Citations

Abstract Background Breast cancer is one of the most common types of cancer, with around 2.3 million cases diagnosed in 2020. One in five cancer patients develops chronic lymphedema caused by multifactorial triggers and treatment-related factors. This can lead to swelling, skin infections, and limb dysfunction, negatively affecting the patient's quality of life. This retrospective cohort study aimed to determine the associations between demographic and breast cancer characteristics and postoperative cellulitis in breast cancer survivors who underwent lymphovenous bypass surgery (LVB) at Mayo Clinic, Florida. Methods We performed a retrospective chart review. Data were collected retrospectively from 2016 to 2022. Sixty adult breast cancer survivors who underwent LVB were included in the final analysis based on specific inclusion and exclusion criteria. Patients were excluded if they did not meet the inclusion criteria or had incomplete follow-up data. Demographic and surgical data were extracted, including body mass index (BMI), type of anastomosis, number of anastomoses, and preoperative cellulitis status. Lymphedema measurements were performed using tape measurements. Fisher's exact test was used to determine statistically significant associations between variables and postoperative cellulitis. Results Postoperative cellulitis was more common in patients aged 60 to 69 years (43.2%), whites (75.0%), overweight or obese (90.9%), with one to four anastomoses (81.8%), and nonsmokers (79.5%). The mean International Society of Lymphology (ISL) criteria for both postoperative cellulitis and no postoperative cellulitis was 1.93. Statistically significant associations with postoperative cellulitis were found for the number of anastomoses (p = 0.021), smoking status (p = 0.049), preoperative cellulitis (p = 0.04), and the length of years with lymphedema diagnosis variable (p = 0.004). Conclusion Our results suggest that a greater number of anastomoses, smoking, preoperative cellulitis, and years with lymphedema are significantly associated with an increased risk of postoperative cellulitis. Awareness of these risk factors is crucial for monitoring and early treatment of infections following surgery.

Medical researchers are increasingly utilizing advanced LLMs like ChatGPT-4 and Gemini to enhance diagnostic processes in the medical field. This research focuses on their ability to comprehend and apply complex medical classification systems for breast conditions, which can significantly aid plastic surgeons in making informed decisions for diagnosis and treatment, ultimately leading to improved patient outcomes. Fifty clinical scenarios were created to evaluate the classification accuracy of each LLM across five established breast-related classification systems. Scores from 0 to 2 were assigned to LLM responses to denote incorrect, partially correct, or completely correct classifications. Descriptive statistics were employed to compare the performances of ChatGPT-4 and Gemini. Gemini exhibited superior overall performance, achieving 98% accuracy compared to ChatGPT-4’s 71%. While both models performed well in the Baker classification for capsular contracture and UTSW classification for gynecomastia, Gemini consistently outperformed ChatGPT-4 in other systems, such as the Fischer Grade Classification for gender-affirming mastectomy, Kajava Classification for ectopic breast tissue, and Regnault Classification for breast ptosis. With further development, integrating LLMs into plastic surgery practice will likely enhance diagnostic support and decision making.

e20097 Background: Malignant Mesothelioma (MMe) is a neoplasm of mesothelium and has a very poor survival rate. Over half the patients of MMe express programmed death-ligand 1 (PD-L1) which binds to the PD-1 receptor on T-cells which in turn suppresses their ability to kill tumors. Pembrolizumab is a PD-1 inhibitor antibody and it has shown efficacy in combating MMe. Our analysis aims to summarize data related to objective response rates (ORR) and adverse effects of Pembrolizumab therapy in MMe patients. Methods: We conducted an online search through PubMed, Cochrane, and Clinicaltrials.gov databases from their inception till January 25, 2024, to identify studies that reported the use of Pembrolizumab in patients with MMe. Data were extracted and analyzed and proportions with their 95% confidence interval have been reported. Onlinemeta v1.0 was used for all analyses. Results: A total of six studies with 549 patients reported the use of Pembrolizumab in patients with MMe. ORR was observed in 36.2% (32.3% - 40.4%; p < 0.01) of patients receiving Pembrolizumab while 20.4% (17.2%- 24.0%; p < 0.01) of patients experienced adverse effects of the therapy. Median survival ranged from 9.5 to 17.3 months whereas progression-free survival ranged from 2.5 to 7.1 months in patients. Conclusions: Pembrolizumab has an ORR and safety profile comparable to other immunotherapies. Further studies are needed to extensively study the drug and its effects in detail in patients with MMe. [Table: see text]

e18073 Background: Selpercatinib is a highly selective RET inhibitor that is used for the treatment of RET-mutant medullary thyroid cancers, but its efficacy and safety is not fully understood. Our objective is to summarize available literature on the safety and efficacy profile of Selpercatinib. Methods: A search was conducted on PubMed, Embase and Cochrane from foundation to 31st January 2024. Patients aged above 18 with Advanced RET-Altered Medullary Thyroid Cancers who received Selpercatinib were identified. Data abstraction was done based on objective response rate, complete response rate, any grade adverse effect (AE) and grade ≥3 AE. Analysis was done using R (v.4.3) and reported as proportions with respective 95% confidence intervals (CI) on forest plots. Results: Four studies were identified that reported the use of Selpercatinib in Advanced RET-Altered Medullary Thyroid Cancers. Our analysis showed that the pooled proportion of objective response rate and complete response rate was 74.8% (95% CI: 70.1% – 79%) and 4.9% (95% CI: 3.1% – 7.7%) respectively. Pooled proportion of patients experiencing any grade AE and grade ≥3 AE was 93.4% (95% CI: 90.4% – 95.6%) and 46.2% (95% CI: 41.1% – 51.3%) respectively. Conclusions: Selpercatinib is a novel RET inhibitor which has potential use against RET-Altered Thyroid Cancers. It has an overall acceptable safety and efficacy profile. However it requires further studies to establish its adverse effects and clinical application. [Table: see text]

Background and Objectives: Large language models (LLMs) are emerging as valuable tools in plastic surgery, potentially reducing surgeons’ cognitive loads and improving patients’ outcomes. This study aimed to assess and compare the current state of the two most common and readily available LLMs, Open AI’s ChatGPT-4 and Google’s Gemini Pro (1.0 Pro), in providing intraoperative decision support in plastic and reconstructive surgery procedures. Materials and Methods: We presented each LLM with 32 independent intraoperative scenarios spanning 5 procedures. We utilized a 5-point and a 3-point Likert scale for medical accuracy and relevance, respectively. We determined the readability of the responses using the Flesch–Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) score. Additionally, we measured the models’ response time. We compared the performance using the Mann–Whitney U test and Student’s t-test. Results: ChatGPT-4 significantly outperformed Gemini in providing accurate (3.59 ± 0.84 vs. 3.13 ± 0.83, p-value = 0.022) and relevant (2.28 ± 0.77 vs. 1.88 ± 0.83, p-value = 0.032) responses. Alternatively, Gemini provided more concise and readable responses, with an average FKGL (12.80 ± 1.56) significantly lower than ChatGPT-4′s (15.00 ± 1.89) (p < 0.0001). However, there was no difference in the FRE scores (p = 0.174). Moreover, Gemini’s average response time was significantly faster (8.15 ± 1.42 s) than ChatGPT’-4′s (13.70 ± 2.87 s) (p < 0.0001). Conclusions: Although ChatGPT-4 provided more accurate and relevant responses, both models demonstrated potential as intraoperative tools. Nevertheless, their performance inconsistency across the different procedures underscores the need for further training and optimization to ensure their reliability as intraoperative decision-support tools.

In the U.S., diagnostic errors are common across various healthcare settings due to factors like complex procedures and multiple healthcare providers, often exacerbated by inadequate initial evaluations. This study explores the role of Large Language Models (LLMs), specifically OpenAI’s ChatGPT-4 and Google Gemini, in improving emergency decision-making in plastic and reconstructive surgery by evaluating their effectiveness both with and without physical examination data. Thirty medical vignettes covering emergency conditions such as fractures and nerve injuries were used to assess the diagnostic and management responses of the models. These responses were evaluated by medical professionals against established clinical guidelines, using statistical analyses including the Wilcoxon rank-sum test. Results showed that ChatGPT-4 consistently outperformed Gemini in both diagnosis and management, irrespective of the presence of physical examination data, though no significant differences were noted within each model’s performance across different data scenarios. Conclusively, while ChatGPT-4 demonstrates superior accuracy and management capabilities, the addition of physical examination data, though enhancing response detail, did not significantly surpass traditional medical resources. This underscores the utility of AI in supporting clinical decision-making, particularly in scenarios with limited data, suggesting its role as a complement to, rather than a replacement for, comprehensive clinical evaluation and expertise.

e16531 Background: Hypoxia-inducible Factor-2α (HIF-2α) is a transcription factor that accumulates in clear cell renal cell carcinoma (ccRCC) and is responsible for genes involved in oncogenesis. Belzutifan is a HIF-2α inhibitor that has shown promising anti-tumor effects in patients with ccRCC. Our objective is to summarize the available literature on the safety and efficacy profile of the drug. Methods: An electronic search was conducted on PubMed, Embase, and Cochrane from inception till January 31st, 2024. We identified patients aged ≥ 18 years with ccRCC who received Belzutifan. We extracted data on objective response rate, complete response rate, any grade adverse effect (AE) and grade ≥3 AE. Data was analyzed using R (v.4.3) and reported as proportions with respective 95% confidence intervals (CI) on forest plots. Results: Three studies (n=168 patients) were identified that reported the use of Belzutifan in ccRCC. Our analysis showed that pooled proportion of objective response rate and complete response rate was 35% (95% CI: 24% – 47%) and 1% (95% CI: 0% – 5%) respectively. Pooled proportion of patients experiencing any grade AE and grade ≥3 AE was 98% (95% CI: 95% – 99%) and 49% (95% CI: 29% – 69%) respectively. Conclusions: Belzutifan is a novel drug that inhibits HIF-2α which in turn results in anti-tumor effects of the drug against ccRCC. The drug has an acceptable safety and efficacy profile from limited data but more studies are needed to establish a complete picture of drug’s adverse effects and clinical benefits. Pooled proportion of patients developing (A) Objective Response, (B) Complete response (C) Any adverse effect and (D) Grade ≥3 adverse effects. [Table: see text]

e24003 Background: Cancer patients undergoing chemotherapy suffer from a variable degree of cancer therapy–related cardiac dysfunction and heart failure (HF). It is known that sodium glucose co-transporter 2 (SGLT2) inhibitors improve outcomes in patients with HF independent of diabetes mellitus and left ventricular ejection fraction; our study aims to investigate the potential benefits of SGLT2 inhibitory therapy specifically in cancer patients undergoing chemotherapy. Methods: We performed an electronic search on PubMed, Embase, and Cochrane, from the inception of databases till January 29, 2024, to identify all the articles that compared the use of SGLT2 inhibitors in adult (≥18 years) with type 2 diabetes mellitus and exposure to cardiotoxic antineoplastic therapy, subsequently diagnosed with cardiomyopathy, heart failure, or LVEF < 40%. The data was extracted and pooled using a random effects model to calculate the odds ratio (OR) with a respective 95% confidence interval (CI). R software (v.4.3) was used for all statistical analyses. Results: Our search identified three observational studies with a total of 3,164 patients (SGLT2- inhibitor group: 1,550; Control group: 1,614). We found that the use of SGLT2 inhibitors was associated with a lower risk of all-cause mortality (OR: 0.25 [0.20, 0.31]; I2 = 16%; p < 0.001), hospitalizations from heart failure (OR: 0.25 [0.10, 0.62]; I2 = 0%; p = 0.003), and acute exacerbation of heart failure (OR: 0.35 [0.27, 0.47]; I2 = 0%; p < 0.001). Conclusions: Our findings suggest a favorable association between the use of SGLT2 inhibitors and reduced mortality, as well as improved cardiac clinical outcomes in cancer patients undergoing chemotherapy. However, further validation through large-scale studies and randomized controlled trials across diverse malignancies is warranted to establish the efficacy of these drugs. [Table: see text]

Since their release, the medical community has been actively exploring large language models’ (LLMs) capabilities, which show promise in providing accurate medical knowledge. One potential application is as a patient resource. This study analyzes and compares the ability of the currently available LLMs, ChatGPT-3.5, GPT-4, and Gemini, to provide postoperative care recommendations to plastic surgery patients. We presented each model with 32 questions addressing common patient concerns after surgical cosmetic procedures and evaluated the medical accuracy, readability, understandability, and actionability of the models’ responses. The three LLMs provided equally accurate information, with GPT-3.5 averaging the highest on the Likert scale (LS) (4.18 ± 0.93) (p = 0.849), while Gemini provided significantly more readable (p = 0.001) and understandable responses (p = 0.014; p = 0.001). There was no difference in the actionability of the models’ responses (p = 0.830). Although LLMs have shown their potential as adjunctive tools in postoperative patient care, further refinement and research are imperative to enable their evolution into comprehensive standalone resources.

Background: Large language models (LLMs) represent a recent advancement in artificial intelligence with medical applications across various healthcare domains. The objective of this review is to highlight how LLMs can be utilized by clinicians and surgeons in their everyday practice. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six databases were searched to identify relevant articles. Eligibility criteria emphasized articles focused primarily on clinical and surgical applications of LLMs. Results: The literature search yielded 333 results, with 34 meeting eligibility criteria. All articles were from 2023. There were 14 original research articles, four letters, one interview, and 15 review articles. These articles covered a wide variety of medical specialties, including various surgical subspecialties. Conclusions: LLMs have the potential to enhance healthcare delivery. In clinical settings, LLMs can assist in diagnosis, treatment guidance, patient triage, physician knowledge augmentation, and administrative tasks. In surgical settings, LLMs can assist surgeons with documentation, surgical planning, and intraoperative guidance. However, addressing their limitations and concerns, particularly those related to accuracy and biases, is crucial. LLMs should be viewed as tools to complement, not replace, the expertise of healthcare professionals.