Damen Johanna A A, Arshi Banafsheh, van Smeden Maarten, Bertagnolio Silvia, Diaz Janet V, Silva Ronaldo, Thwin Soe Soe, Wynants Laure, Moons Karel G M
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, 3508 GA, the Netherlands.
Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands.
Diagn Progn Res. 2024 Dec 19;8(1):17. doi: 10.1186/s41512-024-00181-5.
We evaluated the performance of prognostic models for predicting mortality or ICU admission in hospitalized patients with COVID-19 in the World Health Organization (WHO) Global Clinical Platform, a repository of individual-level clinical data of patients hospitalized with COVID-19, including in low- and middle-income countries (LMICs).
We identified eligible multivariable prognostic models for predicting overall mortality and ICU admission during hospital stay in patients with confirmed or suspected COVID-19 from a living review of COVID-19 prediction models. These models were evaluated using data contributed to the WHO Global Clinical Platform for COVID-19 from nine LMICs (Burkina Faso, Cameroon, Democratic Republic of Congo, Guinea, India, Niger, Nigeria, Zambia, and Zimbabwe). Model performance was assessed in terms of discrimination and calibration.
Out of 144 eligible models, 140 were excluded due to a high risk of bias, predictors unavailable in LIMCs, or insufficient model description. Among 11,338 participants, the remaining models showed good discrimination for predicting in-hospital mortality (3 models), with areas under the curve (AUCs) ranging between 0.76 (95% CI 0.71-0.81) and 0.84 (95% CI 0.77-0.89). An AUC of 0.74 (95% CI 0.70-0.78) was found for predicting ICU admission risk (one model). All models showed signs of miscalibration and overfitting, with extensive heterogeneity between countries.
Among the available COVID-19 prognostic models, only a few could be validated on data collected from LMICs, mainly due to limited predictor availability. Despite their discriminative ability, selected models for mortality prediction or ICU admission showed varying and suboptimal calibration.
我们在世界卫生组织（WHO）全球临床平台上评估了预测2019冠状病毒病（COVID-19）住院患者死亡率或入住重症监护病房（ICU）情况的预后模型的性能。该平台是一个包含COVID-19住院患者个体水平临床数据的数据库，其中包括低收入和中等收入国家（LMICs）的数据。
我们从一项关于COVID-19预测模型的实时综述中，确定了用于预测确诊或疑似COVID-19患者住院期间总体死亡率和入住ICU情况的符合条件的多变量预后模型。这些模型使用来自9个低收入和中等收入国家（布基纳法索、喀麦隆、刚果民主共和国、几内亚、印度、尼日尔、尼日利亚、赞比亚和津巴布韦）贡献给WHO COVID-19全球临床平台的数据进行评估。模型性能通过区分度和校准度进行评估。
在144个符合条件的模型中，有140个因偏倚风险高、LMICs中无法获得预测因素或模型描述不足而被排除。在11338名参与者中，其余模型在预测院内死亡率方面显示出良好的区分度（3个模型），曲线下面积（AUC）在0.76（95%CI 0.71-0.81）至0.84（95%CI 0.77-0.89）之间。预测入住ICU风险（1个模型）的AUC为0.74（95%CI 0.70-0.78）。所有模型均显示出校准错误和过度拟合的迹象，各国之间存在广泛的异质性。
在现有的COVID-19预后模型中，只有少数模型能够在从LMICs收集的数据上得到验证，主要原因是预测因素有限。尽管这些模型具有区分能力，但用于死亡率预测或入住ICU的选定模型显示出不同程度的次优校准。
