ArticleThe American journal of tropical medicine and hygiene2020
Development and Performance of Dengue Diagnostic Clinical Algorithms in Colombia.
Article in The American journal of tropical medicine and hygiene, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A methodological framework for conducting diagnostic meta-analyses using large literature corpora.BMC medical research methodology · 2026Article
- Understanding Dengue Underreporting: An Analysis of the Impacts for the World, Latin America and Brazil.TheScientificWorldJournal · 2025Review
- Using Computational Simulations Based on Fuzzy Cognitive Maps to Detect Dengue Complications.Diagnostics (Basel, Switzerland) · 2024Article
- Diagnostic performance of the WHO definition of probable dengue within the first 5 days of symptoms on Reunion Island.PloS one · 2024Article
- Evaluation of remote radiologist-interpreted point-of-care ultrasound for suspected dengue patients in a primary health care facility in Colombia.Infectious diseases of poverty · 2023Article
- The Effect of Age on Dengue Presentation and the Diagnostic Accuracy of the 2015 Pan American Health Organization Case Criteria in a Puerto Rican Cohort.Open forum infectious diseases · 2023Article
- Dengue algorithms integrated into the IMCI guidelines: An updated assessment in five Southeast-Asian countries.PLoS neglected tropical diseases · 2022Article
- Laboratory Findings in Patients with Probable Dengue Diagnosis from an Endemic Area in Colombia in 2018.Viruses · 2021Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnosing dengue in endemic areas remains problematic because of the low specificity of the symptoms and lack of accurate diagnostic tests. This study aimed to develop and prospectively validate, under routine care, dengue diagnostic clinical algorithms. The study was carried out in two phases. First, diagnostic algorithms were developed using a database of 1,130 dengue and 918 non-dengue patients, expert opinion, and literature review. Algorithms with > 70% sensitivity were prospectively validated in a single-group quasi-experimental trial with an adaptive Bayesian design. In the first phase, the algorithms that were developed with the continuous Bayes formula and included leukocytes and platelet counts, in addition to selected signs and symptoms, showed the highest sensitivities (> 80%). In the second phase, the algorithms were applied on admission to 1,039 consecutive febrile subjects in three endemic areas in Colombia of whom 25 were laboratory-confirmed dengue, 307 non-dengue, 514 probable dengue, and 193 undetermined. Including parameters of the hemogram consistently improved specificity without affecting sensitivity. In the final analysis, considering only confirmed dengue and non-dengue cases, an algorithm with a sensitivity and specificity of 65.4% (95% credibility interval 50-83) and 40.1% (34.7-45.7) was identified. All tested algorithms had likelihood ratios close to 1, and hence, they are not useful to confirm or rule out dengue in endemic areas. The findings support the use of hemograms to aid dengue diagnosis and highlight the challenges of clinical diagnosis of dengue.
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