Hyperoxia results in increased aerobic metabolism following acute brain injury

Acute brain injury is associated with depressed aerobic metabolism. Below a critical mitochondrial pO2 cytochrome c oxidase, the terminal electron acceptor in the mitochondrial respiratory chain, fails to sustain oxidative phosphorylation. After acute brain injury, this ischaemic threshold might be shifted into apparently normal levels of tissue oxygenation. We investigated the oxygen dependency of aerobic metabolism in 16 acutely brain-injured patients using a 120-min normobaric hyperoxia challenge in the acute phase (24–72 h) post-injury and multimodal neuromonitoring, including transcranial Doppler ultrasound-measured cerebral blood flow velocity, cerebral microdialysis-derived lactate-pyruvate ratio (LPR), brain tissue pO2 (pbrO2), and tissue oxygenation index and cytochrome c oxidase oxidation state (oxCCO) measured using broadband spectroscopy. Increased inspired oxygen resulted in increased pbrO2 [ΔpbrO2 30.9 mmHg p < 0.001], reduced LPR [ΔLPR −3.07 p = 0.015], and increased cytochrome c oxidase (CCO) oxidation (Δ[oxCCO] + 0.32 µM p < 0.001) which persisted on return-to-baseline (Δ[oxCCO] + 0.22 µM, p < 0.01), accompanied by a 7.5% increase in estimated cerebral metabolic rate for oxygen (p = 0.038). Our results are consistent with an improvement in cellular redox state, suggesting oxygen-limited metabolism above recognised ischaemic pbrO2 thresholds. Diffusion limitation or mitochondrial inhibition might explain these findings. Further investigation is warranted to establish optimal oxygenation to sustain aerobic metabolism after acute brain injury

Timing of glioblastoma surgery and patient outcomes: a multicenter cohort study.

BACKGROUND: The impact of time-to-surgery on clinical outcome for patients with glioblastoma has not been determined. Any delay in treatment is perceived as detrimental, but guidelines do not specify acceptable timings. In this study, we relate the time to glioblastoma surgery with the extent of resection and residual tumor volume, performance change, and survival, and we explore the identification of patients for urgent surgery. METHODS: Adults with first-time surgery in 2012–2013 treated by 12 neuro-oncological teams were included in this study. We defined time-to-surgery as the number of days between the diagnostic MR scan and surgery. The relation between time-to-surgery and patient and tumor characteristics was explored in time-to-event analysis and proportional hazard models. Outcome according to time-to-surgery was analyzed by volumetric measurements, changes in performance status, and survival analysis with patient and tumor characteristics as modifiers. RESULTS: Included were 1033 patients of whom 729 had a resection and 304 a biopsy. The overall median time-to-surgery was 13 days. Surgery was within 3 days for 235 (23%) patients, and within a month for 889 (86%). The median volumetric doubling time was 22 days. Lower performance status (hazard ratio [HR] 0.942, 95% confidence interval [CI] 0.893–0.994) and larger tumor volume (HR 1.012, 95% CI 1.010–1.014) were independently associated with a shorter time-to-surgery. Extent of resection, residual tumor volume, postoperative performance change, and overall survival were not associated with time-to-surgery. CONCLUSIONS: With current decision-making for urgent surgery in selected patients with glioblastoma and surgery typically within 1 month, we found equal extent of resection, residual tumor volume, performance status, and survival after longer times-to-surgery

Gender differences and determinants of health related quality of life in coronary patients: a follow-up study

<p>Abstract</p> <p>Background</p> <p>The role of gender differences in Health Related Quality Life (HRQL) in coronary patients is controversial, so understanding the specific determinants of HRQL in men and women might be of clinical importance. The aim of this study was to know the gender differences in the evolution of HRQL at 3 and 6 months after a coronary event, and to identify the key clinical, demographic and psychological characteristics of each gender associated with these changes.</p> <p>Methods</p> <p>A follow-up study was carried out, and 175 patients (112 men and 63 women) with acute myocardial infarction (AMI) or unstable angina were studied. The SF-36v1 health questionnaire was used to assess HRQL, and the GHQ-28 (General Health Questionnaire) to measure mental health during follow-up. To study the variables related to changes in HRQL, generalized estimating equation (GEE) models were performed.</p> <p>Results</p> <p>Follow-up data were available for 55 men and 25 women at 3 months, and for 35 men and 12 women at 6 months. Observations included: a) Revascularization was performed later in women. b) The frequency of rehospitalization between months 3 and 6 of follow-up was higher in women c) Women had lower baseline scores in the SF-36. d) Men had progressed favourably in most of the physical dimensions of the SF-36 at 6 months, while at the same time women's scores had only improved for Physical Component Summary, Role Physical and Social Functioning; e) the variables determining the decrease in HRQL in men were: worse mental health and angina frequency; and in women: worse mental health, history of the disease, revascularization, and angina frequency.</p> <p>Conclusions</p> <p>There are differences in the evolution of HRQL, between men and women after a coronary attack. Mental health is the determinant most frequently associated with HRQL in both genders. However, other clinical determinants of HRQL differed with gender, emphasizing the importance of individualizing the intervention and the content of rehabilitation programs. Likewise, the recognition and treatment of mental disorders in these patients could be crucial.</p

Glioblastoma surgery imaging—reporting and data system: Standardized reporting of tumor volume, location, and resectability based on automated segmentations

Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software

Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task

For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime

Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms

This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using manual segmentations from preoperative MRI of glioblastoma (GBM) and low-grade gliomas (LGG) from the BraTS 2021 dataset (1251 in total), in addition to 275 GBM and 205 LGG acquired clinically across 12 hospitals worldwide. Data was split into 80% training, 5% validation, and 15% internal test data. An additional external test-set of 158 GBM and 69 LGG was used to assess generalisability to other hospitals’ data. All models’ median Dice similarity coefficient (DSC) for both test sets were within, or higher than, previously reported human inter-rater agreement (range of 0.74–0.85). For both test sets, nn-Unet achieved the highest DSC (internal = 0.86, external = 0.93) and the lowest Hausdorff distances (10.07, 13.87 mm, respectively) for all tumor classes (p < 0.001). By applying Sparsified training, missing MRI sequences did not statistically affect the performance. nn-Unet achieves accurate segmentations in clinical settings even in the presence of incomplete MRI datasets. This facilitates future clinical adoption of automated glioma segmentation, which could help inform treatment planning and glioma monitoring