Systematic Review of Post-Viral Delayed Inflammation Associated with Hyaluronic Acid Dermal Fillers
Bibliographic record
Abstract
Background and Objectives: Hyaluronic acid (HA) dermal fillers are among the most widely used injectable materials in esthetic medicine. They are generally safe, but delayed inflammatory reactions (DIRs) have been observed, particularly after viral infections or vaccinations. Such events have raised questions about the role of immune activation in filler-related complications. This review examined the available literature on DIRs to HA fillers that occurred in the context of viral illness or immunization, with attention to how these reactions present and how they are managed. Materials and Methods: A systematic search was carried out in PubMed, ScienceDirect, ClinicalKey, and Google Scholar between October and November 2024. Only human case reports and case series were included. The protocol was registered in PROSPERO (CRD420251030918), and study quality was assessed using the Newcastle–Ottawa Scale. Results: Six publications met inclusion criteria: four case series and two case reports, describing 25 women between 22 and 65 years of age. Patients developed swelling, erythema, angioedema, or, in severe cases, marked facial edema after HA filler injections, with symptom onset ranging from several hours to several weeks following viral exposure. Corticosteroids and hyaluronidase were the most common treatments, though milder cases sometimes resolved without intervention. Study quality varied, with some reports providing limited detail on patient characteristics and follow-up. Conclusions: DIRs associated with viral infections or vaccinations remain uncommon but clinically relevant complications of HA filler use. Limited case-based evidence indicates potential effectiveness of corticosteroids and hyaluronidase, though management practices remain inconsistent. Larger prospective studies are needed to clarify underlying mechanisms and to establish standardized guidelines for treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".