We performed a k-means cluster analysis on a large data set of primary care patients with chronic low back pain. We found three clusters that can be characterized as “pensioners with age-associated pain caused by degenerative diseases”, “middle-aged patients with high mental distress and poor coping resources”, and “middle-aged patients who are less pain-affected and better positioned with regard to their mental health”.
Several researchers have stated the need to identify patient groups that could serve as target groups for effective treatment strategies . Turk et al. identified three groups of chronic pain patients by the WHYMPI [22, 39]. The first group, “dysfunctional patients”, corresponds to patients with high pain severity, a low activity level, marked interference with everyday life due to pain, high affective distress, and low perception of life control. The second group, “adaptive copers”, is characterized by a lower pain severity, a higher activity level, lower interference and affective distress, and higher life control. The third group, “interpersonally distressed”, features middle pain severity, general activity, interference and affective distress, and lower social support than the other two groups.
Shaw et al. identified four groups of patients with acute work-related back pain based on disability risk factors (pain, depressive mood, fear avoidance beliefs, work inflexibility, and poor expectations for recovery) . Group one consists of patients who are most affected by pain, concerned with high physical demands at work. This group resembles the “fear avoidance” category and shows low expectations of returning to work. Group two is characterized by a high rate of emotional distress and above average pain intensity. Patients in group three are identified by a high degree of concern about job placement. Finally, patients from group four show low risk factors for disability. They have positive expectations for workplace accommodation and returning to normal work.
Boersma et al. identified groups of acute and subacute spinal pain patients with regard to their risk for permanent pain or disability . Their group profiles “fear-avoidant”, “distressed fear-avoidant”, “low risk” and “low risk depressed mood” are comparable to the results of Shaw et al.
Even though the included variables and patient populations (e.g., acute vs. chronic pain, low vs. no low back pain, different settings) of the aforementioned studies differ, they all have one aspect in common: All analyses revealed one patient group which seems most distressed and shows above average mental distress and high pain severity. In this way, mental health status seems to be a key differential factor.
The primary care setting comprises a high prevalence of older, often multimorbid patients and many chronic diseases. Therefore, groups identified in different settings might not be relevant for general practice . Even though we could confirm the presence of middle-aged groups with minor and major psychosocial distress, further studies are required; diagnostic studies to identify these groups and treatment studies, which would prove effectiveness of group-specific treatments.
Hill et al. developed the STarT Back tool for the primary care setting . The tool classifies patients with low back pain (LBP) into three groups based on nine questions referring to potentially modifiable physical and psychological prognostic indicators for persistent, disabling symptoms. Patients are categorized as “low risk”, “medium risk”, and “high risk” for future disabling LBP. However, the studies from Hill et al. included patients with acute and chronic LBP. Our study focused only on patients with chronic LBP.
Our study is subject to selection bias. GPs may have subconsciously preferred to recruit special cases (e.g., patients with higher disease severity or special personality), or forgotten to recruit patients due to high workload. Furthermore, some patients may have refused to participate in our study due to the long questionnaire, especially considering that our study population included a large proportion of older people with age-associated mental deficiencies. These factors might reduce the external validity of our results. In general, a limitation of cluster analysis is that the results depend on the input variables .
Since the less pain-affected patients of cluster three (average age: 46.8 years) are younger than the more pain-affected patients of cluster two (average age: 57.8 years), it is possible that younger patients from the third cluster will move to the second cluster as they age. This is especially likely because increased age is a proven risk factor for increased pain outcomes (e.g., transition from localized to widespread pain) . We should soon be able to prove this hypothesis using follow-up data from our cohort study.