Developing a clinical case definition for Chikungunya fever in the Democratic Republic of the Congo.

Authors

Florian Vogt, Antoine Nkuba-Ndaye, Anja De Weggheleire, Sheila Makiala-Mandanda, Placide Mbala-Kingebeni, Benoit Mputu-Ngoyi, Eddy Kinganda-Lusamaki, Elisabeth Pukuta, Lorenzo Subissi, Birgit De Smet, Jozefien Buyze, Steve Ahuka-Mundeke

Published in

PloS one. Volume 21. Issue 8. Pages e0355216. Epub Aug 06, 2026.

Abstract

Chikungunya fever presents a diagnostic challenge in Sub-Saharan Africa due to the absence of a region-specific case definition, especially in settings with high disease burden and diverse aetiologies of acute febrile illnesses. Our study aimed to develop an evidence-based clinical case definition for acute Chikungunya virus (CHIKV) infection suitable for use at resource-limited primary health care level.
We conducted a cross-sectional study in Kinshasa, Democratic Republic of the Congo, between June and November 2019 during an ongoing CHIKV outbreak. Patients aged ≥3 years with symptoms suggestive of acute CHIKV infection were enrolled. Clinical examination data and blood samples were collected for CHIKV diagnosis via PCR and ELISA. Malaria rapid diagnostic tests were also performed. We used Classification and Regression Tree (CART) analysis with 10-fold cross-validation and predictive logistic regression models with lasso penalty, with performance assessed via the Area under the Receiver Operating Characteristics Curve (AUC).
Of 132 analysed patients, 40.2% had acute CHIKV infection. CART analysis identified knee and shoulder pain as potential predictors, however classifying only 67.4% of patients correctly while missing 71.7% of cases (cross-validated AUC 53%; 95%CI 43%-63%). Logistic regression achieved 61.1% accuracy, with a corresponding cross-validated AUC of 62% (95%CI 52%-72%). Exploratory analysis excluding malaria co-infections yielded similar CART performance, while logistic regression failed to identify any reliable predictors.
While our study provides valuable insights into the clinical presentation of CHIKV in Sub-Saharan Africa, it highlights the challenges of developing a simple symptom-based diagnostic algorithm suitable for resource-limited primary health care settings. Certain joint pain patterns showed predictive value, however no model achieved sufficient accuracy for clinical use. Improved access to serological testing and integration of epidemiological context are essential for CHIKV diagnosis in such contexts. Further research is needed to refine clinical criteria and support outbreak response in resource-limited regions.

PMID:
42560978
Bibliographic data and abstract were imported from PubMed on 07 Aug 2026.

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