  Human Assumed Central Sensitization is involved in the development and
maintenance of chronic low back pain (CLBP). The Central Sensitization
Inventory (CSI) was developed to evaluate the presence of HACS, with a cut-off
value of 40/100 based on patients with chronic pain. However, various factors
including pain conditions (e.g., CLBP), and gender may influence this cut-off
value. For chronic pain condition such as CLBP, unsupervised clustering
approaches can take these factors into consideration and automatically learn
the HACS-related patterns. Therefore, this study aimed to determine the cut-off
values for a Dutch-speaking population with CLBP, considering the total group
and stratified by gender based on unsupervised machine learning. In this study,
questionnaire data covering pain, physical, and psychological aspects were
collected from patients with CLBP and aged-matched pain-free adults (referred
to as healthy controls, HC). Four clustering approaches were applied to
identify HACS-related clusters based on the questionnaire data and gender. The
clustering performance was assessed using internal and external indicators.
Subsequently, receiver operating characteristic analysis was conducted on the
best clustering results to determine the optimal cut-off values. The study
included 151 subjects, consisting of 63 HCs and 88 patients with CLBP.
Hierarchical clustering yielded the best results, identifying three clusters:
healthy group, CLBP with low HACS level, and CLBP with high HACS level groups.
Based on the low HACS levels group (including HC and CLBP with low HACS level)
and high HACS level group, the cut-off value for the overall groups were 35, 34
for females, and 35 for. The findings suggest that the optimal cut-off values
for CLBP is 35. The gender-related cut-off values should be interpreted with
caution due to the unbalanced gender distribution in the sample.
