Citations

Background: The use of validated risk stratification tools for the workup of suspected pulmonary embolism (PE) is a recommendation of the American Society of Hematology and the American College of Emergency Physicians. Methods: We designed and implemented electronic clinical pretest probability (PTP) tools for use in emergency departments (ED). Overall, 38 EDs (tertiary and regional EDs) in the United States were involved across three health systems. PTP use was analyzed between September 12, 2022 and January 11, 2023 in 270,247 ED visits. PTP use was examined in terms of the percentage of visits for which patients underwent computed tomography pulmonary angiography (CTPA). Each site chose the 3-tier Wells’ score for implementation and Site 2 designed a combined Wells’, pulmonary embolism rule-out criteria (PERC), and YEARS score. Results: At Site 3, forced use resulted in documented PTP scores in 49–53% of ordered CTPAs. At Sites 1 and 2, where PTP scores were optional, documented PTP scores occurred in 2–3% and 1–3% of CTPA orders, respectively. At Site 1, the use of PTP increased slightly over the study period, with signs that PE yield on imaging was also increasing (3.4–5.9%). At Site 2, PE yield on imaging was 9–10%, and it remained similar, with similar use of PTP tools over the study period. PE yield (6–8%) on imaging also remained similar throughout the study at Site 3. Conclusions: Guideline-endorsed PTP scores for PE at three independent health systems in the United States did not increase PE yield on imaging. Novel implementation strategies involving interdisciplinary teams are urgently needed.

Abstract INTRODUCTION Temporal cortical microstructural changes precede cortical atrophy during memory decline. We used neurite orientation dispersion and density imaging (NODDI) to assess such early microstructural change. METHODS Cognitively unimpaired (CU, n = 725) and mildly cognitively impaired (MCI, n = 111) participants from the Mayo Clinic Study of Aging underwent 3T magnetic resonance imaging (MRI), including NODDI and neuropsychological evaluation for calculation of memory z scores. Linear mixed effects modelling assessed the relationship between temporal cortical Neurite Density Index (NDI), Orientation Dispersion Index (ODI), and both baseline and mean annual change in memory z scores. RESULTS NDI was significantly associated with both baseline memory z scores and mean annual change of memory z scores, in the hippocampi and amygdalae. Similar significant associations with ODI were seen in hippocampi, parahippocampal, and fusiform gyri. DISCUSSION Temporal cortical NDI and ODI are early imaging biomarkers of cortical microstructural integrity that may predict memory decline in CU and MCI individuals. Highlights We imaged 836 participants in the Mayo Clinic Study of Aging, who were either cognitively unimpaired (CU) or suffered from mild cognitive impairment (MCI). Neurite orientation dispersion and density imaging (NODDI) is an advanced diffusion magnetic resonance imaging (MRI) technique We found significant associations between new NODDI imaging biomarkers of microstructural integrity and memory function in CU and MCI individuals These relationships were both cross‐sectional in nature and associated with future memory decline Future application of NODDI imaging biomarkers in the setting of anti‐amyloid monoclonal antibody therapy may provide greater insight into memory decline than current cortical atrophy measures.

Abstract Purpose Lattice radiation therapy (LRT) is a form of spatially fractionated radiation therapy that allows increased total dose delivery aiming for improved treatment response without an increase in toxicities, commonly utilized for palliation of bulky tumors. The LRT treatment planning process is complex, while eligible patients often have an urgent need for expedited treatment start. In this study, we aimed to develop a simulation‐free workflow for volumetric modulated arc therapy (VMAT)‐based LRT planning via deep learning‐predicted synthetic CT (sCT) to expedite treatment initiation. Methods Two deep learning models were initially trained using 3D U‐Net architecture to generate sCT from diagnostic CTs (dCT) of the thoracic and abdomen regions using a training dataset of 50 patients. The models were then tested on an independent dataset of 15 patients using image similarity analysis assessing mean absolute error (MAE) and structural similarity index measure (SSIM) as metrics. VMAT‐based LRT plans were generated based on sCT and recalculated on the planning CT (pCT) for dosimetric accuracy comparison. Differences in dose volume histogram (DVH) metrics between pCT and sCT plans were assessed using the Wilcoxon signed‐rank test. Results The final sCT prediction model demonstrated high image similarity to pCT, with a MAE and SSIM of 38.93 ± 14.79 Hounsfield Units (HU) and 0.92 ± 0.05 for the thoracic region, and 73.60 ± 22.90 HU and 0.90 ± 0.03 for the abdominal region, respectively. There were no statistically significant differences between sCT and pCT plans in terms of organ‐at‐risk and target volume DVH parameters, including maximum dose (Dmax), mean dose (Dmean), dose delivered to 90% (D90%) and 50% (D50%) of target volume, except for minimum dose (Dmin) and (D10%). Conclusion With demonstrated high image similarity and adequate dose agreement between sCT and pCT, our study is a proof‐of‐concept for using deep learning predicted sCT for a simulation‐free treatment planning workflow for VMAT‐based LRT.

Thymomas and thymic carcinomas are rare mediastinal tumors that originate in the thymus. Patients with thymoma may experience symptoms associated with autoimmune paraneoplastic diseases (such as myasthenia gravis), which typically do not occur in patients with thymic carcinoma. The NCCN Guidelines for Thymomas and Thymic Carcinomas provide guidance for the diagnosis, treatment, and surveillance of patients with thymoma and thymic carcinoma. Involvement of a multidisciplinary team with experience treating thymomas and thymic carcinomas is highly recommended.

ABSTRACT Introduction Improving organ preservation is essential for expanding the donor pool and enhancing outcomes for pediatric transplant recipients. This report presents three pediatric patients who received donor hearts preserved using the Organ Care System (OCS). All donor hearts were prepared, placed, and monitored on OCS according to standard protocols. Heart function and myocardial edema were visually assessed during monitoring. Case Reports Patient 1: A 17‐year‐old female patient with hypertrophic cardiomyopathy utilized OCS technology due to donor distance. She experienced an uncomplicated post‐transplant course and was discharged on post‐transplant day 10. Patient 2: A 14‐year‐old male patient with complex congenital heart disease, palliated with Fontan circulation, underwent orthotopic heart transplantation (OHT) with OCS support due to both the complexity of the procedure and donor heart distance. His post‐transplant course was complicated by a revision of the ascending aorta anastomosis and prolonged milrinone infusion for diastolic dysfunction. He was discharged on post‐transplant day 31. Patient 3: A 16‐year‐old male patient with hypoplastic left heart syndrome, also palliated with Fontan circulation, underwent OHT using OCS technology due to the complexity of the surgical procedure. His post‐transplant recovery was unremarkable, and he was discharged on post‐transplant day 15. Conclusions All three pediatric patients demonstrated favorable short‐term outcomes using OCS technology. The OCS system facilitated shorter ischemic times despite the need for extended transport distances or complex surgical procedures. However, further studies with larger patient cohorts and longer follow‐up are needed to better understand the long‐term impact of OCS on pediatric heart transplantation outcomes.

Background: Over 2.3 million liposuctions are performed annually with a complication rate of approximately 5%, including a death rate of 1 in 5000 because of blood loss. Artificial intelligence models offer potential for improving blood loss prediction and management in these procedures, analyzing extensive data to identify risk factors and accurately estimate blood loss. Methods: Data from 721 large-volume liposuction patients at 2 centers in Bogotá, Colombia, and Loja, Ecuador, between 2019 and 2023 were evaluated. Both centers followed identical perioperative protocols. The data set was split into training (621 patients) and testing (100 patients) sets. A supervised machine learning model was trained to predict blood loss. Model predictions were compared with clinical data using statistical validation metrics. Results: Most patients were women (79.2%), with median values of age of 37 years; weight, 65 kg; height, 165 cm; body mass index, 24.34 kg/m²; volemia, 3924.41 mL; infiltrated volume, 5800 mL; and aspirated volume, 3900 mL. Previous liposuction was noted in 32%. No significant differences were found between training and testing cohorts. The model achieved a mean absolute error of 22.09 mL, root mean square error of 34.13 mL, and an R ² value of 0.974, indicating high predictive accuracy and excellent model fit. Conclusions: The authors’ study has developed and validated an accurate artificial intelligence–based model to predict blood loss in large-volume liposuction, showing 94.1% accuracy. The authors’ model enhances preoperative planning and intraoperative management, potentially reducing complications and improving outcomes.

Background and purpose New Zealand White Rabbits are a useful preclinical model for neurovascular research. Inducing right common carotid artery aneurysms in New Zealand White Rabbits gives researchers the ability to test neurovascular devices in vivo. This study takes advantage of archived angiograms to obtain vascular measurements of interest for future research in rabbit models. Methods Rabbit angiograms from endovascular studies conducted in-house between 2005 and 2024 were analyzed using ImageJ at points of interest along the thoracic and abdominal aorta. Using scale references, the diameters of various New Zealand White Rabbit (2.5–3.5 kg) arteries were measured for aneurysm models and controls. Anatomic vascular variations were categorized and recorded. Results Measurable artery diameters at 39 points of interest were recorded for 170 female New Zealand White Rabbits. Type 1 vascular anatomy was the most common rabbit aortic arch, characterizing 85% of identifiable rabbit anatomies in this study. Conclusion By making these measurements available, this study simplifies future neurovascular studies, allowing for vasculature to be selected by size depending on the application. Additionally, the study offers insight into the relationship between elastase-induced aneurysms and surrounding vasculature size and draws size comparisons between some key arteries of interest.