A proteomic survival predictor for COVID-19 patients in intensive care.
A proteomic survival predictor for COVID-19 patients in intensive care.
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DOI：
10.1371/journal.pdig.0000007
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发表时间：
2022-01
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全球卫生保健系统受到COVID-19大流行的挑战。由于临床建立的风险评估（如SOFA和APACHE II评分）在预测COVID-19重症患者的生存方面的作用有限，因此有必要优化重症监护的治疗和资源分配。还需要其他工具来监测治疗，包括临床试验中的实验性疗法。全面捕获人体生理学，我们推测蛋白质组学与新的数据驱动分析策略相结合可以产生新一代的预后鉴别器。我们研究了两个独立队列的重症COVID-19患者，他们需要重症监护和有创机械通气。SOFA评分、Charlson合并症指数和APACHE II评分在预测COVID-19预后方面的作用有限。相反，对50名接受有创机械通气的危重患者在349个时间点的321个血浆蛋白组进行量化，发现有14种蛋白质在幸存者和非幸存者之间表现出不同的轨迹。在最大治疗水平（即WHO 7级）的第一个时间点，即在结果出现前几周，对蛋白质组学测量进行训练的预测器实现了幸存者的准确分类（AUROC为0.81）。我们在一个独立验证队列（AUROC 1.0）上检验了所建立的预测因子。预测模型中相关度较高的蛋白大部分属于凝血系统和补体级联。我们的研究表明，血浆蛋白质组学可以在重症监护中产生显著优于当前预后标志物的预后预测因子。世界各地的卫生保健系统正在努力容纳大量COVID-19重症患者。此外，大流行迫切需要加快研究潜在新疗法的临床试验。虽然各种生物标志物可以区分和预测不同疾病严重程度患者的未来病程，但对于疾病严重程度相似的患者群体，如需要重症监护的患者，预后仍然困难。重症监护医学中现有的风险评估，如SOFA或APACHE II，在预测COVID-19未来疾病结局方面的可靠性有限。在这项研究中，我们假设可以利用血浆蛋白质组来预测COVID-19危重患者的生存，血浆蛋白质组反映了生物体表达并存在于血液中的完整蛋白质集，并且已知可以全面捕获宿主对COVID-19的反应。在这里，我们发现了14种蛋白质，随着时间的推移，在重症监护下存活下来的患者和没有存活下来的患者中，蛋白质的变化方向相反。使用结合多种蛋白质测量的机器学习模型，我们能够在结果出来前几周从单个血液样本中准确预测COVID-19危重患者的生存，大大优于既定的风险预测指标。
Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care. Healthcare systems around the world are struggling to accommodate high numbers of the most severely ill patients with COVID-19. Moreover, the pandemic creates a pressing need to accelerate clinical trials investigating potential new therapeutics. While various biomarkers can discriminate and predict the future course of disease for patients of different disease severity, prognosis remains difficult for patient groups with similar disease severity, e.g. patients requiring intensive care. Established risk assessments in intensive care medicine such as the SOFA or APACHE II show only limited reliability in predicting future disease outcomes for COVID-19. In this study we hypothesized that the plasma proteome, which reflects the complete set of proteins that are expressed by an organism and are present in the blood, and which is known to comprehensively capture the host response to COVID-19, can be leveraged to allow for prediction of survival in the most critically ill patients with COVID-19. Here, we found 14 proteins, which over time changed in opposite directions for patients who survive compared to patients who do not survive on intensive care. Using a machine learning model which combines the measurements of multiple proteins, we were able to accurately predict survival in critically ill patients with COVID-19 from single blood samples, weeks before the outcome, substantially outperforming established risk predictors.