Neurological manifestations of dengue infection

Acute Encephalopathy with Biphasic Seizures and Late Reduced Diffusion Associated with Dengue Infection in a Child

Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) is a clinico-radiological syndrome in children secondary to viral or bacterial infections. The causes include viral (influenza, human herpes virus-6, adenovirus, rota) as well as bacterial infections. However, AESD with dengue infection has not been reported earlier. Here, we present an infant with dengue infection and AESD which recovered completely following treatment with intravenous human immunoglobulin therapy. A 9-month-old girl presented with seizures following fever and loose stools. Seizures recurred after 2 days of seizure-free interval. Cerebrospinal fluid analysis was not contributory. Dengue infection was confirmed by lab tests. Magnetic resonance imaging brain after the second seizure revealed diffusion restriction involving the bilateral frontal and parietal white matter, both hemispheres with a typical central perisylvian sparing lesion suggestive of AESD. This case report expands the reported spectrum of neurological manifestations of dengue infection.

Assessment of the new World Health Organization's dengue classification for predicting severity of illness and level of healthcare required

The objective of this study was to assess the validity of the new dengue classification proposed by the World Health Organization (WHO) in 2009 and to develop pragmatic guidelines for case triage and management. This retrospective study involved 357 laboratory-confirmed cases of dengue infection diagnosed at King Abdulaziz University Hospital, Jeddah, Saudi Arabia over a 4-year period from 2014 to 2017. The sensitivity of the new classification for identifying severe cases was limited (65%) but higher than the old one (30%). It had a higher sensitivity for identifying patients who needed advanced healthcare compared to the old one (72% versus 32%, respectively). We propose adding decompensation of chronic diseases and thrombocytopenia-related bleeding to the category of severe dengue in the new classification. This modification improves sensitivity from 72% to 98% for identifying patients who need advanced healthcare without altering specificity (97%). It also improves sensitivity in predicting severe outcomes from 32% to 88%. In conclusion, the new classification had a low sensitivity for identifying patients needing advanced care and for predicting morbidity and mortality. We propose to include decompensation of chronic diseases and thrombocytopenia-related bleeding to the category of severe dengue in the new classification to improve the sensitivity of predicting cases requiring advanced care.

Dengue fever, the most prevalent arthropod-borne viral disease in humans, has been conventionally classified into four main categories: non-classical, classical, dengue hemorrhagic fever, and dengue shock syndrome. Several studies reported lack of correlation between the categories of the conventional classification and the disease severity. As a consequence, the World Health Organization proposed in 2008 a new classification that divides dengue into two categories: non-severe and severe dengue; the non-severe dengue is further divided into two categories: dengue with warning signs and dengue without warning signs. In this retrospective study we reviewed 357 cases of dengue diagnosed in our institution over a 4-year period to assess the validity of the new dengue classification in order to develop pragmatic guidelines for case triage and management in the Emergency Departments. We found that the sensitivity of the new classification for identifying severe cases was limited even though it had a higher sensitivity for identifying patients who needed advanced healthcare compared to the old one. We propose adding decompensation of chronic diseases and low platelets-related bleeding to the category of severe dengue in the new classification. This modification dramatically improves the sensitivity for identifying patients who need advanced healthcare and the sensitivity to predict severe outcomes.