Department of Orthopaedics, LongHua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Department of Bone Tumor Surgery, Changzheng Hospital, Second Military Medical University, Shanghai, China.
Sci Rep. 2019 Mar 12;9(1):4207. doi: 10.1038/s41598-019-39026-3.
The purpose of our study is to identify the predictive factors for a minimum clinically successful therapy after extracorporeal shock wave therapy for chronic plantar fasciitis. The demographic and clinical characteristics were evaluated. The artificial neural networks model was used to choose the significant variables and model the effect of achieving the minimum clinically successful therapy at 6-months' follow-up. The multilayer perceptron model was selected. Higher VAS (Visual Analogue Score) when taking first steps in the morning, presence of plantar fascia spur, shorter duration of symptom had statistical significance in increasing the odd. The artificial neural networks model shows that the sensitivity of predictive factors was 84.3%, 87.9% and 61.4% for VAS, spurs and duration of symptom, respectively. The specificity 35.7%, 37.4% and 22.3% for VAS, spurs and duration of symptom, respectively. The positive predictive value was 69%, 72% and 57% for VAS, spurs and duration of symptom, respectively. The negative predictive value was 82%, 84% and 59%, for VAS, spurs and duration of symptom respectively. The area under the curve was 0.738, 0.882 and 0.520 for VAS, spurs and duration of symptom, respectively. The predictive model showed a good fitting of with an overall accuracy of 92.5%. Higher VAS symptomatized by short-duration, severer pain or plantar fascia spur are important prognostic factors for the efficacy of extracorporeal shock wave therapy. The artificial neural networks predictive model is reasonable and accurate model can help the decision-making for the application of extracorporeal shock wave therapy.
我们的研究目的是确定体外冲击波治疗慢性足底筋膜炎后达到最小临床成功治疗的预测因素。评估了人口统计学和临床特征。使用人工神经网络模型选择显著变量并建立模型，以预测 6 个月随访时达到最小临床成功治疗的效果。选择了多层感知器模型。早晨第一步时 VAS（视觉模拟评分）较高、存在足底筋膜炎骨刺、症状持续时间较短，在增加奇数方面具有统计学意义。人工神经网络模型显示，VAS、骨刺和症状持续时间的预测因素的敏感性分别为 84.3%、87.9%和 61.4%。VAS、骨刺和症状持续时间的特异性分别为 35.7%、37.4%和 22.3%。VAS、骨刺和症状持续时间的阳性预测值分别为 69%、72%和 57%。VAS、骨刺和症状持续时间的阴性预测值分别为 82%、84%和 59%。VAS、骨刺和症状持续时间的曲线下面积分别为 0.738、0.882 和 0.520。预测模型与整体准确性为 92.5%的良好拟合。VAS 较高、症状持续时间较短、疼痛较严重或存在足底筋膜炎骨刺是体外冲击波治疗效果的重要预后因素。人工神经网络预测模型是合理且准确的模型，可以帮助做出是否应用体外冲击波治疗的决策。
