Internal Medicine Department, Faculty of Medicine for Girls, Al-Azhar University, Cairo, Egypt
Internal Medicine Department, Ibn Sina National College for Medical Studies, Jeddah, Saudi Arabia.
J Investig Med. 2022 Feb;70(2):421-427. doi: 10.1136/jim-2021-001940. Epub 2021 Nov 26.
The ISARIC4C consortium developed and internally validated the 4C Score for prediction of mortality only in hospitalized patients. We aimed to assess the validity of the 4C Score in mortality prediction of patients with COVID-19 who had been home isolated or hospitalized.This retrospective cross-sectional study was performed after the first wave of COVID-19. Data of all PCR-positive COVID-19 patients who had been discharged, hospitalized, or died were retrospectively analyzed. Patients were classified into four risk groups according to the 4C Mortality Score. A total of (506) patients were classified as follows: low (57.1%), intermediate (27.9%), high (13%), and very high (2%) risk groups. Clinical, radiological, and laboratory data were significantly more severe in the high and very high-risk groups compared with other groups (p<0.001 for all). Mortality rate was correctly estimated by the model with 71% sensitivity, 88.6% specificity, and area under the curve of 0.9. The mortality rate was underestimated among the very high-risk group (66.2% vs 90%). The odds of mortality were significantly greater in the presence of hypoxia (OR 2.6, 95% CI 1.5 to 4.6, p<0.001) and high respiratory rate (OR 5.3, 95% CI 1.6 to 17.9, p<0.007), C reactive protein (CRP) (OR 3.5, 95% CI 1.8 to 6.8, p<0.001), and blood urea nitrogen (BUN) (OR 1.9, 95% CI 1.3 to 3.1, p<0.002). Other components of the model had non-significant predictions. In conclusion, the 4C Mortality Score has good sensitivity and specificity in early risk stratification and mortality prediction of patient with COVID-19. Within the model, only hypoxia, tachypnea, high BUN, and CRP were the independent mortality predictors with the possibility of overlooking other important predictors.
ISARIC4C 联盟开发并内部验证了 4C 评分，仅用于预测住院患者的死亡率。我们旨在评估 4C 评分在 COVID-19 患者中的死亡率预测中的有效性，这些患者已经在家中隔离或住院。这项回顾性的横断面研究是在 COVID-19 的第一波之后进行的。回顾性分析了所有已出院、住院或死亡的 PCR 阳性 COVID-19 患者的数据。根据 4C 死亡率评分，患者被分为四个风险组。共有（506）名患者被分为以下风险组：低（57.1%）、中（27.9%）、高（13%）和极高（2%）。与其他组相比，高危和极高危组的临床、放射学和实验室数据明显更为严重（所有 p<0.001）。该模型的敏感性为 71%，特异性为 88.6%，曲线下面积为 0.9，正确估计了死亡率。极高危组的死亡率被低估（66.2% 与 90%）。存在低氧血症（OR 2.6，95%CI 1.5 至 4.6，p<0.001）和高呼吸频率（OR 5.3，95%CI 1.6 至 17.9，p<0.007）、C 反应蛋白（CRP）（OR 3.5，95%CI 1.8 至 6.8，p<0.001）和血尿素氮（BUN）（OR 1.9，95%CI 1.3 至 3.1，p<0.002）时，死亡的可能性显著增加。模型的其他组成部分预测结果不显著。总之，4C 死亡率评分在 COVID-19 患者的早期风险分层和死亡率预测中具有良好的敏感性和特异性。在该模型中，只有低氧血症、呼吸急促、高 BUN 和 CRP 是独立的死亡预测因子，可能会忽略其他重要的预测因子。
