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Record W4386726871 · doi:10.1093/asj/sjad299

Unraveling the Intricacies of Early Complications in Hyaluronic Acid Filler Procedures: A Scholarly Critique and Analysis

2023· article· en· W4386726871 on OpenAlexaff
Eqram Rahman, Parinitha Rao, Wolfgang G. Philipp‐Dormston, Richard Webb, Jean Carruthers, Alastair Carruthers

Bibliographic record

VenueAesthetic Surgery Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineHyaluronic acidFiller (materials)MEDLINESurgeryAnatomyBiochemistryComposite material

Abstract

fetched live from OpenAlex

We have diligently reviewed the aforementioned article by Nishikawa et al in conjunction with the subsequent insightful commentary provided by Professor Goodman.1,2 While we commend the authors for presenting a retrospective cohort study of considerable scale concerning Juvéderm products, it is crucial to acknowledge certain methodological and reporting nuances that warrant careful consideration. These nuances, if unaddressed, have the potential to impede a precise interpretation of the presented data, thereby undermining the promotion of standardized reporting practices within the realm of aesthetic medicine. The study in question unfolds as a retrospective exploration of early complications arising from hyaluronic acid (HA) filler injections for facial rejuvenation, encompassing a substantial cohort of 41,775 cases dating back to 2021. Predominantly concentrating on serious complications, particularly those involving vascular compromise, the investigation meticulously examines key aspects, including patient demographics, injection sites, the nature of complications, and subsequent treatment outcomes. The study reports an overall early complication rate of 0.07%, observing an elevated rate of complications among younger patients. Remarkably, the upper eyelids and the nasolabial fold emerge as significant sites of complications. Notably, the study reports that injector experience does not exhibit a direct correlation with the occurrence of complications. In the accompanied commentary by Professor Goodman, he delivered a keen critique on a retrospective study concerning hyaluronic acid filler injections, lauding the safety protocols employed, yet pinpointing significant biases. He argued that the exclusion of known high-risk injection sites might skew the reported acute complication rate. He also scrutinized the under-examined technique of nasal cannulation linked to distressing blindness cases, implying a notable oversight. He highlighted a pronounced lack of clarity within the study, especially regarding an isolated case of conjunctivitis following upper eyelid injection, which he argued is an insufficient basis for risk extrapolation. Professor Goodman expressed surprise over the lesser-documented incidents of intravascular accidents at the nasolabial fold, despite its concerning proximity to the facial artery—a factor inherently laden with risks in standard injection techniques. The commentary insightfully suggests a paradox in which injectors with modest experience may display overconfidence, lacking the vital anatomic understanding seen in their less-experienced or highly seasoned counterparts. Alarmingly, concerns are raised about potential nerve damage, attributed to pre-anesthetizing practices in forehead treatments, coupled with ambiguity surrounding the use and directional approach of cannulas, which could markedly jeopardize patient safety. The initial concern arises from the study's title, which references HA products despite the exclusive inclusion of the Juvéderm HA range (Allergan Aesthetics, AbbVie, Irvine, CA), other than various hyaluronic acid (HA) products approved in Japan. The generalization of their findings solely based on Juvéderm may not be entirely appropriate given the diversity of HA products and the potential variability in their complication rates. Further concerns center around the absence of explicit information regarding the volume of HA administered for aesthetic treatments, particularly instances when complications are reported in Table 2, or in the supplemental material of the authors’ article.1 This absence of data detracts from a comprehensive understanding of the relationship between HA volume and complication rates, which is a significant aspect of complication assessment. In light of the preponderance of complications arising in anatomically sensitive regions, commonly referred to as “danger areas,” it becomes imperative to scrutinize the extent to which the authors explore the intricate relationship between the quantity of HA utilized and the incidence of complications in these high-risk domains. The omission of detailed consideration regarding HA volume potentially leads to an incomplete comprehension of complication management and mitigation within the realm of aesthetic medicine. Moreover, the authors’ assertion that injector experience was not a definitive indicator of treatment outcomes, including the incidence of complications, is based on a relatively small cohort of injectors. The limited number of injectors involved in the study raises questions about the generalizability and statistical robustness of this conclusion. To establish the validity of such a claim, it is crucial to consider the heterogeneity within each experience group, including variations in training, techniques, and patient selection criteria. A more extensive and diverse sample of injectors would be required to draw more conclusive inferences regarding the role of experience in predicting treatment outcomes. The authors did not explore potential confounding variables or nuanced aspects of experience, such as the quality of training, familiarity with diverse patient demographics, or proficiency in managing complications when they do arise. Consequently, the authors’ stance on the limited predictive value of injector experience should be regarded with cautious skepticism until further substantiated by more comprehensive and rigorous investigations. Although the study offers valuable insights into complications related to HA filler treatments, it remains somewhat limited in its statistical analysis. The reliance on descriptive statistics is evident, with advanced analytical techniques such as analysis of variance (ANOVA) or regression analysis notably absent. The implications of these limitations extend to several critical issues: Inferential analysis constraints: The authors predominantly employ descriptive statistics, underscoring frequency and percentage distributions of complications across distinct variables. Although this approach provides a concise overview, its inadequacy in assessing interrelationships and interactions among variables hampers the identification of significant associations or predictive factors. Unexplored multivariate analysis: The application of ANOVA and regression analysis could have facilitated the exploration of potential associations between variables, encompassing factors such as age, injection site, injector experience, and complication rates. Such multivariate analysis would have enabled a comprehensive assessment of several factors concurrently, elucidating their collective impact on complications. Unaddressed confounding factors: The omission of regression analysis precludes the meticulous control for confounding variables that could potentially influence complication rates. Variables like injector technique, patient medical history, and underlying health conditions could conceivably impact complication occurrences but remain unaccounted for in the analysis. Limited insights into risk factors: Incorporating regression analysis would have provided nuanced insights into the relative contributions of diverse factors to the incidence of complications. This knowledge would have proven invaluable to practitioners seeking an enhanced comprehension of variables pivotal to predicting adverse events. Complication rate modeling: By leveraging regression analysis, models could have been formulated to predict complication rates based on patient demographics, injection sites, and injector expertise. Such models are instrumental in facilitating risk assessment and informed decision-making for both clinicians and patients alike. Exploration of interaction effects: The application of regression analysis could have facilitated the exploration of interaction effects. By investigating how associations between age and complications might differ contingent on injection sites or injector proficiency, the study could have provided nuanced recommendations for patient selection and treatment planning. Generalizability and external validity: The dearth of robust statistical analysis compromises the generalizability of the study's findings beyond the specific demographic and context under scrutiny. Enhanced statistical methodologies would have enabled insights transferable to a broader spectrum of patients and clinical settings. The authors' narrow focus on intravascular complications in their study on HA filler injections significantly overlooks other acute reactions, presenting a skewed understanding of the risks. A more robust examination of various acute reactions is crucial for a balanced and thorough comprehension of the overall safety profile of these injections, thereby better equipping readers and practitioners to understand and navigate the myriad of potential risks and complications. In conclusion, the elevation of evidence-based practice within aesthetic medicine remains a paramount pursuit. For this pursuit to gain traction, reporting and analysis in studies must be consistently comprehensive and thorough. The importance of detailed reporting and meticulous analysis cannot be understated, because they serve as cornerstones for informed decision-making among practitioners and researchers. By embracing a rigorous commitment to comprehensive reporting and sophisticated analysis, studies can foster a culture of patient safety and efficacy within aesthetic medicine. Through this synergistic approach, practitioners are empowered to make judicious choices, and patients can partake in treatments characterized by heightened safety and confidence. Dr Rahman served a consultant and speaker for Allergan Aesthetics, an AbbVie Company (Irvine, CA), and is an Evidence-Based Medicine section co-editor of Aesthetic Surgery Journal. Dr Rao is a speaker for Allergan Aesthetics. Dr Philipp-Dormston reports being a clinical trial investigator, scientific adviser, and speaker for Allergan Aesthetics, Galderma (Lausanne, Switzerland), and Merz Pharmaceuticals GmbH (Frankfurt, Germany). Dr J. Carruthers is a consultant and investigator for Allergan Aesthetics, Merz Pharmaceuticals GmbH, Solstice Neurosciences (San Francisco, CA), and Revance Therapeutics, Inc. (Nashville, TN). Dr A. Carruthers is a consultant for and has received research grants from Allergan Aesthetics, Merz Pharmaceuticals GmbH, and Revance Therapeutics, Inc. The remaining author declared no potential conflicts of interest with respect to the research, authorship, and publication of this article. The authors received no financial support for the research, authorship, and publication of this article.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.322
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2023
Admission routes1
Has abstractyes

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