目前尚无识别轻度至中度 COVID-19 患者的工具。我们的目标是确定与非不良结果相关的因素，并制定一个量表来预测医院急诊科的 COVID-19 患者的非不良演变（CoNAE 量表）。对来到我们地区的国家卫生服务医院之一的患者进行的回顾性队列研究用于治疗 2020 年 7 月 1 日至 2021 年 7 月 31 日期间的 SARS-CoV-2 感染。我们从病例记录中收集了社会人口学信息、潜在的合并症和正在进行的治疗、其他相关病史详细信息以及到达分诊时的重要常数。使用多水平多变量逻辑回归模型来确定预测因子。该模型显示，出现非不良结果的患者为年轻、女性且接种了 COVID-19 疫苗（研究时接种了 2 剂）。他们到达时生命体征正常（心率、舒张压和收缩压、体温和血氧饱和度），并且没有以下伴随疾病或因素：心力衰竭、其他心脏病、高血压、糖尿病、肝病、痴呆、恶性病史肿瘤，并且他们没有接受口服或其他全身性皮质类固醇或免疫抑制剂治疗。该模型的受试者工作特征曲线下面积为 0.840（95% CI，0.834-0.847）。我们开发了 CoNAE 量表来预测非不良结果。该量表可能有助于对 COVID-19 患者进行分类评估。它还可以帮助预测安全出院或规划患者不仅在医院急诊科而且在紧急初级保健机构或院外紧急护理中所需的护理水平。

Tools to identify patients with mild to moderate COVID-19 are as yet unavailable. Our aims were to identify factors associated with nonadverse outcomes and develop a scale to predict nonadverse evolution in patients with COVID-19 (the CoNAE scale) in hospital emergency departments.Retrospective cohort study of patients who came to one of our area's national health service hospitals for treatment of SARS-CoV-2 infection from July 1, 2020, to July 31, 2021. From case records we collected sociodemographic information, underlying comorbidity and ongoing treatments, other relevant medical history details, and vital constants on arrival for triage. Multilevel multivariable logistic regression models were used to identify predictors.The model showed that patients who had nonadverse outcomes were younger, female, and vaccinated against COVID-19 (2 doses at the time of the study). They arrived with normal vital signs (heart rate, diastolic and systolic pressures, temperature, and oxygen saturation) and had none of the following concomitant diseases or factors: heart failure other heart disease, hypertension, diabetes, liver disease, dementia, history of malignant tumors, and they were not being treated with oral or other systemic corticosteroids or immunosuppressant therapy. The area under the receiver operating characteristic curve for the model was 0.840 (95% CI, 0.834-0.847).We developed the CoNAE scale to predict nonadverse outcomes. This scale may be useful in triage for evaluating patients with COVID-19. It may also help predict safe discharge or plan the level of care that patients require not only in a hospital emergency department but also in urgent primary care settings or out-of-hospital emergency care.