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.

翻译：人类假定的中枢敏化（HACS）参与慢性下背痛（CLBP）的发生和维持。中枢敏化量表（CSI）旨在评估HACS的存在，基于慢性疼痛患者其截断值设为40/100。然而，疼痛状况（如CLBP）和性别等多种因素可能影响该截断值。对于CLBP这类慢性疼痛状况，无监督聚类方法可综合考虑这些因素并自动学习与HACS相关的模式。因此，本研究旨在确定荷兰语CLBP人群的截断值，考虑总体组及基于无监督机器学习按性别分层的情况。本研究收集了CLBP患者及年龄匹配的无痛成年人（称为健康对照组，HC）的问卷数据，涵盖疼痛、身体及心理方面。基于问卷数据和性别，应用四种聚类方法识别与HACS相关的聚类。使用内部和外部指标评估聚类性能。随后，对最佳聚类结果进行受试者工作特征分析以确定最优截断值。本研究纳入151名受试者，包括63名HC和88名CLBP患者。层次聚类结果最佳，识别出三个聚类：健康组、低HACS水平CLBP组和高HACS水平CLBP组。基于低HACS水平组（包括HC和低HACS水平CLBP）与高HACS水平组，总体组的截断值为35，女性为34，男性为35。研究结果表明，CLBP的最佳截断值为35。由于样本中性别分布不均衡，应谨慎解读与性别相关的截断值。