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[基于生命系统衰老动力学理论预测前列腺癌患者的预期寿命]

[Prediction of life expectancy for prostate cancer patients based on the kinetic theory of aging of living systems].

作者信息

Viktorov A A, Zharinov G M, Neklasova N Ju, Morozova E E

机构信息

State Research Center - Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency (SRC-FMBC), 46, Zhivopisnaya str., Moscow, 123182, Russian Federation;

Federal State Budget Institution Russian Research Center of Radiology and Surgical Technologies, 70, Leningradskaya str., Pesochny, St. Petersburg, 197758, Russian Federation;

出版信息

Adv Gerontol. 2017;30(3):356-362.

Abstract

The article presents a methodical approach for prediction of life expectancy for people diagnosed with prostate cancer based on the kinetic theory of aging of living systems. The life expectancy is calculated by solving the differential equation for the rate of aging for three different stage of life - «normal» life, life with prostate cancer and life after combination therapy for prostate cancer. The mathematical model of aging for each stage of life has its own parameters identified by the statistical analysis of healthcare data from the Zharinov's databank and Rosstat CDR NES databank. The core of the methodical approach is the statistical correlation between growth rate of the prostate specific antigen level (PSA-level) or the PSA doubling time (PSA DT) before therapy, and lifespan: the higher the PSA DT is, the greater lifespan. The patients were grouped under the «fast PSA DT» and «slow PSA DT» categories. The satisfactory matching between calculations and experiment is shown. The prediction error of group life expectancy is due to the completeness and reliability of the main data source. A detailed monitoring of the basic health indicators throughout the each person life in each analyzed group is required. The absence of this particular information makes it impossible to predict the individual life expectancy.

摘要

本文提出了一种基于生命系统衰老动力学理论预测前列腺癌患者预期寿命的系统方法。预期寿命通过求解生命三个不同阶段(“正常”生活、患前列腺癌的生活以及前列腺癌联合治疗后的生活)的衰老速率微分方程来计算。每个生命阶段的衰老数学模型都有其通过对扎里诺夫数据库和俄罗斯联邦国家统计局国民经济核算数据库的医疗数据进行统计分析确定的自身参数。该系统方法的核心是治疗前前列腺特异性抗原水平(PSA水平)的增长率或PSA倍增时间(PSA DT)与寿命之间的统计相关性:PSA DT越高,寿命越长。患者被分为“快速PSA DT”和“缓慢PSA DT”两类。计算结果与实验结果显示出令人满意的匹配度。群体预期寿命的预测误差归因于主要数据源的完整性和可靠性。需要对每个分析组中的每个人在其整个生命过程中的基本健康指标进行详细监测。缺乏这些特定信息使得无法预测个体预期寿命。

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