Wellstar Atlanta Medical Center is a hospital in Atlanta, Georgia operated by Wellstar Health System. It has 460 beds and over 700 physicians. The hospital is a Level I Trauma Center, and an Advanced Primary Stroke Center. It houses a Neurointensive Care Unit and a Level III Neonatal ICU. It was formerly known as Georgia Baptist Hospital.
Objective Posttraumatic Stress Disorder (PTSD) is a highly prevalent condition, and current treatments have limitations. Vagal Nerve Stimulation (VNS) is a new approach that potentially has promise for PTSD. Understanding the neurobiology of treatment response is important for developing new treatments. The purpose of this study was to assess neural correlations of long-term transcutaneous cervical VNS (tcVNS) in patients with PTSD. Methods Patients with PTSD underwent randomization to active tcVNS (N=6) or sham stimulation (N=5) twice daily for three months. High-Resolution Positron Emission Tomography scanning with radiolabeled water was used to measure brain blood flow measurements before and after treatment during exposure to personalized traumatic scripts paired with active or sham stimulation. Results Three months of active tcVNS resulted in activation in response to traumatic scripts in the sham stimulation group not seen in the tcVNS group in brain areas mediating the fear response, including posterior cingulate, thalamus, temporal and parietal cortex, and parahippocampal gyrus, with an increase in medial prefrontal cortex with tcVNS, in patients with PTSD. Conclusion TcVNS affects brain areas mediating fear and emotion which may underlie a therapeutic effect for PTSD.
Apparent treatment-resistant hypertension (aTRH) is a clinically challenging condition with heterogeneous etiologies. Understanding the biological pathways underlying resistance to antihypertensive treatment could inform targeted therapeutic strategies. To evaluate how methodological choices in SNP selection influence biological inference, we applied two approaches to select aTRH-associated variants for clustering: PRS-CSx and pruning and thresholding (P&T). Using k-means clustering, we grouped aTRH-associated variants based on their association profiles across 91 cardiometabolic-related phenotypes. We then performed pathway and tissue enrichment analyses to evaluate the biological processes represented by each cluster. Both methods identified multiple genetic clusters, but the distribution of variants and biological signals differed. Clustering based on PRS-CSx produced unequally distributed clusters of SNPs and yielded limited tissue enrichment, while P&T-based clustering captured more uniform trends across cardiometabolic traits and broader tissue and pathway enrichment. These results demonstrate that methodological choices in SNP selection influence downstream clustering and biological interpretation. Despite some overlap in identified pathways and tissue enrichment, each approach identified unique biological signals, highlighting the potential of pairing polygenic methods and k-means clustering to elucidate the biological heterogeneity of aTRH and guide future mechanistic studies.
As more specialties have begun to use Point-of-Care Ultrasound (POCUS) in patient care, hospitals and healthcare systems have been investing increasing resources in POCUS infrastructure (training, equipment, and administration). Since each specialty uses different POCUS applications, healthcare systems seek to identify commonalities and differences between specialties to make thoughtful investments in POCUS infrastructure to support each specialty’s use of POCUS while minimizing redundancies. Historically, past studies have focused on POCUS use in individual specialties, primarily emergency medicine and critical care, but comparative studies of different specialties are needed to guide investment in POCUS infrastructure and bolster POCUS implementation across healthcare systems. We conducted a cross-sectional survey of all Veterans Affairs (VA) medical centers in the United States and compared data from 5 different specialties on current usage, training needs, and barriers to POCUS implementation. Data were collected from facility chiefs of staff (n = 130; 100
BACKGROUND:Despite guideline recommendations, our prior work revealed more than half of low-risk bladder cancer patients within the Department of Veterans Affairs (VA) undergo too many surveillance procedures and about a third of high-risk patients do not undergo enough procedures. Thus, we developed and integrated implementation strategies to improve risk-aligned bladder cancer surveillance for the VA. METHODS:Prior work used Implementation Mapping to develop nine implementation strategies: change record systems, educational meetings, champions, tailoring, preparing patients to be active participants, external facilitation, remind clinicians, audit & feedback, and a blueprint. We integrated these strategies as improvement approaches across four VA urology clinics. Primary implementation outcomes were qualitatively measured via coding of semi-structured interviews with clinicians and co-occurrence of codes. Implementation outcomes included: appropriateness, acceptability, and feasibility. Exploratory quantitative outcomes included clinicians' recommendations for guideline-concordant bladder cancer surveillance intervals and sustainability. RESULTS:Eleven urologists were interviewed. Co-occurrence analysis of codes across strategies indicated that urologists most commonly reported on the acceptability and appropriateness of changing the record system, preparing patients to be active participants ("surveillance grid"), reminders (i.e., cheat sheet), and educational sessions. We confirmed feasibility of all implementation strategies. Urologists indicated that changing the record system had a high impact, reduced documentation time, and guided resident physicians. Preparing patients to be active participants using the "surveillance grid" was seen as an effective but time-consuming tool. Educational sessions were seen as critical to support implementation. In quantitative analyses, clinicians recommended guideline-concordant surveillance about 65% of the time at baseline for low-risk patients, and this improved to 70% during evaluation. Across all risk levels, the largest improvement was observed at site 2 while site 3 did not improve. All sites sustained use of the changed record system, while sustainability of other strategies was variable. CONCLUSIONS:Based on summative interpretation of results, the most appropriate, acceptable, and feasible strategies include changing record systems via a template and educational meetings focused on guideline-concordant surveillance. Future work should assess the impact of the improvement approaches on clinical care processes, particularly on reducing overuse of surveillance procedures among low-risk patients. TRIAL REGISTRATION:The implementation strategies were not considered a healthcare intervention on human participants by the governing funding agency and IRB. Rather, they were seen as quality improvement interventions. Thus, this study did not meet criteria for a clinical trial and was not registered as such.
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)
