things like initial levels of pain and disability. Psychosocial factors like distress, depression, self efficacy come in and out of focus despite no shortage of data. I wonder if we could ever get this data homogenous and convincing enough to drive better clinical research/therapy provision?

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