Interstitial Lung Disease Unit, Department of Respiratory Medicine, Royal Brompton and Harefield NHS Foundation Trust, London, UK
National Heart and Lung Institute, Imperial College London, London, UK.
Thorax. 2021 Jul;76(7):696-703. doi: 10.1136/thoraxjnl-2020-216425. Epub 2021 Mar 10.
Risk factors of adverse outcomes in COVID-19 are defined but stratification of mortality using non-laboratory measured scores, particularly at the time of prehospital SARS-CoV-2 testing, is lacking.
Multivariate regression with bootstrapping was used to identify independent mortality predictors in patients admitted to an acute hospital with a confirmed diagnosis of COVID-19. Predictions were externally validated in a large random sample of the ISARIC cohort (N=14 231) and a smaller cohort from Aintree (N=290).
983 patients (median age 70, IQR 53-83; in-hospital mortality 29.9%) were recruited over an 11-week study period. Through sequential modelling, a five-predictor score termed SOARS (pO2, besity, ge, espiratory rate, troke history) was developed to correlate COVID-19 severity across low, moderate and high strata of mortality risk. The score discriminated well for in-hospital death, with area under the receiver operating characteristic values of 0.82, 0.80 and 0.74 in the derivation, Aintree and ISARIC validation cohorts, respectively. Its predictive accuracy (calibration) in both external cohorts was consistently higher in patients with milder disease (SOARS 0-1), the same individuals who could be identified for safe outpatient monitoring. Prediction of a non-fatal outcome in this group was accompanied by high score sensitivity (99.2%) and negative predictive value (95.9%).
The SOARS score uses constitutive and readily assessed individual characteristics to predict the risk of COVID-19 death. Deployment of the score could potentially inform clinical triage in preadmission settings where expedient and reliable decision-making is key. The resurgence of SARS-CoV-2 transmission provides an opportunity to further validate and update its performance.
COVID-19 不良结局的危险因素已得到明确界定，但缺乏使用非实验室测量评分对死亡率进行分层的方法，特别是在进行 SARS-CoV-2 检测前。
采用具有自举功能的多元回归分析方法，确定在一家急性医院确诊 COVID-19 的患者中与死亡独立相关的预测因子。该预测模型在 ISARIC 队列（N=14231）的一个大型随机样本和来自安特里（Aintree）的一个较小队列（N=290）中进行了外部验证。
在为期 11 周的研究期间，共招募了 983 例患者（中位年龄 70 岁，IQR 53-83；院内死亡率 29.9%）。通过逐步建模，开发了一个五因素评分（SOARS，即 pO2、肥胖、年龄、呼吸频率、中风史），可用于将 COVID-19 的严重程度与低、中和高死亡率风险分层相关联。该评分在预测院内死亡方面具有良好的区分度，在推导、安特里和 ISARIC 验证队列中的受试者工作特征曲线下面积分别为 0.82、0.80 和 0.74。该评分在两个外部队列中的预测准确性（校准）在疾病较轻的患者（SOARS 0-1）中始终更高，这些患者可用于安全的门诊监测。在这一组患者中，预测非致死性结局的同时，评分具有很高的敏感性（99.2%）和阴性预测值（95.9%）。
SOARS 评分使用个体的固有且易于评估的特征来预测 COVID-19 死亡风险。在需要快速和可靠决策的入院前环境中，该评分的应用可能有助于临床分诊。SARS-CoV-2 传播的再次出现为进一步验证和更新该评分的性能提供了机会。
