A Systematic Review of Diffusion Microstructure Imaging (Dmi): Current and Future Applications in Neurology Research (preprint)

Background: Diffusion microstructure imaging (DMI) is a novel diffusion magnetic resonance imaging (MRI) technique that provides rich estimates of microscopic tissue properties, such as axon morphologies and fiber configurations. DMI has potential applications in neurology, where various diseases and disorders affect the brain tissue's microstructure and connectivity.Objectives: To investigate the current and future applications of DMI in neurology, covering various diseases and disorders such as brain tumors and metastases, Parkinson's syndromes, COVID-19-related neurological symptoms, temporal lobe epilepsy, and acute ischemic stroke.Methods: The PRISMA 2020 statement was followed. Four electronic databases were searched from inception to May the 5th 2023. Two reviewers independently screened, selected, and extracted data from the eligible studies.Results: Seven studies were included in the review. The studies showed that DMI can differentiate between various neurological diseases or disorders based on alterations in brain tissue microstructure and connectivity. The studies also showed that DMI can be superior to conventional diffusion imaging techniques, such as diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), in detecting subtle differences between pathological conditions.Conclusions: DMI is a powerful diffusion imaging technique that can provide rich estimates of microscopic tissue properties and differentiate between various neurological diseases or disorders. However, more research is needed to compare DMI with other imaging modalities or clinical measures and to evaluate longitudinal changes or treatment effects using DMI in neurological diseases or disorders.

Invariant Scattering Transform for Medical Imaging (preprint)

Invariant scattering transform introduces new area of research that merges the signal processing with deep learning for computer vision. Nowadays, Deep Learning algorithms are able to solve a variety of problems in medical sector. Medical images are used to detect diseases brain cancer or tumor, Alzheimer's disease, breast cancer, Parkinson's disease and many others. During pandemic back in 2020, machine learning and deep learning has played a critical role to detect COVID-19 which included mutation analysis, prediction, diagnosis and decision making. Medical images like X-ray, MRI known as magnetic resonance imaging, CT scans are used for detecting diseases. There is another method in deep learning for medical imaging which is scattering transform. It builds useful signal representation for image classification. It is a wavelet technique; which is impactful for medical image classification problems. This research article discusses scattering transform as the efficient system for medical image analysis where it's figured by scattering the signal information implemented in a deep convolutional network. A step by step case study is manifested at this research work.

Newly discovered Pituitary Adenoma in the Emergency Center during the COVID-19 Pandemic in Serbia: Treatment and Early Postoperative Outcome (preprint)

Introduction:Pituitary tumors represent 10-15% of all intracranial tumors. Clinical manifestations depend on the size of the tumor such as microadenoma, macroadenoma or giant adenoma, and type of the tumor (secreting or non-secreting). Surgical treatment of pituitary adenoma can be managed via transcranial or transsphenoidal approach. Depending on the approach, there is a possibillity of postoperative complications such as meningitis, pneumocephalus, liquorrhea, transient diabetes insipidus and ect. Aim:The aim of this study was to establish the frequency of newly discovered pituitary tumors in the Emergency Center, University Clinical Center Of Serbia, during the COVID-19 pandemic in Serbia, and early postoperative complications in patients treated with an endoscopic and microscope transsphenoidal approach. Material and methods: During the time period during the pandemic, the study contains 119 patients, from January 1, 2020 to March 1, 2023, of which 64 are male (53.8%) and 55 are female (46.2%), age range is 14 to 85 years with a mean of 52.10. As for statistical analysis, assessment of frequency rate and relative numbers were used as methods of descriptive statistics. Results:Macroadenoma was present in 95 patients (79.83%), microadenoma in 22 patients (18.49%), and giant adenoma in 2 patients (1.69%). Transient DI developed postoperatively in 11 patients (9.24%). The average duration of hospitalization after surgery was 8 days. Conclusion: The duration of hospitalization depends on numerous factors where the COVID-19 pandemic can serve as an example for future similar crisis situations so that better organization and preoperative preparation of patients can be implemented.

Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing Tasks (preprint)

Feature-Imitating-Networks (FINs) are neural networks with weights that are initialized to approximate closed-form statistical features. In this work, we perform the first-ever evaluation of FINs for biomedical image processing tasks. We begin by training a set of FINs to imitate six common radiomics features, and then compare the performance of networks with and without the FINs for three experimental tasks: COVID-19 detection from CT scans, brain tumor classification from MRI scans, and brain-tumor segmentation from MRI scans; we find that FINs provide best-in-class performance for all three tasks, while converging faster and more consistently when compared to networks with similar or greater representational power. The results of our experiments provide evidence that FINs may provide state-of-the-art performance for a variety of other biomedical image processing tasks.

Pre-treatment Emotional Distress in Patients Irradiated for Malignant Glioma.

BACKGROUND/AIM: A recommendation of radiotherapy for patients with malignant gliomas may trigger emotional distress. Frequency and risk factors of this complication were investigated. PATIENTS AND METHODS: Prevalence of six emotional problems and 11 potential risk factors were evaluated in 103 patients irradiated for grade II-IV gliomas. p-Values <0.0045 were considered significant. RESULTS: Seventy-six patients (74%) had ≥1 emotional problem. Prevalence of specific emotional problems ranged between 23% and 63%. Associations were found between ≥5 physical problems and worry (p=0.0010), fear (p=0.0001), sadness (p=0.0023), depression (p=0.0006), and loss of interest (p=0.0006), and Karnofsky performance score ≤80 and depression (p=0.0002). Trends were found for physical problems and nervousness (p=0.040), age ≥60 years and depression (p=0.043) or loss of interest (p=0.045), grade IV glioma and sadness (p=0.042), and ≥2 involved sites and loss of interest (p=0.022). CONCLUSION: Three-fourths of glioma patients had pre-radiotherapy emotional distress. Psychological support should be offered very soon, particularly for high-risk patients.

Feasibility and preliminary efficacy of a virtual reality intervention targeting distress and anxiety in primary brain tumor patients at the time of clinical evaluation: Study protocol for a phase 2 clinical trial.

BACKGROUND: Primary brain tumor (PBT) patients experience higher levels of distress and anxiety than other solid tumor patients, particularly at the time of clinical evaluation when uncertainty about disease status is high ("scanxiety"). There is promising evidence supporting use of virtual reality (VR) to target psychological symptoms in other solid tumor patients, though PBT patients have not been studied extensively in this context. The primary aim of this phase 2 clinical trial is to establish the feasibility of a remote VR-based relaxation intervention for a PBT population, with secondary aims designed to determine preliminary efficacy of improving distress and anxiety symptoms. METHODS: PBT patients (N = 120) with upcoming MRI scans and clinical appointments who meet eligibility will be recruited to participate in a single arm trial conducted remotely through the NIH. Following completion of baseline assessments, participants will complete a 5-min VR intervention via telehealth using a head-mounted immersive device while under supervision of the research team. Following the intervention, over the course of 1 month patients can use VR at their discretion with follow-up assessments done immediately post-VR intervention, as well as 1 week and 4 weeks later. Additionally, a qualitative phone interview will be conducted to assess patient satisfaction with the intervention. DISCUSSION: Use of immersive VR is an innovative interventional approach to target distress and scanxiety symptoms in PBT patients who are at high risk for experiencing these symptoms leading into their clinical appointments. Findings from this study may inform design of a future multicenter randomized VR trial for PBT patients and may aid in development of similar interventions for other oncology populations. TRIAL REGISTRATION: Clinicaltrials.gov (NCT04301089), registered 9 March 2020.

Variation in postoperative outcomes of patients with intracranial tumors: insights from a prospective international cohort study during the COVID-19 pandemic.

BACKGROUND: This study assessed the international variation in surgical neuro-oncology practice and 30-day outcomes of patients who had surgery for an intracranial tumor during the COVID-19 pandemic. METHODS: We prospectively included adults aged ≥18 years who underwent surgery for a malignant or benign intracranial tumor across 55 international hospitals from 26 countries. Each participating hospital recorded cases for 3 consecutive months from the start of the pandemic. We categorized patients' location by World Bank income groups (high [HIC], upper-middle [UMIC], and low- and lower-middle [LLMIC]). Main outcomes were a change from routine management, SARS-CoV-2 infection, and 30-day mortality. We used a Bayesian multilevel logistic regression stratified by hospitals and adjusted for key confounders to estimate the association between income groups and mortality. RESULTS: Among 1016 patients, the number of patients in each income group was 765 (75.3%) in HIC, 142 (14.0%) in UMIC, and 109 (10.7%) in LLMIC. The management of 200 (19.8%) patients changed from usual care, most commonly delayed surgery. Within 30 days after surgery, 14 (1.4%) patients had a COVID-19 diagnosis and 39 (3.8%) patients died. In the multivariable model, LLMIC was associated with increased mortality (odds ratio 2.83, 95% credible interval 1.37-5.74) compared to HIC. CONCLUSIONS: The first wave of the pandemic had a significant impact on surgical decision-making. While the incidence of SARS-CoV-2 infection within 30 days after surgery was low, there was a disparity in mortality between countries and this warrants further examination to identify any modifiable factors.

Impact of Covid-19 pandemic on neuro-oncology multidisciplinary tumor board in the pre-vaccine era: the Normandy experience.

INTRODUCTION: The COVID19 pandemic had a strong impact on the healthcare system, particularly in oncology. Brain tumor are usually revealed by acute and life threatening symptoms. We wanted to evaluate the possible consequences of the COVID19 pandemic in 2020 on the activity of neuro-oncology multidisciplinary tumor board in a Normandy region (France). METHODS: A descriptive, retrospective, multicenter study was conducted in the four referent centers (two universitary hospitals and two cancer centers). The main objective was to compare the average number of neuro-oncology patients presented per multidisciplinary tumor board per week between a pre-COVID19 reference period (period 1 from December 2018 to December 2019) and the pre-vaccination period (period 2 from December 2019 to November 2020). RESULTS: Across Normandy, 1540 cases were presented in neuro-oncology multidisciplinary tumor board in 2019 and 2020. No difference was observed between period 1 and 2: respectively 9.8 per week versus 10.7, P=0.36. The number of cases per week also did not significantly differ during the lockdown periods: 9.1/week versus 10.4 during the non-lockdown periods, P=0.26. The only difference observed was a higher proportion of tumor resection during the lockdown periods: 81.4% (n=79/174) versus 64.5% (n=408/1366), P=0.001. CONCLUSION: The pre-vaccination era of the COVID19 pandemic did not impact the activity of neuro-oncology multidisciplinary tumor board in the Normandy region. The possible consequences in terms of public health (excess mortality) due to this tumor location should now be investigated.

Primary Human Herpes Virus-6 Causing Recalcitrant Pyrexia after Pilocytic Astrocytoma Resection.

INTRODUCTION: Human herpes virus-6 (HHV-6) is a ubiquitous virus but can lead to deleterious clinical manifestations due to its predilection for the pediatric central nervous system. Despite significant literature describing its common clinical course, it is rarely considered as a causative agent in CSF pleocytosis in the setting of craniotomy and external ventricular drainage device. Identification of a primary HHV-6 infection allowed for timely treatment with an antiviral agent along with earlier discontinuation of antibiotic regimen and expedited placement of a ventriculoperitoneal shunt. CASE PRESENTATION: A two-year-old girl presented with 3 months of progressive gait disturbance and intranuclear ophthalmoplegia. Following craniotomy for removal of 4th ventricular pilocytic astrocytoma and decompression of hydrocephalus, she suffered a prolonged clinical course due to persistent fevers and worsening CSF leukocytosis despite multiple antibiotic regimens. The patient was admitted to the hospital during the COVID-19 pandemic and isolated with her parents in the intensive care unit with strict infection control measures. FilmArray Meningitis/Encephalitis (FAME) panel ultimately detected HHV-6. Clinical confirmation of HHV-6-induced meningitis was proposed given improvement in CSF leukocytosis and fever reduction following the initiation of antiviral medications. Pathologic analysis of brain tumor tissue failed to show HHV-6 genome positivity, suggesting a primary peripheral etiology of infection. CONCLUSION: Here, we present the first known case of HHV-6 infection detected by FAME following intracranial tumor resection. We propose a modified algorithm for persistent fever of unknown origin which may decrease symptomatic sequelae, minimize additional procedures, and shorten length of ICU stay.

What Can We Still Learn from Brain Autopsies in COVID-19?

Neuropathological findings have been published from ∼900 patients who died with or from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, representing less than 0.01% of the close to 6.4 million deaths reported to the World Health Organization 2 years into the coronavirus disease 2019 (COVID-19) pandemic. In this review, we extend our prior work summarizing COVID-19 neuropathology by including information on published autopsies up to June 2022, and neuropathological studies in children, COVID-19 variants, secondary brain infections, ex vivo brain imaging, and autopsies performed in countries outside of the United States or Europe. We also summarize research studies that investigate mechanisms of neuropathogenesis in nonhuman primates and other models. While a pattern of cerebrovascular pathology and microglial-predominant inflammation remains the primary COVID-19-associated neuropathological finding, there is no singular understanding of the mechanisms that underlie neurological symptoms in acute COVID-19 or the post-acute COVID-19 condition. Thus, it is paramount that we incorporate microscopic and molecular findings from brain tissue into what we know about the clinical disease so that we attain best practice guidance and direct research priorities for the study of the neurological morbidity of COVID-19.

Impact of the SARS-CoV-2 pandemic on the survival of patients with high-grade glioma and best practice recommendations.

The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has changed the clinical day-to-day practice. The aim of this study was to evaluate the impact of the pandemic on patients with high-grade glioma (HGG) as well as to derive best practice recommendations. We compared a multi-institutional cohort with HGG (n = 251) from 03/2020 to 05/2020 (n = 119) to a historical cohort from 03/2019 to 05/2019 (n = 132). The endpoints were outcome (progression-free survival (PFS) and overall survival (OS)) as well as patterns of care and time intervals between treatment steps. The median OS for WHO grade 4 gliomas was 12 months in 2019 (95% Confidence Interval 9.7-14.3 months), and not reached in 2020 (p = .026). There were no other significant differences in the Kaplan-Meier estimates for OS and PFS between cohorts of 2019 and 2020, neither did stratification by WHO grade reveal any significant differences for OS, PFS or for patterns of care. The time interval between cranial magnetic resonance imaging (cMRI) and biopsy was significantly longer in 2020 cohort (11 versus 21 days, p = .031). Median follow-up was 10 months (range 0-30 months). Despite necessary disease containment policies, it is crucial to ensure that patients with HGG are treated in line with the recent guidelines and standard of care (SOC) algorithms. Therefore, we strongly suggest pursuing no changes to SOC treatment, a timely diagnosis and treatment with short time intervals between first symptoms, initial diagnosis, and treatment, as well as a guideline-based cMRI follow-up.

Treatment Patterns and Outcomes of Patients with Grade 4 Glioma Treated with Radiation during the COVID-19 Pandemic.

During the first year of the COVID-19 pandemic there was a global disruption in the provision of healthcare. Grade 4 gliomas are rapidly progressive tumors, and these patients are at risk of poorer outcomes due to delays in diagnosis or treatment. We retrospectively evaluated the impact of the pandemic on treatment patterns and outcomes of patients with grade 4 gliomas in British Columbia. We identified a cohort of 85 patients treated with radiotherapy between March 2020-2021 (COVID era) and compared baseline characteristics, treatments, and outcomes with a control cohort of 79 patients treated between March 2018-2019 (pre-COVID era). There were fewer patients treated with radiotherapy over age 65 in the COVID era compared to the pre-COVID era (p = 0.037). Significantly more patients were managed with biopsy relative to partial or gross total resection during the COVID era compared to the pre-COVID era (p = 0.04), but there were no other significant differences in time to assessment, time to treatment, or administration of adjuvant therapy. There was no difference in overall survival between eras (p = 0.189). In this assessment of outcomes of grade 4 gliomas during the pandemic, we found that despite less aggressive surgical intervention during the COVID era, outcomes were similar between eras.

MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis (preprint)

Coronavirus disease 2019 (COVID-19) has spread all over the world for three years, but medical facilities in many areas still aren't adequate. There is a need for rapid COVID-19 diagnosis to identify high-risk patients and maximize the use of limited medical resources. Motivated by this fact, we proposed the deep learning framework MEDNC for automatic prediction and diagnosis of COVID-19 using computed tomography (CT) images. Our model was trained using two publicly available sets of COVID-19 data. And it was built with the inspiration of transfer learning. Results indicated that the MEDNC greatly enhanced the detection of COVID-19 infections, reaching an accuracy of 98.79% and 99.82% respectively. We tested MEDNC on a brain tumor and a blood cell dataset to show that our model applies to a wide range of problems. The outcomes demonstrated that our proposed models attained an accuracy of 99.39% and 99.28%, respectively. This COVID-19 recognition tool could help optimize healthcare resources and reduce clinicians' workload when screening for the virus.

Regulatory role of endoplasmic reticulum resident chaperone protein ERp29 in anti-murine ß-coronavirus host cell response.

Gap junctional intercellular communication (GJIC) involving astrocytes is important for proper CNS homeostasis. As determined in our previous studies, trafficking of the predominant astrocyte GJ protein, Connexin43 (Cx43), is disrupted in response to infection with a neurotropic murine ß-coronavirus (MHV-A59). However, how host factors are involved in Cx43 trafficking and the infection response is not clear. Here, we show that Cx43 retention due to MHV-A59 infection was associated with increased ER stress and reduced expression of chaperone protein ERp29. Treatment of MHV-A59-infected astrocytes with the chemical chaperone 4-sodium phenylbutyrate increased ERp29 expression, rescued Cx43 transport to the cell surface, increased GJIC, and reduced ER stress. We obtained similar results using an astrocytoma cell line (delayed brain tumor) upon MHV-A59 infection. Critically, delayed brain tumor cells transfected to express exogenous ERp29 were less susceptible to MHV-A59 infection and showed increased Cx43-mediated GJIC. Treatment with Cx43 mimetic peptides inhibited GJIC and increased viral susceptibility, demonstrating a role for intercellular communication in reducing MHV-A59 infectivity. Taken together, these results support a therapeutically targetable ERp29-dependent mechanism where ß-coronavirus infectivity is modulated by reducing ER stress and rescuing Cx43 trafficking and function.

Development and validation of MedDRA Tagger: a tool for extraction and structuring medical information from clinical notes (preprint)

Rapid and automated extraction of clinical information from patients' notes is a desirable though difficult task. Natural language processing (NLP) and machine learning have great potential to automate and accelerate such applications, but developing such models can require a large amount of labeled clinical text, which can be a slow and laborious process. To address this gap, we propose the MedDRA tagger, a fast annotation tool that makes use of industrial level libraries such as spaCy, biomedical ontologies and weak supervision to annotate and extract clinical concepts at scale. The tool can be used to annotate clinical text and obtain labels for training machine learning models and further refine the clinical concept extraction performance, or to extract clinical concepts for observational study purposes. To demonstrate the usability and versatility of our tool, we present three different use cases: we use the tagger to determine patients with a primary brain cancer diagnosis, we show evidence of rising mental health symptoms at the population level and our last use case shows the evolution of COVID-19 symptomatology throughout three waves between February 2020 and October 2021. The validation of our tool showed good performance on both specific annotations from our development set (F1 score 0.81) and open source annotated data set (F1 score 0.79). We successfully demonstrate the versatility of our pipeline with three different use cases. Finally, we note that the modular nature of our tool allows for a straightforward adaptation to another biomedical ontology. We also show that our tool is independent of EHR system, and as such generalizable.

Moderately hypofractionated versus conventionally fractionated radiation therapy with temozolomide for young and fit patients with glioblastoma: an institutional experience and meta-analysis of literature.

PURPOSE: Shorter hypofractionated radiation therapy (HF-RT) schedules may have radiobiological, patient convenience and healthcare resource advantages over conventionally fractionated radiation therapy (CF-RT) in glioblastoma (GBM). We report outcomes of young, fit GBM patients treated with HF-RT and CF-RT during the COVID-19 pandemic, and a meta-analysis of HF-RT literature in this patient subgroup. METHODS: Hospital records of patients with IDH-wildtype GBM treated with HF-RT (50 Gy/20 fractions) and CF-RT (60 Gy/30 fractions) between January 2020 and September 2021 were reviewed. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan-Meier method. Univariable analysis was performed using Cox regression analysis. A systematic search and meta-analysis of studies from January 2000 to January 2022 was performed. RESULTS: 41 patients were treated (HF-RT:15, CF-RT:26). For both HF-RT and CF-RT groups, median age was 58 years and 80-90% were ECOG 0-1. There were more methylated tumours in the HF-RT group. All patients received concurrent/adjuvant temozolomide. At 19.2 months median follow-up, median OS was 19.8 months and not-reached for HF-RT and CF-RT (p = 0.5), and median PFS was 7.7 and 5.8 months, respectively (p = 0.8). HF-RT or CF-RT did not influence OS/PFS on univariable analysis. Grade 3 radionecrosis rate was 6.7% and 7.7%, respectively. 15 of 1135 studies screened from a systematic search were eligible for meta-analysis. For studies involving temozolomide, pooled median OS and PFS with HF-RT were 17.5 and 9.9 months (927 and 862 patients). Studies using shortened HF-RT schedules reported 0-2% Grade 3 radionecrosis rates. CONCLUSION: HF-RT may offer equivalent outcomes and reduce treatment burden compared to CF-RT in young, fit GBM patients.

Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.

Influence of COVID-19 pandemic on the decision making of patients in undergoing gamma knife radiosurgery.

PURPOSE: Gamma knife radiosurgery (GK) is a commonly used approach for the treatment of intracranial lesions. Its radiation response is typically not immediate, but delayed. In this study, we analyzed cases from a prospectively collected database to assess the influence of COVID-19 pandemic on the decision making in patients treated by gamma knife radiosurgery. METHODS: From January 2019 to August 2021, 540 cases of intracranial lesions were treated by GK with 207 cases before COVID-19 pandemic as a control. During the COVID-19 pandemic, 333 cases were similarly treated on patients with or without the COVID-19 vaccination. All the GK treated parameters as well as time profile in the decision making were analyzed. The parameters included age, sex, characteristic of lesion, targeted volume, peripheral radiation dose, neurological status, Karnofsky Performance Status (KPS), time interval from MRI diagnosis to consultation, time interval from the approval to treatment, frequency of outpatient department (OPD) visit, and frequency of imaging follow-up. RESULTS: Longer time intervals from diagnosis to GK consultation and treatment were found in the pandemic group (36.8 ± 25.5/54.5 ± 27.6 days) compared with the pre-COVID control (17.1 ± 22.4/45.0 ± 28.0 days) or vaccination group (12.2 ± 7.1/29.6 ± 10.9 days) (p < 0.001, and p < 0.001, respectively). The fewer OPD visits and MRI examinations also showed the same trends. High proportion of neurological deficits were found in the pandemic group (65.4%) compared with the control (45.4%) or vaccination group (58.1%) (p < 0.001). The Charlson comorbidity in the pandemic group was 3.9 ± 3.3, the control group was 4.6 ± 3.2, and the vaccination group was 3.1 ± 3.1. There were similar inter-group difference (p < 0.001). In multiple variant analyses, longer time intervals from the diagnosis to consultation or treatment, OPD frequency and MRI examination were likely influenced by the status of the COVID-19 pandemic as they were alleviated by the vaccination. CONCLUSIONS: The decision making in patients requiring gamma knife treatment was most likely influenced by the status of the COVID-19 pandemic, while vaccination appeared to attenuate their hesitant behaviors. Patients with pre-treatment neurological deficits and high co-morbidity undergoing the gamma knife treatment were less affected by the COVID-19 pandemic.

Neurosurgical Services in the Northern Zone of Sarawak in Malaysia: The Way Forward Amid the COVID-19 Pandemic.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) pandemic has set a huge challenge to the delivery of neurosurgical services, including the transfer of patients. We aimed to share our strategy in handling neurosurgical emergencies at a remote center in Borneo island. Our objectives included discussing the logistic and geographic challenges faced during the COVID-19 pandemic. METHODS: Miri General Hospital is a remote center in Sarawak, Malaysia, serving a population with difficult access to neurosurgical services. Two neurosurgeons were stationed here on a rotational basis every fortnight during the pandemic to handle neurosurgical cases. Patients were triaged depending on their urgent needs for surgery or transfer to a neurosurgical center and managed accordingly. All patients were screened for potential risk of contracting COVID-19 prior to the surgery. Based on this, the level of personal protective equipment required for the health care workers involved was determined. RESULTS: During the initial 6 weeks of the Movement Control Order in Malaysia, there were 50 urgent neurosurgical consultations. Twenty patients (40%) required emergency surgery or intervention. There were 9 vascular (45%), 5 trauma (25%), 4 tumor (20%), and 2 hydrocephalus cases (10%). Eighteen patients were operated at Miri General Hospital, among whom 17 (94.4%) survived. Ninety percent of anticipated transfers were avoided. None of the medical staff acquired COVID-19. CONCLUSIONS: This framework allowed timely intervention for neurosurgical emergencies (within a safe limit), minimized transfer, and enabled uninterrupted neurosurgical services at a remote center with difficult access to neurosurgical care during a pandemic.