Abstract：
OBJECTIVE: Pain often accompanies carpal tunnel syndrome and affects patients\' health-related quality of life. The aim was to develop and validate a predictive model for the pain intensity of carpal tunnel syndrome using demographic, clinical, electrophysiological, and ultrasound findings.
METHODS: We conducted a secondary analysis of data from a large sample of patients (May 2017 to December 2022) with carpal tunnel syndrome. A total of 520 (53.0 %) mild, 276 (28.1 %) moderate, and 186 (18.9 %) severe syndromes were included in the complete data set of 982 hands (61.1 % female). The mean age was 57.8 (10.7) years and the median duration [interquartile range] of the symptoms was 4 [2,10] months. A regression model was developed and validated to predict pain intensity on a numerical rating scale using a tree-based machine learning algorithm.
RESULTS: The validation of the regression model showed good performance with a root mean squared error, R-squared, and mean absolute error of 1.35, 0.42, and 1.05, respectively. Overall, the top significant predictors of pain intensity were compound motor nerve action potential latency, nocturnal pain, and thenar weakness. These were followed by the cross-sectional area of the median nerve, sensory nerve action potential, bowing of the flexor retinaculum, disease duration, and body mass index. We did not find strong associations between the median nerve transcarpal latency, age, sex, and diabetes with the pain intensity of carpal tunnel syndrome.
CONCLUSIONS: Our model showed good performance in predicting the subjective pain intensity of carpal tunnel syndrome, even in the context of non-linear relations.
METHODS: We conducted a secondary analysis of data from a large sample of patients (May 2017 to December 2022) with carpal tunnel syndrome. A total of 520 (53.0 %) mild, 276 (28.1 %) moderate, and 186 (18.9 %) severe syndromes were included in the complete data set of 982 hands (61.1 % female). The mean age was 57.8 (10.7) years and the median duration [interquartile range] of the symptoms was 4 [2,10] months. A regression model was developed and validated to predict pain intensity on a numerical rating scale using a tree-based machine learning algorithm.
RESULTS: The validation of the regression model showed good performance with a root mean squared error, R-squared, and mean absolute error of 1.35, 0.42, and 1.05, respectively. Overall, the top significant predictors of pain intensity were compound motor nerve action potential latency, nocturnal pain, and thenar weakness. These were followed by the cross-sectional area of the median nerve, sensory nerve action potential, bowing of the flexor retinaculum, disease duration, and body mass index. We did not find strong associations between the median nerve transcarpal latency, age, sex, and diabetes with the pain intensity of carpal tunnel syndrome.
CONCLUSIONS: Our model showed good performance in predicting the subjective pain intensity of carpal tunnel syndrome, even in the context of non-linear relations.
摘要：
目的：疼痛常伴随腕管综合征并影响患者健康相关生活质量。目的是使用人口统计来开发和验证腕管综合征疼痛强度的预测模型，临床,电生理学,和超声检查结果。
方法：我们对大量腕管综合征患者（2017年5月至2022年12月）的数据进行了二次分析。共520例(53.0%)轻度,276（28.1%）中度，186例(18.9%)严重综合征纳入982例(61.1%女性)的完整数据集。平均年龄为57.8（10.7）岁，症状的中位持续时间[四分位数范围]为4[2,10]个月。使用基于树的机器学习算法，开发并验证了回归模型以在数字评分量表上预测疼痛强度。
结果：回归模型的验证显示出良好的性能，具有均方根误差，R平方，和平均绝对误差分别为1.35、0.42和1.05。总的来说,疼痛强度的主要预测因子是复合运动神经动作电位潜伏期，夜间疼痛，和鱼际弱点。然后是正中神经的横截面积，感觉神经动作电位,屈肌支持带的弯曲，疾病持续时间,和体重指数。我们没有发现正中神经经腕潜伏期之间的强烈关联，年龄,性别,和糖尿病与腕管综合征的疼痛强度有关。
结论：我们的模型在预测腕管综合征的主观疼痛强度方面表现良好，即使在非线性关系的背景下。
方法：我们对大量腕管综合征患者（2017年5月至2022年12月）的数据进行了二次分析。共520例(53.0%)轻度,276（28.1%）中度，186例(18.9%)严重综合征纳入982例(61.1%女性)的完整数据集。平均年龄为57.8（10.7）岁，症状的中位持续时间[四分位数范围]为4[2,10]个月。使用基于树的机器学习算法，开发并验证了回归模型以在数字评分量表上预测疼痛强度。
结果：回归模型的验证显示出良好的性能，具有均方根误差，R平方，和平均绝对误差分别为1.35、0.42和1.05。总的来说,疼痛强度的主要预测因子是复合运动神经动作电位潜伏期，夜间疼痛，和鱼际弱点。然后是正中神经的横截面积，感觉神经动作电位,屈肌支持带的弯曲，疾病持续时间,和体重指数。我们没有发现正中神经经腕潜伏期之间的强烈关联，年龄,性别,和糖尿病与腕管综合征的疼痛强度有关。
结论：我们的模型在预测腕管综合征的主观疼痛强度方面表现良好，即使在非线性关系的背景下。
