Fernández Eduardo, Smieja Marek, Walter Stephen D, Loeb Mark
Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada.
Department of Pathology and Molecular Medicine, Institute for Infectious Disease Research McMaster University, Hamilton, ON, Canada.
BMC Infect Dis. 2017 Oct 11;17(1):676. doi: 10.1186/s12879-017-2800-3.
An important challenge in the identification of dengue is how to predict which patients will go on to experience severe illness, which is typically characterized by fever, thrombocytopenia, haemorrhagic manifestations, and plasma leakage. Accurate prediction could result in the appropriate hospital triage of high risk patients. The objective of this study was to identify clinical factors observed within the first 24 h of hospital admission that could predict subsequent severe dengue.
We conducted a retrospective cohort study of 320 patients with febrile illness who had confirmation of dengue within one week of admission, using data from the 2009-2010 Honduras Epidemiological Survey for Dengue. The outcome measure was plasma leakage defined using hemoconcentration ≥15% as determined by serial hematocrit testing. We conducted univariable analysis and multivariable logistic regression analysis to construct a predictive model for severe dengue.
Thirty-four (10.6%) of patients in the 320 patient cohort had hemoconcentration ≥15%. In the final multivariable logistic regression model the presence of ascites, OR 7.29, 95% CI 1.85 to 28.7, and a platelet count <50,000 platelets/mm at admission, OR 3.02, 95% CI 1.42 to 6.42, were significantly associated with plasma leakage, while the presence of petechiae, OR 0.24 95% CI 0.080 to 0.73, and headache, OR 0.38, 95% CI 0.15 to 0.95, were negatively associated with leakage. Using an estimated probability of 7% as a threshold for a person being considered a severe case correctly predicted 26 of the 34 severe cases (sensitivity 76.4%) and 201 of the 286 non-severe cases (specificity of 70.3%) for a percentage correctly classified of 70.9%.
We identified signs and symptoms that can correctly identify a majority of patients who eventually develop severe dengue in Honduras. It will be important to further refine our models and validate them in other populations.
登革热诊断中的一个重要挑战是如何预测哪些患者会发展为重症疾病，其典型特征为发热、血小板减少、出血表现和血浆渗漏。准确的预测有助于对高危患者进行适当的医院分诊。本研究的目的是确定入院后24小时内观察到的可预测后续重症登革热的临床因素。
我们利用2009 - 2010年洪都拉斯登革热流行病学调查的数据，对320例发热性疾病患者进行了一项回顾性队列研究，这些患者在入院一周内确诊为登革热。结局指标是通过连续血细胞比容检测确定的血液浓缩≥15%来定义的血浆渗漏。我们进行了单变量分析和多变量逻辑回归分析，以构建重症登革热的预测模型。
320例患者队列中有34例（10.6%）血液浓缩≥15%。在最终的多变量逻辑回归模型中，腹水的存在（比值比7.29，95%置信区间1.85至28.7）以及入院时血小板计数<50,000/mm³（比值比3.02，95%置信区间1.42至6.42）与血浆渗漏显著相关，而瘀点的存在（比值比0.24，95%置信区间0.080至0.73）和头痛（比值比0.38，95%置信区间0.15至0.95）与渗漏呈负相关。以估计概率7%作为判定重症病例的阈值，正确预测了34例重症病例中的26例（敏感性76.4%）和286例非重症病例中的201例（特异性70.3%），正确分类百分比为70.9%。
我们确定了一些体征和症状，它们能够正确识别洪都拉斯大多数最终发展为重症登革热的患者。进一步完善我们的模型并在其他人群中进行验证将很重要。
