使用多种方法量化一家大型安全网医院 COVID-19 住院患者的需氧量

Sky Vanderburg, Tyler Law, Priya B. Shete, Elisabeth D. Riviello, Carolyn M. Hendrickson, Gregory D. Burns, Vivek Jain, Michael S. Lipnick
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引用次数: 0

摘要

背景:在 COVID-19 大流行期间,全球许多机构都在努力预测氧气需求量,氧气需求量往往超过氧气供应量,从而影响了患者护理。对 COVID-19 患者需氧量的准确估计很少,而且提出的估计方法尚未得到充分评估或实施。为了填补这一知识空白,美国一家大型安全网医院利用患者消耗(需求)数据、氧气采购(供应)数据以及新型计算工具的模型数据计算了 COVID-19 患者的需氧量。计算方法从 2020 年 3 月至 2022 年 3 月期间扎克伯格旧金山综合医院(ZSFG)收治的 COVID-19 患者的电子病历中提取数据,包括每次记录的外周血氧饱和度(SpO2)测量值以及供氧设备和设置。患者的总耗氧量按住院期间每个记录时间间隔的供氧量总和计算。供氧量使用供氧设备的特定公式计算。此外,还报告了可能影响氧气需求量的患者和治疗特定因素。为了进行比较,我们查看了研究期间的氧气采购记录,以估算消耗的供氧量,并使用 Oxygencalculator.com 工具来模拟相同规模的实验患者的氧气需求量。结果:共分析了 1,076 名患者的 282,095 个时间点。三分之二的患者接受了氧疗,其中 24.3% 的患者接受了高流量氧疗 (HFO),16.0% 的患者接受了有创机械通气 (IMV)。总体院内死亡率为 7.5%,吸氧患者的死亡率为 10.8%,接受 IMV 的患者死亡率为 28.3%。氧疗持续时间的中位数(IQR)为 3.1(0.8-8.9)天,平均(标清)氧流量为每分钟 5.6(5.0)升,平均(标清)每次住院总供氧量为 180,115 (510,330) 升。与根据患者数据估算的需求量相比,基于供氧和模型的方法都高估了耗氧量。结论:本研究是计算 COVID-19 患者需氧量的最大规模研究之一,其中包括有助于解释需氧量变化的患者临床特征。此外,氧需求量的量化方法适用于任何环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantifying Oxygen Demand by Patients Hospitalized with COVID-19 at a Large Safety-Net Hospital Using Multiple Methodologies
Background: During the COVID-19 pandemic, many facilities worldwide struggled to forecast oxygen demand, which often exceeded oxygen supply to the detriment of patient care. Accurate estimates of oxygen demand by patients with COVID-19 are scarce, and proposed estimation methods have not been fully evaluated or implemented. To address this knowledge gap, oxygen demand by COVID-19 patients was calculated at a large safety-net hospital in the United States using patient consumption (demand) data, oxygen procurement (supply) data, and modeled data with a novel calculator tool. Methods: Data were extracted from electronic medical records of patients admitted with COVID-19 to Zuckerberg San Francisco General Hospital (ZSFG) from March 2020 to March 2022, including every recorded peripheral oxygen saturation (SpO2) measurement as well as oxygen delivery device(s) and settings. Total patient oxygen consumption was calculated as the sum of oxygen delivery amounts for each recorded time interval during hospitalization. Oxygen delivery amounts were calculated using delivery device-specific formulas. Patient and treatment-specific factors which may impact oxygen demand were also reported. For comparison, oxygen procurement logs from the study period were reviewed to estimate supply consumed, and the Oxygencalculator.com tool was used to model oxygen demand using an experimental patient population of the same size. Results: In total, 282,095 time points from 1,076 patients were analyzed. Two-thirds of patients received oxygen, of which 24.3% received high-flow oxygen (HFO) therapy and 16.0% received invasive mechanical ventilation (IMV) at some point. In-hospital mortality was 7.5% overall, 10.8% for patients who received oxygen, and 28.3% for patients who received IMV. The median (IQR) duration of oxygen therapy was 3.1 (0.8-8.9) days, mean (SD) oxygen flow was 5.6 (5.0) liters per minute (LPM), and mean (SD) total volume of oxygen delivered was 180,115 (510,330) liters (L) per hospitalization. Both the supply- and model-based methods overestimated oxygen consumption compared to demand estimated from patient data. Conclusions: This study represents one of the largest cohorts of patients with COVID-19 for which oxygen demand has been calculated, including patient clinical characteristics which may help explain variations in oxygen demand. Moreover, oxygen demand was quantified using a methodology that could be applied in any setting.
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