Comparison of Ductal Stent Versus Surgical Shunt as Initial Intervention for Neonates with Pulmonary Atresia with Intact Ventricular Septum.

Data comparing surgical systemic-to-pulmonary artery shunt and patent ductus arteriosus (PDA) stent as the initial palliation procedure for patients with pulmonary atresia with intact ventricular septum (PA-IVS) are limited. We sought to compare characteristics and outcomes in a multicenter cohort of patients with PA-IVS undergoing surgical shunts versus PDA stents. We retrospectively reviewed neonates with PA-IVS from 2009 to 2019 in 19 United States centers. Bivariate comparisons and multivariable logistic regression analysis were performed to determine the relationship between initial palliation strategy and outcomes including major adverse cardiovascular events (MACE): stroke, mechanical circulatory support, cardiac arrest, or death. 187 patients were included: 38 PDA stents and 149 surgical shunts. Baseline characteristics did not differ statistically between groups. Post-procedural MACE occurred in 4 patients (11%) with PDA stents versus 38 (26%) with surgical shunts, p = 0.079. Overall, the initial palliation strategy was not significantly associated with MACE (aOR:0.37; 95% CI,0.13-1.02). In patients with moderate-to-severe right ventricle hypoplasia, PDA stents were significantly associated with decreased odds of MACE (aOR:0.36; 95% CI,0.13-0.99). PDA stents were associated with lower vasoactive inotrope scores (median 0 versus 5, p < 0.001), greater likelihood to be extubated at the end of their procedure (37% versus 4%, p < 0.001), and shorter duration of mechanical ventilation (median 24 versus 96 h, p < 0.001). PDA stents were associated with significantly more unplanned reinterventions for hypoxemia compared to surgical shunts (42% vs. 20%, p = 0.009). In this multicenter study, neonates with PA-IVS who underwent PDA stenting received less vasoactive and ventilatory support postoperatively compared to those who had surgical shunts. Furthermore, patients with the most severe morphology had decreased odds of MACE.

Treatment Outcomes of Cesarean Scar Pregnancy Under a Novel Classification System: A Retrospective Cohort Study.

OBJECTIVES: To determine whether the new classification system published by Jordans et al for cesarean scar pregnancy (CSP) can guide management and treatment outcomes. METHODS: A retrospective study of women diagnosed with CSP from October 2010 to December 2022 in a single tertiary center was performed. Sonographic records of these women were classified into three types according to the classification published by Jordans et al. Treatment outcomes were compared across each type of CSP. RESULTS: The study included a total of 84 women, where 60 (71.4%), 17 (20.2%), and 7 (8.3%) of them were categorized into Type 1, 2, and 3 CSP, respectively. A total of 47 (55.9%) women were managed with methotrexate, 22 (26.2%) underwent surgical management of the CSP without removal of the Cesarean section (CS) niche, and 11 (13.1%) underwent surgery to remove the CSP and the CS niche. Overall treatment success rates for medical management and surgical management were 70 and 97%, respectively. Four women were managed expectantly and continued their pregnancies, among which three carried beyond 34 weeks and had good neonatal outcomes. CONCLUSIONS: The classification as published by Jordans et al is easily replicable and readily implemented clinically. Our findings show that a higher proportion of Type 1 and Type 2 CSP were treated successfully by a less invasive medical approach with a high success rate, whereas most Type 3 CSP required surgical resection to successfully remove the CSP and the CS niche. Prospective studies are required to confirm these findings and further validate the clinical utility of this nomenclature system.

Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach.

BACKGROUND: MRI magnetization-prepared rapid acquisition (MPRAGE) is an easily available imaging modality for dementia diagnosis. Previous studies suggested that volumetric analysis plays a crucial role in various stages of dementia classification. In this study, volumetry, radiomics and demographics were integrated as inputs to develop an artificial intelligence model for various stages, including Alzheimer's disease (AD), mild cognitive decline (MCI) and cognitive normal (CN) dementia classifications. METHOD: The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset was separated into training and testing groups, and the Open Access Series of Imaging Studies (OASIS) dataset was used as the second testing group. The MRI MPRAGE image was reoriented via statistical parametric mapping (SPM12). Freesurfer was employed for brain segmentation, and 45 regional brain volumes were retrieved. The 3D Slicer software was employed for 107 radiomics feature extractions from within the whole brain. Data on patient demographics were collected from the datasets. The feed-forward neural network (FFNN) and the other most common artificial intelligence algorithms, including support vector machine (SVM), ensemble classifier (EC) and decision tree (DT), were used to build the models using various features. RESULTS: The integration of brain regional volumes, radiomics and patient demographics attained the highest overall accuracy at 76.57% and 73.14% in ADNI and OASIS testing, respectively. The subclass accuracies in MCI, AD and CN were 78.29%, 89.71% and 85.14%, respectively, in ADNI testing, as well as 74.86%, 88% and 83.43% in OASIS testing. Balanced sensitivity and specificity were obtained for all subclass classifications in MCI, AD and CN. CONCLUSION: The FFNN yielded good overall accuracy for MCI, AD and CN categorization, with balanced subclass accuracy, sensitivity and specificity. The proposed FFNN model is simple, and it may support the triage of patients for further confirmation of the diagnosis.

Association of post-operative ICU requirements with early extubation in the fontan procedure.

OBJECTIVES: This study investigated the association between early extubation (EE) and the degree of postoperative intensive care unit (ICU) support after the Fontan procedure, specifically evaluating the volume of postoperative intravenous fluid (IVF) and vasoactive-inotropic score (VIS). METHODS: Retrospective analysis of patients who underwent Fontan palliation from 2008 to 2018 at a single center was completed. Patients were initially divided into pre-institutional initiative towards EE (control) and post-initiative (modern) cohorts. Differences between the cohorts were assessed using t-test, Wilcoxon, or chi-Square. Following stratification by early or late extubation, four groups were compared via ANOVA or Kruskal-Wallis Test. RESULTS: There was a significant difference in the rate of EE between the control and modern cohorts (mean 42.6 versus 75.7%, p = 0.01). The modern cohort demonstrated lower median VIS (5 versus 8, p = 0.002), but higher total mean IVF (101±42 versus 82 ±27 cc/kg, p < 0.001) versus control cohort. Late extubated (LE) patients in the modern cohort had the highest VIS and IVF requirements. This group received 67% more IVF (140 ± 53 versus 84 ± 26 cc/kg, p < 0.001) and had a higher median VIS at 24 hours (10 (IQR, 5-10) versus 4 (IQR, 2-7), p < 0.001) versus all other groups. In comparison, all EE patients had a 5-point lower median VIS when compared to LE patients (3 versus 8, p= 0.001). CONCLUSIONS: EE following the Fontan procedure is associated with reduced post-operative VIS. LE patients in the modern cohort received more IVF, potentially identifying a high-risk subgroup of Fontan patients deserving of further investigation.

AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis.

BACKGROUND: Glioblastoma (GBM) is one of the most common malignant primary brain tumors, which accounts for 60-70% of all gliomas. Conventional diagnosis and the decision of post-operation treatment plan for glioblastoma is mainly based on the feature-based qualitative analysis of hematoxylin and eosin-stained (H&E) histopathological slides by both an experienced medical technologist and a pathologist. The recent development of digital whole slide scanners makes AI-based histopathological image analysis feasible and helps to diagnose cancer by accurately counting cell types and/or quantitative analysis. However, the technology available for digital slide image analysis is still very limited. This study aimed to build an image feature-based computer model using histopathology whole slide images to differentiate patients with glioblastoma (GBM) from healthy control (HC). METHOD: Two independent cohorts of patients were used. The first cohort was composed of 262 GBM patients of the Cancer Genome Atlas Glioblastoma Multiform Collection (TCGA-GBM) dataset from the cancer imaging archive (TCIA) database. The second cohort was composed of 60 GBM patients collected from a local hospital. Also, a group of 60 participants with no known brain disease were collected. All the H&E slides were collected. Thirty-three image features (22 GLCM and 11 GLRLM) were retrieved from the tumor volume delineated by medical technologist on H&E slides. Five machine-learning algorithms including decision-tree (DT), extreme-boost (EB), support vector machine (SVM), random forest (RF), and linear model (LM) were used to build five models using the image features extracted from the first cohort of patients. Models built were deployed using the selected key image features for GBM diagnosis from the second cohort (local patients) as model testing, to identify and verify key image features for GBM diagnosis. RESULTS: All five machine learning algorithms demonstrated excellent performance in GBM diagnosis and achieved an overall accuracy of 100% in the training and validation stage. A total of 12 GLCM and 3 GLRLM image features were identified and they showed a significant difference between the normal and the GBM image. However, only the SVM model maintained its excellent performance in the deployment of the models using the independent local cohort, with an accuracy of 93.5%, sensitivity of 86.95%, and specificity of 99.73%. CONCLUSION: In this study, we have identified 12 GLCM and 3 GLRLM image features which can aid the GBM diagnosis. Among the five models built, the SVM model proposed in this study demonstrated excellent accuracy with very good sensitivity and specificity. It could potentially be used for GBM diagnosis and future clinical application.

Early Treatment of Multisystem Inflammatory Syndrome in Children.

BACKGROUND AND OBJECTIVES: Multisystem inflammatory syndrome in children (MIS-C) is a severe hyperinflammatory illness occurring after severe acute respiratory syndrome coronavirus 2 infection. The optimal treatment of MIS-C is unknown, although prior studies have indicated benefits of intravenous immunoglobulin (IVIG) and glucocorticoids. We hypothesize that early treatment with glucocorticoids is associated with shorter hospital length of stay (LOS). METHODS: This study is a multicenter retrospective cohort study of patients hospitalized with MIS-C over a roughly 1-year period. The primary outcome was hospital LOS comparing subjects who received glucocorticoids within 48 hours of arrival to the treating hospital to those who did not. Secondary outcomes included ICU LOS. Unadjusted and adjusted analyses were performed. RESULTS: The final analysis included 131 subjects. Subjects who received early glucocorticoids were more likely to receive early IVIG and to require ICU admission. Early glucocorticoid administration was associated with shorter ICU LOS (4 vs 9 days, P = .004) in the unadjusted analysis. In the adjusted analysis, early glucocorticoid administration and early IVIG administration were both independently associated with shorter hospital LOS (incidence rate ratio 0.75, P = .025; incidence rate ratio 0.56, P = .026, respectively). CONCLUSIONS: Glucocorticoids and intravenous immunoglobulin were independently associated with shorter hospital length of stay when given early in hospitalization to MIS-C patients after accounting for potential confounding factors. The optimal dose and duration of treatment require further investigation, but this study supports early combination therapy with both IVIG and glucocorticoids for all children hospitalized with MIS-C.

Elevated cardiac biomarkers and outcomes in children and adolescents with acute COVID-19.

Cardiac involvement associated with multi-system inflammatory syndrome in children has been extensively reported, but the prevalence of cardiac involvement in children with SARS-CoV-2 infection in the absence of inflammatory syndrome has not been well described. In this retrospective, single centre, cohort study, we describe the cardiac involvement found in this population and report on outcomes of patients with and without elevated cardiac biomarkers. Those with multi-system inflammatory syndrome in children, cardiomyopathy, or complex CHD were excluded. Inclusion criteriaz were met by 80 patients during the initial peak of the pandemic at our institution. High-sensitivity troponin T and/or N-terminal pro-brain type natriuretic peptide were measured in 27/80 (34%) patients and abnormalities were present in 5/27 (19%), all of whom had underlying comorbidities. Advanced respiratory support was required in all patients with elevated cardiac biomarkers. Electrocardiographic abnormalities were identified in 14/38 (37%) studies. Echocardiograms were performed on 7/80 patients, and none demonstrated left ventricular dysfunction. Larger studies to determine the true extent of cardiac involvement in children with COVID-19 would be useful to guide recommendations for standard workup and management.

Procedural Outcomes of Pulmonary Atresia With Intact Ventricular Septum in Neonates: A Multicenter Study.

BACKGROUND: Multicenter contemporary data describing short-term outcomes after initial interventions of neonates with pulmonary atresia with intact ventricular septum (PA-IVS) are limited. This multicenter study describes characteristics and outcomes of PA-IVS neonates after their initial catheter or surgical intervention and identifies factors associated with major adverse cardiac events (MACE). METHODS: Neonates with PA-IVS who underwent surgical or catheter intervention between 2009 and 2019 in 19 centers were reviewed. Risk factors for MACE, defined as cardiopulmonary resuscitation, mechanical circulatory support, stroke, or in-hospital mortality, were analyzed using multivariable logistic regression models. RESULTS: We reviewed 279 neonates: 79 (28%) underwent right ventricular decompression, 151 (54%) underwent systemic-to-pulmonary shunt or ductal stent placement only, 36 (13%) underwent right ventricular decompression with shunt or ductal stent placement, and 11 (4%) underwent transplantation. MACE occurred in 57 patients (20%): 26 (9%) received mechanical circulatory support, 37 (13%) received cardiopulmonary resuscitation, stroke occurred in 16 (6%), and 23 (8%) died. The presence of 2 major coronary artery stenoses (adjusted odds ratio, 4.99; 95% CI, 1.16-21.39) and lower weight at first intervention (adjusted odds ratio, 1.52; 95% CI, 1.01-2.27) were significantly associated with MACE. Coronary ischemia was the most frequent presumed mechanism of death (n = 10). CONCLUSIONS: In a multicenter cohort, 1 in 5 neonates with PA-IVS experienced MACE after their initial intervention. Patients with 2 major coronary artery stenoses or lower weight at the time of the initial procedure were most likely to experience MACE and warrant vigilance during preintervention planning and postintervention management.

Level of Amyloid-ß (Aß) Binding Leading to Differential Effects on Resting State Functional Connectivity in Major Brain Networks.

INTRODUCTION: Amyloid-ß protein (Aß) is one of the biomarkers for Alzheimer's disease (AD). The recent application of interhemispheric functional connectivity (IFC) in resting-state fMRI has been used as a non-invasive diagnostic tool for early dementia. In this study, we focused on the level of Aß accumulated and its effects on the major functional networks, including default mode network (DMN), central executive network (CEN), salience network (SN), self-referential network (SRN) and sensory motor network (SMN). METHODS: 58 participants (27 Hi Aß (HiAmy) and 31 low Aß (LowAmy)) and 25 healthy controls (HC) were recruited. [18F]flutemetamol PET/CT was performed for diseased groups, and MRI scanning was done for all participants. Voxel-by-voxel correlation analysis was done for both groups in all networks. RESULTS: In HiAmy, IFC was reduced in all networks except SN. A negative correlation in DMN, CEN, SRN and SMN suggests high Aß related to IFC reduction; However, a positive correlation in SN suggests high Aß related to an increase in IFC. In LowAmy, IFC increased in CEN, SMN, SN and SRN. Positive correlation in all major brain networks. CONCLUSION: The level of Aß accumulated demonstrated differential effects on IFC in various brain networks. As the treatment to reduce Aß plaque deposition is available in the market, it may be an option for the HiAmy group to improve their IFC in major brain networks.

Radiomics from Various Tumour Volume Sizes for Prognosis Prediction of Head and Neck Squamous Cell Carcinoma: A Voted Ensemble Machine Learning Approach.

Background: Traditionally, cancer prognosis was determined by tumours size, lymph node spread and presence of metastasis (TNM staging). Radiomics of tumour volume has recently been used for prognosis prediction. In the present study, we evaluated the effect of various sizes of tumour volume. A voted ensemble approach with a combination of multiple machine learning algorithms is proposed for prognosis prediction for head and neck squamous cell carcinoma (HNSCC). Methods: A total of 215 HNSCC CT image sets with radiotherapy structure sets were acquired from The Cancer Imaging Archive (TCIA). Six tumour volumes, including gross tumour volume (GTV), diminished GTV, extended GTV, planning target volume (PTV), diminished PTV and extended PTV were delineated. The extracted radiomics features were analysed by decision tree, random forest, extreme boost, support vector machine and generalized linear algorithms. A voted ensemble machine learning (VEML) model that optimizes the above algorithms was used. The receiver operating characteristic area under the curve (ROC-AUC) were used to compare the performance of machine learning methods, including accuracy, sensitivity and specificity. Results: The VEML model demonstrated good prognosis prediction ability for all sizes of tumour volumes with reference to GTV and PTV with high accuracy of up to 88.3%, sensitivity of up to 79.9% and specificity of up to 96.6%. There was no significant difference between the various target volumes for the prognostic prediction of HNSCC patients (chi-square test, p > 0.05). Conclusions: Our study demonstrates that the proposed VEML model can accurately predict the prognosis of HNSCC patients using radiomics features from various tumour volumes.

Pulmonary Atresia with Intact Ventricular Septum: Midterm Outcomes from a Multicenter Cohort.

Contemporary multicenter data regarding midterm outcomes for neonates with pulmonary atresia with intact ventricular septum are lacking. We sought to describe outcomes in a contemporary multicenter cohort, determine factors associated with end-states, and evaluate the effect of right ventricular coronary dependency and coronary atresia on transplant-free survival. Neonates treated during 2009-2019 in 19 United States centers were reviewed. Competing risks analysis was performed to determine cumulative risk of each end-state, and multivariable regression analyses were performed to identify factors associated with each end-state and transplant-free survival. We reviewed 295 patients. Median tricuspid valve Z-score was - 3.06 (25%, 75%: - 4.00, - 1.52). Final end-state was biventricular repair for 45 patients (15.2%), one-and-a half ventricle for 16 (5.4%), Fontan for 75 (25.4%), cardiac transplantation for 29 (9.8%), and death for 54 (18.3%). Seventy-six patients (25.7%) remained in mixed circulation. Cumulative risk estimate of death was 10.9%, 16.1%, 16.9%, and 18.8% at 1, 6 months, 1 year, and 5 years, respectively. Tricuspid valve Z-score was inversely, and coronary atresia positively associated with death or transplantation [odds ratio (OR) = 0.46, (95% confidence interval (CI) = 0.29-0.75, p < 0.001) and OR = 3.75 (95% CI 1.46-9.61, p = 0.011), respectively]. Right ventricular coronary dependency and left coronary atresia had a significant effect on transplant-free survival (log-rank p < 0.001). In a contemporary multicenter cohort of patients with PAIVS, consisting predominantly of patients with moderate-to-severe right ventricular hypoplasia, we observed favorable survival outcomes. Right ventricular coronary dependency and left, but not right, coronary atresia significantly worsens transplant-free survival.

Radiomics-Based Artificial Intelligence Differentiation of Neurodegenerative Diseases with Reference to the Volumetry.

This study aimed to build automated detection models-one by brain regional volume (V-model), and the other by radiomics features of the whole brain (R-model)-to differentiate mild cognitive impairment (MCI) from cognitive normal (CN), and Alzheimer's Disease (AD) from mild cognitive impairment (MCI). The objectives are to compare the models and identify whether radiomics or volumetry can provide a better prediction for differentiating different types of dementia. METHOD: 582 MRI T1-weighted images were retrieved from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, which is a multicenter operating open source database for AD. In total, 97 images of AD, 293 images of MCI patient and 192 images of cognitive normal were divided into a training, a validation and a test group at a ratio of 70:15:15. For each T1-weighted image, volumetric segmentation was performed with the image analysis software FreeSurfer, and radiomics features were retrieved by imaging research software 3D slicers. Brain regional volume and radiomics features were used to build the V-model and R-model, respectively, using the random forest algorithm by R. The receiver operating characteristics (ROC) curve of both models were used to evaluate their diagnostic accuracy and reliability to differentiate AD, MCI and CN. RESULTS: To differentiate MCI and CN, both V-model and R-model achieved excellent performance, with an AUC of 0.9992 ± 0.0022 and 0.9850 ± 0.0032, respectively. No significant difference was found between the two AUCs, indicating both models attained similar good performance. In MCI and AD differentiation, the V-model and R-model yielded AUC of 0.9986 ± 0.0013 and 0.9714 ± 0.0175, respectively. The best performance was to differentiate AD from CN, where the V-model and R-model yielded AUC of 0.9994 ± 0.0019 and 0.9830 ± 0.009, respectively. The results suggested that both volumetry and radiomics approaches could be used in differentiating AD, MCI and CN, based on T1 weighted MR images using random forest algorithm successfully. CONCLUSION: This study showed that the radiomics features from T1-weighted MR images achieved excellence performance in differentiating AD, MCI and CN. Compared to the volumetry method, the accuracy, sensitivity and specificity are slightly lower in using radiomics, but still attained very good and reliable classification of the three stages of neurodegenerations. In view of the convenience and operator independence in feature extraction, radiomics can be a quantitative biomarker to differentiate the disease groups.

Multi-Planar VMAT Plans for High-Grade Glioma and Glioblastoma Targeting the Hypothalamic-Pituitary Axis Sparing.

BACKGROUND: This study aimed to identify the better arc configuration of volumetric modulated arc therapy (VMAT) for high-grade glioma and glioblastoma, focusing on a dose reduction to the hypothalamic-pituitary axis through an analysis of dose-volumetric parameters, as well as a correlation analysis between the planned target volume (PTV) to organs at risk (OAR) distance and the radiation dose. METHOD: Twenty-four patients with 9 high-grade glioma and 15 glioblastomas were included in this study. Identical CT, MRI and structure sets of each patient were used for coplanar VMAT (CO-VMAT), dual planar VMAT (DP-VMAT) and multi-planar VMAT (MP-VMAT) planning. The dose constraints adhered to the RTOG0825 and RTOG9006 protocols. The dose-volumetric parameters of each plan were collected for statistical analysis. Correlation analyses were performed between radiation dose and PTV-OARs distance. RESULTS: The DP-VMAT and MP-VMAT achieved a significant dose reduction to most nearby OARs when compared to CO-VMAT, without compromising the dose to PTV, plan homogeneity and conformity. For centrally located OARs, including the hypothalamus, pituitary, brain stem and optic chiasm, the dose reductions ranged from 2.65 Gy to 3.91 Gy (p < 0.001) in DP-VMAT and from 2.57 Gy to 4 Gy (p < 0.001) in MP-VMAT. Similar dose reduction effects were achieved for contralaterally located OARs, including the hippocampus, optic nerve, lens and retina, ranging from 1.06 Gy to 4.37 Gy in DP-VMAT and from 0.54 Gy to 3.39 Gy in MP-VMAT. For ipsilaterally located OARs, DP-VMAT achieved a significant dose reduction of 1.75 Gy to Dmax for the optic nerve. In the correlation analysis, DP-VMAT and MP-VMAT showed significant dose reductions to centrally located OARs when the PTV-OAR distance was less than 4 cm. In particular, DP-VMAT offered better sparing to the optic chiasm when it was located less than 2 cm from the PTV than that of MP-VMAT and CO-VMAT. DP-VMAT and MP-VMAT also showed better sparing to the contralateral hippocampus and retina when they were located 3-8 cm from the PTV. CONCLUSION: The proposed DP-VMAT and MP-VMAT demonstrated significant dose reductions to centrally located and contralateral OARs and maintained the high plan qualities to PTV with good homogeneity and conformity when compared to CO-VMAT for high-grade glioma and glioblastoma. The benefit in choosing DP-VMAT and MP-VMAT over CO-VMAT was substantial when the PTV was located near the hypothalamus, pituitary, optic chiasm, contralateral hippocampus and contralateral retina.

Extracorporeal Membrane Oxygenation (ECMO) and its complications in newborns with congenital diaphragmatic hernia.

BACKGROUND: Extracorporeal Membrane Oxygenation (ECMO) is offered to patients with congenital diaphragmatic hernia (CDH) who are in severe respiratory and cardiac failure. We aim to describe the types of complications among these patients and their impact on survival. METHODS: A single-center, retrospective review of CDH patients cannulated onto ECMO between January 2005 and November 2020 was conducted. ECMO complications, as categorized by the Extracorporeal Life Support Organization (ELSO), were correlated with survival status. Descriptive statistics were used to compare observed complications between survivors and non-survivors. RESULTS: In our cohort of CDH neonates, 21% (54/258) were supported with ECMO, of whom, 61% (33/54) survived. Survivors and non-survivors were similar in baseline characteristics except for birthweight z-score (p = 0.043). Seventy percent of CDH neonates experienced complications during their ECMO run, with the most common categories being metabolic (48.1%) and mechanical (38.9%), followed by hemorrhage (22.2%), neurological (18.5%), renal (11.1%), pulmonary (7.4%), and cardiovascular (7.4%). The median number of complications per patient was higher in the non-survivor group  (2 (IQR: 1-4) vs 1 (IQR: 0-2), p = 0.043). In addition, mechanical (57.1% vs 27.3%, p = 0.045) and renal (28.6% vs 0%, p = 0.002) complications were more common among non-survivors compared to survivors. CONCLUSION: Complications occur frequently among ECMO-treated newborns with CDH, some of which have serious long-term consequences. Survivors had higher birth weight z-scores, shorter ECMO runs, and fewer complications per patient. Mechanical and renal complications were independently associated with mortality, emphasizing the utility of more focused strategies to target fluid balance and renal protection and to prevent circuit and cannula complications.

Multisystem Inflammatory Syndrome in Children (MIS-C): Experiences With a New Disease Process.

On March 20, 2021, the Columbia University Department of Anesthesiology hosted the Papper virtual event dedicated to an academic discussion of various aspects of coronavirus disease-2019. Dr. Eva Cheung, a pediatric intensivist and pediatric cardiologist, spoke about the clinical challenges associated with tackling multisystem inflammatory syndrome in children, a novel clinical entity in pediatric patients related to coronavirus disease-2019, and the experience with confronting multisystem inflammatory syndrome in children in New York.

A scoping review of resting-state brain functional alterations in Type 2 diabetes.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been actively used in the last decade to investigate brain functional connectivity alterations in Type 2 Diabetes Mellitus (T2DM) to understand the neuropathophysiology of T2DM in cognitive degeneration. Given the emergence of new analysis techniques, this scoping review aims to map the rs-fMRI analysis techniques that have been applied in the literature and reports the latest rs-fMRI findings that have not been covered in previous reviews. Graph theory, the contemporary rs-fMRI analysis, has been used to demonstrate altered brain topological organisations in people with T2DM, which included altered degree centrality, functional connectivity strength, the small-world architecture and network-based statistics. These alterations were correlated with T2DM patients' cognitive performances. Graph theory also contributes to identify unbiased seeds for seed-based analysis. The expanding rs-fMRI analytical approaches continue to provide new evidence that helps to understand the mechanisms of T2DM-related cognitive degeneration.

Modified VMAT Plans for Locally Advanced Centrally Located Non-Small Cell Lung Cancer (NSCLC).

OBJECTIVES: This study aimed to find the optimal radiotherapy VMAT plans, that achieved high conformity and homogeneity to the planned target volume (PTV), and minimize the dose to nearby organs at risk including the non-PTV lung, heart and oesophagus for patients with centrally located non-small Cell Lung Cancer. METHODS: A total of 18 patients who were treated for stage III centrally located non-small Cell Lung Cancer were selected retrospectively for this study. Identical CT datasets, 4D CT and structure dataset were used for radiotherapy planning based on single-planar VMAT (SP-VMAT), dual-planar VMAT (DP-VMAT) and Hybrid VMAT (H-VMAT). For SP-VMAT, one full arc and two half arcs were created on single-plane with couch at 0°. For DP-VMAT, one full arc was created with couch at 0°, and two half arcs with couch rotation of 330° or 30°. For H-VMAT, anterior-posterior opposing fixed beam and two half arcs were planned at couch at 0°. Dose constraints were adhered to the RTOG0617. Dose volumetric parameters were collected for statistical analysis. RESULTS: There were no significant differences for the PTV, HI, CI between the SP-VMAT, DP-VMAT and H-VMAT. For the non-PTV lungs, Dmean, V20, V10, V5, D1500 and D1000 were significantly lower (2.05 Gy, 6.47%, 15.89%, 11.66% 4.17 Gy and 5.47 Gy respectively) in H-VMAT than that of SP-VMAT (all p < 0.001). For the oesophagus, Dmax, Dmean, V30 and V18.8 of H-VMAT were 0.08 Gy, 1.73 Gy, 5.54% and 7.17% lower than that of the SP-VMAT plan. For the heart, Dmean, V34, V28, V20 and V10 of DP-VMAT were lower than that of SP-VMAT by 1.45 Gy, 0.65%, 1.74%, 4.8% and 7.11% respectively. CONCLUSION: The proposed H-VMAT showed more favourable plan quality than the SP-VMAT for centrally located stage III NSCLC, in particular for non-PTV lungs and the oesophagus. It will benefit patients, especially those who planned for immunotherapy (Durvalumab) after standard chemo-irradiation. The proposed DP-VMAT plan showed significant dose reduction to the heart when compared to the H-VMAT plan.

Diagnostic Efficacy of Voxel-Mirrored Homotopic Connectivity in Vascular Dementia as Compared to Alzheimer's Related Neurodegenerative Diseases-A Resting State fMRI Study.

Previous studies have demonstrated that functional connectivity (FC) of different brain regions in resting state function MRI were abnormal in patients suffering from mild cognitive impairment (MCI) and Alzheimer's disease (AD) when comparing to healthy controls (HC) using seed based, independent component analysis (ICA) or small world network techniques. A new technique called voxel-mirrored homotopic connectivity (VMHC) was used in the current study to evaluate the value of interhemispheric functional connectivity (IFC) as a diagnostic tool to differentiate vascular dementia (VD) from other Alzheimer's related neurodegenerative diseases. Eighty-three participants were recruited from the university hospital memory clinic. A multidisciplinary panel formed by a neuroradiologist and two geriatricians classified the participants into VD (13), AD (16), MCI (29), and HC (25) based on clinical history, Montreal Cognitive Assessment Hong Kong version (HK­MoCA) neuropsychological score, structural MRI, MR perfusion, and 18-F Flutametamol (amyloid) PET-CT findings of individual subjects. We adopted the calculation method used by Kelly et al. (2011) and Zuo et al. (2010) in obtaining VMHC maps. Specific patterns of VMHC maps were obtained for VD, AD, and MCI to HC comparison. VD showed significant reduction in VMHC in frontal orbital gyrus and gyrus rectus. Increased VMHC was observed in default mode network (DMN), executive control network (ECN), and the remaining salient network (SN) regions. AD showed a reduction of IFC in all DMN, ECN, and SN regions; whereas MCI showed VMHC reduction in vSN, and increased VMHC in DMN and ECN. When combining VMHC values of relevant brain regions, the accuracy was improved to 87%, 92%, and 83% for VD, AD, and MCI from HC, respectively, in receiver operating characteristic (ROC) analysis. Through studying the VMHC maps and using VMHC values in relevant brain regions, VMHC can be considered as a reliable diagnostic tool for VD, AD, and MCI from HC.

Non-coplanar VMAT plans for postoperative primary brain tumour to reduce dose to hippocampus, temporal lobe and cochlea: a planning study.

OBJECTIVES: This study aimed to compare radiotherapy plan quality of coplanar volumetric modulated arc therapy (CO-VMAT) and non-coplanar VMAT (NC-VMAT) for post-operative primary brain tumour. METHODS: A total of 16 patients who were treated for primary brain tumours were retrospectively selected for this study. For each patient, identical CT sets with structures were used for both CO-VMAT and NC-VMAT planning. For CO-VMAT, one full arc and two coplanar half arcs were used. For NC-VMAT, one full coplanar and two non-coplanar half arcs with couch rotation of 315° or 45° were used. Dose constraints were adhered to the RTOG 0614, RTOG 0933 and TMH protocol. Dose volumetric parameters were collected for statistical analysis. RESULTS: .NC-VMAT achieved significant dose reduction in contralateral hippocampus, both temporal lobes and cochleae, and other OARs while the plan qualities remained the same. In particular, NC-VMAT decreased contralateral hippocampus mean dose by 1.67Gy. Similarly, the NC-VMAT decreased temporal lobe mean dose by 6.29Gy and 2.8Gy for ipsilateral and contralateral side respectively. Furthermore, it decreased cochlea mean dose by 5.34Gy and 0.97Gy for ipsilateral and contralateral side respectively. Overall, there was a reduction of 5.4% of normal brain tissue volume receiving low dose irradiation. CONCLUSION: The proposed NC-VMAT showed more favourable plan quality than the CO-VMAT for primary brain tumours, in particular to hippocampus, temporal lobes, cochleae and OARs located to the contralateral side of tumours. ADVANCES IN KNOWLEDGE: For primary brain tumours radiotherapy, NC-VMAT can reduce doses to the hippocampus, both temporal lobes, and cochleae, as well as OARs located to the contralateral side of tumours.