Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy.
J Med Internet Res. 2021 Oct 11;23(10):e31400. doi: 10.2196/31400.
Many countries have experienced 2 predominant waves of COVID-19-related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic.
In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic.
Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19.
Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain.
Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.
许多国家经历了 COVID-19 相关住院治疗的两波高峰。比较大流行期间分波次住院患者的临床轨迹，有助于进一步了解 COVID-19 流行病学、病理生理学和医疗动态的演变。
在这项回顾性队列研究中，我们分析了来自参与医疗保健系统的 SARS-CoV-2 感染住院患者的电子健康记录（EHR）数据，这些患者来自 6 个国家的 315 家医院。我们比较了第一波和第二波大流行期间住院患者的住院率、严重 COVID-19 风险和平均实验室值。
使用联邦方法，每个参与的医疗保健系统从他们的第一波和第二波队列中提取患者水平的临床数据，并将汇总数据提交给中央站点。中央站点采用了数据质量控制步骤，以纠正不合理的值并协调单位。通过计算单个医疗保健系统的效应量，并使用随机效应荟萃分析对其进行综合，以解释异质性，从而进行统计分析。我们根据 C 反应蛋白（CRP）、铁蛋白、纤维蛋白原、降钙素原、D-二聚体和肌酐与严重 COVID-19 的关联，将实验室分析重点放在这些标志物上。
共有 79613 名患者的数据可用，其中 32467 名患者在第一波住院，47146 名患者在第二波住院。第一波和第二波之间，男性患者和 50 至 69 岁患者的比例显著下降。与第一波相比，第二波住院患者严重 COVID-19 的风险降低了 9.9%（95%CI 8.5%-11.3%）。人口亚组分析表明，26 至 49 岁和 50 至 69 岁、男性和女性、以及黑人患者在第二波的严重疾病风险显著低于第一波。入院时，第二波的 CRP 均值明显低于第一波。入院后第 7 天，第二波的 CRP、铁蛋白、纤维蛋白原和降钙素原均值明显低于第一波。总体而言，各国从第一波到第二波的实验室检测率变化不同。入院时，法国、德国和西班牙的 D-二聚体检测率明显更高。
第二波住院患者严重 COVID-19 的风险显著降低。这与第二波入院后第 7 天的 CRP 等实验室值更接近典型生理范围的均值相对应。我们的联邦方法证明了从多个国际医疗保健系统协调异质 EHR 数据以快速进行大规模研究来描述 COVID-19 临床轨迹演变的可行性和有效性。
