Sivkov A V, Golovanov S A, Zhukova L V
N.A. Lopatkin Scientific Research Institute of Urology and Interventional Radiology Branch of the National Medical Research Centre of Radiology of the Ministry of Health of Russian Federation, Moscow, Russia.
National Research University Higher School of Economics, Moscow, Russia.
Urologiia. 2019 Jul(3):14-22.
For the treatment of LUTS/BPH is used a wide range of drugs that patients have to take for a long time. Therefore, it is important to develop methods for predicting long-term results of therapy. The purpose of this work is to evaluate the possibility to predict long-term results of drug therapy of LUTS/BPH using mathematical modeling on the example of treatment with Serenoa repens extract (ESR - Permixon).
For prediction using the methods of predictive analytics of the therapeutic ESR effect in the long term, materials from the open study "Clinical and biological long-term tolerance of a lipidosterolic extract of Serenoa repens (Permixon) in patients with symptomatic benign prostatic hypertrophy" (No. P0048 95 GP 401) were used. The study took place in 1995-1999 in 3 Moscow medical centers: Research Institute of Urology of the Ministry of Health of the Russian Federation, Urological Clinic of the Moscow Medical Academy named after Sechenov and the urology department of Moscow Clinical Hospital No 60. The study included 155 patients aged 52 to 87 years (65.3) who received the drug in 320 mg capsules per day for two years. The target indicators of the prognosis identified key clinical parameters: a decrease IPSS of>25% or>3 points and an increase in Qmax>25% at 12 and 24 months of treatment. When evaluating the results, a binary approach was used: improvement achieved (1), not achieved (0).
Using the methods of predictive analytics, mathematical models were built to predict the long-term results of treatment according to the most significant 7 initial criterias (predictors): IPSS; Qmax; average urine flow rate; urination volume, urination time, residual urine volume, prostate volume. For each target field and time interval, mathematical models were built using ensembles from 7 selected machine learning algorithms with the best predictive qualities: BNet; C5.0; SVM; KNN; NNet; CHAID; C&RT. Verification of models on internal randomized samples showed their high prognostic properties: sensitivity 82.4-99.0; specificity 75.0-96.1; AUC 0,864-0,965.
The potential for effective prediction by the methods of predictive analytics and data mining of the separated results of drug therapy of LUTS / BPH according to the main clinical criteria was demonstrated. It is necessary to continue training and testing the model with the inclusion of new clinical observations in the data set. This approach is applicable to the creation of similar models for predicting the effect of other drugs.
用于治疗下尿路症状/良性前列腺增生（LUTS/BPH）的药物种类繁多，患者需要长期服用。因此，开发预测治疗长期效果的方法很重要。本研究的目的是通过数学建模，以锯叶棕果实提取物（ESR - 保列治）治疗为例，评估预测LUTS/BPH药物治疗长期效果的可能性。
为了使用预测分析方法长期预测ESR的治疗效果，采用了开放研究“锯叶棕果实脂质甾醇提取物（保列治）在有症状的良性前列腺增生患者中的临床和生物学长期耐受性”（编号P0048 95 GP 401）的材料。该研究于1995 - 1999年在3个莫斯科医疗中心进行：俄罗斯联邦卫生部泌尿学研究所、以谢马什克命名的莫斯科医学科学院泌尿外科诊所和莫斯科第60临床医院泌尿外科。研究纳入了155例年龄在52至87岁（平均65.3岁）的患者，他们每天服用320毫克胶囊装药物，持续两年。预后的目标指标确定了关键临床参数：治疗12个月和24个月时，国际前列腺症状评分（IPSS）降低>25%或>3分，最大尿流率（Qmax）增加>25%。评估结果时采用二元方法：达到改善（1），未达到改善（0）。
使用预测分析方法，根据7个最显著的初始标准（预测因子）建立了数学模型来预测治疗的长期效果：IPSS；Qmax；平均尿流率；排尿量、排尿时间、残余尿量、前列腺体积。对于每个目标领域和时间间隔，使用7种具有最佳预测质量的选定机器学习算法的集成构建数学模型：贝叶斯网络（BNet）；C5.0；支持向量机（SVM）；K近邻算法（KNN）；神经网络（NNet）；卡方自动交互检测（CHAID）；分类回归树（C&RT）。在内部随机样本上对模型进行验证，结果显示其具有较高的预后性能：灵敏度82.4 - 99.0；特异性75.0 - 96.1；曲线下面积（AUC）0.864 - 0.965。
证明了通过预测分析和数据挖掘方法根据主要临床标准有效预测LUTS/BPH药物治疗单独结果的潜力。有必要继续对模型进行训练和测试，并将新的临床观察结果纳入数据集。这种方法适用于创建预测其他药物效果的类似模型。
