Wirries André, Geiger Florian, Hammad Ahmed, Bäumlein Martin, Schmeller Julia Nadine, Blümcke Ingmar, Jabari Samir
Spine Center, Hessing Foundation, Hessingstrasse 17, 86199 Augsburg, Germany.
Center for Orthopaedics and Trauma Surgery, Philipps University of Marburg, Baldingerstrasse, 35043 Marburg, Germany.
Biomedicines. 2022 Jun 4;10(6):1319. doi: 10.3390/biomedicines10061319.
The treatment options for neuropathic pain caused by lumbar disc herniation have been debated controversially in the literature. Whether surgical or conservative therapy makes more sense in individual cases can hardly be answered. We have investigated whether a machine learning-based prediction of outcome, regarding neuropathic pain development, after lumbar disc herniation treatment is possible. The extensive datasets of 123 consecutive patients were used to predict the development of neuropathic pain, measured by a visual analogue scale (VAS) for leg pain and the Oswestry Disability Index (ODI), at 6 weeks, 6 months and 1 year after treatment of lumbar disc herniation in a machine learning approach. Using a decision tree regressor algorithm, a prediction quality within the limits of the minimum clinically important difference for the VAS and ODI value could be achieved. An analysis of the influencing factors of the algorithm reveals the important role of psychological factors as well as body weight and age with pre-existing conditions for an accurate prediction of neuropathic pain. The machine learning algorithm developed here can enable an assessment of the course of treatment after lumbar disc herniation. The early, comparative individual prediction of a therapy outcome is important to avoid unnecessary surgical therapies as well as insufficient conservative therapies and prevent the chronification of neuropathic pain.
腰椎间盘突出症所致神经性疼痛的治疗选择在文献中一直存在争议。在个别病例中，手术治疗还是保守治疗更有意义，这很难回答。我们研究了基于机器学习预测腰椎间盘突出症治疗后神经性疼痛发展的结果是否可行。我们采用机器学习方法，利用123例连续患者的大量数据集，预测腰椎间盘突出症治疗后6周、6个月和1年时通过腿痛视觉模拟量表（VAS）和奥斯威斯利功能障碍指数（ODI）测量的神经性疼痛的发展情况。使用决策树回归算法，可以在VAS和ODI值的最小临床重要差异范围内实现预测质量。对该算法影响因素的分析揭示了心理因素以及体重和年龄与既往疾病在准确预测神经性疼痛方面的重要作用。这里开发的机器学习算法可以对腰椎间盘突出症后的治疗过程进行评估。对治疗结果进行早期、比较性的个体预测对于避免不必要的手术治疗以及保守治疗不足，并防止神经性疼痛慢性化非常重要。
