Borisova Inna S, Stepansky Dmitry O
SE «Dnipropetrovsk Medical Academy Of Health Ministry Of Ukraine», Dnipro, Ukraine.
Wiad Lek. 2019;72(3):395-400.
Introduction: At the present stage, the medicine development is based on the principles of evidence-based medicine, which requires using of statistical methods and forecasting. Using statistical analysis and possibilities and mathematical forecasting emphasizes the probability of obtained data in scientific medical research. Identifying the factors that determine the survival of patients with acute leukemia and pneumonia causes the conduct of this study. The aim: To create a mathematical model of poor outcome prognosis in patients with acute leukemia, which was complicated by pneumonia, to determine the treatment place and timely optimize the treatment.
Materials and methods: An electronic database of formalized disease history of 360 patients with acute leukemia and pneumonia was created. The data base contained data of objective survey and additional research methods. In our study we used non-parametric dispersion analysis of Kraskele-Wallis, correlation analysis with the calculation of Spierman's rank correlation coefficients, simple and multiple logistic regression analysis with the calculation of the odds ratio; ROC analysis. The significance level p <0,05 was considered statistically significant.
Results: It was determined that with the onset of the lethal outcome of patients with pneumonia, developed on the background of acute leukemia, the indicators of leukocytes, lymphocytes, neutrophils, platelets, erythrocytes, hemoglobin and immunity indexes (B(CD19+), T(CD4+), CD4+/CD8+, IgG). According to the results of our study, a mathematical model of prediction poor outcome in patients with acute leukemia, which was complicated by pneumonia, was created: PPO=exp(-10,317+0,410* В(CD19+) -2,149* IgG)/[1+exp(-10,317+0,410* В(CD19+) -2,149* IgG)].
Conclusion: Using in clinical practice the proposed mathematical model of prediction poor outcome in patients with acute leukemia, which was complicated by pneumonia, will allow determining the treatment place and timely optimizing the treatment program.
引言:现阶段,医学发展基于循证医学原则,这需要运用统计方法和预测。使用统计分析、可能性分析及数学预测强调了科学医学研究中所获数据的概率。确定影响急性白血病合并肺炎患者生存的因素促使了本研究的开展。目标:创建急性白血病合并肺炎患者不良预后的数学模型,以确定治疗方案并及时优化治疗。
材料与方法:创建了360例急性白血病合并肺炎患者的规范化病史电子数据库。该数据库包含客观检查数据及其他研究方法的数据。在本研究中,我们使用了Kraskele-Wallis非参数离散分析、计算斯皮尔曼等级相关系数的相关分析、计算比值比的简单和多元逻辑回归分析;ROC分析。显著性水平p<0.05被认为具有统计学意义。
结果:已确定,在急性白血病背景下发生肺炎的患者出现致命结局时,白细胞、淋巴细胞、中性粒细胞、血小板、红细胞、血红蛋白及免疫指标(B(CD19+)、T(CD4+)、CD4+/CD8+、IgG)的指标。根据我们的研究结果,创建了急性白血病合并肺炎患者不良预后预测的数学模型:PPO=exp(-10.317 + 0.410B(CD19+) - 2.149IgG)/[1 + exp(-10.317 + 0.410B(CD19+) - 2.149IgG)]。
结论:在临床实践中使用所提出的急性白血病合并肺炎患者不良预后预测数学模型,将有助于确定治疗方案并及时优化治疗方案。