Development of clinical algorithms for the diagnosis of dengue in Colombia

Abstract

Introduction: Due to the increase in dengue incidence and mortality, its diagnosis is relevant for endemic countries. Clinical classifications and laboratory tests have a variable performance in clinical practice with a sensitivity level between 45% and 98%, and a specificity level between 4% and 98% partly due to the variety of contexts where they are applied.
Objective: To develop clinical algorithms for the diagnosis of dengue in the Colombian context.
Materials and methods: A cross-sectional study was conducted based on secondary sources. We constructed clinical diagnostic algorithms of dengue based on Bayesian methods combining symptoms, signs, and blood count parameters, and then we compared them in terms of diagnostic accuracy with gold standard tests. In addition, an external validation of the algorithm with greater accuracy and sensibility was performed comparing it with the WHO-1997 and the WHO-2009 clinical classifications, the Colombian guide for 2010, and the diagnostic scale recommended by the Ministerio de Salud y Protección Social of Colombia for 2013.
Results: Four algorithms were generated, two for signs and symptoms, and two that included leukocytes (≤4,500/mm3) and/or platelets (≤160,000/mm3) counts. The most accurate algorithm included blood count parameters with a sensitivity of 76.5% (95%CI: 71.9-80.5) and a specificity of 46.0% (95%CI: 37.6-54.7). In the external validation we found a sensitivity of 11.1% (95%CI: 4.9-20.7) and a specificity of 91.9% (95%CI: 87.5-93.9). The scale of the Ministerio de Salud had a sensitivity of 76.4% (95%CI: 64.9-85.6) and a specificity of 38.0% (95%CI: 32.8-43.4).
Conclusion: The inclusion of blood count parameters improved the sensitivity of diagnostics algorithms based on signs and symptoms. Clinical diagnosis of dengue remains a challenge for health research.