Clinical Symptoms of Dengue Infection among Patients from a Non-Endemic Area and Potential for a Predictive Model: A Multiple Logistic Regression Analysis and Decision Tree
Clinical Symptoms of Dengue Infection among Patients from a Non-Endemic Area and Potential for a Predictive Model: A Multiple Logistic Regression Analysis and Decision Tree
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DOI：
10.4269/ajtmh.20-0192
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发表时间：
2021-01-01
影响因子：
3.3
通讯作者：
Messer, William B.
中科院分区：
文献类型：
--
作者：
Khosavanna, Ruchira R.;Kareko, Bettie W.;Messer, William B.
对登革热感染认识不足可能导致发病率和死亡率增加，而早期发现有助于改善患者预后。最近在美国和其他温带地区登革热病毒的发病率和暴发报告，其中登革热通常没有看到，引起了对不熟悉该疾病的医疗保健提供者的适当诊断和管理的关注。本研究旨在描述非地方性队列中自我报告的登革热临床症状，并建立一种基于呈现特征的临床有用的预测算法，以帮助早期评估潜在的登革热感染。本研究招募了在登革热流行国家旅行时经历发热性疾病的志愿者。在入组时收集病史和血液样本。根据中和抗体滴度，将参与者分为登革热初治者或登革热暴露者。进行统计学分析以比较两组之间的特征。包括关节/肌肉/骨骼疼痛、皮疹、呼吸困难和白带的回归模型预测登革热感染的敏感性为78%，特异性为63%，阳性预测值为80%，阴性预测值为61%。包括关节/肌肉/骨骼疼痛、呼吸困难和皮疹的决策树模型产生77%的灵敏度和67%的特异性。登革热的诊断是具有挑战性的，因为临床表现的非特异性。敏感的预测模型有助于在非流行性环境中对疑似登革热感染进行分类，但特异性需要额外的测试，包括实验室评估。
Under-recognition of dengue infection may lead to increased morbidity and mortality, whereas early detection is shown to help improve patient outcomes. Recent incidence and outbreak reports of dengue virus in the United States and other temperate regions where dengue was not typically seen have raised concerns regarding appropriate diagnosis and management by healthcare providers unfamiliar with the disease. This study aimed to describe self reported clinical symptoms of dengue fever in a non-endemic cohort and to establish a clinically useful predictive algorithm based on presenting features that can assist in the early evaluation of potential dengue infection. Volunteers who experienced febrile illness while traveling in dengue-endemic countries were recruited for this study. History of illness and blood samples were collected at enrollment. Participants were classified as dengue naive or dengue exposed based on neutralizing antibody titers. Statistical analysis was performed to compare characteristics between the two groups. A regression model including joint/muscle/bone pain, rash, dyspnea, and rhinorrhea predicts dengue infection with 78% sensitivity, 63% specificity, 80% positive predictive value, and 61% negative predictive value. A decision tree model including joint/muscle/bone pain, dyspnea, and rash yields 77% sensitivity and 67% specificity. Diagnosis of dengue fever is challenging because of the nonspecific nature of clinical presentation. A sensitive predicting model can be helpful to triage suspected dengue infection in the non-endemic setting, but specificity requires additional testing including laboratory evaluation.