SBIR Phase I: A Clinical Decision Support System for Fluid Resuscitation for Patients with Sepsis

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力包括为需要液体管理的重症监护病房（ICU）患者提供可靠和持续的液体复苏（即静脉注射液体）。危重病人液体复苏的目标是将循环系统的血容量恢复到可接受的水平，以确保足够的组织灌注（即血液输送到组织）。然而，患者内部和患者间生理参数的巨大差异以及不同疾病和药物的影响可能导致临床工作人员对ICU患者的复苏不足和过度。最佳液体复苏对于严重脓毒症（即脓毒症合并急性器官功能障碍的患者）或脓毒症休克（即脓毒症合并持续或难治性低血压或组织灌注不足的患者，尽管进行了适当的液体复苏）的恢复尤为重要。在这些患者中，由于血管舒张（即血管扩张）和毛细血管渗漏（即液体进入间隙的分布增加）导致的动脉循环无效需要通过液体管理来补偿。拟议的项目包括开发一个临床决策支持系统，用于重症监护室严重败血症或感染性休克患者的液体复苏。具体来说，将开发一个基于云的临床决策支持系统，该系统将使用来自血流动力学监测设备的连续测量，为临床医生提供可操作的反馈，以优化流体管理。在这个项目中，将开发一个临床决策支持算法，指导临床医生进行流体管理，并在我们的合作医院进行临床研究。使用基于模型的方法计算患者液体需要量的一个关键缺点是，模型需要精确的参数值。然而，高保真模型并不存在，目前的模型不能完全解释病人对液体的生理和反应。拟议的框架不需要任何患者特定信息（例如，年龄、性别、体重、诊断、伴随用药等）。此外，该框架不需要患者动力学和患者特定生理参数的准确模型。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project includes reliable and consistent fluid resuscitation (i.e., intravenous administration of fluids) for patients in the intensive care unit (ICU) requiring fluid management. The goal of fluid resuscitation in critically ill patients is to restore blood volume in the circulatory system to an acceptable level in order to ensure adequate tissue perfusion (i.e., blood delivery to tissue). However, large intrapatient and interpatient variability in physiological parameters as well as the effect of different illnesses and medications can result in under- and over-resuscitation of ICU patients by the clinical staff. Optimal fluid resuscitation is especially critical for the recovery of patients with severe sepsis (i.e., patients with sepsis and acute organ dysfunction) or septic shock (i.e., patients with sepsis and persistent or refractory hypotension or tissue hypoperfusion despite adequate fluid resuscitation). In these patients, ineffective arterial circulation due to vasodilation (i.e., dilation of blood vessels) and capillary leakage (i.e., increased distribution of fluids into the interstitial space) needs to be compensated by fluid management.The proposed project involves developing a clinical decision support system for fluid resuscitation for patients with severe sepsis or septic shock in the ICU. Specifically, a cloud-based clinical decision support system will be developed, which will use continuous measurements from hemodynamic monitoring devices to provide actionable feedback for clinicians to optimize fluid management. In this project, a clinical decision support algorithm that will guide the clinician in fluid management will be developed and a clinical study at our partner hospital will be performed. A critical drawback with using a model-based approach to compute the patient's fluid requirement is that accurate parameter values are needed for the model. However, high-fidelity models do not exist and current models cannot fully account for the physiology and response of the patient to fluids. The proposed framework does not need any patient-specific information (e.g., age, gender, weight, diagnosis, concomitant medication, etc.). Furthermore, the framework does not require an accurate model of the patient dynamics and the patient specific physiological parameters.