Central Sensitivity Is Associated with Poor Recovery of Pain: Prediction, Cluster, and Decision Tree Analyses.

Shu Morioka,Masayuki Koga,Vahid Rakhshan,Hayato Shigetoh,Yoichi Tanaka

doi:10.1155/2020/8844219

Shu Morioka, Masayuki Koga + Show 3 more

Open Access

https://doi.org/10.1155/2020/8844219

Copy DOI

Abstract

The process of pain recovery varies and can include the recovery, maintenance, or worsening of symptoms. Many cases of patients with pain show a tendency of recovering as predicted; however, some do not. The characteristics of cases that do not fit the prediction of pain recovery remain unclear. We performed cluster and decision tree analyses to reveal the characteristics in cases that do not fit the prediction of pain recovery. A total of 43 patients with musculoskeletal pain (nonoperative: 22 patients, operative: 13 patients) and central pain (brain disease: 5 patients, spinal cord disease: 3 patients) were included in this longitudinal study. Central sensitivity syndrome (CSS) outcome measures (Central Sensitisation Inventory), pain intensity-related outcome measures (Short-Form McGill Pain Questionnaire-2 (SFMPQ-2)), and cognitive-emotional outcome measures (Hospital Anxiety and Depression Scale and Pain Catastrophising Scale-4) of all patients were assessed at baseline and after 1-2 months. Regression analysis was used to calculate pain recovery prediction values. A hierarchical cluster analysis based on the predicted change of SFMPQ-2 and the observed change of SFMPQ-2 was used to extract subgroups that fit and those that do not fit pain recovery prediction. To extract the characteristics of subgroups that do not fit the prediction of pain recovery, a decision tree analysis was performed. The level of significance was set at 5%. In the results of cluster analysis, patients were classified into three subgroups. Cluster 1 was characterised by worse pain intensity from baseline, cluster 2 by pain, having recovered less and mildly than the predicted value, and Cluster 3 by a marked recovery of pain. In the results of the decision tree analysis, the CSI change was extracted as an indicator related to the classification of all clusters. Our findings suggest that the poor improvement of CSS is characteristic in cases that do not fit the prediction of pain recovery.

Highlights

Pain is a symptom of musculoskeletal disorders [1,2,3] and a symptom of many other diseases such as central nervous system disorders [4, 5], and the severity of pain varies from mild to severe [6,7,8]
Data from 25 patients were used to create a pain recovery prediction equation. e pain recovery prediction equation was ∆Short-Form McGill Pain Questionnaire-2 (SFMPQ-2) −0.52∗SFMPQ-2 baseline − 3.34. e adjusted coefficient of determination (R2′) was 0.56. e coefficient of determination was used to measure the goodness of fit between actual and predicted data
Cluster Analysis and Comparison between Clusters. e cluster analysis based on ∆SFMPQ-2 predicted and

Summary

Introduction

Pain is a symptom of musculoskeletal disorders [1,2,3] and a symptom of many other diseases such as central nervous system disorders [4, 5], and the severity of pain varies from mild to severe [6,7,8]. Several longitudinal studies have reported central sensitivity syndrome (CSS) [15, 16], catastrophic thinking [17], anxiety [18], and depression [14, 18, 19] in the initial state as predictors of postintervention pain. These predictors are predictors of postintervention pain severity, and the relationships between cognitive-emotional factors, CSS, and predictors of pain recovery have not been clarified. Pain recovery prediction models have been developed, and the accuracy of the agreement between predicted and observed

Objectives

Methods

Results

Conclusion