Proposal of a Clinical Decision Tree Algorithm Using Factors Associated with Severe Dengue Infection
Proposal of a Clinical Decision Tree Algorithm Using Factors Associated with Severe Dengue Infection
复制标题
DOI：
10.1371/journal.pone.0161696
复制
发表时间：
2016-08-23
期刊：
影响因子：
3.7
通讯作者：
Muninathan, Prema
中科院分区：
文献类型：
--
作者：
Tamibmaniam, Jayashamani;Hussin, Narwani;Muninathan, Prema
背景世界卫生组织在2009年的新分类：有或无警告迹象的登革热和严重登革热，使得大量登革热患者必须入院，这反过来又给地球仪的许多医院带来了巨大的经济和身体负担，特别是东南亚和马来西亚，近年来该疾病的人数迅速激增。缺乏一个简单的工具来区分轻微的威胁生命的感染，导致不必要的住院治疗的登革热patients.MethodsWe进行了一个单中心，回顾性研究，涉及血清学确诊的登革热患者，在一个单一的病房，在医院吉隆坡，马来西亚。从2014年2月至5月收集了4个月的数据。记录社会人口统计学、共病、入院前患病天数、症状、警告体征、生命体征和实验室结果。结果657例确诊登革热患者中，59例（9.0%）为重症登革热患者，重症登革热的发生率为9.0%（59/657）。总体而言，最常见的警告体征是呕吐（36.1%）和腹痛（32.1%）。使用简单logistic回归分析发现，既往合并症、呕吐、腹泻、胸腔积液、低收缩压、高红细胞压积、低白蛋白和高尿素是严重登革热的重要风险因素。然而，多重logistic回归分析显示，严重登革热的显著危险因素仅为呕吐、胸腔积液和低收缩压。使用这3个风险因素，我们绘制了一个预测严重登革热的算法。与WHO标准的重症登革热分类相比，决策树算法的敏感性为0.81，特异性为0.54，阳性预测值为0.16，阴性预测值为0.96。结论本研究提出的决策树算法在预测重症登革热患者是否需要入院方面具有较高的敏感性和NPV。经过进一步验证研究，该工具可用于帮助临床医生在首次就诊时决定进一步管理患者。它还将对卫生资源产生重大影响，因为低风险患者可以作为门诊病人进行管理，从而保留了稀缺的医院床位和医疗资源。
BackgroundWHO's new classification in 2009: dengue with or without warning signs and severe dengue, has necessitated large numbers of admissions to hospitals of dengue patients which in turn has been imposing a huge economical and physical burden on many hospitals around the globe, particularly South East Asia and Malaysia where the disease has seen a rapid surge in numbers in recent years. Lack of a simple tool to differentiate mild from life threatening infection has led to unnecessary hospitalization of dengue patients.MethodsWe conducted a single-centre, retrospective study involving serologically confirmed dengue fever patients, admitted in a single ward, in Hospital Kuala Lumpur, Malaysia. Data was collected for 4 months from February to May 2014. Socio demography, co-morbidity, days of illness before admission, symptoms, warning signs, vital signs and laboratory result were all recorded. Descriptive statistics was tabulated and simple and multiple logistic regression analysis was done to determine significant risk factors associated with severe dengue.Results657 patients with confirmed dengue were analysed, of which 59 (9.0%) had severe dengue. Overall, the commonest warning sign were vomiting (36.1%) and abdominal pain (32.1%). Previous co-morbid, vomiting, diarrhoea, pleural effusion, low systolic blood pressure, high haematocrit, low albumin and high urea were found as significant risk factors for severe dengue using simple logistic regression. However the significant risk factors for severe dengue with multiple logistic regressions were only vomiting, pleural effusion, and low systolic blood pressure. Using those 3 risk factors, we plotted an algorithm for predicting severe dengue. When compared to the classification of severe dengue based on the WHO criteria, the decision tree algorithm had a sensitivity of 0.81, specificity of 0.54, positive predictive value of 0.16 and negative predictive of 0.96.ConclusionThe decision tree algorithm proposed in this study showed high sensitivity and NPV in predicting patients with severe dengue that may warrant admission. This tool upon further validation study can be used to help clinicians decide on further managing a patient upon first encounter. It also will have a substantial impact on health resources as low risk patients can be managed as outpatients hence reserving the scarce hospital beds and medical