I’m a nurse working with a group of health care professionals in a pain clinic. Most of our patients have chronic back pain. We’ve been asked to put together a good way to measure results before and after treatment. I have two standard tests I’d like to use. Is there a certain amount of change we should use to signify a positive (or negative) response to treatment?

As you probably know, there are many tests available to measure outcomes of treatment for back pain. Some, like the Visual Analogue Scale (VAS) or the Numerical Rating Scale (NRS), measure pain levels. Others, such as the Short-Form Health Survey (SF-12 or SF-36) focus on general physical and mental health.

Specific back function and disability can also be measured before and after treatment. The Oswestry Disability Index (ODI), Roland Disability Questionnaire (RDI), and the Quebec Back Pain Disability Questionnaire work well for this type of assessment.

Each test has its own standard of measure but most of these are based on a range from zero to 100. There are no hard and fast cut-off points. And sometimes the test results suggest significant change but the patient’s level of satisfaction or clinical improvement is still low.

An international panel of experts met in June of 2006 to discuss this problem. Their task was to agree on a minimal important change (MIC) for pain and back specific function. They based their guidance on the five tests mentioned here.

They proposed absolute cut-off values for each test and suggested an MIC of 30 per cent from the baseline. These figures are only a guideline. Further studies are needed to verify it. As always, each patient’s case should be evaluated for any factors that might change how the results are weighted.

You may want to consider setting up a research design to track the results. The hope is that with a standard baseline MIC value, treatment can be compared from center to center. The goal is to find out what works best for each patient or groups of patients. Using standard measurements and pooling research data of this type can really advance our knowledge in these areas.