Related Experiment Video
Updated: Dec 13, 2025

The Quantification of Injectability by Mechanical Testing
Published on: May 13, 2020
Physician survey on pre-/postprocedure measures for injectable treatments
Michael H Gold1,2, Anneke Andriessen3, Shasa Hu4,5
1Gold Skin Care Center and Tennessee Clinical Research Center, Nashville, TN, USA.
Introduction:
Every year in the United States, over 1 billion dollars are spent on aesthetic injectables, such as soft tissue fillers and neurotoxins. In 2018, the total amount of injectable treatments performed surpassed 2 671 130 procedures. While often mild and transient, adverse events (AEs) can occur following these procedures. AEs may include common side effects such as bruising, or rare, but serious AEs such as infections. While previous investigators have evaluated methods of reducing risks of AEs due to the treatment procedure itself, few investigations have evaluated measures employed before and/or after treatment (ie, peri-procedure).
Methods:
An electronic survey was sent to aesthetic clinicians with experience performing injectable treatments. The survey collected information regarding general information (eg, demographics and specialty), type of injectable devices used, current peri-procedures, and an exploration of future options for peri-procedural measures.
Results:
Most aesthetic clinicians did not use prophylactic topical or systemic antimicrobials, nor prophylactic topical antiviral therapy. However, approximately 65% of clinicians reported using prophylactic systemic antivirals for patients with a history of herpes simplex virus. A variety of products were used to prepare the skin prior to injectable procedures. Postprocedure, multiple over-the-counter wound repair products were recommended by >70% of injectors. However, there was a large variety of products recommended with no majority consensus.
Conclusions:
Currently, there are no peri-procedural standards of practice when performing aesthetic injectable treatments. Efforts are underway for the development of best-practice algorithms.
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