Mizrahi Reuveni Miri, Kertes Jennifer, Shapiro Ben David Shirley, Shahar Arnon, Shamir-Stein Naama, Rosen Keren, Liran Ori, Bar-Yishay Mattan, Adler Limor
Health Division, Maccabi Healthcare Services, Tel Aviv 6812509, Israel.
Department of Family Medicine, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel.
Biomedicines. 2023 Mar 2;11(3):767. doi: 10.3390/biomedicines11030767.
Risk stratification models have been developed to identify patients that are at a higher risk of COVID-19 infection and severe illness. Objectives To develop and implement a scoring tool to identify COVID-19 patients that are at risk for severe illness during the Omicron wave.
This is a retrospective cohort study that was conducted in Israel's second-largest healthcare maintenance organization. All patients with a new episode of COVID-19 between 26 November 2021 and 18 January 2022 were included. A model was developed to predict severe illness (COVID-19-related hospitalization or death) based on one-third of the study population (the train group). The model was then applied to the remaining two-thirds of the study population (the test group). Risk score sensitivity, specificity, and positive predictive value rates, and receiver operating characteristics (ROC) were calculated to describe the performance of the model.
A total of 409,693 patients were diagnosed with COVID-19 over the two-month study period, of which 0.4% had severe illness. Factors that were associated with severe disease were age (age > 75, OR-70.4, 95% confidence interval [CI] 42.8-115.9), immunosuppression (OR-4.8, 95% CI 3.4-6.7), and pregnancy (5 months or more, OR-82.9, 95% CI 53-129.6). Factors that were associated with a reduced risk for severe disease were vaccination status (patients vaccinated in the previous six months OR-0.6, 95% CI 0.4-0.8) and a prior episode of COVID-19 (OR-0.3, 95% CI 0.2-0.5). According to the model, patients who were in the 10th percentile of the risk severity score were considered at an increased risk for severe disease. The model accuracy was 88.7%.
This model has allowed us to prioritize patients requiring closer follow-up by their physicians and outreach services, as well as identify those that are most likely to benefit from anti-viral treatment during the fifth wave of infection in Israel, dominated by the Omicron variant.
已开发出风险分层模型，以识别感染新冠病毒和罹患重症风险较高的患者。目的：开发并应用一种评分工具，以识别在奥密克戎毒株流行期间有重症风险的新冠患者。
这是一项在以色列第二大医疗保健维护组织中进行的回顾性队列研究。纳入了2021年11月26日至2022年1月18日期间首次感染新冠病毒的所有患者。基于三分之一的研究人群（训练组）开发了一个预测重症（与新冠相关的住院或死亡）的模型。然后将该模型应用于其余三分之二的研究人群（测试组）。计算风险评分的敏感性、特异性、阳性预测值率和受试者工作特征（ROC），以描述该模型的性能。
在为期两个月的研究期间，共有409,693名患者被诊断为感染新冠病毒，其中0.4%患有重症。与重症相关的因素包括年龄（年龄>75岁，OR=70.4，95%置信区间[CI]42.8 - 115.9）、免疫抑制（OR=4.8，95%CI 3.4 - 6.7）和妊娠（5个月及以上，OR=82.9，95%CI 53 - 129.6）。与重症风险降低相关的因素包括疫苗接种状况（在过去六个月内接种疫苗的患者，OR=0.6，95%CI 0.4 - 0.8）和既往新冠病毒感染史（OR=0.3，95%CI 0.2 - 0.5）。根据该模型，风险严重程度评分处于第10百分位的患者被认为重症风险增加。该模型的准确率为88.7%。
该模型使我们能够确定医生和外联服务需要密切随访的患者优先级，并识别出在以色列以奥密克戎变种为主导的第五波感染期间最有可能从抗病毒治疗中获益的患者。
