Lis-Studniarska Dorota, Lipnicka Marta, Studniarski Marcin, Irzmański Robert
Central Clinical Hospital, Medical University of Łódź, Pomorska 251, 92-213 Łódź, Poland.
Faculty of Mathematics and Computer Science, University of Łódź, Banacha 22, 90-238 Łódź, Poland.
Life (Basel). 2023 Aug 13;13(8):1738. doi: 10.3390/life13081738.
: Osteoporosis is a socio-economic problem of modern aging societies. Bone fractures and the related treatments generate the highest costs. The occurrence of osteoporotic fractures is a cause of chronic disability, many complications, reduced quality of life, and often premature death. The aim of the study was to determine which of the patient's potential risk factors pertaining to various diseases and lifestyle have an essential impact on the occurrence of low-energy fractures and the hierarchy of these factors. The study was retrospective. The documentation of 222 patients (206 women and 16 men) from an osteoporosis treatment clinic in Łódź, Poland was analyzed. Each patient was described by a vector consisting of 27 features, where each feature was a different risk factor. Using artificial neural networks, an attempt was made to create a model that, based on the available data, would be able to predict whether the patient would be exposed to low-energy fractures. We developed a neural network model that achieved the best result for the testing data. In addition, we used other methods to solve the classification problem, i.e., correctly dividing patients into two groups: those with fractures and those without fractures. These methods were logistic regression, -nearest neighbors and SVM. The obtained results gave us the opportunity to assess the effectiveness of various methods and the importance of the features describing patients. Using logistic regression and the recursive elimination of features, a ranking of risk factors was obtained in which the most important were age, chronic kidney disease, neck T-score, and serum phosphate level. Then, we repeated the learning procedure of the neural network considering only these four most important features. The average mean squared error on the test set was about 27% for the best variant of the model. The comparison of the rankings with different numbers of patients shows that the applied method is very sensitive to changes in the considered data (adding new patients significantly changes the result). Further cohort studies with more patients and more advanced methods of machine learning may be needed to identify other significant risk factors and to develop a reliable fracture risk system. The obtained results may contribute to the improved identification patients at risk of low-energy fractures and early implementation of comprehensive treatment.
骨质疏松症是现代老龄化社会的一个社会经济问题。骨折及相关治疗产生的费用最高。骨质疏松性骨折的发生是导致慢性残疾、多种并发症、生活质量下降以及常导致过早死亡的原因。本研究的目的是确定患者与各种疾病和生活方式相关的潜在风险因素中，哪些对低能量骨折的发生有重要影响以及这些因素的层级关系。该研究为回顾性研究。对来自波兰罗兹一家骨质疏松症治疗诊所的222名患者（206名女性和16名男性）的病历进行了分析。每位患者由一个包含27个特征的向量描述，其中每个特征是一个不同的风险因素。利用人工神经网络，试图创建一个基于现有数据能够预测患者是否会遭受低能量骨折的模型。我们开发了一个神经网络模型，该模型在测试数据上取得了最佳结果。此外，我们使用了其他方法来解决分类问题，即将患者正确分为两组：有骨折的患者和无骨折的患者。这些方法包括逻辑回归、k近邻法和支持向量机。所获结果使我们有机会评估各种方法的有效性以及描述患者特征的重要性。使用逻辑回归和特征递归消除法，获得了一个风险因素排名，其中最重要的是年龄、慢性肾病、颈部T值和血清磷酸盐水平。然后，我们仅考虑这四个最重要的特征重复神经网络的学习过程。对于模型的最佳变体，测试集上的平均均方误差约为27%。对不同患者数量的排名比较表明，所应用的方法对所考虑数据的变化非常敏感（增加新患者会显著改变结果）。可能需要进行更多患者参与的进一步队列研究以及更先进的机器学习方法，以识别其他重要风险因素并开发可靠的骨折风险系统。所获结果可能有助于更好地识别有低能量骨折风险的患者并尽早实施综合治疗。
