Central Laboratory of Clinical Biology, University Hospital Center of Blida, 9000 Blida, Algeria.
Department of Internal Medicine and Cardiology, University Hospital Center of Blida. 9000 Blida, Algeria.
Intensive Crit Care Nurs. 2021 Jun;64:103012. doi: 10.1016/j.iccn.2021.103012. Epub 2021 Jan 9.
Coronavirus Disease 2019 is characterized by a spectrum of clinical severity. This study aimed to develop a laboratory score system to identify high-risk individuals, to validate this score in a separate cohort, and to test its accuracy in the prediction of in-hospital mortality.
In this cohort study, biological data from 330 SARS-CoV-2 infected patients were used to develop a risk score to predict progression toward severity. In a second stage, data from 240 additional COVID-19 patients were used to validate this score. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC).
In the development cohort, a step-wise decrease in the average survival duration was noted with the increment of the risk score (p < 0.0001). A similar trend was confirmed when analyzing this association in the validation cohort (p < 0.0001). The AUC was 0.74 [0.66-0.82] and 0.90 [0.87-0.94], p < 0.0001, respectively for severity and mortality prediction.
This study provides a useful risk score based on biological routine parameters assessed at the time of admission, which has proven its effectiveness in predicting both severity and short-term mortality of COVID-19. Improved predictive scores may be generated by including other clinical and radiological features.
2019 年冠状病毒病（COVID-19）的临床严重程度表现出一定的范围。本研究旨在开发一种实验室评分系统，以识别高危人群，并在独立队列中验证该评分，同时检验其对住院死亡率预测的准确性。
在这项队列研究中，使用了 330 名 SARS-CoV-2 感染患者的生物学数据来开发预测疾病严重程度进展的风险评分。在第二阶段，使用了 240 名额外的 COVID-19 患者的数据来验证该评分。采用受试者工作特征曲线下面积（AUC）来衡量评分的准确性。
在开发队列中，随着风险评分的增加，平均存活时间呈逐步下降趋势（p<0.0001）。在验证队列中分析这种相关性时也得到了类似的趋势（p<0.0001）。严重程度和死亡率预测的 AUC 分别为 0.74[0.66-0.82]和 0.90[0.87-0.94]，p<0.0001。
本研究基于入院时评估的生物学常规参数提供了一种有用的风险评分，该评分已被证明可有效预测 COVID-19 的严重程度和短期死亡率。通过纳入其他临床和影像学特征，可能会生成更具预测能力的评分。
