Elaboration and validation of a novelty nomogram for the prognostication of anxiety susceptibility in individuals suffering from low back pain.

Journal: Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia

Volume: 122

Issue: 

Year of Publication: 2024

Affiliated Institutions:  Department of Neurosurgery, Tangdu Hospital, Affiliated Hospital of the Air Force Medical University, Xi'an, China. Department of Respiratory and Critical Care Medicine, Tangdu Hospital, Affiliated Hospital of the Air Force Medical University, Xi'an, China. Department of Rehabilitation, Southeast Hospital, Affiliated Hospital of Xiamen University, Xiamen, China. Department of Neurosurgery, the Second Affiliated Hospital of the Xi'an Jiaotong University, Xi'an, China. Department of Psychiatry, Xiaogan Mental Health Center, Xiaogan, China. Department of Health Statistics and Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational, China. Electronic address: zhyh@fmmu.edu.cn. Department of Neurosurgery, Tangdu Hospital, Affiliated Hospital of the Air Force Medical University, Xi'an, China. Electronic address: tangdunaowai@.com.

Abstract summary 

Low back pain (LBP) constitutes a distressing emotional ordeal and serves as a potent catalyst for adverse emotional states, notably anxiety. We dedicated to discerning methodologies for identifying patients who are predisposed to heightened levels of anxiety and pain. A self-assessment questionnaire was administered to patients afflicted with LBP. The pain scores were subjected to analysis in conjunction with anxiety scores, and a clustering procedure was executed using the scientific k-means methodology. Subsequently, six machine learning algorithms, including Logistics Regression (LR), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB), were employed. Next, five pertinent variables were identified, namely Age, Course, Body Mass Index (BMI), Education, and Marital status. Furthermore, a LR model was utilized to construct a nomogram, which was subsequently subjected to assessment for discrimination, calibration, and evaluation of its clinical utility. As a result, 599 questionnaires were valid (effective rate: 99 %). The correlation analysis revealed a significant association between anxiety and pain scores (r = 0.31, P < 0.001). LBP patients could be divided into two clusters, Cluster1 had higher pain scores (P < 0.05) and SAS scores (P < 0.001). The proposed nomogram demonstrated an area under the receiver operating characteristics curve (ROC) of 0.841 (95 %CI: 0.804-0.878) and 0.800 (95 %CI: 0.733-0.867) in the training and test groups, respectively. Briefly, the established nomogram has demonstrated remarkable proficiency in discerning individuals afflicted with LBP who are at a heightened risk of experiencing anxiety.

Authors & Co-authors:  Wang Liu Tian Gu Chen Huang Lv Zhang Li

Study Outcome 

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Statistics
Citations : 
Authors :  9
Identifiers
Doi : 10.1016/j.jocn.2024.03.003
SSN : 1532-2653
Study Population
Male,Female
Mesh Terms
Humans
Other Terms
Anxiety;Low back pain;Machine learning;Nomogram;Validation
Study Design
Study Approach
Country of Study
Publication Country
Scotland