BACKGROUND 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. RESULTS 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. CONCLUSIONS 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.

中文翻译：

---

基于临床数据的感染性休克的数学模型。


背景技术疾病的数学模型可以为建立基于基本病理生理学的有效治疗策略提供统一的方法。然而，对临床实践有用的模型必须克服人类生理学的巨大复杂性和患者环境条件的多样性。为了对复杂疾病进行建模，我们选择脓毒症，这是一种高度复杂、危及生命的全身性疾病，死亡率很高。我们特别关注脓毒性休克，这是脓毒症的一个亚型，其中潜在的循环和细胞/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、神经系统模型和药理学模型作为子模型，并将它们集成以创建基于病理事实的脓毒症模型。结果模型验证分两步完成。首先，我们为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，我们检查了以病原体生长率定义的感染严重程度与以恢复所需的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一致性。然后，我们将相同的模拟应用于患有心力衰竭作为基础疾病的患者。该模型表明，即使是非常轻微的感染，如果不进行治疗，也不会自然恢复。这与临床经验是一致的。接下来，我们使用三名脓毒症患者的临床数据验证了该模型。根据验证第一部分中使用的标准患者的模型，对这些患者的模型参数进行了调整。 在这些情况下，模拟与临床数据非常吻合。事实上，只需调整少数参数即可使模拟与数据匹配。结论 我们构建了感染性休克模型，并证明它可以很好地重现治疗过程和疾病进展。可以轻松调整每个患者的模型参数。这项研究证明了疾病模型的可行性，表明临床用于预测疾病进展、决定药物剂量时间和估计感染时间的可能性。