Endotypes of thrombocytopenia in the critically ill

血小板减少症在危重病人中极为常见。然而，急性血小板反应在 危重患者的研究还不够深入，而重症监护室中血小板减少症的多因素病因使研究变得困难。 了解，或了解是否治疗它。在几种情况下，如创伤性损伤或败血症，非常低 血小板计数与出血、血栓形成和终末器官损伤有关。血小板作为一种关键因素被广泛研究 止血的成分，但一个迅速出现的概念是，血小板也是全身系统的关键效应细胞 炎症过程既是局部和全身炎症反应的煽动者，也是 导致组织损伤的炎症。血小板和炎症之间的联系是复杂的和双向的， 作为炎症配体，已被证明可调节血小板功能，激活的血小板可诱导炎症反应 其他细胞类型的反应。这项建议的主要主题是研究危重病患者的血小板动力学。 患者，构建该人群中血小板减少的临床内型，并将这些内型与 通过计算建模的潜在中尺度机制。我们将使用大型电子健康记录为基础 数据库和一个三州创伤数据库作为构建这些内型的源数据。我们将内型定义为临床 按四个维度定义的模式：(1)基线信息(人口、慢性病负担、严重程度 疾病和承认诊断)，(2)血小板计数时间序列的特征(减少率，最低值等)，(3) 同时干预，以及(4)结果。计算方法将尝试将临床内型根植于 机械的解释(或替代解释的集合)，有助于聚焦基础科学 研究，并填补阻止设计和使用靶向抗血小板炎症药物的关键知识空白 危重病人的治疗方法。计算模型将在不同的复杂程度上开发，具有特定的 注意将潜在的机制与血小板减少患者常规进行的功能分析联系起来，例如 凝血酶原时间、激活凝血时间和血栓弹性图。

Thrombocytopenia is extremely frequent in critically ill patients. However, the role of acute platelet responses in critically ill patients is not well studied, and the multifactorial etiology of thrombocytopenia in the ICU makes it difficult to understand, or understand whether or not to treat it. In several situations such as traumatic injury or sepsis, very low platelet counts have been to bleeding, thrombosis and end-organ injury. Platelets have been extensively studied as a key component of hemostasis, but a rapidly emerging concept is that platelets are also key effector cells in systemic inflammatory processes as both instigators of local and systemic inflammatory reactions and also participants in the inflammation that contributes to tissue injury. The link between platelets and inflammation is complex and bidirectional, as inflammatory ligands have been shown to regulate platelet function and activated platelets induce inflammatory responses in other cell types. The overarching theme of this proposal is to study platelet dynamics in critically ill patients, construct clinical endotypes of thrombocytopenia in this population, and to relate these endotypes to underlying mesoscale mechanisms through computational modeling. We will use a large electronic health record-based database and a tri-state trauma database as source data to construct these endotypes. We define endotype as clinical patterns defined along four dimensions: (1) baseline information (demographic, chronic disease burden, severity of illness and admitting diagnosis), (2) features of the platelet count time series (rate of decrease, nadir, etc.), (3) concurrent interventions, and (4) outcome. The computational approach will attempt to root clinical endotypes in mechanistic interpretations (or collections of alternative interpretations), contributing to focus basic science investiagtions, and to close key knowledge gaps preventing the design and use of targeted anti-platelet-inflammatory therapies in the critically ill. Computational models will be developed at different levels of complexity, with a specific attention to tie underlying mechanisms to functional assays routinely performed in thrombocytopenic patients, such as prothrombin time, activated coagulation time, and thromboelastogram.