论文标题
中风网络的患者流量的排队模型，以估计急性中风转移能力
A Queueing Model of Patient Flow for Stroke Networks to Estimate Acute Stroke Transfer Capacity
论文作者
论文摘要
背景：美国大多数急性中风（AS）患者最初是在初次中风中心（PSC）评估的，很大一部分需要转移到全面的中风中心（CSC）进行晚期治疗。 CSC通常接受其网络中多个PSC的患者，从而导致容量限制。这项研究使用排队模型来估计由于PSC转移导致CSC容量的影响。 方法：该模型假定到达每个PSC的患者数量，患者转移的比例以及通过AS类型的CSC神经系统重症监护病房（NEURO-ICU）的停留时间是随机的，而患者的缺血性和出血性的转移率是对照变量。主要结果度量是“溢出”概率，即CSC没有能力（神经ICU床不可用）接受转移的概率。使用基本情况和扩展情况进行了模型的数据模拟，以说明更改关键参数的影响，例如从PSCS和CSC Neuro-ICU容量对溢出容量的转移率。 结果：使用基本案例的模型数据模拟表明，PSC的缺血性中风转移率从15％增加到55％，将溢流概率从30.62％提高到36.13％。对扩展案例的进一步模拟表明，要使AS传输率为15％的PSC保持30.62％的先验CSC溢出概率为30.62％，其他PSC需要将其转移率降低12.5％，或者CSC Neuro-ICU将需要添加2张床。 讨论：排队模型可用于估计PSC-CSC网络大小的变化，AS转移速率的变化或CSC的CSC Neuro-ICU床的数量变化其容量对CSC中溢出概率的影响。
Background: Most acute stroke (AS) patients in the United States are initially evaluated at a primary stroke center (PSC) and a significant proportion requires transfer to a comprehensive stroke center (CSC) for advanced treatment. A CSC typically accepts patients from multiple PSCs in its network, leading to capacity limits. This study uses a queueing model to estimate impacts on CSC capacity due to transfers from PSCs. Methods: The model assumes that the number of AS patients arriving at each PSC, proportion of AS patients transferred, and length of stay in the CSC Neurologic Intensive Care Unit (Neuro-ICU) by type of AS are random, while the transfer rates of ischemic and hemorrhagic AS patients are control variables. The main outcome measure is the "overflow" probability, namely, the probability of a CSC not having capacity (unavailability of a Neuro-ICU bed) to accept a transfer. Data simulations of the model, using a base case and an expanded case, were performed to illustrate the effects of changing key parameters, such as transfer rates from PSCs and CSC Neuro-ICU capacity on overflow capacity. Results: Data simulations of the model using a base case show that an increase of a PSC's ischemic stroke transfer rate from 15% to 55% raises the overflow probability from 30.62% to 36.13%. Further simulations of the expanded case show that to maintain an a priori CSC overflow probability of 30.62% when adding a PSC with a AS transfer rate of 15% to the network, other PSCs would need to decrease their transfer rate by 12.5% or the CSC Neuro-ICU would need to add 2 beds. Discussion: A queuing model can be used to estimate the effects of change in the size of a PSC-CSC network, change in AS transfer rates, or change in number of CSC Neuro-ICU beds of a CSC on its capacity on the overflow probability in the CSC.
