Risk Factors and Diagnostic Model Construction of Chronic Pain with Cognitive Impairment

Changteng Zhang; Ying Su; Xianzheng Zeng; Xiaoyu Zhu; Rui Gao; Wangyang Liu; Runzi Du; Chan Chen; Jin Liu

doi:10.2147/JPR.S485000

Risk Factors and Diagnostic Model Construction of Chronic Pain with Cognitive Impairment

J Pain Res. 2024 Dec 17:17:4331-4342. doi: 10.2147/JPR.S485000. eCollection 2024.

Authors

Changteng Zhang^#^{1

2}, Ying Su^#^{1

2}, Xianzheng Zeng³, Xiaoyu Zhu^{1

2}, Rui Gao^{1

2}, Wangyang Liu^{1

2}, Runzi Du^{1

2}, Chan Chen^{1

2}, Jin Liu^{1

2}

Affiliations

¹ Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, People's Republic of China.
² The Research Units of West China (2018RU012)-Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, People's Republic of China.
³ Department of Pain Management, West China Hospital, Sichuan University, Chengdu, People's Republic of China.

^# Contributed equally.

Abstract

Background: 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.

Methods: 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.

Results: The study ultimately included 163 eligible patients. Based on analysis, age (OR, 1.037 [95% CI, 1.007-1.070]; P=0.018), duration of pain (OR, 2.546 [95% CI, 1.099-6.129]; P=0.032), VAS score (OR, 1.724 [95% CI, 0.819-3.672]; P=0.153), LMR (OR, 0.091 [95% CI, 0.024-0.275]; P<0.001), absolute neutrophil value (OR, 0.306 [95% CI, 0.115-0.767]; P=0.014), and lymphocyte percentage (OR, 6.551 [95% CI, 2.143-25.039]; P=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.

Conclusion: 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.

Keywords: chronic pain; cognitive impairment; complete blood count parameters; diagnostic model; risk factors.

Grants and funding

Not applicable.