Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

鉴于为预测 COVID-19 死亡率而开发的预测算法数量不断增加，我们使用跨国医疗保健系统网络评估了死亡率预测算法的可移植性。我们使用基线常用实验室测量值以及跨医疗保健系统、国家和大陆的标准人口统计和临床协变量来预测 COVID-19 死亡率。具体来说，我们使用九个测量的实验室测试值、入院时的标准人口统计数据以及入院前的合并症负担来训练 Cox 回归模型。这些模型在地点、国家和大陆级别进行了比较。在 39,969 名住院的 COVID-19 患者（68.6% 男性）中，有 5717 人（14.3%）死亡。在 Cox 模型中，年龄、白蛋白、AST、肌酸、CRP 和白细胞计数最能预测死亡率。基线协变量更能预测 COVID-19 住院初期的死亡率。在具有较大队列规模的医疗保健系统中训练的模型在移植到不同地点时很大程度上保留了良好的可移植性性能。入院时的常规实验室检测值与基本人口统计特征相结合可以预测住院的 COVID-19 患者的死亡率。重要的是，这种潜在的可部署模型与之前的工作不同，它不仅展示了一致的性能，而且在美国和欧洲的医疗保健系统中具有可靠的可移植性，突出了该模型和整体方法的普遍性。