Microvascular dysfunction in septic and dengue shock: pathophysiology and implications for clinical management

The microcirculation comprising of arterioles, capillaries and post-capillary venules is the terminal vascular network of the systemic circulation. Microvascular homeostasis, comprising of a balance between vasoconstriction, vasodilation and endothelial permeability in healthy states, regulates tissue perfusion. In severe infections, systemic inflammation occurs irrespective of the infecting microorganism(s), resulting in microcirculatory dysregulation and dysfunction, which impairs tissue perfusion and often precedes end-organ failure. The common hallmarks of microvascular dysfunction in both septic shock and dengue shock, are endothelial cell activation, glycocalyx degradation and plasma leak through a disrupted endothelial barrier. Microvascular tone is also impaired by a reduced bioavailability of nitric oxide. In vitro and in vivo studies have however demonstrated that the nature and extent of microvascular dysfunction as well as responses to volume expansion resuscitation differ in these two clinical syndromes. This review compares and contrasts the pathophysiology of microcirculatory dysfunction in septic versus dengue shock and the attendant effects of fluid administration during resuscitation

The Diagnosis of Dengue in Patients Presenting With Acute Febrile Illness Using Supervised Machine Learning and Impact of Seasonality

BackgroundSymptomatic dengue infection can result in a life-threatening shock syndrome and timely diagnosis is essential. Point-of-care tests for non-structural protein 1 and IgM are used widely but performance can be limited. We developed a supervised machine learning model to predict whether patients with acute febrile illnesses had a diagnosis of dengue or other febrile illnesses (OFI). The impact of seasonality on model performance over time was examined.MethodsWe analysed data from a prospective observational clinical study in Vietnam. Enrolled patients presented with an acute febrile illness of &amp;lt;72 h duration. A gradient boosting model (XGBoost) was used to predict final diagnosis using age, sex, haematocrit, platelet, white cell, and lymphocyte count collected on enrolment. Data was randomly split 80/20% into a training and hold-out set, respectively, with the latter not used in model development. Cross-validation and hold out set testing was used, with performance over time evaluated through a rolling window approach.ResultsWe included 8,100 patients recruited between 16th October 2010 and 10th December 2014. In total 2,240 (27.7%) patients were diagnosed with dengue infection. The optimised model from training data had an overall median area under the receiver operator curve (AUROC) of 0.86 (interquartile range 0.84–0.86), specificity of 0.92, sensitivity of 0.56, positive predictive value of 0.73, negative predictive value (NPV) of 0.84, and Brier score of 0.13 in predicting the final diagnosis, with similar performances in hold-out set testing (AUROC of 0.86). Model performances varied significantly over time as a function of seasonality and other factors. Incorporation of a dynamic threshold which continuously learns from recent cases resulted in a more consistent performance throughout the year (NPV &amp;gt;90%).ConclusionSupervised machine learning models are able to discriminate between dengue and OFI diagnoses in patients presenting with an early undifferentiated febrile illness. These models could be of clinical utility in supporting healthcare decision-making and provide passive surveillance across dengue endemic regions. Effects of seasonality and changing disease prevalence must however be taken into account—this is of significant importance given unpredictable effects of human-induced climate change and the impact on health.</jats:sec