Functional Impairment is Associated with Poor Long-Term Outcomes after Arteriovenous Access Creation.

BACKGROUND: Functional impairment affects outcomes after a variety of procedures. However, the impact of functional impairment on outcomes of arteriovenous (AV) access creation is unclear. We aimed to evaluate the association of patients' ability to ambulate and perform activities of daily living (ADL) with AV access outcomes. METHODS: We retrospectively reviewed patients undergoing AV access creation at an urban, safety-net hospital from 2014 to 2022. We evaluated associations of impaired ambulatory and assisted ADL status with 90-day readmission, 1-year primary patency, and 5-year mortality. RESULTS: Among the 689 patients receiving AV access, mean age was 59.6 ± 13.9 years, 59% were male, and 60% were Black. Access types included brachiocephalic (42%), brachiobasilic (26%), radiocephalic (14%), other autogenous (5%) fistulas, and prosthetic grafts (13%). Impaired ambulatory status was identified in 35% and assisted ADL status, when assessed, was identified in 21% of patients. Ninety-day readmission was more likely in patients with impaired ambulatory (58% vs. 39%, P < 0.001) and assisted ADL (56% vs. 41%, P = 0.004) status. On Kaplan-Meier analysis, 1-year primary patency was lower for patients with impaired ambulatory status (44% ± 3% vs. 29% ± 3%, P = 0.001), but was not significantly different for patients with assisted ADL status (41% ± 3% vs. 32% ± 5%, P = 0.12). Five-year survival was lower for patients with impaired ambulatory status (53% ± 5% vs. 74% ± 4%, P < 0.001), but was not significantly different for patients with assisted ADL status (45% ± 9% vs. 71% ± 4%, P = 0.1). On multivariable analysis, increased likelihood of 90-day readmission was significantly associated with impaired ambulatory status (odds ratio (OR) 2.03, 95% confidence interval (CI) 1.4-2.94, P < 0.001) and assisted ADL status (OR 1.66, 95% CI 1.07-2.57, P = 0.02). One-year primary patency was not significantly associated with impaired ambulatory (hazard ratio (HR) 1.25, 95% CI 0.98-1.6, P = 0.07) or assisted ADL status (HR 1.13, 95% CI 0.87-1.48, P = 0.36). Increased likelihood of 5-year mortality was associated with impaired ambulatory (HR 1.65, 95% CI 1.04-2.62, P = 0.04) and assisted ADL status (HR 2.63, 95% CI 1.35-5.11, P = 0.004). CONCLUSIONS: Impaired ambulatory and assisted ADL statuses were associated with increased readmissions and long-term mortality after AV access creation. Approximately half of patients with functional impairment were not alive at 5 years. Setting outcome expectations as well as prospectively examining the impact of physical therapy and visiting nursing services for functionally impaired patients undergoing AV access creation are warranted.

Impact of pregnancy on airway complications after intubation for COVID-19 infection: A case series.

Coronavirus disease (COVID-19) is associated with severe acute respiratory illness, often requiring intensive care unit admission. Some patients require prolonged intubation and mechanical ventilation. Post-intubation laryngotracheal stenosis occurs in approximately four to 13 % of adult patients after prolonged intubation in the absence of COVID-19 infection. The rate of COVID-19 related post-intubation laryngotracheal stenosis may be higher. Of 339 pregnant patients with COVID-19, we identified seven who required intubation and mechanical ventilation. Four of the seven developed persistent airway complications, and laryngotracheal stenosis, the most severe, was present in three. Each patient had variations in duration of intubation, endotracheal tube size, re-intubation, presence of superimposed infections, and pre-existing comorbidities. We speculate that underlying physiologic changes of pregnancy in addition to the increased inflammatory state caused by COVID-19 are associated with an increased risk of post-intubation laryngotracheal stenosis. Otolaryngology physicians should have a low threshold for considering this pathophysiology when consulting on obstetric patients who have previously been intubated with COVID-19. Otolaryngologists can educate obstetricians when caring for pregnant patients who have laryngotracheal stenosis, especially those who may require emergency airway management for obstetric indications.

DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation.

Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the-art techniques rely on convolutional neural networks (CNNs) that use pixel-to-pixel loss to update network parameters. However, stereotactic body radiotherapy (SBRT) dose is often heterogeneous, making it difficult to model using pixel-level loss. Generative adversarial networks (GANs) utilize adversarial learning that incorporates image-level loss and is better suited to learn from heterogeneous labels. However, GANs are difficult to train and rely on compromised architectures to facilitate convergence. This study suggests an attention-gated generative adversarial network (DoseGAN) to improve learning, increase model complexity, and reduce network redundancy by focusing on relevant anatomy. DoseGAN was compared to alternative state-of-the-art dose prediction algorithms using heterogeneity index, conformity index, and various dosimetric parameters. All algorithms were trained, validated, and tested using 141 prostate SBRT patients. DoseGAN was able to predict more realistic volumetric dosimetry compared to all other algorithms and achieved statistically significant improvement compared to all alternative algorithms for the V100 and V120 of the PTV, V60 of the rectum, and heterogeneity index.

Attention-Aware Discrimination for MR-to-CT Image Translation Using Cycle-Consistent Generative Adversarial Networks.

PURPOSE: To suggest an attention-aware, cycle-consistent generative adversarial network (A-CycleGAN) enhanced with variational autoencoding (VAE) as a superior alternative to current state-of-the-art MR-to-CT image translation methods. MATERIALS AND METHODS: An attention-gating mechanism is incorporated into a discriminator network to encourage a more parsimonious use of network parameters, whereas VAE enhancement enables deeper discrimination architectures without inhibiting model convergence. Findings from 60 patients with head, neck, and brain cancer were used to train and validate A-CycleGAN, and findings from 30 patients were used for the holdout test set and were used to report final evaluation metric results using mean absolute error (MAE) and peak signal-to-noise ratio (PSNR). RESULTS: A-CycleGAN achieved superior results compared with U-Net, a generative adversarial network (GAN), and a cycle-consistent GAN. The A-CycleGAN averages, 95% confidence intervals (CIs), and Wilcoxon signed-rank two-sided test statistics are shown for MAE (19.61 [95% CI: 18.83, 20.39], P = .0104), structure similarity index metric (0.778 [95% CI: 0.758, 0.798], P = .0495), and PSNR (62.35 [95% CI: 61.80, 62.90], P = .0571). CONCLUSION: A-CycleGANs were a superior alternative to state-of-the-art MR-to-CT image translation methods.© RSNA, 2020.

Attention-enabled 3D boosted convolutional neural networks for semantic CT segmentation using deep supervision.

A deeply supervised attention-enabled boosted convolutional neural network (DAB-CNN) is presented as a superior alternative to current state-of-the-art convolutional neural networks (CNNs) for semantic CT segmentation. Spatial attention gates (AGs) were incorporated into a novel 3D cascaded CNN framework to prioritize relevant anatomy and suppress redundancies within the network. Due to the complexity and size of the network, incremental channel boosting was used to decrease memory usage and facilitate model convergence. Deep supervision was used to encourage semantically meaningful deep features and mitigate local minima traps during training. The accuracy of DAB-CNN is compared to seven architectures: a variation of U-Net (UNet), attention-enabled U-Net (A-UNet), boosted U-Net (B-UNet), deeply-supervised U-Net (D-UNet), U-Net with ResNeXt blocks (ResNeXt), life-long learning segmentation CNN (LL-CNN), and deeply supervised attention-enabled U-Net (DA-UNet). The accuracy of each method was assessed based on Dice score compared to manually delineated contours as the gold standard. One hundred and twenty patients who had definitive prostate radiotherapy were used in this study. Training, validation, and testing followed Kaggle competition rules, with 80 patients used for training, 20 patients used for internal validation, and 20 test patients used to report final accuracies. Comparator p -values indicate that DAB-CNN achieved significantly superior Dice scores than all alternative algorithms for the prostate, rectum, and penile bulb. This study demonstrated that attention-enabled boosted convolutional neural networks (CNNs) using deep supervision are capable of achieving superior prediction accuracy compared to current state-of-the-art automatic segmentation methods.

Competency to stand trial evaluations in a multicultural population: Associations between psychiatric, demographic, and legal factors.

Data were examined from an archival sample of Competency to Stand Trial (CST) reports of 200 consecutive New York City pre-trial defendants evaluated over a five-month period. Approximately a fourth of defendants in the present study were immigrants; many required the assistance of interpreters. The examiners conducting the CST evaluation diagnosed approximately half of the defendants with a primary diagnosis of a psychotic disorder and deemed over half not competent. Examiners reached the same conclusion about competency in 96% of cases, about the presence of a psychotic disorder in 91% of cases, and affective disorder in 85% of cases. No significant differences between psychologists and psychiatrists were found for rates of competency/incompetency opinions. Compared to those deemed competent, defendants deemed not competent had significantly higher rates of prior psychiatric hospitalization and diagnosis of psychotic illness at the time of the CST evaluation but lower rates of reported substance abuse.

Dry-heat Depyrogenation Ovens for Pharmaceutical Compounding Facilities.

Sterilization kills microorganisms in compounded preparations, on the implements used to prepare them, and on the vessels that contain them, but depyrogenation incinerates the remaining debris and renders the treated tool, container, or meditation pyrogen free. Depyrogenation is thus an essential step in the preparation of sterile compounds, and the pharmacist who dispenses those formulations is directly responsible for ensuring their safety, potency, and purity. Dry heat provided by a depyrogenation oven or tunnel is the pharmaceutical gold standard for ensuring the elimination of pyrogens. In this report, we describe several depyrogenation ovens that are compliant with Current Good Manufacturing Practice standards and are appropriate for use in aseptic-compounding facilities that meet the guidelines set forth in United States Pharmacopela Chapter <797>.