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严重急性呼吸综合征冠状病毒 2 感染患者生物标志物轨迹的预后分层分析。

Outcome-Stratified Analysis of Biomarker Trajectories for Patients Infected With Severe Acute Respiratory Syndrome Coronavirus 2.

出版信息

Am J Epidemiol. 2021 Oct 1;190(10):2094-2106. doi: 10.1093/aje/kwab138.

Abstract

Longitudinal trajectories of vital signs and biomarkers during hospital admission of patients with COVID-19 remain poorly characterized despite their potential to provide critical insights about disease progression. We studied 1884 patients with severe acute respiratory syndrome coronavirus 2 infection from April 3, 2020, to June 25, 2020, within 1 Maryland hospital system and used a retrospective longitudinal framework with linear mixed-effects models to investigate relevant biomarker trajectories leading up to 3 critical outcomes: mechanical ventilation, discharge, and death. Trajectories of 4 vital signs (respiratory rate, ratio of oxygen saturation (Spo2) to fraction of inspired oxygen (Fio2), pulse, and temperature) and 4 laboratory values (C-reactive protein (CRP), absolute lymphocyte count (ALC), estimated glomerular filtration rate, and D-dimer) clearly distinguished the trajectories of patients with COVID-19. Before any ventilation, log(CRP), log(ALC), respiratory rate, and Spo2-to-Fio2 ratio trajectories diverge approximately 8-10 days before discharge or death. After ventilation, log(CRP), log(ALC), respiratory rate, Spo2-to-Fio2 ratio, and estimated glomerular filtration rate trajectories again diverge 10-20 days before death or discharge. Trajectories improved until discharge and remained unchanged or worsened until death. Our approach characterizes the distribution of biomarker trajectories leading up to competing outcomes of discharge versus death. Moving forward, this model can contribute to quantifying the joint probability of biomarkers and outcomes when provided clinical data up to a given moment.

摘要

尽管生命体征和生物标志物在 COVID-19 患者住院期间的变化轨迹可能为疾病进展提供重要信息,但这些数据仍未得到充分描述。我们研究了 2020 年 4 月 3 日至 6 月 25 日马里兰州 1 家医院系统内的 1884 例严重急性呼吸综合征冠状病毒 2 感染患者,使用回顾性纵向框架和线性混合效应模型来研究导致 3 个关键结局(机械通气、出院和死亡)的相关生物标志物变化轨迹。4 项生命体征(呼吸频率、血氧饱和度(Spo2)与吸氧分数(Fio2)比值、脉搏和体温)和 4 项实验室值(C 反应蛋白(CRP)、绝对淋巴细胞计数(ALC)、估算肾小球滤过率和 D-二聚体)的变化轨迹清楚地区分了 COVID-19 患者的变化轨迹。在任何通气之前,大约在出院或死亡前 8-10 天,log(CRP)、log(ALC)、呼吸频率和 Spo2-to-Fio2 比值轨迹就开始出现差异。通气后,log(CRP)、log(ALC)、呼吸频率、Spo2-to-Fio2 比值和估算肾小球滤过率轨迹再次在死亡或出院前 10-20 天出现差异。轨迹在出院前改善,在死亡前保持不变或恶化。我们的方法描述了导致出院与死亡的竞争结局的生物标志物变化轨迹的分布。未来,当提供的临床数据截止到某一时刻时,该模型可以有助于量化生物标志物和结局的联合概率。

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