Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.
Department of Medicine, Swiss Tropical and Public Health Institute, Basel, Switzerland.
PLoS Negl Trop Dis. 2022 May 4;16(5):e0010281. doi: 10.1371/journal.pntd.0010281. eCollection 2022 May.
Dengue fever is highly endemic in Vietnam, but scrub typhus-although recognized as an endemic disease-remains underappreciated. These diseases together are likely to account for more than half of the acute undifferentiated fever burden in Vietnam. Scrub typhus (ST) is a bacterial disease requiring antimicrobial treatment, while dengue fever (DF) is of viral etiology and does not. The access to adequate diagnostics and the current understanding of empirical treatment strategies for both illnesses remain limited. In this study we aimed to contribute to the clinical decision process in the management of these two important etiologies of febrile illness in Vietnam.
Using retrospective data from 221 PCR-confirmed scrub typhus cases and 387 NS1 protein positive dengue fever patients admitted to five hospitals in Khanh Hoa province (central Vietnam), we defined predictive characteristics for both diseases that support simple clinical decision making with potential to inform decision algorithms in future. We developed models to discriminate scrub typhus from dengue fever using multivariable logistic regression (M-LR) and classification and regression trees (CART). Regression trees were developed for the entire data set initially and pruned, based on cross-validation. Regression models were developed in a training data set involving 60% of the total sample and validated in the complementary subsample. Probability cut points for the distinction between scrub typhus and dengue fever were chosen to maximise the sum of sensitivity and specificity.
Using M-LR, following seven predictors were identified, that reliably differentiate ST from DF; eschar, regional lymphadenopathy, an occupation in nature, increased days of fever on admission, increased neutrophil count, decreased ratio of neutrophils/lymphocytes, and age over 40. Sensitivity and specificity of predictions based on these seven factors reached 93.7% and 99.5%, respectively. When excluding the "eschar" variable, the values dropped to 76.3% and 92.3%, respectively. The CART model generated one further variable; increased days of fever on admission, when eschar was included, the sensitivity and specificity was 95% and 96.9%, respectively. The model without eschar involved the following six variables; regional lymphadenopathy, increased days of fever on admission, increased neutrophil count, increased lymphocyte count, platelet count ≥ 47 G/L and age over 28 years as predictors of ST and provided a sensitivity of 77.4% and a specificity of 90.7%.
The generated algorithms contribute to differentiating scrub typhus from dengue fever using basic clinical and laboratory parameters, supporting clinical decision making in areas where dengue and scrub typhus are co-endemic in Vietnam.
登革热在越南高度流行，但恙虫病虽然被认为是一种地方病，但仍未得到充分重视。这两种疾病加在一起，可能占越南急性未分化发热负担的一半以上。恙虫病（ST）是一种需要抗菌治疗的细菌病，而登革热（DF）则是病毒性病因，不需要。目前对这两种疾病的充分诊断和经验性治疗策略的了解仍然有限。在这项研究中，我们旨在为越南这两种重要发热病因的治疗提供临床决策过程的帮助。
使用来自五个医院（越南中部庆和省）的 221 例经 PCR 确诊的恙虫病病例和 387 例 NS1 蛋白阳性登革热患者的回顾性数据，我们确定了这两种疾病的预测特征，这些特征支持简单的临床决策，并有潜力为未来的决策算法提供信息。我们使用多变量逻辑回归（M-LR）和分类回归树（CART）为恙虫病和登革热建立了模型。回归树最初是在整个数据集上建立的，并根据交叉验证进行了修剪。回归模型是在包含总样本 60%的训练数据集中建立的，并在互补子样本中进行了验证。选择区分恙虫病和登革热的概率切点，以最大化灵敏度和特异性的总和。
使用 M-LR，确定了以下七个预测因子，可以可靠地区分 ST 和 DF；焦痂、局部淋巴结肿大、从事自然相关职业、入院时发热天数增加、中性粒细胞计数增加、中性粒细胞/淋巴细胞比值降低和年龄大于 40 岁。基于这七个因素的预测的敏感性和特异性分别达到 93.7%和 99.5%。当排除“焦痂”变量时，值分别下降到 76.3%和 92.3%。CART 模型生成了另一个变量；入院时发热天数增加，如果包括焦痂，敏感性和特异性分别为 95%和 96.9%。不包括焦痂的模型涉及以下六个变量；局部淋巴结肿大、入院时发热天数增加、中性粒细胞计数增加、淋巴细胞计数增加、血小板计数≥47 G/L 和年龄大于 28 岁，作为 ST 的预测因子，敏感性为 77.4%，特异性为 90.7%。
这些生成的算法有助于使用基本的临床和实验室参数区分恙虫病和登革热，支持在越南登革热和恙虫病共同流行的地区进行临床决策。
