Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo, Brazil.
Laboratório de Inferência Causal em Epidemiologia da Universidade de São Paulo, São Paulo, Brazil.
Trop Med Int Health. 2020 Nov;25(11):1385-1394. doi: 10.1111/tmi.13476. Epub 2020 Sep 7.
COVID-19 diagnosis is a critical problem, mainly due to the lack or delay in the test results. We aimed to obtain a model to predict SARS-CoV-2 infection in suspected patients reported to the Brazilian surveillance system.
We analysed suspected patients reported to the National Surveillance System that corresponded to the following case definition: patients with respiratory symptoms and fever, who travelled to regions with local or community transmission or who had close contact with a suspected or confirmed case. Based on variables routinely collected, we obtained a multiple model using logistic regression. The area under the receiver operating characteristic curve (AUC) and accuracy indicators were used for validation.
We described 1468 COVID-19 cases (confirmed by RT-PCR) and 4271 patients with other illnesses. With a data subset including 80% of patients from Sao Paulo (SP) and Rio Janeiro (RJ), we obtained a function which reached an AUC of 95.54% (95% CI: 94.41-96.67%) for the diagnosis of COVID-19 and accuracy of 90.1% (sensitivity 87.62% and specificity 92.02%). In a validation dataset including the other 20% of patients from SP and RJ, this model exhibited an AUC of 95.01% (92.51-97.5%) and accuracy of 89.47% (sensitivity 87.32% and specificity 91.36%).
We obtained a model suitable for the clinical diagnosis of COVID-19 based on routinely collected surveillance data. Applications of this tool include early identification for specific treatment and isolation, rational use of laboratory tests, and input for modelling epidemiological trends.
COVID-19 诊断是一个关键问题，主要是由于检测结果的缺乏或延迟。我们旨在建立一个模型，以预测报告给巴西监测系统的疑似患者中 SARS-CoV-2 感染。
我们分析了报告给国家监测系统的疑似患者，这些患者符合以下病例定义：有呼吸道症状和发热的患者，他们曾前往有局部或社区传播的地区，或与疑似或确诊病例有密切接触。根据常规收集的变量，我们使用逻辑回归获得了一个多模型。使用接收者操作特征曲线下的面积（AUC）和准确性指标进行验证。
我们描述了 1468 例 COVID-19 病例（通过 RT-PCR 确诊）和 4271 例其他疾病患者。在包括来自圣保罗州（SP）和里约热内卢州（RJ）的 80%患者的数据子集中，我们获得了一个函数，其对 COVID-19 的诊断 AUC 为 95.54%（95%CI：94.41-96.67%），准确性为 90.1%（敏感性 87.62%和特异性 92.02%）。在包括来自 SP 和 RJ 的其他 20%患者的验证数据集中，该模型的 AUC 为 95.01%（92.51-97.5%），准确性为 89.47%（敏感性 87.32%和特异性 91.36%）。
我们根据常规收集的监测数据获得了一个适合 COVID-19 临床诊断的模型。该工具的应用包括早期识别进行特定治疗和隔离、合理使用实验室检测以及为流行病学趋势建模提供输入。
