ArticlePloS one2023
Development and validation of a clinical rule for the diagnosis of chikungunya fever in a dengue-endemic area.
Article in PloS one, 2023. 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 machine learning approach for differential classification of chikungunya virus: an explainable AI study based on surveillance data.Infection · 2026Article
- Development and validation of an interpretable machine learning model for early hospital-based differentiation of chikungunya and dengue fever using routine clinical data.BMC infectious diseases · 2026Observational
- Post-chikungunya chronic arthralgia in Burkina Faso: Frequency and associated factors.PLoS neglected tropical diseases · 2026Article
- AI-Assisted Differentiation of Dengue and Chikungunya Using Big, Imbalanced Epidemiological Data.Tropical medicine and infectious disease · 2026Article
- Proof-of-Concept Machine Learning Framework for Arboviral Disease Classification Using Literature-Derived Synthetic Data: Methodological Development Preceding Clinical Validation.Healthcare (Basel, Switzerland) · 2026Article
- Unraveling chikungunya's viral tapestry through molecular mechanisms and clinical insights.Infectious diseases & immunity · 2026Review
- Contribution of imaging in the diagnosis of three neglected diseases in the Southern Cone: Leishmaniasis, Dengue, and Chikungunya.Pediatric radiology · 2025Review
- Joint Involvement Can Predict Chikungunya in a Dengue Syndemic Setting in India.Journal of epidemiology and global health · 2023Article
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4 authors.
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Abstract
Rio de Janeiro is a dengue-endemic city that experienced Zika and chikungunya epidemics between 2015 and 2019. Differential diagnosis is crucial for indicating adequate treatment and assessing prognosis and risk of death. This study aims to derive and validate a clinical rule for diagnosing chikungunya based on 3,214 suspected cases consecutively treated at primary and secondary health units of the sentinel surveillance system (up to 7 days from onset of symptoms) in Rio de Janeiro, Brazil. Of the total sample, 624 were chikungunya, 88 Zika, 51 dengue, and 2,451 were negative for all these arboviruses according to real-time polymerase chain reaction (RT-qPCR). The derived rule included fever (1 point), exanthema (1 point), myalgia (2 points), arthralgia or arthritis (2 points), and joint edema (2 points), providing an AUC (area under the receiver operator curve) = 0.695 (95% CI: 0.662-0.725). Scores of 4 points or more (validation sample) showed 74.3% sensitivity (69.0% - 79.2%) and 51.5% specificity (48.8% - 54.3%). Adding more symptoms improved the specificity at the expense of a lower sensitivity compared to definitions proposed by government agencies based on fever alone (European Center for Disease Control) or in combination with arthralgia (World Health Organization) or arthritis (Pan American Health Organization, Brazilian Ministry of Health). The proposed clinical rule offers a rapid, low-cost, easy-to-apply strategy to differentiate chikungunya fever from other arbovirus infections during epidemics.
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