Israel Ariel, Schäffer Alejandro A, Merzon Eugene, Green Ilan, Magen Eli, Golan-Cohen Avivit, Vinker Shlomo, Ruppin Eytan
Leumit Health Services, Tel-Aviv 6473817, Israel.
Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.
Microorganisms. 2022 Jun 16;10(6):1238. doi: 10.3390/microorganisms10061238.
Vaccines have allowed for a significant decrease in COVID-19 risk, and new antiviral medications can prevent disease progression if given early in the course of the disease. The rapid and accurate estimation of the risk of severe disease in new patients is needed to prioritize the treatment of high-risk patients and maximize lives saved. We used electronic health records from 101,039 individuals infected with SARS-CoV-2, since the beginning of the pandemic and until 30 November 2021, in a national healthcare organization in Israel to build logistic models estimating the probability of subsequent hospitalization and death of newly infected patients based on a few major risk factors (age, sex, body mass index, hemoglobin A1C, kidney function, and the presence of hypertension, pulmonary disease, and malignancy) and the number of BNT162b2 mRNA vaccine doses received. The model's performance was assessed by 10-fold cross-validation: the area under the curve was 0.889 for predicting hospitalization and 0.967 for predicting mortality. A total of 50%, 80%, and 90% of death events could be predicted with respective specificities of 98.6%, 95.2%, and 91.2%. These models enable the rapid identification of individuals at high risk for hospitalization and death when infected, and they can be used to prioritize patients to receive scarce medications or booster vaccination. The calculator is available online.
疫苗已使新冠病毒感染风险显著降低，新型抗病毒药物若在疾病早期使用，可预防疾病进展。为了优先治疗高危患者并最大程度挽救生命，需要快速准确地评估新患者的重症风险。自疫情开始至2021年11月30日，我们利用以色列一家全国性医疗保健机构中101039名感染新冠病毒个体的电子健康记录，构建逻辑模型，根据一些主要风险因素（年龄、性别、体重指数、糖化血红蛋白、肾功能，以及是否存在高血压、肺部疾病和恶性肿瘤）和接种BNT162b2 mRNA疫苗的剂量，估算新感染患者随后住院和死亡的概率。通过10倍交叉验证评估模型性能：预测住院的曲线下面积为0.889，预测死亡率的曲线下面积为0.967。分别以98.6%、95.2%和91.2%的特异性，可预测总共50%、80%和90%的死亡事件。这些模型能够快速识别感染时具有高住院和死亡风险的个体，可用于优先安排患者接受稀缺药物治疗或加强疫苗接种。该计算器可在线获取。
