Anderson David R, Aydinliyim Tolga, Bjarnadóttir Margrét V, Çil Eren B, Anderson Michaela R
School of Business Villanova University Philadelphia Pennsylvania USA.
Zicklin School of Business Baruch College, CUNY New York USA.
Prod Oper Manag. 2023 Jan 22. doi: 10.1111/poms.13934.
In the United States, even though national guidelines for allocating scarce healthcare resources are lacking, 26 states have specific ventilator allocation guidelines to be invoked in case of a shortage. While several states developed their guidelines in response to the recent COVID-19 pandemic, New York State developed these guidelines in 2015 as "pandemic influenza is a foreseeable threat, one that we cannot ignore." The primary objective of this study is to assess the existing procedures and priority rules in place for allocating/rationing scarce ventilator capacity and propose alternative (and improved) priority schemes. We first build machine learning models using inpatient records of COVID-19 patients admitted to New York-Presbyterian/Columbia University Irving Medical Center and an affiliated community health center to predict survival probabilities as well as ventilator length-of-use. Then, we use the resulting point estimators and their uncertainties as inputs for a multiclass priority queueing model with abandonments to assess three priority schemes: (i) SOFA-P (Sequential Organ Failure Assessment based prioritization), which most closely mimics the existing practice by prioritizing patients with sufficiently low SOFA scores; (ii) ISP (incremental survival probability), which assigns priority based on patient-level survival predictions; and (iii) ISP-LU (incremental survival probability per length-of-use), which takes into account survival predictions and resource use duration. Our findings highlight that our proposed priority scheme, ISP-LU, achieves a demonstrable improvement over the other two alternatives. Specifically, the expected number of survivals increases death risk while waiting for ventilator use decreases. We also show that ISP-LU is a robust priority scheme whose implementation yields a Pareto-improvement over both SOFA-P and ISP in terms of maximizing saved lives after mechanical ventilation while limiting racial disparity in access to the priority queue.
在美国，尽管缺乏分配稀缺医疗资源的国家指导方针，但有26个州制定了在短缺情况下可援引的特定呼吸机分配指导方针。虽然几个州是针对近期的新冠疫情制定了这些指导方针，但纽约州在2015年就制定了这些指导方针，因为“大流行性流感是一个可预见的威胁，我们不能忽视”。本研究的主要目的是评估现有的分配/配给稀缺呼吸机能力的程序和优先级规则，并提出替代的（且经过改进的）优先级方案。我们首先使用纽约长老会/哥伦比亚大学欧文医学中心及一家附属社区卫生中心收治的新冠患者的住院记录建立机器学习模型，以预测生存概率以及呼吸机使用时长。然后，我们将所得的点估计值及其不确定性作为输入，用于一个带有放弃情况的多类优先级排队模型，以评估三种优先级方案：（i）SOFA-P（基于序贯器官衰竭评估的优先级划分），它通过优先考虑SOFA评分足够低的患者来最紧密地模仿现有做法；（ii）ISP（增量生存概率），它根据患者层面的生存预测来分配优先级；（iii）ISP-LU（每使用时长的增量生存概率），它考虑了生存预测和资源使用持续时间。我们的研究结果表明，我们提出的优先级方案ISP-LU相较于其他两种方案有显著改进。具体而言，预期存活人数增加，而等待使用呼吸机时的死亡风险降低。我们还表明，ISP-LU是一种稳健的优先级方案，其实施在机械通气后挽救生命最大化的同时，限制了优先队列获取方面的种族差异，从而在帕累托意义上优于SOFA-P和ISP。
