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通过模拟呼吸机分配协议来研究危机标准护理中的伦理权衡。

Investigating ethical tradeoffs in crisis standards of care through simulation of ventilator allocation protocols.

机构信息

Department of Health Humanities and Bioethics, University of Rochester, Rochester, New York, United States of America.

Department of Philosophy, University of Rochester, Rochester, New York, United States of America.

出版信息

PLoS One. 2024 Sep 12;19(9):e0300951. doi: 10.1371/journal.pone.0300951. eCollection 2024.

DOI:10.1371/journal.pone.0300951
PMID:39264928
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11392394/
Abstract

INTRODUCTION

Arguments over the appropriate Crisis Standards of Care (CSC) for public health emergencies often assume that there is a tradeoff between saving the most lives, saving the most life-years, and preventing racial disparities. However, these assumptions have rarely been explored empirically. To quantitatively characterize possible ethical tradeoffs, we aimed to simulate the implementation of five proposed CSC protocols for rationing ventilators in the context of the COVID-19 pandemic.

METHODS

A Monte Carlo simulation was used to estimate the number of lives saved and life-years saved by implementing clinical acuity-, comorbidity- and age-based CSC protocols under different shortage conditions. This model was populated with patient data from 3707 adult admissions requiring ventilator support in a New York hospital system between April 2020 and May 2021. To estimate lives and life-years saved by each protocol, we determined survival to discharge and estimated remaining life expectancy for each admission.

RESULTS

The simulation demonstrated stronger performance for age-sensitive protocols. For a capacity of 1 bed per 2 patients, ranking by age bands saves approximately 29 lives and 3400 life-years per thousand patients. Proposed protocols from New York and Maryland which allocated without considering age saved the fewest lives (13.2 and 8.5 lives) and life-years (416 and 420 years). Unlike other protocols, the New York and Maryland algorithms did not generate significant disparities in lives saved and life-years saved between White non-Hispanic, Black non-Hispanic, and Hispanic sub-populations. For all protocols, we observed a positive correlation between lives saved and life-years saved, but also between lives saved overall and inequality in the number of lives saved in different race and ethnicity sub-populations.

CONCLUSION

While there is significant variance in the number of lives saved and life-years saved, we did not find a tradeoff between saving the most lives and saving the most life-years. Moreover, concerns about racial discrimination in triage protocols require thinking carefully about the tradeoff between enforcing equality of survival rates and maximizing the lives saved in each sub-population.

摘要

引言

围绕公共卫生紧急情况下适当的危机标准护理(CSC)的争论,往往假设在挽救最多生命、挽救最多寿命年和预防种族差异之间存在权衡。然而,这些假设很少得到实证研究。为了定量描述可能的伦理权衡,我们旨在模拟在 COVID-19 大流行背景下实施五种拟议的 CSC 协议来配给呼吸机的情况。

方法

使用蒙特卡罗模拟来估计在不同短缺情况下实施基于临床严重程度、合并症和年龄的 CSC 协议所挽救的生命数量和寿命年数。该模型使用 2020 年 4 月至 2021 年 5 月期间在纽约医院系统接受呼吸机支持的 3707 名成年患者的数据进行填充。为了估计每种协议所挽救的生命和寿命年数,我们确定了出院时的存活情况,并估计了每次入院的剩余预期寿命。

结果

模拟表明,年龄敏感协议的性能更强。对于每 2 名患者 1 张床位的容量,按年龄带排序可使每千名患者约节省 29 条生命和 3400 个寿命年。纽约和马里兰州提出的不考虑年龄分配的协议挽救的生命最少(约 13.2 和 8.5 条)和寿命年数(约 416 和 420 年)。与其他协议不同,纽约和马里兰州的算法没有在白种非西班牙裔、黑种非西班牙裔和西班牙裔亚人群体之间的拯救生命和寿命年数上产生显著差异。对于所有协议,我们观察到拯救生命和寿命年数之间存在正相关,但也观察到拯救生命总数和不同种族和族裔亚群体之间拯救生命数量不平等之间存在正相关。

结论

虽然挽救生命和寿命年数的数量存在显著差异,但我们没有发现挽救最多生命和挽救最多寿命年数之间的权衡。此外,对分类协议中种族歧视的担忧需要仔细考虑在执行生存率平等和最大限度地提高每个亚群体中拯救生命之间的权衡。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb0/11392394/38a9b734121a/pone.0300951.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb0/11392394/e0c31a022f79/pone.0300951.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb0/11392394/13e199428f6e/pone.0300951.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb0/11392394/b2b84a6c80d6/pone.0300951.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb0/11392394/38a9b734121a/pone.0300951.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb0/11392394/e0c31a022f79/pone.0300951.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb0/11392394/13e199428f6e/pone.0300951.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb0/11392394/b2b84a6c80d6/pone.0300951.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb0/11392394/38a9b734121a/pone.0300951.g004.jpg

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