A better understanding of potentially high therapeutic failure rates in pain management may be a first step toward doing better with currently available treatments. Clinically, this means expecting analgesic failure, assessing pain, and considering options for stopping and switching therapies.
This also requires casting aside a reliance on what works for “average” patients, and asking what works best, for whom, in what circumstances.
Most analgesic medications work well, but in only a relatively small percentage of people, according to Andrew Moore from Oxford University and colleagues writing in the British Medical Journal [Moore et al. 2013].
They propose a transformation in thinking about how analgesic efficacy and harm should be assessed, and suggest several practical implications of a better understanding and appreciation of therapeutic failure rates:
- “No single drug will treat successfully more than a minority of patients with a painful condition.
- Successful pain relief is also likely to improve sleep, depression, fatigue, quality of life, function, and ability to work.
- Experience (and some evidence) suggests that failure with one drug does not necessarily mean failure with others, even within a class.
- We do not know the best order in which to use drugs, in terms of efficacy, harm, or cost.
- Success or failure can be determined within 2-4 weeks, and success, when achieved, tends to be long lasting.
- Because success rates are low, a wide range of drugs is needed to do the best for most patients, especially in complex chronic conditions.”
Individual patient responses to any therapy vary greatly, Moore et al. observe. Pain relief measurements delineating successful outcomes typically are not distributed along a normal bell-shaped curve, but are usually bimodal; that is,
most patient responses are either very good (above 50% pain relief) or very poor (below 15%). Therefore, the frequency distribution curve is more “U-shaped”; rather than the classic bell curve
Due to the U-shaped response distribution, research outcomes based on averages are unhelpful and misleading since “average” pain relief is actually experienced by few, if any, patients.
“Averages” are a problem in medicine in general because they don’t indicate the distribution of data. To illustrate the problem:
On average, a human being has one breast and one testicle.
All that matters is the individual response, which is never “average”.
The mean score tells us nothing about how many patients will experience clinically useful pain relief; hence, Moore and colleagues suggest that research should be moving toward “responder analyses” — focusing and reporting on the proportion of patients achieving outcomes that patients themselves consider to be worthwhile.
Patients who get better (responders) typically do well, experiencing improvements in fatigue, depression, and sleep interference, plus better function and quality of life.
Defining Analgesic Failure
Overall, and with but a few exceptions, less than half of patients achieved at least a 50% reduction in pain intensity (responder definition), and failure rates were highest over the long term in patients with chronic pain conditions.
Of the 44 studies, success rates were above 50% for only 4 drugs in acute postoperative pain (acetaminophen + ibuprofen; acetaminphen + oxycodone; etoricoxib; ibuprofen + codeine) and 1 drug for migraine (zolmitriptan).
For all other drugs and in all other conditions, fewer than half of patients achieved at least a 50% reduction in pain intensity.
Analgesic failure rates generally ranged from 55% to ≥87%.
in a combined analysis of osteoarthritis and chronic low back pain trials, 30% of tapentadol-group patients and 21% taking oxycodone did experience analgesic success.
Moving Toward Pragmatic Approaches
Of some importance regarding chronic pain, Moore et al. observe that typical clinical trials may inadvertently underestimate treatment efficacy if the data are closely examined
Fixed-dose regimens may exacerbate adverse events and discontinuations, resulting in higher failure rates.
An alternate approach would be to allow patient-directed drug titration to achieve adequate pain relief with tolerable adverse events;
A most essential pragmatic implication of high failure rates is that populations with pain need access to a broad range of analgesics and/or other interventions to have a better chance of success.
In other conditions, like depression, switching medications is often effective; randomized trials have shown that any antidepressant used initially may benefit fewer than half of patients, but the majority can benefit when failures are followed by switching to other medications for depression
Practical Implications of Therapeutic Failures
Essential practice principles for pain management should include
- assessing pain,
- expecting and recognizing analgesic failure, and
- reacting to it by pursuing analgesic success rather than blindly accepting failure.
The authors further observe that guidelines developers often restrict treatment recommendations to 1 or 2 drugs for any pain condition.
The developers consider similar drugs to operate as a class, overlooking the fact that there can be important differences in pharmacokinetics or drug interactions across similar medications.
Less restrictive guidance recommendations, centered on patient-practitioner interactions
The authors additionally suggest that regulatory authorities need to recognize that therapeutic failure is the norm and set standards of acceptance based on real-world expectations.
Coupled with the nuances of neurobiological pain modulation, central nervous system transformations, and genetic influences, high failure rates with single pharmacologic interventions are unsurprising.
some current arguments against opioid analgesics for chronic noncancer pain seem to demand that either the therapy works well and for all patients or it is unacceptable — there is no middle-ground or acceptance of failure.
expectations need to be lowered to the level of clinical reality; eg, while long-term opioid therapy may not benefit all patients with chronic noncancer pain, it does help a certain proportion of patients and significantly so.
There is no such thing as an “average” patient, even though research trials tend to define outcomes based on average, or mean, scores on efficacy measures. In doing this, research can be misleading
The most vital question is: what works, for whom, in what circumstances?
A degree of failure should not defeat the pursuit of success when it comes to pain management.