Vela Emili, Carot-Sans Gerard, Clèries Montse, Monterde David, Acebes Xènia, Comella Adrià, García Eroles Luís, Coca Marc, Valero-Bover Damià, Pérez Sust Pol, Piera-Jiménez Jordi
Servei Català de la Salut (CatSalut), Barcelona, Spain.
Digitalization for the Sustainability of the Healthcare System (DS3), IDIBELL, Barcelona, Spain.
Sci Rep. 2022 Feb 28;12(1):3277. doi: 10.1038/s41598-022-07138-y.
The shortage of recently approved vaccines against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has highlighted the need for evidence-based tools to prioritize healthcare resources for people at higher risk of severe coronavirus disease (COVID-19). Although age has been identified as the most important risk factor (particularly for mortality), the contribution of underlying comorbidities is often assessed using a pre-defined list of chronic conditions. Furthermore, the count of individual risk factors has limited applicability to population-based "stratify-and-shield" strategies. We aimed to develop and validate a COVID-19 risk stratification system that allows allocating individuals of the general population into four mutually-exclusive risk categories based on multivariate models for severe COVID-19, a composite of hospital admission, transfer to intensive care unit (ICU), and mortality among the general population. The model was developed using clinical, hospital, and epidemiological data from all individuals among the entire population of Catalonia (North-East Spain; 7.5 million people) who experienced a COVID-19 event (i.e., hospitalization, ICU admission, or death due to COVID-19) between March 1 and September 15, 2020, and validated using an independent dataset of 218,329 individuals with COVID-19 confirmed by reverse transcription-polymerase chain reaction (RT-PCR), who were infected after developing the model. No exclusion criteria were defined. The final model included age, sex, a summary measure of the comorbidity burden, the socioeconomic status, and the presence of specific diagnoses potentially associated with severe COVID-19. The validation showed high discrimination capacity, with an area under the curve of the receiving operating characteristics of 0.85 (95% CI 0.85-0.85) for hospital admissions, 0.86 (0.86-0.97) for ICU transfers, and 0.96 (0.96-0.96) for deaths. Our results provide clinicians and policymakers with an evidence-based tool for prioritizing COVID-19 healthcare resources in other population groups aside from those with higher exposure to SARS-CoV-2 and frontline workers.
近期获批的针对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的疫苗短缺，凸显了基于证据的工具对于为罹患严重冠状病毒病（COVID-19）风险较高人群优先分配医疗资源的必要性。虽然年龄已被确定为最重要的风险因素（尤其是对于死亡率而言），但潜在合并症的影响通常是使用预先定义的慢性病列表来评估的。此外，个体风险因素的计数对于基于人群的“分层与保护”策略的适用性有限。我们旨在开发并验证一种COVID-19风险分层系统，该系统能够基于严重COVID-19的多变量模型，将普通人群个体分为四个相互排斥的风险类别，严重COVID-19是住院、转入重症监护病房（ICU）以及普通人群死亡率的综合指标。该模型是利用2020年3月1日至9月15日期间在加泰罗尼亚（西班牙东北部；750万人）全体人口中经历COVID-19事件（即因COVID-19住院、入住ICU或死亡）的所有个体的临床、医院和流行病学数据开发的，并使用一个独立数据集进行验证，该数据集包含218,329名经逆转录聚合酶链反应（RT-PCR）确诊为COVID-19的个体，这些个体是在模型开发后被感染的。未定义排除标准。最终模型包括年龄、性别、合并症负担的综合指标、社会经济地位以及存在可能与严重COVID-19相关的特定诊断。验证显示该模型具有较高的区分能力，对于住院情况，接受者操作特征曲线下面积为0.85（95%置信区间0.85 - 0.85）；对于转入ICU情况，曲线下面积为0.86（0.86 - 0.97）；对于死亡情况，曲线下面积为0.96（0.96 - 0.96）。我们的结果为临床医生和政策制定者提供了一种基于证据的工具，用于在除了接触SARS-CoV-2风险较高人群和一线工作人员之外的其他人群中，为COVID-19医疗资源分配确定优先顺序。
