Predicting the development of vulvar lichen sclerosus

Ekaterina V. Kolesnikova, A. V. Zharov, Lyudmila K. Osipova, Artem I. Dupleev
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Abstract

Relevance. The issue of timely diagnosis and treatment of vulvar lichen sclerosus has become especially acute in recent years due to the “rejuvenation” of the disease and the risk of its malignancy. In this regard, it is urgent to search for effective methods for predicting and early detection of the disease. The aim of the study - to develop amodel for predicting vulvar lichen sclerosus based on established clinical and anamnestic risk factors. Materials and Methods. The prospective case-control study included 404 women aged 20 to 70 years, of which 344 were patients with vulvar lichen sclerosus and 60 were women without vulvar diseases. At the first stage, acomparative statistical correlation analysis of the clinical and anamnestic data of the subjects was carried out using the Spearman correlation coefficient (R0.15), Chi-square tests, Phi and Cramer statistics, the Mann-­Whitney U test and the Student t test (p 0.05). The data obtained were used to develop aneural network model for predicting vulvar lichen sclerosus in the second stage of the study. Results and Discussion. Based on established reliably significant (p0.05) obstetric-­gynecological, somatic, infectious, hygienic and household factors influencing the risk of developing vulvar lichen sclerosus (Rindicator - from 0.16 to 0.38 confirms the statistical significance of correlations), aneural network model for predicting vulvar lichen sclerosus was developed (the percentage of correct classification on the test sample is the maximum possible value - 100%) and acomputer program was written that automates the procedure for predicting the disease. Conclusion. The neural network model for predicting the disease, developed on the basis of reliably (p0.05) significant risk factors for vulvar lichen sclerosus, has high prognostic properties, and acomputer program written on its basis allows the doctor in amatter of minutes to identify the patient at risk for the development of vulvar lichen sclerosus and give she needs preventive recommendations aimed at preventing or early detection of the disease.
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预测外阴硬皮病的发展情况
相关性。近年来,由于外阴硬化性苔藓的 "年轻化 "及其恶变的风险,及时诊断和治疗外阴硬化性苔藓的问题变得尤为突出。因此,寻找预测和早期发现该疾病的有效方法迫在眉睫。本研究的目的是根据已确定的临床和病理风险因素,建立预测外阴硬化性苔藓的模型。材料和方法。这项前瞻性病例对照研究包括 404 名 20 至 70 岁的妇女,其中 344 人为外阴苔藓硬化症患者,60 人为无外阴疾病的妇女。在第一阶段,使用斯皮尔曼相关系数(R0.15)、卡方检验、Phi 和 Cramer 统计、曼-惠特尼 U 检验和学生 t 检验(P0.05)对受试者的临床和内科数据进行了比较统计相关分析。获得的数据用于在第二阶段研究中建立预测外阴硬皮病的神经网络模型。结果与讨论。根据已确定的影响外阴硬化性苔藓发病风险的妇产科、躯体、感染、卫生和家庭因素(Rindicator--从 0.16 到 0.38,证实了相关性的统计学意义),建立了预测外阴硬化性苔藓的神经网络模型(对测试样本的正确分类百分比为最大可能值--100%),并编写了计算机程序,使预测该疾病的程序自动化。结论根据外阴硬化性苔藓的可靠(P0.05)重要危险因素开发的预测疾病的神经网络模型具有很高的预后特性,在其基础上编写的计算机程序可让医生在几分钟内识别出有可能患外阴硬化性苔藓的病人,并向她提供所需的预防建议,以预防或及早发现该疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.50
自引率
0.00%
发文量
43
审稿时长
8 weeks
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