Liliane de Fátima Antonio Oliveira, Lúcia Regina do Nascimento Brahim Paes, Luiz Claudio Ferreira, Gabriel Garcez de Araújo Souza, Guilherme Souza Weigert, Layla Lorena Bezerra de Almeida, Rafael Kenji Fonseca Hamada, Lyz Tavares de Sousa, Andreza Pain Marcelino, Cláudia Maria Valete
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A multiple binary logistic regression model of controls (negative confirmation for COVID-19 or confirmation of other influenza-like illness) versus COVID-19 was developed to obtain an odds ratio (OR) and a 95% confidence interval (CI). In the final binary logistic regression model, six factors were considered significant: presence of rhinorrhea, ocular symptoms, abdominal pain, rhinosinusopathy, and wheezing/asthma and bronchospasm were more frequent in controls, thus indicating a greater chance of flu-like illnesses than COVID-19. The presence of tiredness and fatigue was three times more prevalent in COVID-19 cases (OR = 3.631; CI = 1.138-11.581; <i>p</i>-value = 0.029). 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引用次数: 0
摘要
冠状病毒病(COVID-19)是由SARS-CoV-2病毒引起的传染病,该病毒在2020年爆发,被世界卫生组织定性为大流行。限制措施改变了医疗保健服务,远程医疗在整个大流行期间提供了一种可行的替代方案。本研究分析了远程医疗平台数据库,目的是建立COVID-19患者的诊断预测模型。这是对2022年在Conexa Saúde远程医疗平台上就诊的患者进行的纵向研究。建立了对照(COVID-19阴性确诊或其他流感样疾病确诊)与COVID-19的多元二元logistic回归模型,以获得优势比(or)和95%置信区间(CI)。在最终的二元logistic回归模型中,六个因素被认为是显著的:鼻溢、眼部症状、腹痛、鼻窦病、喘息/哮喘和支气管痉挛在对照组中更常见,因此表明流感样疾病的可能性比COVID-19更大。疲倦和疲劳在COVID-19病例中的发生率是其3倍(OR = 3.631; CI = 1.138-11.581; p值= 0.029)。我们的研究结果表明,与流感样疾病和COVID-19相关的潜在预测因子可能会区分这些感染。
COVID-19 Clinical Predictors in Patients Treated via a Telemedicine Platform in 2022.
Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus, whose 2020 outbreak was characterized as a pandemic by the World Health Organization. Restriction measures changed healthcare delivery, with telehealth providing a viable alternative throughout the pandemic. This study analyzed a telemedicine platform database with the goal of developing a diagnostic prediction model for COVID-19 patients. This is a longitudinal study of patients seen on the Conexa Saúde telemedicine platform in 2022. A multiple binary logistic regression model of controls (negative confirmation for COVID-19 or confirmation of other influenza-like illness) versus COVID-19 was developed to obtain an odds ratio (OR) and a 95% confidence interval (CI). In the final binary logistic regression model, six factors were considered significant: presence of rhinorrhea, ocular symptoms, abdominal pain, rhinosinusopathy, and wheezing/asthma and bronchospasm were more frequent in controls, thus indicating a greater chance of flu-like illnesses than COVID-19. The presence of tiredness and fatigue was three times more prevalent in COVID-19 cases (OR = 3.631; CI = 1.138-11.581; p-value = 0.029). Our findings suggest potential predictors associated with influenza-like illness and COVID-19 that may distinguish between these infections.