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Top 5 Predictors of Persistent Pain

There are many factors that contribute to pain. However, some of them may not be well known to patients. In an effort to increase health literacy, it's our goal at Riverside Chiropractic to offer you access to information that is current and useful.

A new research article in with a very large data set of 50,000 people who have pain has again emphasised the role of certain factors in predicting long lasting pain and poorer prognosis. The top of the list might strike you as surprising.

First, let's clarify a definition. Pain isn't classified according to its severity, but, is rather classified to how long it lasts. Pain is often classified according to the length of time it persists. Acute pain is pain that is less than 3 months in duration. Most injuries heal up by themselves more quickly than 3 months. This is a category of sprains and strains.

However, if pain persists beyond 3 months, it becomes classified as persistent pain, or sometimes as chronic pain. The nature of these conditions changes because tissues have all healed themselves by 3 months time (often more quickly than this). But pain can still persist. Some people like to think of the analogy of a car alarm; the damage was done quite some time ago, but the alarm can still go off for a variety of reasons. Sometimes, the alarm can be dysfunctional; or sometimes, it can be tricked, like when a heavy wind shakes a car and the alarm begins.

Whatever the reason, we want to avoid chronic pain. Chronic pain is a sign that our nervous system is not coping well, and this can cause unnecessary suffering.

Some of the interesting findings in this study are that we can see hints of a maladaptive nervous system when it comes to predicting chronic pain.

"The features with the strongest prognosis included sleeplessness, feeling 'fed-up', tiredness, stressful life events and a BMI >30."

Interestingly, "the cause of chronic pain and its prognosis often remains unknown, as tissue damage following injury is a poor predictor of clinical outcomes'. This is important to understand and is well studied as a phenomena. The amount of tissue damage doesn't always predict the amount of pain someone will experience. This is also why scans are sometimes unhelpful for conditions. They certainly have their place; however, it is just as important to emphasise other components of healthcare, what we call the psychosocial factors, which are a stronger predictor of someone having a poor outcome.

Notice the list above again:

  • Sleeplessness

  • Feeling fed-up

  • Tiredness

  • Stressful life events

  • BMI > 30

The top 5 predictors of poor recovery are psychological and social domains. Only 1 of the top 6 includes a biological component, that of high body mass index, or being overweight.

While this is consistent with many other data sets, the study design has both advantages and disadvantages.

One obvious advantage is that it pulled the data from the UK biobank, and it has a large sample size. When detecting for statistical changes, large numbers of participants and data sets that are really large help buffer any anomaly in the data. They also used machine learning to sift through the data so they could do calculations that were much more complicated than many single variable studies.

From this machine learning, the researchers hypothesized that a certain set of patients would be more likely to experience ongoing, persistent pain. 9 years after their initial intake, factors like mood, sleep and BMI were predictive of chronic pain. Those who experienced pain in more than one sight at the start of the study were more likely to also have pain that spread to multiple, distal sites 9 years later.

One potential disadvantage is that it is a retrospective analysis with a few assumptions. While the calculations are fascinating, it might also be based on false assumptions, which could certainly skew the conclusions. Also, this is based on retrospective data, which is never as strong as prospective data. And lastly, the paper is now in pre-print as of August 2022, so, it is awaiting peer review, which can sometimes find fault with the data.

And yet, this large data set again suggests that factors such as sleep, mood, tiredness, stressful life events and feeling-fed up are all components of chronic pain. This is consistent with many other findings in health care, and is yet another reason why prioritising health includes prioritising sleep, decreasing stress, and considering your health from a wholistic perspective rather than honing in on anatomical, simplistic models of pain.

Sources: A Data-Driven Biopsychosocial Framework Determining the Spreading of Chronic Pain

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