A predictive model to differentiate dengue from other febrile illness Academic Article uri icon

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abstract

  • BACKGROUND: Dengue is a major public health problem in tropical and subtropical countries and has a presentation similar to other febrile illnesses. Since laboratory confirmation is frequently delayed, the majority of dengue cases are diagnosed based on symptoms. The objective of this study was to identify clinical, hematological and demographical parameters that could be used as predictors of dengue fever among patients with febrile illness. METHODS: We conducted a retrospective cohort study of 548 patients presenting with febrile syndrome to the largest public hospitals in Honduras. Patients' clinical, laboratory, and demographic data as well as dengue laboratory detection by either serology or viral isolation were used to build a predictive statistical model to identify dengue cases. RESULTS: Of 548 patients, 390 were confirmed with dengue infection while 158 had negative results. Univariable analysis revealed seven variables associated with dengue: male sex, petechiae, skin rash, myalgia, retro-ocular pain, positive tourniquet test, and gingival bleeding. In multivariable logistic regression analysis, retro-ocular pain petechiae and gingival bleeding were associated with increased risk, while epistaxis and paleness of skin were associated with reduced risk of dengue. Using a value of 0.6 (i.e., 60% probability for a case to be positive based on the equation values), our model had a sensitivity of 86.2%, a specificity of 27.2%, and an overall accuracy of 69.2%; allowing for the diagnosis of dengue to be ruled out and for other febrile conditions to be investigated. CONCLUSIONS: Among Honduran patients presenting with febrile illness, our analysis identified key symptoms associated with dengue fever, however the overall accuracy of our model was still low and specificity remains a concern. Our model requires validation in other populations with a similar pattern of dengue transmission.

publication date

  • December 2016