Citations

Abstract In this study, we quantified differences in iris stiffness between female and male subjects in healthy and postlaser peripheral iridotomy (post-LPI) groups using an image-based inverse modeling approach. We analyzed anterior segment optical coherence tomography (AS-OCT) images from 25 participants across four groups. Finite element models were created using **solidworks, **abaqus, and a custom C program, modeling the iris as a neo-Hookean material. We found that post-LPI females had significantly higher normalized elastic modulus (E′=3.81±1.74) than healthy females (E′=0.92±0.31,p=0.004), while no significant difference was observed in males. Post-LPI females also showed significantly higher stiffness than post-LPI males (p=0.003). Here, p denotes the probability value, with p<0.05 considered statistically significant. Our findings suggest that sex-based differences in iris biomechanics may contribute to the higher susceptibility of females to primary angle-closure disease. Despite the small sample size, this preliminary study highlights the need for larger, sex-stratified investigations into glaucoma pathophysiology.

6073 Background: Laryngeal Squamous Cell Carcinoma (LaSCC) has varying outcomes based on the stage of cancer patients present with. Currently, a high proportion of patients are diagnosed with advanced-stage LaSCC complicating the treatment landscape and over-treating low risk patients. Risk stratification of LaSCC can help tailor treatment plans. Numerous studies have identified spatial architecture of tumor-infiltrating lymphocytes (TILs) as a prognostic biomarker in oral cavity and oropharyngeal SCC. In this work, we evaluate the prognostic value of an artificial intelligence (AI)-leveraged approach that characterizes the spatial architecture of TILs on digitized hematoxylin and eosin (H&E)-stained slides from patients with LaSCC. Methods: H&E slides from 192 patients with LaSCC were collected from Baylor Medical Center. This dataset was randomly divided into two equal cohorts, A and B. The slides were digitized as whole slide images at 40x magnification. The nuclei of all cells were automatically segmented using a deep-learning model (Hover-Net). Each nucleus was then classified as TIL or non-TIL based on morphological features. TILs and non-TILs were clustered based on proximity, and features related to the density and spatial distribution were extracted. The top features, determined by the least absolute shrinkage and selection operator, were used to train a Cox Proportional Hazards regression model that assigned a risk score for recurrence of cancer to each patient in cohort A. For validation, the model was applied to patients in cohort B. The 25 th percentile training risk score was used as a cutoff for classifying patients as high or low risk. The performance of the model in prognosticating loco-regional recurrence (LRR) was evaluated using survival analysis. Results: Patients in Cohort B identified as “high risk” by the model based on spatial organization of TILs had a significantly shorter survival time. Univariate survival analysis showed this model was prognostic for DFS with a hazard ratio of 2.57 (95% Confidence Interval: 1.12-5.89, p-value=0.048), meaning that patients classified as “high risk” are approximately 2.5 times more likely to develop LLR. Conclusions: We used computational pathology to characterize the architecture of TILs and develop a model to predict risk of LLR in LaSCC. With additional validation, this approach can be used to assist clinicians with making clinical decisions. Multivariate analysis. Variable Reference vs Comparison Pr(>|z|) HR (95% CI) Race Black vs Caucasian 0.93 0.92 (0.40 - 2.31) N N0 vs N+ 0.67 1.80 (0.12 - 26.06) T 1-2 vs 3-4 0.83 1.28 (0.13 - 12.38) Chemo Yes vs No 0.38 3.49 (0.21 - 56.90) Tobacco Yes vs No 0.26 2.08 (0.58 - 7.47) Alcohol Yes vs No 0.43 1.53 (0.54 - 4.35) TIL Architecture Risk High vs Low 0.03 * 0.26 (0.08 - 0.89)

Abstract Objectives Sleep disturbance is a key symptom of fibromyalgia and a risk factor for chronic widespread pain. This systematic review and meta-analysis aims to assess the effectiveness of pharmacological treatments and cognitive behavioural therapy (CBT) in improving sleep quality in fibromyalgia patients. Methods A systematic search of PubMed, MEDLINE, Embase, Cochrane CENTRAL and CINAHL was conducted for randomized controlled trials (RCTs) published up to April 2023. Studies assessing pharmacological or CBT interventions with sleep-related outcomes were included. Data were extracted, and meta-analyses were performed where applicable. Study quality and bias were evaluated using the Cochrane Risk of Bias tool. Results Forty-seven RCTs, including 11 094 participants, were reviewed. CBT for insomnia (CBT-I) showed a significant improvement in sleep quality (SMD −0.63, 95% CI −0.98 to −0.27), while CBT for pain (CBT-P) had no significant impact. Pharmacological agents such as pregabalin and sodium oxybate moderately improved sleep, but there was uncertainty around this evidence. Amitriptyline, milnacipran and duloxetine showed no significant benefit for sleep. Study heterogeneity was moderate, and no publication bias was detected. Conclusion CBT-I is a promising treatment for enhancing sleep quality in fibromyalgia. Pharmacological treatments like pregabalin may be beneficial but should be used cautiously due to potential risks. Future research should prioritize trials focusing on sleep as a primary outcome and explore the comparative effectiveness of pharmacological treatments and CBT-I in fibromyalgia. Understanding the mechanisms linking sleep and fibromyalgia will also help guide future therapies.

TPS272 Background: Prostate-specific membrane antigen (PSMA)-targeting positron emission tomography (PET) radiopharmaceuticals enable early detection of metastatic and recurrent prostate cancer (PCa). High urinary radioactivity of some PSMA-PET agents can affect interpretation of scans in the pelvic region (prostate/prostate bed [PB]; pelvic lymph nodes [PLN]). 18 F-Piflufolastat and 18 F-flotufolastat are FDA-approved, mainly renally cleared, diagnostic PSMA-PET agents; data suggest 18 F-flotufolastat has lower urinary radioactivity than 18 F-piflufolastat, but no clinical differences have been confirmed by head-to-head intra-patient studies. The present study will directly compare the urinary radioactivity of 18 F-piflufolastat with 18 F-flotufolastat in men with low prostate-specific antigen (PSA) biochemical recurrence (BCR) of PCa after radical prostatectomy (RP). Methods: This multicenter, prospective, intra-patient comparator study will include men (≥ 18 years old) with low PSA (≤ 0.5 ng/mL) BCR of PCa who have had RP ≥ 6 months before enrollment (with undetectable PSA post surgery) and are due to have routine 18 F-piflufolastat PET. Men who have had prior 18 F-piflufolastat PET or salvage therapy, or with conditions affecting urinary output, will be excluded. Eligible patients will undergo 18 F-piflufolastat PET followed by 18 F-flotufolastat PET ≥ 24 hours but ≤ 10 days later. No diuretic will be administered for scanning purposes. Each radiopharmaceutical will be administered as a single intravenous bolus per approved US prescribing information (USPI). Scanning (with the same scanner for both scans) will start 50–90 minutes post injection of 18 F-piflufolastat and 50–70 minutes post injection of 18 F-flotufolastat. Each patient will be their own control. The primary endpoint will be difference in mean standardized uptake value (SUV mean ) between 18 F-piflufolastat and 18 F-flotufolastat as assessed in volumes of interest drawn on the urinary bladder. Secondary endpoints for both agents will include detection rates: at the patient level (overall; by baseline PSA); for local recurrences in PB subregions; and for PLN lesions. For secondary endpoints, PET scans will be interpreted per product-specific USPI by 2 specially trained, blinded, central readers (a third reader will adjudicate disagreements). Safety will be monitored until 24 hours after the second scan. Around 60 men will be screened and ≥ 52 with evaluable PET scans for both agents will be included in the primary analysis. The primary endpoint will be assessed for normality and a difference in SUV mean will be tested using 2-sided paired tests (Wilcoxon signed-rank test [non-normal]; t-test [normal]). All further endpoints, including safety, will be summarized descriptively. The trial has been open for enrollment since 10/24. Clinical trial information: NCT06604442 .

BACKGROUND: The ability to predict recovery of left ventricular ejection fraction (LVEF) in response to guideline-directed therapy among patients with nonischemic cardiomyopathy is desired. We sought to determine whether left ventricular endocardial unipolar voltage measured during invasive electroanatomic mapping could be used to predict LVEF recovery among those with recent-onset nonischemic cardiomyopathy. METHODS: We analyzed the left ventricular voltage maps of patients included in the eMAP trial (Electrogram-Guided Myocardial Advanced Phenotyping; NCT03293381), a prospective, nonrandomized, interventional trial conducted at 2 institutions between 2017 and 2020. Patients had recent-onset nonischemic cardiomyopathy defined by LVEF ≤45% and development of symptoms or signs of heart failure within the past 6 months. Detailed voltage maps of the left ventricular endocardium were generated using the Carto electroanatomic mapping system. Abnormal unipolar amplitude was defined as <8.27 mV. The primary end point was recovery of LVEF (Recovery) defined by a 1-year LVEF ≥50% or ≥45% with ≥10% increase from baseline. RESULTS: Of the 29 enrolled patients (median age, 49 years [25th percentile, 39; 75th percentile, 59], 8 females [27.6%]), LVEF recovered in 13 (44.8%) by 1-year follow-up. The percentage of total endocardial surface area with unipolar voltage abnormality (AUA) was significantly lower among Recovery patients than No Recovery patients (18.2% [25th percentile, 6.4; 75th percentile, 22.4] versus 80.0% [25th percentile, 29.5; 75th percentile, 90.9]; P =0.004). Percent AUA was associated with lower likelihood of Recovery (odds ratio, 0.64 per 10% increase in AUA; 95% CI, 0.47–0.88; P =0.006). A 28% cutoff value for percent AUA was 92% sensitive and 75% specific with an area under the receiver operating characteristic curve of 0.81 (95% CI, 0.63–0.99; P =0.001) for predicting recovery versus no recovery. The majority of patients (12 of 13; 92.3%) with a percent AUA >28% did not recover. CONCLUSIONS: Left ventricular unipolar voltage abnormality is a potent predictor of LVEF recovery among patients recently diagnosed with nonischemic cardiomyopathy. Detailed left ventricular unipolar voltage mapping could therefore be used as a valuable prognostic tool in guiding treatment decisions.

Abstract Objectives Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented in narrative clinical notes rather than as structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of extraction of such information. Materials and Methods Psychiatric encounter notes from Mount Sinai Health System (MSHS, n = 300) and Weill Cornell Medicine (WCM, n = 225) were annotated to create a gold-standard corpus. A rule-based system (RBS) involving lexicons and a large language model (LLM) using FLAN-T5-XL were developed to identify mentions of SS and SI and their subcategories (eg, social network, instrumental support, and loneliness). Results For extracting SS/SI, the RBS obtained higher macroaveraged F1-scores than the LLM at both MSHS (0.89 versus 0.65) and WCM (0.85 versus 0.82). For extracting the subcategories, the RBS also outperformed the LLM at both MSHS (0.90 versus 0.62) and WCM (0.82 versus 0.81). Discussion and Conclusion Unexpectedly, the RBS outperformed the LLMs across all metrics. An intensive review demonstrates that this finding is due to the divergent approach taken by the RBS and LLM. The RBS was designed and refined to follow the same specific rules as the gold-standard annotations. Conversely, the LLM was more inclusive with categorization and conformed to common English-language understanding. Both approaches offer advantages, although additional replication studies are warranted.