Department of Anaesthesia and Intensive Care, Chelsea and Westminster Hospital NHS Foundation Trust, London, UK
National Heart and Lung Institute, Imperial College London, London, UK.
Emerg Med J. 2020 Oct;37(10):630-636. doi: 10.1136/emermed-2020-210199. Epub 2020 Sep 18.
Common causes of death in COVID-19 due to SARS-CoV-2 include thromboembolic disease, cytokine storm and adult respiratory distress syndrome (ARDS). Our aim was to develop a system for early detection of disease pattern in the emergency department (ED) that would enhance opportunities for personalised accelerated care to prevent disease progression. A single Trust's COVID-19 response control command was established, and a reporting team with bioinformaticians was deployed to develop a real-time traffic light system to support clinical and operational teams. An attempt was made to identify predictive elements for thromboembolism, cytokine storm and ARDS based on physiological measurements and blood tests, and to communicate to clinicians managing the patient, initially via single consultants. The input variables were age, sex, and first recorded blood pressure, respiratory rate, temperature, heart rate, indices of oxygenation and C-reactive protein. Early admissions were used to refine the predictors used in the traffic lights. Of 923 consecutive patients who tested COVID-19 positive, 592 (64%) flagged at risk for thromboembolism, 241/923 (26%) for cytokine storm and 361/923 (39%) for ARDS. Thromboembolism and cytokine storm flags were met in the ED for 342 (37.1%) patients. Of the 318 (34.5%) patients receiving thromboembolism flags, 49 (5.3% of all patients) were for suspected thromboembolism, 103 (11.1%) were high-risk and 166 (18.0%) were medium-risk. Of the 89 (9.6%) who received a cytokine storm flag from the ED, 18 (2.0% of all patients) were for suspected cytokine storm, 13 (1.4%) were high-risk and 58 (6.3%) were medium-risk. Males were more likely to receive a specific traffic light flag. In conclusion, ED predictors were used to identify high proportions of COVID-19 admissions at risk of clinical deterioration due to severity of disease, enabling accelerated care targeted to those more likely to benefit. Larger prospective studies are encouraged.
导致 COVID-19 患者死亡的常见原因包括血栓栓塞性疾病、细胞因子风暴和成人呼吸窘迫综合征 (ARDS)。我们的目标是开发一种在急诊科早期检测疾病模式的系统，以增加提供个性化加速护理的机会，防止疾病进展。建立了一个单一信托基金的 COVID-19 反应控制指挥部，并部署了一个由生物信息学家组成的报告团队，以开发一个实时信号灯系统，为临床和运营团队提供支持。我们试图根据生理测量和血液测试来识别血栓栓塞、细胞因子风暴和 ARDS 的预测因素，并将其传达给管理患者的临床医生，最初是通过单一顾问。输入变量包括年龄、性别以及首次记录的血压、呼吸频率、体温、心率、氧合指数和 C 反应蛋白。早期入院有助于完善信号灯中使用的预测因素。在连续 923 例 COVID-19 检测呈阳性的患者中，592 例(64%)被标记为血栓栓塞风险，241/923 例(26%)为细胞因子风暴风险，361/923 例(39%)为 ARDS 风险。342 例(37.1%)患者在急诊科出现血栓栓塞和细胞因子风暴标志。在接受血栓栓塞标志的 318 例(34.5%)患者中，49 例(所有患者的 5.3%)为疑似血栓栓塞，103 例(11.1%)为高危，166 例(18.0%)为中危。在从急诊科收到细胞因子风暴标志的 89 例(9.6%)患者中，18 例(所有患者的 2.0%)为疑似细胞因子风暴，13 例(1.4%)为高危，58 例(6.3%)为中危。男性更有可能收到特定的信号灯标志。总之，急诊科的预测因素被用于识别出大量 COVID-19 住院患者因疾病严重程度而有临床恶化的风险，从而能够为更有可能受益的患者提供针对性的加速护理。鼓励进行更大规模的前瞻性研究。
