Zhang Changteng, Su Ying, Zeng Xianzheng, Zhu Xiaoyu, Gao Rui, Liu Wangyang, Du Runzi, Chen Chan, Liu Jin
Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, People's Republic of China.
The Research Units of West China (2018RU012)-Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, People's Republic of China.
J Pain Res. 2024 Dec 17;17:4331-4342. doi: 10.2147/JPR.S485000. eCollection 2024.
Cognitive impairment (CI) is frequently observed in patients with chronic pain (CP). CP progression increases the risk of dementia and accelerates Alzheimer's disease pathogenesis. However, risk diagnostic models and biomarkers for CP-related CI remain insufficient. Previous research has highlighted the relationships between several complete blood count parameters for CP or CI-related diseases, such as Alzheimer's disease, while the specific values of complete blood count parameters in CP-related CI patients remain unclear. This study aimed to explore the correlation between complete blood count parameters and CP-related CI to establish a risk diagnostic model for the early detection of CP-related CI.
This cross-sectional study was conducted at West China Hospital, Sichuan University. The Montreal Cognitive Assessment (MoCA) was used to classify patients into either the CP with CI group or the CP without CI group. Univariate analysis and multivariate logistic regression analysis were used to screen the related factors of CP-related CI for constructing a risk diagnostic model, and the model was evaluated using receiver operating characteristic (ROC) curve analysis.
The study ultimately included 163 eligible patients. Based on analysis, age (OR, 1.037 [95% CI, 1.007-1.070]; =0.018), duration of pain (OR, 2.546 [95% CI, 1.099-6.129]; =0.032), VAS score (OR, 1.724 [95% CI, 0.819-3.672]; =0.153), LMR (OR, 0.091 [95% CI, 0.024-0.275]; <0.001), absolute neutrophil value (OR, 0.306 [95% CI, 0.115-0.767]; =0.014), and lymphocyte percentage (OR, 6.551 [95% CI, 2.143-25.039]; =0.002) were identified as critical factors of CP-related CI. The diagnostic model was evaluated by the ROC curve, demonstrating good diagnostic value with an area under the curve (AUC) of 0.803, a sensitivity of 0.603 and a specificity of 0.871.
The risk diagnostic model developed in this study for CP-related CI has significant value and enables clinicians to customize interventions based on each patient's needs.
慢性疼痛（CP）患者中经常出现认知障碍（CI）。CP进展会增加患痴呆症的风险，并加速阿尔茨海默病的发病机制。然而，CP相关CI的风险诊断模型和生物标志物仍然不足。先前的研究强调了CP或CI相关疾病（如阿尔茨海默病）的几个全血细胞计数参数之间的关系，而CP相关CI患者全血细胞计数参数的具体值仍不清楚。本研究旨在探讨全血细胞计数参数与CP相关CI之间的相关性，以建立一个用于早期检测CP相关CI的风险诊断模型。
本横断面研究在四川大学华西医院进行。使用蒙特利尔认知评估量表（MoCA）将患者分为CP合并CI组或CP不合并CI组。采用单因素分析和多因素逻辑回归分析筛选CP相关CI的相关因素以构建风险诊断模型，并使用受试者工作特征（ROC）曲线分析对该模型进行评估。
该研究最终纳入了163例符合条件的患者。经分析，年龄（比值比[OR]，1.037[95%置信区间（CI），1.007 - 1.070]；P = 0.018）、疼痛持续时间（OR，2.546[95%CI，1.099 - 6.129]；P = 0.032）、视觉模拟评分（VAS）（OR，1.724[95%CI，0.819 - 3.672]；P = 0.153）、淋巴细胞与单核细胞比值（LMR）（OR，0.091[95%CI，0.024 - 0.275]；P < 0.001）、中性粒细胞绝对值（OR，0.306[95%CI，0.115 - 0.767]；P = 0.014）和淋巴细胞百分比（OR，6.551[95%CI，2.143 - 25.039]；P = 0.002）被确定为CP相关CI的关键因素。通过ROC曲线对诊断模型进行评估，曲线下面积（AUC）为0.803，灵敏度为0.603，特异度为0.871，显示出良好的诊断价值。
本研究中开发的CP相关CI风险诊断模型具有重要价值，使临床医生能够根据每个患者的需求定制干预措施。
