Centre for Clinical Investigation - Clinical Epidemiology (CIC 1410), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire de La Réunion, Saint Pierre, Reunion.
Centre for Clinical Investigation - Clinical Epidemiology (CIC 1410), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire de La Réunion, Saint Pierre, Reunion.
Travel Med Infect Dis. 2022 Jan-Feb;45:102232. doi: 10.1016/j.tmaid.2021.102232. Epub 2021 Dec 9.
The purpose of this cohort study was to develop two scores able to differentiate coronavirus 2019 (COVID-19) from dengue and other febrile illnesses (OFIs).
All subjects suspected of COVID-19 who attended the SARS-CoV-2 testing center of Saint-Pierre hospital, Reunion, between March 23 and May 10, 2020, were assessed for identifying predictors of both infectious diseases from a multinomial logistic regression model. Two scores were developed after weighting the odd ratios then validated by bootstrapping.
Over 49 days, 80 COVID-19, 60 non-severe dengue and 872 OFIs were diagnosed. The translation of the best fit model yielded two scores composed of 11 criteria: contact with a COVID-19 positive case (+3 points for COVID-19; 0 point for dengue), return from travel abroad within 15 days (+3/-1), previous individual episode of dengue (+1/+3), active smoking (-3/0), body ache (0/+5), cough (0/-2), upper respiratory tract infection symptoms (-1/-1), anosmia (+7/-1), headache (0/+5), retro-orbital pain (-1/+5), and delayed presentation (>3 days) to hospital (+1/0). The area under the receiver operating characteristic curve was 0.79 (95%CI 0.76-0.82) for COVID-19 score and 0.88 (95%CI 0.85-0.90) for dengue score. Calibration was satisfactory for COVID-19 score and excellent for dengue score. For predicting COVID-19, sensitivity was 97% at the 0-point cut-off and specificity 99% at the 10-point cut-off. For predicting dengue, sensitivity was 97% at the 3-point cut-off and specificity 98% at the 11-point cut-off.
COVIDENGUE scores proved discriminant to differentiate COVID-19 and dengue from OFIs in the context of SARS-CoV-2 testing center during a co-epidemic.
本队列研究旨在开发两种评分系统，以区分 2019 年冠状病毒病（COVID-19）与登革热和其他发热性疾病（OFIs）。
2020 年 3 月 23 日至 5 月 10 日期间，所有疑似 COVID-19 而到留尼汪圣皮埃尔医院 SARS-CoV-2 检测中心就诊的患者，均采用多项逻辑回归模型来评估这两种传染病的预测指标。对优势比进行加权后，建立了两种评分系统，并通过自举法进行验证。
在 49 天内，共诊断出 80 例 COVID-19、60 例非重症登革热和 872 例 OFIs。最佳拟合模型的翻译产生了两种评分系统，共包含 11 个标准：与 COVID-19 阳性病例接触（COVID-19 为+3 分，登革热为 0 分）、15 天内从国外返回（+3/-1）、曾有个人登革热发作（+1/+3）、吸烟（-3/0）、全身疼痛（0/+5）、咳嗽（0/-2）、上呼吸道感染症状（-1/-1）、嗅觉丧失（+7/-1）、头痛（0/+5）、眼眶后疼痛（-1/+5）、以及延迟就诊（>3 天）（+1/0）。COVID-19 评分的受试者工作特征曲线下面积为 0.79（95%CI 0.76-0.82），登革热评分的曲线下面积为 0.88（95%CI 0.85-0.90）。COVID-19 评分的校准情况良好，登革热评分的校准情况极佳。预测 COVID-19 时，截断值为 0 分时，灵敏度为 97%，特异性为 99%；截断值为 10 分时，灵敏度为 97%，特异性为 98%。预测登革热时，截断值为 3 分时，灵敏度为 97%，特异性为 98%。
在 SARS-CoV-2 检测中心同时流行 COVID-19 和登革热的情况下，COVIDENGUE 评分可用于区分 COVID-19 和登革热与 OFIs。
