Yamanaka Yukihiro, Uchida Kenko, Akashi Momoka, Watanabe Yuta, Yaguchi Arino, Shimamoto Shuji, Shimoda Shingo, Yamada Hitoshi, Yamashita Masashi, Kimura Hidenori
Waseda University, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo, Japan.
Tokyo Women's Medical University, Kawada-cho, Shinjuku-ku, Tokyo, Japan.
Theor Biol Med Model. 2019 Mar 6;16(1):5. doi: 10.1186/s12976-019-0101-9.
Mathematical models of diseases may provide a unified approach for establishing effective treatment strategies based on fundamental pathophysiology. However, models that are useful for clinical practice must overcome the massive complexity of human physiology and the diversity of patients' environmental conditions. With the aim of modeling a complex disease, we choose sepsis, which is highly complex, life-threatening systemic disease with high mortality. In particular, we focused on septic shock, a subset of sepsis in which underlying circulatory and cellular/metabolic abnormalities are profound enough to substantially increase mortality. Our model includes cardiovascular, immune, nervous system models and a pharmacological model as submodels and integrates them to create a sepsis model based on pathological facts.
Model validation was done in two steps. First, we established a model for a standard patient in order to confirm the validity of our approach in general aspects. For this, we checked the correspondence between the severity of infection defined in terms of pathogen growth rate and the ease of recovery defined in terms of the intensity of treatment required for recovery. The simulations for a standard patient showed good correspondence. We then applied the same simulations to a patient with heart failure as an underlying disease. The model showed that spontaneous recovery would not occur without treatment, even for a very mild infection. This is consistent with clinical experience. We next validated the model using clinical data of three sepsis patients. The model parameters were tuned for these patients based on the model for the standard patient used in the first part of the validation. In these cases, the simulations agreed well with clinical data. In fact, only a handful parameters need to be tuned for the simulations to match with the data.
We have constructed a model of septic shock and have shown that it can reproduce well the time courses of treatment and disease progression. Tuning of model parameters for each patient could be easily done. This study demonstrates the feasibility of disease models, suggesting the possibility of clinical use in the prediction of disease progression, decisions on the timing of drug dosages, and the estimation of time of infection.
疾病的数学模型可为基于基本病理生理学建立有效的治疗策略提供统一方法。然而，对临床实践有用的模型必须克服人体生理学的巨大复杂性以及患者环境条件的多样性。为了对一种复杂疾病进行建模，我们选择了脓毒症，它是一种高度复杂、危及生命且死亡率高的全身性疾病。特别是，我们专注于感染性休克，它是脓毒症的一个子集，其潜在的循环和细胞/代谢异常严重到足以大幅增加死亡率。我们的模型包括心血管、免疫、神经系统模型以及一个药理学模型作为子模型，并将它们整合起来以创建一个基于病理事实的脓毒症模型。
模型验证分两步进行。首先，我们为一名标准患者建立了一个模型，以确认我们的方法在一般方面的有效性。为此，我们检查了根据病原体生长率定义的感染严重程度与根据恢复所需治疗强度定义的恢复难易程度之间的对应关系。对标准患者的模拟显示出良好的对应关系。然后，我们将相同的模拟应用于一名患有心力衰竭基础疾病的患者。模型显示，即使是非常轻微的感染，如果不进行治疗也不会自发恢复。这与临床经验一致。接下来，我们使用三名脓毒症患者的临床数据对模型进行验证。基于验证第一部分中使用的标准患者模型，为这些患者调整了模型参数。在这些情况下，模拟结果与临床数据吻合良好。事实上，只需调整少数几个参数，模拟结果就能与数据匹配。
我们构建了一个感染性休克模型，并表明它能够很好地再现治疗和疾病进展的时间进程。为每个患者调整模型参数很容易做到。这项研究证明了疾病模型的可行性，暗示了其在疾病进展预测、药物剂量时机决策以及感染时间估计方面临床应用的可能性。
