Department of Biomaterials, Bioengineering Institute, New York University, 433 First Avenue, Room 820, New York, NY 10010-4086, USA.
Lab Chip. 2020 Jun 21;20(12):2075-2085. doi: 10.1039/d0lc00373e. Epub 2020 Jun 3.
SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.
SARS-CoV-2 是导致冠状病毒病（COVID-19）的病毒，该病已达到大流行水平，导致发病率和死亡率显著上升，影响到每个有人居住的大洲。大量需要重症监护的患者有可能使全球的医疗体系不堪重负。同样，迫切需要 COVID-19 疾病严重程度测试来为处于高死亡风险的患者优先分配护理和资源。在这里，我们提出了一种基于 C 反应蛋白（CRP）、N 末端 pro B 型利钠肽（NT-proBNP）、肌红蛋白（MYO）、D-二聚体、降钙素原（PCT）、肌酸激酶-心肌带（CK-MB）和心肌肌钙蛋白 I（cTnI）等生物标志物测量值的即时护理 COVID-19 严重程度评分和临床决策支持系统。COVID-19 严重程度评分结合了多元生物标志物测量值和风险因素，使用统计学习算法来预测死亡率。COVID-19 严重程度评分使用来自中国武汉的 160 名住院 COVID-19 患者的数据进行了训练和评估。我们的分析发现，死亡组的 COVID-19 严重程度评分明显高于出院组，中位数（四分位距）评分分别为 59（40-83）和 9（6-17），曲线下面积为 0.94（95%CI 0.89-0.99）。尽管这项分析代表了有心脏合并症（高血压）的患者，但纳入 COVID-19 中涉及的其他病理生理学的生物标志物（例如，D-二聚体用于血栓事件，CRP 用于感染或炎症，PCT 用于细菌合并感染和败血症）可能会改善对更广泛人群的未来预测。这些有前途的初步模型为即时护理 COVID-19 严重程度评分系统铺平了道路，在使用外部收集的临床数据进一步验证后，该系统将对患者护理产生影响。COVID-19 的临床决策支持工具具有通过为高风险不良结局患者优先提供重症监护来拯救生命的强大潜力。
