Construction of risk prediction model for chronic pain in elderly patients
10.3760/cma.j.cn115682-20220401-01564
- VernacularTitle:老年患者慢性疼痛风险预测模型的构建
- Author:
Tingting WANG
1
;
Tong ZHU
;
Benxiang NING
;
Jin XU
Author Information
1. 南京大学医学院附属鼓楼医院疼痛科，南京 210008
- Keywords:
Aged;
Predictive model;
Chronic pain;
Risk factors
- From:
Chinese Journal of Modern Nursing
2022;28(34):4773-4778
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a risk prediction model for chronic pain in elderly patients and verify its predictive value.Methods:From January 2020 to June 2021, 320 elderly patients admitted to the Pain Department of Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School were selected by convenience sampling. The general information of patients was collected, and patients were divided into chronic pain group ( n=185) and non-chronic pain group ( n=135) according to whether they had chronic pain. The patients were investigated with the Numerical Rating Scale (NRS) , Self-Rating Anxiety Scale (SAS) , Self-Rating Depression Scale (SDS) , Pittsburgh Sleep Quality Index (PSQI) , Morse Fall Scale, Constipation Symptom and Efficacy Scale, and bioelectrical impedance method. The binomial Logistic regression analysis was used to explore the risk factors of chronic pain in elderly patients and establish a risk prediction model. The predictive value of the risk prediction model was evaluated by the receiver operating characteristic (ROC) curve. Results:The NRS score of 185 patients with chronic pain was (4.34±1.50) . Lower limbs, lumbosacral region and neck were the most common parts of chronic pain. Osteoarthritis and cervical spondylosis were the main diseases causing chronic pain. Binomial Logistic regression analysis showed that high SDS score, high Morse Fall Scale score, high constipation symptom score, high SAS score and low appendicular skeletal muscle mass index were independent risk factors for chronic pain in elderly patients ( P<0.05) . The area under the ROC curve of the risk prediction model for chronic pain in elderly patients was 0.878, the sensitivity was 92.53%, and the specificity was 67.04%. Conclusions:The risk prediction model based on anxiety, depression, falls, myasthenia and constipation has good clinical value in predicting chronic pain in elderly patients, and can provide support for early identification and intervention of chronic pain in elderly patients.
