Citations

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.

AbstractIn response to the evolving treatment landscape for new‐onset refractory status epilepticus (NORSE) and the publication of consensus recommendations in 2022, we conducted a comparative analysis of NORSE management over time. Seventy‐seven patients were enrolled by 32 centers, from July 2016 to August 2023, in the NORSE/FIRES biorepository at Yale. Immunotherapy was administered to 88% of patients after a median of 3 days, with 52% receiving second‐line immunotherapy after a median of 12 days (anakinra 29%, rituximab 25%, and tocilizumab 19%). There was an increase in the use of second‐line immunotherapies (odds ratio [OR] = 1.4, 95% CI = 1.1–1.8) and ketogenic diet (OR = 1.8, 95% CI = 1.3–2.6) over time. Specifically, patients from 2022 to 2023 more frequently received second‐line immunotherapy (69% vs 40%; OR = 3.3; 95% CI = 1.3–8.9)—particularly anakinra (50% vs 13%; OR = 6.5; 95% CI = 2.3–21.0), and the ketogenic diet (OR = 6.8; 95% CI = 2.5–20.1)—than those before 2022. Among the 27 patients who received anakinra and/or tocilizumab, earlier administration after status epilepticus onset correlated with a shorter duration of status epilepticus (ρ = .519, p = .005). Our findings indicate an evolution in NORSE management, emphasizing the increasing use of second‐line immunotherapies and the ketogenic diet. Future research will clarify the impact of these treatments and their timing on patient outcomes.

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.

Background: The compensatory reserve metric (CRM) provides a novel marker of hemodynamic status in prediction of sudden cardiovascular decompensation during central hypovolemia. The CRM has previously been calculated using arterial waveforms obtained from a photoplethysmographic volume-clamp technique. However, the in-field use of pulse oximetry is becoming more prominent in research and medicine. To improve in-field applicability of the CRM, this project aimed to validate pulse-oximetry derived CRM values relative to physiologic data during simulated hemorrhage through application of lower body negative pressure (LBNP). Methods: Forty-nine healthy adults (25 females) underwent a graded LBNP protocol with the following 5-minute stages: -0, -15, -30, -45, -60, -70, -80, -90, and 100 mmHg, or until the onset of pre-syncopal symptoms (tolerance) was reached. Arterial waveforms were sampled using pulse oximetry (Massimo Radical 7). The CRM was calculated using a validated one-dimensional convolutional neural network. A brachial artery catheter was used to measure intraarterial pressure. A 3-lead ECG was used to measure heart rate. SpO2 was measured using pulse oximetry. Cardiac output and stroke volume were estimated using the photoplethysmographic volume-clamp. Data were binned for the last thirty seconds of each completed stage, and at tolerance. Fixed-effect linear mixed models with repeated measures were used to examine the association between CRM values and physiologic variables. A priori significance was set at P<0.05. Results: The median LBNP stage reached was 70 mmHg (Range: 45-100 mmHg). Relative to baseline, at tolerance there was a 50±12% ( P<0.001) mean reduction in stroke volume, 65±27% ( P<0.001) increase in heart rate, 18±8% ( P<0.001) reduction in mean arterial pressure, 21±7% ( P<0.001) systolic blood pressure, 8±9% ( P<0.001) diastolic blood pressure, and 39±12% ( P<0.001) pulse pressure. The CRM obtained using pulse oximetry was significantly associated with a reduction in stroke volume ( P<0.001), increase in heart rate ( P<0.001), and reductions in systolic ( P=0.033), diastolic ( P=0.036), and pulse pressure ( P=0.002). There was no statistically significant association between CRM and cardiac output ( P=0.421), SpO2 ( P=0.408), or mean arterial pressure ( P=0.214). Conclusion: The CRM obtained using pulse oximetry may be a valid, reliable marker of hemodynamic status with promising in-field applications. Future studies warrant investigation of feasibility and applicability within various contexts ranging from the operating room to the battlefield. The presented work was funded in part by the Congressionally Directed Medical Research Program (Award No. DM180240) and Offce of Naval Research (Awards No. N00014-18-D-7001 and N00014-19-C-2017). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA’s responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes.

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI’s role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI’s role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers’ effectiveness and well-being.