Chikungunya and the Heart
Bibliographic record
Abstract
INTRODUCTION: Neglected tropical diseases are a group of communicable diseases that occur in tropical and subtropical conditions and are closely related to poverty and inadequate sanitation conditions. Among these entities, chikungunya remains one of the most widely spread diseases. Although the main symptoms are related to a febrile syndrome, cardiovascular (CV) involvement has been reported, with short- and long-term implications. As part of the "Neglected Tropical Diseases and other Infectious Diseases involving the Heart" (NET-Heart) Project, the aim of this review is to compile all the information available regarding CV involvement of this disease, to help healthcare providers gain knowledge in this field, and contribute to improving early diagnosis, treatment, and prevention strategies. METHODS: We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement in conducting and reporting this systematic review. The search was conducted using MEDLINE/PubMed, SciELO, and LILACS databases to identify any relevant studies or reviews detailing an association between chikungunya and cardiac involvement published from January 1972 to May 31, 2020. RESULTS: Despite its mechanism not being fully understood, CV involvement has been described as the most frequent atypical presentation of chikungunya (54.2%). Myocarditis is the most prevalent CV complication. Different rhythm disturbances have been reported in 52% of cases, whereas heart failure was reported in 15% of cases, pericarditis in 5%, and acute myocardial infarction in 2%. Overall estimated CV mortality is 10%, although in patients with other comorbidities, it may increase up to 20%. In the proper clinical setting, the presence of fever, polyarthralgia, and new-onset arrhythmia suggests chikungunya virus-related myocarditis. CONCLUSION: Although most cases are rarely fatal, CV involvement in chikungunya infection remains the most frequent atypical presentation of this disease and may have severe manifestations. Timely diagnosis and appropriate management are necessary to improve patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".