Citations

Abstract Aims Mitral valve prolapse (MVP) is a common valvular disorder associated with ventricular arrhythmias (VAs) in a subgroup of patients, known as arrhythmic MVP (AMVP). Arrhythmic MVP patients with malignant VAs typically receive an implantable cardioverter-defibrillator (ICD) for secondary prevention. However, data regarding the incidence of appropriate ICD therapy and the type of arrhythmia recurrence in this population remain scarce. This study aimed to evaluate the incidence of appropriate ICD therapies and type of arrhythmia recurrence in AMVP patients with an ICD implanted for secondary prevention. Methods and results A systematic literature search was conducted in PubMed/MEDLINE and EMBASE databases until January 2025. Studies reporting the incidence of appropriate ICD therapy in AMVP patients were included. A total of 9 studies met the inclusion criteria, resulting in 166 AMVP patients with ICDs for secondary prevention. The mean age was 41.8 years, with 55.8% of patients being female. Over a weighted mean follow-up of 4.9 ± 3.1 years, 34.9% [95% confidence interval (CI): 28.1–42.5] of the patients experienced recurrence of VA. The pooled incidence of appropriate ICD therapies during follow-up was 8.1 per 100 person-years (95% CI: 5.6–10.5, I2: 11.7%). Regarding the type of arrhythmic recurrence, the majority of arrhythmias (53.8%, 95% CI: 44.3–63.0%) were in the form of ventricular fibrillation or polymorphic ventricular tachycardia (VT), while the remaining arrhythmias were reported as monomorphic VT. Among 67 ventricular arrhythmic episodes reported in 7 studies with available data, 65 required shock therapy, corresponding to a shock proportion of 97.0% (95% CI: 89.6–99.6%). Conclusion Arrhythmic MVP patients with ICDs for secondary prevention exhibit a high risk of recurrent arrhythmias, with recurrences in the form of polymorphic VA occurring in more than half of the cases. PROSPERO registration number: CRD42024588610

The application of artificial intelligence (AI), large language models and generative AI (GenAI) is accelerating rapidly across many industries worldwide. Innovative models and tools are being piloted to understand their business and societal value. Models such as ChatGPT (OpenAI), Gemini (Google) and Copilot (Microsoft) have become increasingly sophisticated and widely adopted. Understanding the advantages and drawbacks of GenAI models is crucial for organisations seeking to leverage GenAI to maximise their efficiency and expand their capabilities. This paper describes the strategic approach, implementation and findings of using GenAI in a health care business context. A GenAI model was used to analyse and summarise strategic and operational information submitted by external organisations seeking to collaborate with Mayo Clinic. The goal was to reduce process cycle time and manual resource intensity. In parallel, a comparative human review was conducted by functional experts. Both the AI-generated and human-reviewed results were evaluated using sentiment analysis (SA) and then categorised as positive, neutral or negative. Attention was paid to possible inaccuracies and biases associated with the use of the GenAI model. GenAI reduced the manual workload by approximately 20−25 per cent and presented information in a well-structured format. However, the SA showed the GenAI results as positively biased and the human-generated results as neutral. These results were most likely influenced by a positive bias in the input documents, the lack of contextual comparators/ standards in the GenAI model and an inherent bias within the model’s algorithm(s). Additionally, limitations in input size and language translation hindered the GenAI model’s effectiveness. The possibility of AI hallucinations/inaccuracies emphasised the importance of validation by a human expert. Furthermore, carefully formatting the input data enhanced the value and relevance of the GenAI model’s outputs. GenAI has the potential to increase efficiency and alleviate manual burden, but it must be thoughtfully implemented to address inaccuracies, biases and privacy concerns. This article is also included in The Business & Management Collection which can be accessed at https:// hstalks.com/business/.

261 Background: Clinicians use next-generation sequencing (NGS) to tailor targeted treatments for PCa patients. However, the results are often available in unstructured formats and are saturated with technical jargon, making them inaccessible for patients. Therefore, we aimed to assess the efficacy of LLMs to accurately extract genomic characteristics from unstructured reports and generate patient-friendly summaries for ease of variant interpretation in PCa patients. Methods: This retrospective study included patients with PCa who underwent NGS from 2023-2024. The GPT4 model was utilized using structured zero-shot prompts to extract genomic characteristics from unstructured reports in a non-decomposed manner. Prompt development was conducted iteratively using a 2% random sample of the dataset. Genomic characteristics ascertained by LLM were assessed against held out vendor-curated test dataset for evaluation. Performance was assessed using weighted evaluation metrics (precision, recall, and F1-score). Additionally, the extracted variables were organized using rule-based criterion and presented as a separate prompt to GPT4 to generate summaries for interpretation at the level of each biologically relevant variant. Mean Flesch-Kincaid scores and Vocd-D statistic with standard deviations were computed to assess readability and lexical diversity of LLM-generated variant interpretation summaries. Results: A total of 331 patients (370 NGS reports) were included in the study. Evaluation on the held-out test dataset (Table) showed that the weighted average precision ranged from 80% for extracting tumor mutational burden to 100% for extracting the CtDNA fraction from unstructured reports. The weighted average recall ranged from 85% for extracting CtDNA tumor fraction to 99% for ascertaining variant/alteration. The weighted average F1-scores varied from 0.83 for extracting tumor mutational burden to 1.0 for extracting specimen site from NGS reports. The mean Flesch-Kincaid score was 8.29 ± 1 indicating average/standard readability ease. The mean Vocd-D statistic was 7.55 ± 1 indicating focused summaries, however, with limited lexical diversity. Conclusions: This study suggests that large language models can effectively extract genomic characteristics from unstructured reports and can potentially improve genomic literacy among prostate cancer patients by providing easy-to-interpret variant summaries. However, targeted prompting may be required to increase lexical diversity for improved engagement. Performance across different genomic characteristics. Category Precision Recall F1-Score Altered gene 0.86 0.97 0.89 Variant/alteration type 0.98 0.99 0.98 Variant allele fraction 0.96 0.96 0.96 Test name/type 1 0.99 1 Specimen site 1 0.99 1 Tumor mutational burden 0.8 0.93 0.83 CtDNA tumor fraction 1 0.85 0.92 MSI status 0.99 0.97 0.98

Background: C10603/RATFIY was the first AML trial to show the benefit of adding a targeted agent to intensive chemotherapy for a specific genetically determined subset (Stone R et al, NEJM 2017). The addition of the multi-kinase inhibitor midostaurin (M) to chemotherapy in previously untreated adults with FLT3-mutant AML resulted in superior event-free (EFS) and overall survival (OS) compared to placebo (P) and led to the approval of this agent in combination therapy. Subsequently, additional targeted drugs including gilteritinib for relapsed/refractory FLT3-mutant AML and quizartinib plus chemotherapy in untreated adults with AML with FLT3-ITD disease have gained approval. Long-term follow-up after reporting of the primary endpoint, especially if met, is important in oncology. Objective: We report the EFS and OS data from C10603 using 10 years of follow up to determine the persistence of a midostaurin benefit and to assess the nature of late relapses or toxicity. Methods: C10603 enrolled 717 pts (360 on the M and 357 on the P arm; median age 47.8 years (range 18-61), 398 women (55.5%) of whom 51.7% were randomized to M and 59.4% to P (p=0.04), and 89% white) from 2011-2015 with previously untreated AML who had either a FLT3-TKD or ITD mutation with allelic ratio of >0.05 to receive daunorubicin/cytarabine (3+7) induction (one reinduction permitted) followed by up to 4 consolidation cycles with cytarabine 3 g/m2 every 12h on days 1, 3 and 5. M (50 mg bid) or P was given orally on days 8-22 of each chemotherapy cycle as well as daily during one year of single-agent maintenance. Allogeneic transplantation (alloHCT) was employed at investigator's discretion; study drug was not continued post-HCT. Pts stayed on their randomized arm and were not re-randomized prior to maintenance. Pts were followed for relapse and survival after the end of protocol therapy, regardless of subsequent therapy, for 10 years. Log-rank tests, Kaplan-Meier methods, and Cox proportional hazard models were used to evaluate the treatment effect in EFS and OS, as well as the alloHCT effect. All p values are two-sided. Results: We have completed the 10-year FU on this trial and data collection has concluded. The median EFS was 8.2 months (95% CI, 5.5-11.4) on the M arm compared with 3.0 months for P (95% CI, 1.9-6.0) with a HR of 0.79 (95% CI 0.67-0.94, p=0.0067). Among the 420 CR pts who achieved complete remission (CR) by the protocol specified time of 60 days, 253 have had an event (death in 69 and relapse in 184); these events were relatively equal on each arm. OS, the co-primary endpoint, remained marginally superior for those randomized to M compared to P; the 10-year OS estimate was 43.7% (95% CI, 38.7-49.3%) vs 38.6% (95% CI, 33.6-44.4%) (p= 0.0485). The hazard ratios for OS favored randomization to M in each key biological subgroup (FLT3-ITD low or high allelic ratio or FLT3-TKD), but smaller subsets precluded statistical significance. There was a 10-year OS benefit in favor of M in men (p=0.005), but not women (p=0.9). A total of 414 transplants were performed: only 5 in the latter half of the follow-up period. Transplantation in CR1 was highly beneficial overall (10-year OS, 56% vs 35.8% without transplantation, p=0.001). Among those transplanted in CR1, randomization to M was marginally better than P (61.5% vs 49%, p=.064). After censoring for alloHCT in CR1, there was an OS trend in favor of M (51.8% v 48.0%, p=0.102). In CR1 patients who were randomized to maintenance therapy, M reduced the cumulative incidence of relapse in ELN 2017 favorable and intermediate risk pts, but not in those with adverse risk disease (HR 0.71, 0.47, and 1.01, respectively); maintenance M had no effect on OS. Only 3 pts relapsed after 5 years (all randomized to M). 25 pts randomized to M vs 13 P pts died after 5 years of follow-up. 5 pts on each arm died of a new primary cancer whereas 17 M and 5 P pts were coded as having died from treatment-related causes. None of the 3 pts who relapsed after 4 years had spliceosome mutations at diagnosis; whereas 18% (21/117) patients pts who had relapsed earlier had spliceosome mutations at diagnosis (p=0.419). Conclusions: The EFS benefit of randomization to midostaurin vs placebo when added to chemotherapy was maintained over time, although the benefit for OS was diminished, likely due in part to aging. Patient and disease factors differed between early vs late relapses, which could suggest the use of alternative therapies during distinct therapeutic stages.

Abstract Accumulating evidence suggests dysregulated immune checkpoint (IC) signaling can exacerbate COVID-19 severity, but the role of these molecules in the pathogenesis of fatal COVID-19-related diffuse alveolar damage (DAD) remains elusive. Understanding how IC proteins influence acute lung injury due to COVID-19 can provide insights into potential therapeutic strategies to modulate immune responses and improve patient outcomes. Here, in a single-center autopsy cohort, we determined the cellular localization of ICs in lung tissue from cases of fatal COVID-19, DAD-comparators, and non-fibrotic controls by using immunohistochemistry, and investigated their association with clinical outcomes. We expanded our findings by performing analyses of publicly available single-cell RNA sequencing datasets from patients with fatal COVID-19 and non-fibrotic controls. We demonstrated the presence of protein-protein interaction networks of ICs in the lung cellular niche by performing transcriptomic profiling of lung tissue-derived RNA counts from patients with fatal COVID-19. Further, we leveraged data from the largest international, multi-center COVID-19 autopsy cohort and validated that, among patients with fatal COVID-19, those with higher PD-L1/CD274 expression in lung endothelial cells had more rapid clinical deterioration. Lastly, in a cohort of individuals with early COVID-19, IC plasma protein levels were elevated in those with persistent SARS-CoV-2 RNAemia and adverse clinical outcomes. Collectively, our data provide unique pathological insights into the role of IC dysregulation and differential disease severity in COVID-19.

Abstract Witte, BC, Schouten, TC, Westphal, JA, VanZile, AW, Jones, DD, Widenhoefer, TL, Dobbs, WC, Jagim, AR, Luedke, JA, and Almonroeder, TG. The modified reactive strength index is a valid measure of lower-body explosiveness in male and female high school athletes. J Strength Cond Res 38(8): 1428–1432, 2024—The modified reactive strength index (mRSI) is a commonly used metric to quantify lower-body explosiveness during countermovement jump (CMJ) performance. However, few studies have attempted to examine its validity as a measure of explosiveness, particularly among high school athletes. The purpose of this study was to examine the validity of the mRSI as a measure of lower-body explosiveness among a relatively large sample of male and female high school athletes from various sports. As part of this study, male ( n = 132) and female ( n = 43) high school athletes performed CMJs, while ground reaction forces were recorded using a force platform. The vertical ground reaction force data collected during the CMJs were used to derive the following variables: peak force (PF), peak power, time to PF, time to take-off, peak rate of force development, and the mRSI. Principal component analysis was applied and reduced these variables into 2 components related to “force” and “speed.” The mRSI loaded on both the force (loading = 0.82) and speed (loading = −0.46) components, indicating that it incorporates elements of both force and speed, although it loaded more strongly on the force component than the speed component. The observed pattern of cross-loading suggests that the mRSI is generally a valid measure of lower-body explosiveness for male and female high school athletes.

Ever since its introduction as a diagnostic imaging tool the potential of magnetic resonance imaging (MRI) in radiation therapy (RT) treatment simulation and planning has been recognized. Recent technical advances have addressed many of the impediments to use of this technology and as a result have resulted in rapid and growing adoption of MRI in RT. The purpose of this article is to provide a broad review of the multiple uses of MR in the RT treatment simulation and planning process, identify several of the most used clinical scenarios in which MR is integral to the simulation and planning process, highlight existing limitations and provide multiple unmet needs thereby highlighting opportunities for the diagnostic MR imaging community to contribute and collaborate with our oncology colleagues.Evidence Level5Technical EfficacyStage 5