Background

Coronavirus disease 2019 (COVID-19) has rapidly become a global pandemic. Because the severity of the disease is highly variable, predictive models to stratify patients according to their mortality risk are needed.

Objective

Our aim was to develop a model able to predict the risk of fatal outcome in patients with COVID-19 that could be used easily at the time of patients' arrival at the hospital.

Methods

We constructed a prospective cohort with 611 adult patients in whom COVID-19 was diagnosed between March 10 and April 12, 2020, in a tertiary hospital in Madrid, Spain. The analysis included 501 patients who had been discharged or had died by April 20, 2020. The capacity of several biomarkers, measured at the beginning of hospitalization, to predict mortality was assessed individually. Those biomarkers that independently contributed to improve mortality prediction were included in a multivariable risk model.

Results

High IL-6 level, C-reactive protein level, lactate dehydrogenase (LDH) level, ferritin level, d-dimer level, neutrophil count, and neutrophil-to-lymphocyte ratio were all predictive of mortality (area under the curve >0.70), as were low albumin level, lymphocyte count, monocyte count, and ratio of peripheral blood oxygen saturation to fraction of inspired oxygen (SpO₂/FiO₂). A multivariable mortality risk model including the SpO₂/FiO₂ ratio, neutrophil-to-lymphocyte ratio, LDH level, IL-6 level, and age was developed and showed high accuracy for the prediction of fatal outcome (area under the curve 0.94). The optimal cutoff reliably classified patients (including patients with no initial respiratory distress) as survivors and nonsurvivors with 0.88 sensitivity and 0.89 specificity.

Conclusion

This mortality risk model allows early risk stratification of hospitalized patients with COVID-19 before the appearance of obvious signs of clinical deterioration, and it can be used as a tool to guide clinical decision making.

中文翻译：

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基于IL-6的COVID-19住院患者的死亡风险模型。

背景

2019年冠状病毒病（COVID-19）已迅速成为全球大流行病。由于疾病的严重程度变化很大，因此需要根据死亡风险对患者进行分层的预测模型。

目的

我们的目标是开发一种能够预测COVID-19患者致命结果风险的模型，该模型可以在患者到达医院时轻松使用。

方法

我们在2020年3月10日至4月12日之间，在西班牙马德里的一家三级医院中建立了611名成年患者的前瞻性队列，其中COVID-19被诊断为COVID-19。该分析包括501位在2020年4月20日之前出院或死亡的患者。在住院之初测量的几种生物标志物预测死亡率的能力进行了单独评估。那些独立有助于改善死亡率预测的生物标志物已包括在多变量风险模型中。

结果

高IL-6水平，C反应蛋白水平，乳酸脱氢酶（LDH）水平，铁蛋白水平，d-二聚体水平，中性粒细胞计数以及中性粒细胞与淋巴细胞的比率均可以预测死亡率（曲线下面积> 0.70）。 ，低白蛋白水平，淋巴细胞计数，单核细胞计数以及外周血氧饱和度与吸入氧分数（SpO ₂ / FiO ₂）的比率也是如此。包括SpO ₂ / FiO ₂在内的多变量死亡率风险模型比率，嗜中性白血球/淋巴细胞比率，LDH水平，IL-6水平和年龄均得到了发展，并且在预测致命结局方面具有很高的准确性（曲线下面积0.94）。最佳临界值将患者（包括没有初始呼吸窘迫的患者）可靠地分类为幸存者和非幸存者，敏感性为0.88，特异性为0.89。

结论

该死亡率风险模型允许在出现明显的临床恶化迹象之前对住院的COVID-19患者进行早期风险分层，并且可以将其用作指导临床决策的工具。