Popa Alina Delia, Gavril Radu Sebastian, Popa Iolanda Valentina, Mihalache Laura, Gherasim Andreea, Niță George, Graur Mariana, Arhire Lidia Iuliana, Niță Otilia
Faculty of Medicine, University of Medicine and Pharmacy "Grigore T Popa", 700115 Iasi, Romania.
Faculty of Medicine and Biological Sciences, University "Ștefan cel Mare" of Suceava, 720229 Suceava, Romania.
J Clin Med. 2023 Sep 7;12(18):5816. doi: 10.3390/jcm12185816.
Our paper proposes the first machine learning model to predict long-term mortality in patients with diabetic foot ulcers (DFUs). The study includes 635 patients with DFUs admitted from January 2007 to December 2017, with a follow-up period extending until December 2020. Two multilayer perceptron (MLP) classifiers were developed. The first MLP model was developed to predict whether the patient will die in the next 5 years after the current hospitalization. The second MLP classifier was built to estimate whether the patient will die in the following 10 years. The 5-year and 10-year mortality models were based on the following predictors: age; the University of Texas Staging System for Diabetic Foot Ulcers score; the Wagner-Meggitt classification; the Saint Elian Wound Score System; glomerular filtration rate; topographic aspects and the depth of the lesion; and the presence of foot ischemia, cardiovascular disease, diabetic nephropathy, and hypertension. The accuracy for the 5-year and 10-year models was 0.7717 and 0.7598, respectively (for the training set) and 0.7244 and 0.7087, respectively (for the test set). Our findings indicate that it is possible to predict with good accuracy the risk of death in patients with DFUs using non-invasive and low-cost predictors.
我们的论文提出了首个用于预测糖尿病足溃疡（DFU）患者长期死亡率的机器学习模型。该研究纳入了2007年1月至2017年12月期间收治的635例DFU患者，随访期延长至2020年12月。开发了两个多层感知器（MLP）分类器。第一个MLP模型用于预测患者在当前住院后的未来5年内是否会死亡。第二个MLP分类器用于估计患者在接下来的10年内是否会死亡。5年和10年死亡率模型基于以下预测因素：年龄；德克萨斯大学糖尿病足溃疡分期系统评分；瓦格纳 - 梅吉特分类；圣埃利安伤口评分系统；肾小球滤过率；病变的地形学特征和深度；以及足部缺血、心血管疾病、糖尿病肾病和高血压的存在情况。5年和10年模型在训练集上的准确率分别为0.7717和0.7598，在测试集上的准确率分别为0.7244和0.7087。我们的研究结果表明，使用非侵入性和低成本的预测因素可以较为准确地预测DFU患者的死亡风险。
