Daily Cosmetic Research Analysis
Three impactful studies in cosmetic and aesthetic surgery stand out today: a multinational registry analysis harmonizing real-world data on breast implant revision incidence; a registry–primary care linkage study showing symptom reductions after cosmetic breast implant explantation but persistent excess vs nonrecipients; and an expert-validated appraisal of large language models for patient information in aesthetic surgery, highlighting transparency gaps.
Summary
Three impactful studies in cosmetic and aesthetic surgery stand out today: a multinational registry analysis harmonizing real-world data on breast implant revision incidence; a registry–primary care linkage study showing symptom reductions after cosmetic breast implant explantation but persistent excess vs nonrecipients; and an expert-validated appraisal of large language models for patient information in aesthetic surgery, highlighting transparency gaps.
Research Themes
- Real-world safety and revision outcomes of breast implants
- Breast implant illness symptom trajectories after explantation
- AI-driven patient education quality in aesthetic procedures
Selected Articles
1. Comparing International Revision Incidence of Commonly Used Breast Implants.
In a pooled, multicountry registry cohort (Australia and Netherlands) including 150,969 breast implants, overall complication-related revision was 6.3% (reconstructive) and 1.2% (cosmetic). Cosmetic anatomical polyurethane–silicone implants had a lower revision risk vs anatomical textured–silicone (HR 0.38), while no significant 5-year cumulative differences were seen between types.
Impact: Demonstrates feasibility and value of harmonized international registries for real-world device safety, informing implant selection and future regulatory/clinical studies.
Clinical Implications: For cosmetic augmentation, anatomical polyurethane–silicone implants may offer lower revision risk versus anatomical textured–silicone; however, 5-year cumulative differences are minimal across types. Shared decision-making should emphasize comparable medium-term outcomes and the value of continued registry participation.
Key Findings
- Successful harmonization and pooling of Australian and Dutch implant registries enabled multinational time-to-event analysis.
- Overall complication-related revision incidence: 6.3% (reconstructive) vs 1.2% (cosmetic).
- Cosmetic anatomical polyurethane–silicone implants had lower revision risk vs anatomical textured–silicone (HR 0.38; 95% CI 0.22–0.64).
- No significant differences in 5-year cumulative revision incidence across implant types in both reconstructive and cosmetic indications.
Methodological Strengths
- Large, multicenter, population-based registries with harmonized variables across countries.
- Robust time-to-event modeling using frailty Cox regression to account for clustering.
Limitations
- Observational design with potential residual confounding and unmeasured factors (e.g., surgical technique, surgeon experience).
- Five-year horizon may miss longer-term differences; registry capture depends on reporting completeness.
Future Directions: Expand harmonization to additional national registries, include manufacturer-level and surgical technique variables, and extend follow-up to assess long-term outcomes and rare events.
IMPORTANCE: Despite the widespread use of breast implants, to date, no large-scale international studies on long-term revision incidence of different implant types have been conducted. OBJECTIVE: To determine whether international data could be combined using a harmonized dataset to increase power and investigate clinically relevant differences in complication-related revision incidence between breast implant types. DESIGN, SETTING, AND PARTICIPANTS: This multicenter, population-based cohort study used data from the Australian Breast Device Registry and the Dutch Breast Implant Registry from 2016 to 2021. Time-to-event analysis was performed using a frailty Cox proportional hazards regression model with pooled data. The study included all permanent breast implants that were inserted for primary postmastectomy or benign breast reconstruction or cosmetic augmentation. Data were analyzed from January 1, 2016, through December 31, 2021. EXPOSURE: Permanent breast implants grouped based on implant shape, shell, and fill. MAIN OUTCOMES AND MEASURES: Complication-related revision incidence between breast implant types. RESULTS: Data exchange between registries was successful. In total, 21 115 reconstructive and 129 854 cosmetic breast implants inserted in patients with a median (IQR) age of 47 (38-55) years and 31 (25-38) years, respectively, were included. Overall complication-related revision was 6.3% for reconstructive and 1.2% for cosmetic implants. For reconstructive implants, hazard ratios (HRs) for implant types showed no significant differences compared with anatomical textured-silicone implants. For cosmetic implants, anatomical polyurethane-silicone implants showed a lower risk of revision (HR, 0.38; 95% CI, 0.22-0.64) compared with anatomical-textured-silicone implants. At 5 years, no significant differences in cumulative revision incidence were observed between implant types for either reconstructive or cosmetic implants. CONCLUSIONS AND RELEVANCE: This study showed that international datasets can be pooled to assess real-world incidence of breast implant revision, which is anticipated to generate a foundation on which future breast implant studies can be based.
2. The Occurrence of Health Symptoms in General Practice Before and After the Explantation of Cosmetic Breast Implants.
Linking a national implant registry with primary care records, explantation of cosmetic breast implants was associated with significant reductions in multiple symptom burdens within one year, yet symptom likelihood remained higher than in women without implants. Replacement surgery also showed reductions from pre- to postsurgery.
Impact: Provides real-world, clinician-recorded symptom trajectories around explantation, informing the contested discourse on breast implant illness and patient counseling.
Clinical Implications: Patients considering explantation can be counseled that symptom burden often decreases within a year, though not to the level of nonrecipients; expectations should be managed and follow-up planned.
Key Findings
- Retrospective linkage of registry and primary care datasets enabled pre/post comparison around explantation (n=217) with replacement (n=228) and nonrecipient (n=433) comparators.
- Explantation reduced the odds of ≥3 symptoms (OR 0.26), ≥3 consultations (OR 0.56), and substantial symptoms (OR 0.36) within one year.
- Post-explant, symptom likelihood remained higher vs nonrecipients; replacement surgery also showed symptom reductions from pre- to postsurgery.
Methodological Strengths
- Data linkage across a national implant registry and general practice EHRs.
- Multiple comparator groups and pre/post within-subject assessment.
Limitations
- Retrospective design with potential confounding and coding biases.
- One-year window; lack of symptom severity scales and causal inference.
Future Directions: Prospective, longer-term studies incorporating validated symptom scales and biomarkers to elucidate mechanisms and predictors of response to explantation.
BACKGROUND: Explantation of breast implants is increasingly performed in response to concerns about breast implant illness (BII), an array of various health symptoms. However, the benefits of explantation remain unclear because of methodological limitations in previous studies. OBJECTIVES: To examine the occurrence of health symptoms before and after explantation of cosmetic breast implants. METHODS: This is a retrospective cohort study linking data from the Dutch Breast Implant Registry and Nivel Primary Care Database. The study included 217 cosmetic explantation patients, control groups of 228 cosmetic replacement patients, and 433 female patients without breast implants (nonrecipients). BII-related health symptoms presented in general practice were compared between groups 1 year before and after explantation. Outcomes included any symptom, ≥2 symptoms, ≥3 symptoms, ≥3 consultations, and substantial symptoms (≥3 symptoms with ≥2 consultations for 2 symptoms). RESULTS: The likelihood of ≥3 symptoms, ≥3 consultations, and substantial symptoms reduced significantly after explantation (odds ratio [OR] ≥3 symptoms 0.26, 95% CI [0.08-0.85]; OR ≥3 consultations 0.56, 95% CI [0.32-0.96]; OR substantial symptoms 0.36, 95% CI [0.14-0.94]). However, compared with nonrecipients, explantation patients still had higher odds of any symptom, ≥2 symptoms, and ≥3 consultations after explantation. Replacement patients also had a reduced likelihood of any symptom, ≥2 symptoms, and ≥3 consultations from pre- to postsurgery. CONCLUSIONS: Although explantation appears to improve BII-related health symptoms within a year, patients still showed an increased likelihood of symptoms compared with nonrecipients.
3. Assessing the Informational Value of Large Language Models Responses in Aesthetic Surgery: A Comparative Analysis with Expert Opinions.
In a comparative expert evaluation using real patient questions, ChatGPT 3.5 and Google Bard provided moderately accurate and clear answers on dermal fillers, botulinum toxin, and aesthetic blepharoplasty. Lack of source transparency limited credibility assessment, warranting cautious clinical use.
Impact: Addresses a timely and cross-cutting issue by benchmarking AI-generated patient information against expert standards in aesthetic surgery, highlighting current limitations and guiding safe integration.
Clinical Implications: Clinicians should supervise and contextualize AI-derived information, cautioning patients about missing citations and potential omissions. Development and adoption of LLMs with verifiable sources are needed before relying on them as primary educational tools.
Key Findings
- Two LLMs (ChatGPT 3.5 and Google Bard v1.53) answered frequent patient questions across three common aesthetic procedures.
- Thirteen expert aesthetic surgeons rated responses as moderately accurate, clear, and useful overall.
- Source transparency was insufficient, precluding full assessment of credibility and limiting clinical reliance.
Methodological Strengths
- Use of real patient-derived questions spanning multiple procedures.
- Blinded expert panel ratings using standardized Likert scales across key dimensions.
Limitations
- Small sample size and rapidly evolving LLM versions may limit generalizability.
- Lack of quantitative source attribution analysis beyond expert perception.
Future Directions: Conduct larger, multilingual, and longitudinal assessments including versioned LLMs with source citation features; evaluate patient comprehension, safety, and bias correction.
BACKGROUND: The increasing popularity of Large Language Models (LLMs) in various healthcare settings has raised questions about their ability to provide accurate and reliable information. This study aimed to evaluate the informational value of Large Language Models responses in aesthetic plastic surgery by comparing them with the opinions of experienced surgeons. METHODS: Thirty patients undergoing three common aesthetic procedures-dermal fillers, botulinum toxin injections, and aesthetic blepharoplasty-were selected. The most frequently asked questions by these patients were recorded and submitted to ChatGpt 3.5 and Google Bard v.1.53. The answers provided by the Large Language Models were then evaluated by 13 experienced aesthetic plastic surgeons on a Likert scale for accessibility, accuracy, and overall usefulness. RESULTS: The overall ratings of the chatbot responses were moderate, with surgeons generally finding them to be accurate and clear. However, the lack of transparency regarding the sources of the information provided by the LLMs made it impossible to fully evaluate their credibility. CONCLUSIONS: While chatbots have the potential to provide patients with convenient access to information about aesthetic plastic surgery, their current limitations in terms of transparency and comprehensiveness warrant caution in their use as a primary source of information. Further research is needed to develop more robust and reliable LLMs for healthcare applications. LEVEL OF EVIDENCE I: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .