Evaluation of host and viral factors associated with severe dengue based on the 2009 WHO classification

Abstract

Background: Dengue fever (DF) is the most prevalent arthropod-borne viral disease affecting humans. The World Health Organization (WHO) proposed a revised classification in 2009 to enable the more effective identification of cases of severe dengue (SD). This was designed primarily as a clinical tool, but it also enables cases of SD to be differentiated into three specific subcategories (severe vascular leakage, severe bleeding, and severe organ dysfunction). However, no study has addressed whether this classification has advantage in estimating factors associated with the progression of disease severity or dengue pathogenesis. We evaluate in a dengue outbreak associated risk factors that could contribute to the development of SD according to the 2009 WHO classification.

Methods: A prospective cross-sectional study was performed during an epidemic of dengue in 2009 in Chiapas, Mexico. Data were analyzed for host and viral factors associated with dengue cases, using the 1997 and 2009 WHO classifications. The cost-benefit ratio (CBR) was also estimated.

Results: The sensitivity in the 1997 WHO classification for determining SD was 75%, and the specificity was 97.7%. For the 2009 scheme, these were 100% and 81.1%, respectively. The 2009 classification showed a higher benefit (537%) with a lower cost (10.2%) than the 1997 WHO scheme. A secondary antibody response was strongly associated with SD. Early viral load was higher in cases of SD than in those with DF. Logistic regression analysis identified predictive SD factors (secondary infection, disease phase, viral load) within the 2009 classification. However, within the 1997 scheme it was not possible to differentiate risk factors between DF and dengue hemorrhagic fever or dengue shock syndrome. The critical clinical stage for determining SD progression was the transition from fever to defervescence in which plasma leakage can occur.

Conclusions: The clinical phenotype of SD is influenced by the host (secondary response) and viral factors (viral load). The 2009 WHO classification showed greater sensitivity to identify SD in real time. Timely identification of SD enables accurate early decisions, allowing proper management of health resources for the benefit of patients at risk for SD. This is possible based on the 2009 WHO classification.

Figure 1

Correspondence analysis between clinical manifestations and severity of illness. (A) Correspondences between dengue severity and main clinical manifestations are shown. SD showed a close correspondence with rash and retro-orbital pain (ROP). (B) Correspondences between dengue severity and warnings signs are shown. A closer correspondence between SD and vomiting and abdominal pain was observed. Diarrhoea exhibited an equal correspondence with SD and D ± WS. D–WS was distinguished by a close correspondence with an absence of vomiting, abdominal pain, diarrhoea, nausea, and back pain (BP).

Figure 2

Sensitivity (S) and specificity (E) of platelet count for prediction of severe dengue. Several intersection (cut-off) points were selected and S and E calculated for each. On the coordinates (abscissas) axis (1-E) and S on the ordinates axis. The ideal examination (S = 1 and E = 1) should be on the top-left angle of the graph. Cut-off value of 35,000 plat/μL showed the greatest sensitivity and specificity (S = 75% and E = 72.86%). A cut-off value of 100,000 plat/μL showed S of 97.25% and E of 37%. (N = 389, area under the curve ROC = 0.8199).

Figure 3

Evaluation of the 1997 and 2009 WHO classifications to identify SD. (A) The percentage of laboratory-confirmed dengue cases classified as DF, DHF, or DSS in the traditional scheme (1997 WHO), or classified as D–WS, D + WS, or SD according to the 2009 WHO scheme is shown. (B) Correspondence between traditional and revised classification.

Figure 4

Linear regression of viremia according to time. (A, C) Viremia levels in D ± WS are explained by day of illness or defervescence day, respectively. (B, D) In contrast, linear regression can explain the viremia behavior in SD either according to illness day or according to defervescence day. (E, F) Slopes of viremia levels significantly differ between SD and D ± WS according to day of illness or defervescence day.

Figure 5

Viremia levels during illness phase. (A) Viremia levels of 219 D ± WS and SD patients from day 1 to day 10 of illness. During the first 4 days of illness, viremia levels in SD were higher than in D ± WS. In D ± WS, viremia levels significantly diminished over time. In contrast, in SD, viremia levels persistently rose. (B) According to defervescence, in the febrile stage, viremia levels were higher in SD than in D ± WS. During defervescence, viremia levels could still be observed in SD and in D ± WS. During defervescence days 2–4, viremia levels persistently rose in SD. In contrast, viremia levels diminished in D ± WS (Kruskal–Wallis test, multiple comparison Dunn’s post hoc test, P = 0.0001).