Kurban Lutfi Ali S, AlDhaheri Sharina, Elkkari Abdulbaset, Khashkhusha Ramzi, AlEissaee Shaikha, AlZaabi Amna, Ismail Mohamed, Bakoush Omran
Department of Radiology, Tawam Hospital, Al Ain, United Arab Emirates.
Department of Internal Medicine, Tawam Hospital, Al Ain, United Arab Emirates.
Front Med (Lausanne). 2022 Feb 10;9:817549. doi: 10.3389/fmed.2022.817549. eCollection 2022.
This study was conducted to develop, validate, and compare prediction models for severe disease and critical illness among symptomatic patients with confirmed COVID-19.
For development cohort, 433 symptomatic patients diagnosed with COVID-19 between April 15th 2020 and June 30th, 2020 presented to Tawam Public Hospital, Abu Dhabi, United Arab Emirates were included in this study. Our cohort included both severe and non-severe patients as all cases were admitted for purpose of isolation as per hospital policy. We examined 19 potential predictors of severe disease and critical illness that were recorded at the time of initial assessment. Univariate and multivariate logistic regression analyses were used to construct predictive models. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Calibration and goodness of fit of the models were assessed. A cohort of 213 patients assessed at another public hospital in the country during the same period was used to validate the models.
One hundred and eighty-six patients were classified as severe while the remaining 247 were categorized as non-severe. For prediction of progression to severe disease, the three independent predictive factors were age, serum lactate dehydrogenase (LDH) and serum albumin (ALA model). For progression to critical illness, the four independent predictive factors were age, serum LDH, kidney function (eGFR), and serum albumin (ALKA model). The AUC for the ALA and ALKA models were 0.88 (95% CI, 0.86-0.89) and 0.85 (95% CI, 0.83-0.86), respectively. Calibration of the two models showed good fit and the validation cohort showed excellent discrimination, with an AUC of 0.91 (95% CI, 0.83-0.99) for the ALA model and 0.89 (95% CI, 0.80-0.99) for the ALKA model. A free web-based risk calculator was developed.
The ALA and ALKA predictive models were developed and validated based on simple, readily available clinical and laboratory tests assessed at presentation. These models may help frontline clinicians to triage patients for admission or discharge, as well as for early identification of patients at risk of developing critical illness.
本研究旨在开发、验证和比较确诊COVID-19的有症状患者中重症和危重症的预测模型。
对于开发队列，纳入2020年4月15日至2020年6月30日期间在阿拉伯联合酋长国阿布扎比的塔瓦姆公立医院就诊的433例确诊COVID-19的有症状患者。我们的队列包括重症和非重症患者，因为根据医院政策，所有病例均因隔离目的入院。我们检查了初始评估时记录的19个重症和危重症的潜在预测因素。使用单因素和多因素逻辑回归分析构建预测模型。通过受试者操作特征曲线下面积（AUC）评估辨别力。评估模型的校准和拟合优度。同期在该国另一家公立医院评估的213例患者队列用于验证模型。
186例患者被分类为重症，其余247例被分类为非重症。对于预测进展为重症，三个独立预测因素是年龄、血清乳酸脱氢酶（LDH）和血清白蛋白（ALA模型）。对于进展为危重症，四个独立预测因素是年龄、血清LDH、肾功能（eGFR）和血清白蛋白（ALKA模型）。ALA和ALKA模型的AUC分别为0.88（95%CI，0.86-0.89）和0.85（95%CI，0.83-0.86）。两个模型的校准显示拟合良好，验证队列显示出优异的辨别力，ALA模型的AUC为0.91（95%CI，0.83-0.99），ALKA模型的AUC为0.89（95%CI，0.80-0.99）。开发了一个基于网络的免费风险计算器。
基于就诊时评估的简单、易于获得的临床和实验室检查，开发并验证了ALA和ALKA预测模型。这些模型可能有助于一线临床医生对患者进行入院或出院分诊，以及早期识别有发展为危重症风险的患者。
