Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China.
PLoS Negl Trop Dis. 2023 Mar 15;17(3):e0011161. doi: 10.1371/journal.pntd.0011161. eCollection 2023 Mar.
Establishing reliable early warning models for severe dengue cases is a high priority to facilitate triage in dengue-endemic areas and optimal use of limited resources. However, few studies have identified the complex interactive relationship between potential risk factors and severe dengue. This research aimed to assess the potential risk factors and detect their high-order combinative effects on severe dengue. A structured questionnaire was used to collect detailed dengue outbreak data from eight representative hospitals in Dhaka, Bangladesh, in 2019. Logistic regression and machine learning models were used to examine the complex effects of demographic characteristics, clinical symptoms, and biochemical markers on severe dengue. A total of 1,090 dengue cases (158 severe and 932 non-severe) were included in this study. Dyspnoea (Odds Ratio [OR] = 2.87, 95% Confidence Interval [CI]: 1.72 to 4.77), plasma leakage (OR = 3.61, 95% CI: 2.12 to 6.15), and hemorrhage (OR = 2.33, 95% CI: 1.46 to 3.73) were positively and significantly associated with the occurrence of severe dengue. Classification and regression tree models showed that the probability of occurrence of severe dengue cases ranged from 7% (age >12.5 years without plasma leakage) to 92.9% (age ≤12.5 years with dyspnoea and plasma leakage). The random forest model indicated that age was the most important factor in predicting severe dengue, followed by education, plasma leakage, platelet, and dyspnoea. The research provides new evidence to identify key risk factors contributing to severe dengue cases, which could be beneficial to clinical doctors to identify and predict the severity of dengue early.
建立可靠的重症登革热病例预警模型对于在登革热流行地区进行分诊和优化利用有限资源至关重要。然而，很少有研究确定潜在危险因素之间的复杂交互关系与重症登革热的关系。本研究旨在评估潜在危险因素，并检测其对重症登革热的高阶组合效应。使用结构化问卷从孟加拉国达卡的 8 家代表性医院收集了 2019 年登革热爆发的详细数据。使用逻辑回归和机器学习模型来研究人口统计学特征、临床症状和生化标志物对重症登革热的复杂影响。本研究共纳入 1090 例登革热病例（158 例重症和 932 例非重症）。呼吸困难（比值比 [OR] = 2.87，95%置信区间 [CI]：1.72 至 4.77）、血浆渗漏（OR = 3.61，95%CI：2.12 至 6.15）和出血（OR = 2.33，95%CI：1.46 至 3.73）与重症登革热的发生呈正相关且具有统计学意义。分类回归树模型显示，重症登革热病例的发生概率范围为 7%（无血浆渗漏且年龄＞12.5 岁）至 92.9%（有呼吸困难和血浆渗漏且年龄≤12.5 岁）。随机森林模型表明，年龄是预测重症登革热的最重要因素，其次是教育程度、血浆渗漏、血小板和呼吸困难。该研究提供了新的证据，确定了导致重症登革热病例的关键危险因素，这可能有助于临床医生早期识别和预测登革热的严重程度。
