Initial antihypertensive agent effects on acute blood pressure after intracerebral haemorrhage.

INTRODUCTION: Current guidelines recommend blood pressure (BP) lowering in patients after acute intracerebral haemorrhage (ICH) without guidance on initial choice of antihypertensive class. This study sought to determine if initial antihypertensive class differentially effects acute BP lowering in a large multiethnic ICH cohort. METHODS: Subjects enrolled in the Ethnic/Racial Variations in ICH study between August 2010 and August 2017 with elevated admission BP and who received labetalol, nicardipine or hydralazine monotherapy as initial antihypertensive were analysed. Primary outcomes were systolic and diastolic BP changes from baseline to first BP measurement after initial antihypertensive treatment. Secondary outcomes included haematoma expansion (HE), hospital length of stay (LOS) and modified Rankin Score (mRS) up to 12 months after ICH. Exploratory outcomes assessed effects of race/ethnicity. Linear and logistic regression analyses, adjusted for relevant covariates, were performed to determine associations of antihypertensive class with outcomes. RESULTS: In total, 1156 cases were used in analyses. Antihypertensive class was associated with diastolic BP change (p=0.003), but not systolic BP change (p=0.419). Initial dosing with nicardipine lowered acute diastolic BP than labetalol (least square mean difference (labetalol-nicardipine)=5.47 (2.37, 8.57), p<0.001). Initial antihypertensive class was also found to be associated with LOS (p=0.028), but not with HE (p=0.406), mortality (p=0.118), discharge disposition (p=0.083) or mRS score at discharge, 3, 6 and 12 months follow-up (p=0.262, 0.276, 0.152 and 0.36, respectively). Race/ethnicity variably affected multivariable models. CONCLUSION: In this large acute ICH cohort, initial antihypertensive class was associated with acute diastolic, but not systolic, BP-lowering suggesting differential effects of antihypertensive agents. TRIAL REGISTRATION NUMBER: NCT01202864.

Identifying risk factors of intracerebral hemorrhage stability using explainable attention model.

Segmentation of intracerebral hemorrhage (ICH) helps improve the quality of diagnosis, draft the desired treatment methods, and clinically observe the variations with healthy patients. The clinical utilization of various ICH progression scoring systems has limitations due to the systems' modest predictive value. This paper proposes a single pipeline of a multi-task model for end-to-end hemorrhage segmentation and risk estimation. We introduce a 3D spatial attention unit and integrate it into the state-of-the-art segmentation architecture, UNet, to enhance the accuracy by bootstrapping the global spatial representation. We further extract the geometric features from the segmented hemorrhage volume and fuse them with clinical features such as CT angiography (CTA) spot, Glasgow Coma Scale (GCS), and age to predict the ICH stability. Several state-of-the-art machine learning techniques such as multilayer perceptron (MLP), support vector machine (SVM), gradient boosting, and random forests are applied to train stability estimation and to compare the performances. To align clinical intuition with model learning, we determine the shapely values (SHAP) and explain the most significant features for the ICH risk scoring system. A total of 79 patients are included, of which 20 are found in critical condition. Our proposed single pipeline model achieves a segmentation accuracy of 86.3%, stability prediction accuracy of 78.3%, and precision of 82.9%; the mean square error of exact expansion rate regression is observed to be 0.46. The SHAP analysis reveals that CTA spot sign, age, solidity, location, and length of the first axis of the ICH volume are the most critical characteristics that help define the stability of the stroke lesion. We also show that integrating significant geometric features with clinical features can improve the ICH progression scoring by predicting long-term outcomes. Graphical abstract Overview of our proposed method comprising of spatial attention and feature extraction mechanisms. The architecture is trained on the input CT images, and the first step output is the predicted segmentation of the hemorrhagic region. The output is fed into a geometric feature extractor and is fused with clinical features to estimate ICH stability using a multilayer perceptron (MLP).

An open label randomized trial to assess the efficacy of tranexamic acid in reducing post-operative recurrence of chronic subdural haemorrhage.

Chronic subdural haemorrhage (CSDH) is a common neurosurgical entity with complex pathophysiological pathways. The generally favourable surgical outcome may be affected by its associated risks including recurrence rates. We performed a prospective randomized multi-center clinical trial comparing the addition of tranexamic acid (TXA) to standard neurosurgical procedures for patients with symptomatic CSDH. The primary endpoint was CSDH requiring repeat surgery within 6-month post-operatively. Secondary endpoints were comparison of post-operative volumes between the treatment arms and safety evaluation of the dosing regime. 90 patients were analyzed with 49 patients in the observation arm and 41 patients in the TXA arm. The observation arm had five (10.2%) recurrences compared to two (4.8%, p = 0.221) in the TXA arm. Patients in the TXA arm demonstrated a greater reduction of their CSDH volume at 6 weeks follow up (36.6%) compared to the observation arm (23.3%, p = 0.6648). There were no reportable serious adverse events recorded in the observation arm, compared to 4 (9.8%) patients in the TXA arm. The addition of TXA treatment to standard surgical drainage of CSH did not significantly reduce symptomatic post-operative recurrence. Patients in the TXA arm had a delay in the CSDH recurrence with a comparative reduction of residual hematoma volume at the 6-week follow up although the effect was unsustained. Larger randomized trials with dose adjustments should be considered to investigate subgroups of patients that may benefit from this medical adjunct.

Neurological manifestations in COVID-19: a systematic review and meta-analysis.

OBJECTIVE: Following the outbreak of coronavirus 2019 (COVID-19), there is strong evidence of neurological involvement in these patients. We aimed to determine the clinical characteristics of neurological manifestations in COVID-19. METHOD: A systematic review of studies reporting neurological manifestations published between 1 December, 2019 and 11 May, 2020 was performed. Studies were grouped based on neurological manifestation. Pooled analyses of individual patient's clinical characteristics and olfactory and gustatory dysfunction prevalence were performed. RESULTS: Of 486 studies identified, 48 were included. 70 patients with 73 neurological manifestations were reported. 39 (53.4%) patients had stroke, 18 (24.7%) had Guillain-Barré syndrome and variants, 11 (15.1%) had meningitis, encephalitis, encephalopathy, or myelitis, and five (6.8%) had seizures. They had a mean age of 61.9 ± 17.7 years (60.6% male). Neurological disease occurred 8.1 ± 6.8 days from initial symptoms. Average mortality rate was 17.8%. Stroke has a mortality rate of 25.6%. Olfactory and gustatory dysfunction occurred in 59.9% and 57.5%, respectively. CONCLUSIONS: Stroke is the most frequently reported neurological manifestation in COVID-19 and has the highest mortality rate. Neurological manifestations tend to develop one to two weeks after the onset of respiratory disease. There is significant morbidity and mortality associated with COVID-19 neurological manifestations.

Automated detection of subthalamic nucleus in deep brain stimulation surgery for Parkinson's disease using microelectrode recordings and wavelet packet features.

BACKGROUND: Deep brain stimulation (DBS) to the subthalamic nucleus (STN) is an effective neurosurgery that overcomes the motor system alternations of patients with advanced Parkinson's disease. The most challenging aspect of DBS surgery is the accurate identification of STN and its borders. In general, it is performed manually by a neurophysiologist using the microelectrode recordings (MERs). This process is subjective, and tedious and further, interpretation of MERs is difficult because of its inherent nonstationary variations. NEW METHODS: In this work, the wavelet-packet based features are proposed to automatically localize the STN and its subcortical structures using microelectrode recorded signals during DBS surgery. The study analyses 2904 MERs of 26 PD patients who underwent DBS implantation. The low and high order statistical parameters are extracted from the wavelet packet coefficients of MERs and used in the classifications, namely, non-STN vs. STN, pre-STN vs. STN and STN vs. post-STN. RESULTS: Most of the features are significantly different in STN and its subcortical regions, namely, pre-STN and post-STN. The proposed features achieve an average accuracy of 85 % in non-STN vs. STN, 87.2 % in pre-STN vs. STN and 77.7 % in STN vs. post-STN. The accuracy is improved by around 10 % in non-STN vs. STN and STN vs. post-STN when the transition error is 1 mm. COMPARISON WITH EXISTING METHODS: The proposed features are found to be better than the wavelet features. CONCLUSIONS: The proposed approach could be a potential useful adjunct for the real-time rapid intraoperative identification of STN and its anatomical borders.

Longitudinal medication profile and cost savings in Parkinson's disease patients after bilateral subthalamic nucleus deep brain stimulation.

INTRODUCTION: Deep brain stimulation of the subthalamic nucleus (STN DBS) has been shown to reduce antiparkinsonian medication in Parkinson's disease. We aimed to investigate the changes in long-term medication profile with STN DBS. METHODS: Antiparkinsonian medication data for 56 patients were collected from as early as 3 years before STN DBS up to 10 years after. Cost spending on medication changes was analyzed. Mean levodopa equivalent daily dose (LEDD) was projected 10 years into the future based on preoperative data to create a comparator group wherein the patients did not undergo STN DBS. Use of neuroleptics and antidepressants was also recorded. RESULTS: LEDD requirement was significantly reduced by a mean of 31 ± 2% over 10 years after DBS, from 1049 ± 381 mg at pre-DBS baseline, to 713 ± 392 mg at 1 year post-DBS, and 712 ± 385 mg at 10 years post-DBS. This was associated with a mean reduction of 35 ± 3% in medicine cost. Modeled LEDD requirements for not having STN DBS were in the range of 1489 mg to 2721 mg at 10 years post-DBS (109-282% higher than the observed mean LEDD in DBS cohort). The proportion of patients increased from 5% before STN DBS to 14% at 10 year post-DBS for neuroleptics, and 11-23% for antidepressants. CONCLUSION: STN DBS led to LEDD reduction and antiparkinsonian medication cost savings in our South-East Asian cohort. Medication reduction with STN DBS in our cohort over the 10-year period was comparable to those reported in Western populations.

Selective Serotonin Reuptake Inhibitors and Intracerebral Hemorrhage Risk and Outcome.

Background and Purpose- Selective serotonin reuptake inhibitors (SSRIs) have a well-established association with bleeding complications and conflicting reports on outcome after stroke. We sought to evaluate whether pre-intracerebral hemorrhage (ICH) SSRI use increased ICH risk and post-ICH SSRI use improved ICH outcome. Methods- Through post hoc analysis of the ERICH study (Ethnic/Racial Variations of Intracerebral Hemorrhage), SSRI use was categorized into no use, pre-ICH only, pre- and post-ICH use (termed "continuous"), and post-ICH only (termed "new"). Using multivariable modeling, associations were sought between pre-ICH SSRI use and ICH risk in the case-control set, and associations between post-ICH SSRI use and 3-month outcome were analyzed in the ICH case set. Exploratory analyses sought to assess influence of race/ethnicity in models. Results- The final study cohort consisted of 2287 ICH cases and 2895 controls. Pre-ICH SSRI use was not associated with ICH risk (odds ratio, 0.824 [95% CI, 0.632-1.074]) nor potentiation of ICH risk with anticoagulant or antiplatelet use. New post-ICH SSRI use was associated with unfavorable modified Rankin Scale score at 3 months after ICH (odds ratio, 1.673 [95% CI, 1.162-2.408]; P=0.006) in multivariable analyses. Additional propensity score analysis indicated a similar trend but did not reach statistical significance (P=0.107). When stratified by race/ethnicity, multivariable modeling demonstrated reduced ICH risk with pre-ICH SSRI use in Hispanics (odds ratio, 0.513 [95% CI, 0.301-0.875]; P=0.014), but not non-Hispanic whites or blacks, and no associations between post-ICH SSRI use and 3-month outcome in any racial/ethnic group. Conclusions- In a large multiethnic cohort, pre-ICH SSRI use was not associated with increased ICH risk, but post-ICH SSRI use was associated with unfavorable 3-month neurological outcome after ICH. Registration- URL: https://www.clinicaltrials.gov; Unique identifier: NCT01202864.

Incidence of biomarkers in high-grade gliomas and their impact on survival in a diverse SouthEast Asian cohort - a population-based study.

BACKGROUND: Gliomas consist of a heterogeneous group of tumors. This study aimed to report the incidences of O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, 1p19q co-deletion, isocitrate dehydrogenase (IDH) gene mutations, and inactivating mutations of alpha-thalassemia/mental retardation syndrome X-linked (ATRX) in high-grade gliomas in an ethnically diverse population. METHODS: Records of patients who underwent surgery for high-grade gliomas from January 2013 to March 2017 at our institution were obtained. The patients' age, gender, ethnicity, Karnofsky Performance Scale (KPS) score, ability to perform activities of daily living (ADLs), tumor location and biomarkers status were recorded. Data were analyzed using chi-square and Mann-Whitney U tests, Kaplan-Meier estimates and log-rank test. RESULTS: 181 patients were selected (56 with grade III gliomas, 125 with grade IV gliomas). In the grade III group, 55% had MGMT promoter methylation, 41% had 1p19q co-deletion, 35% had IDH1 mutation and none had ATRX loss. In the grade IV group, 30% had MGMT promoter methylation, 2% had 1p19q co-deletion, 15% had IDH1 mutation and 8% had ATRX loss. After adjusting for effects of age, surgery and pre-operative ADL statuses, only MGMT promoter methylation was found to be significantly associated with longer overall survival time in grade III (p = 0.024) and IV patients (p = 0.006). CONCLUSIONS: The incidences of MGMT promoter methylation and IDH1 mutation were found to be comparable to globally reported rates, but those of 1p19q co-deletion and ATRX loss seemed to be lower in our cohort. MGMT promoter methylation was associated with increased overall survival in our cohort and might serve as favorable prognostic factor.

Survey on Willingness to Pay for Life-Saving Treatment, Functional Recovery, and Cosmesis in a Neuroscience Outpatient Clinic Setting in Singapore.

BACKGROUND: Neurologic disorders impose a heavy burden on healthcare in Singapore. To date, no data on the willingness to pay (WTP) for neurologic treatments has been reported in the local population. OBJECTIVES: We aimed to quantify the value of various health domains to neuroscience patients and their caregivers by comparing their WTP for different types of treatments. METHODS: A questionnaire using a mixed open-ended and closed-ended contingent valuation method was developed to elicit WTP and self-administered by 112 visitors to a neuroscience outpatient clinic. The WTP for treatments in 3 health domains (advanced restoration of function, life extension, and cosmesis) was evaluated and compared. Subgroup regression analysis was performed to investigate the impact of demographic and socioeconomic factors. RESULTS: Treatment that improved cosmesis had the highest median WTP of Singapore dollar (SGD) 35 000, followed by treatment that provided 1 year of life extension (SGD 20 000) and 1 year of advanced restored function (SGD 10 000; P < .001). Respondents with a university education were willing to pay as much as 2 to 3 times of those without across all health domains. CONCLUSION: This is the first study to provide data on how different health domains are valued by neuroscience patients and caregivers in our population. Respondents valued treatment that restored or improved their physical appearances the most. These findings could contribute to future policies on the improvement of neuroscience care.

Effects of anticoagulant and antiplatelet agents in severe traumatic brain injury in an asian population - A matched case-control study.

The use of anticoagulation and antiplatelet agents (ACAP) has steadily increased over recent years. However, the effects of ACAP on traumatic brain injuries (TBI) are not well investigated. The aim of this study was to investigate the effects of pre-injury ACAP use on clinical outcome and mortality in severe TBI. A retrospective case-control study was performed for all patients who presented with severe TBI (GCS < 8) to the National Neuroscience Institute, Singapore, between 2006 and 2009. Patients with pre-injury ACAP use were compared to matched controls. Outcome measures were mortality at 14 days and 6 months, and Glasgow Outcome Score (GOS) at 6 months using a sliding dichotomy approach. Univariate analysis was performed using Chi-square and student's t-test and logistic regression was used to model the effect of ACAP on mortality rate. Forty-five patients with pre-injury use of ACAP were compared with matched controls. The mortality at 14 days (OR = 0.5, 95% CI 0.2-1.4) and 6 months (OR = 0.7, 95% CI 0.2-1.9) were not significantly different between the 2 groups. Using the sliding dichotomy approach, there was no difference in the odds for unfavorable functional outcomes at 6 months (OR = 1.2, 95% CI 0.4-3.7). In this case-control study, the use of ACAP did not have a significant effect on mortality and adverse outcomes in patients with severe TBI. This would suggest that in severe TBI, ACAP use may not contribute significantly to the overall prognosis.

Regression based overall survival prediction of glioblastoma multiforme patients using a single discovery cohort of multi-institutional multi-channel MR images.

Glioblastoma multiforme (GBM) are malignant brain tumors, associated with poor overall survival (OS). This study aims to predict OS of GBM patients (in days) using a regression framework and assess the impact of tumor shape features on OS prediction. Multi-channel MR image derived texture features, tumor shape, and volumetric features, and patient age were obtained for 163 GBM patients. In order to assess the impact of tumor shape features on OS prediction, two feature sets, with and without tumor shape features, were created. For the feature set with tumor shape features, the mean prediction error (MPE) was 14.6 days and its 95% confidence interval (CI) was 195.8 days. For the feature set excluding shape features, the MPE was 17.1 days and its 95% CI was observed to be 212.7 days. The coefficient of determination (R2) value obtained for the feature set with shape features was 0.92, while it was 0.90 for the feature set excluding shape features. Although marginal, inclusion of shape features improves OS prediction in GBM patients. The proposed OS prediction method using regression provides good accuracy and overcomes the limitations of GBM OS classification, like choosing data-derived or pre-decided thresholds to define the OS groups. Graphical abstract Two feature sets: with and without tumor shape features were extracted from T1-weighted contrast-enhanced, T2-weighted and FLAIR MRI. These feature sets were analyzed using the Mean Prediction Error (MPE) and its 95% Confidence Interval (CI) obtained from the Bland-Altman plot, along with the coefficient of determination (R2) value to assess the impact of tumor shape features on overall survival prediction of glioblastoma multiforme patients.

External Validation of Hematoma Expansion Scores in Spontaneous Intracerebral Hemorrhage in an Asian Patient Cohort.

BACKGROUND: Hematoma expansion (HE) occurs in approximately one-third of patients with intracerebral hemorrhage (ICH) and is known to be a strong predictor of neurological deterioration as well as poor functional outcome. This study aims to externally validate three risk prediction models of HE (PREDICT, 9-point, and BRAIN scores) in an Asian population. METHODS: A prospective cohort of 123 spontaneous ICH patients admitted to a tertiary hospital (certified stroke center) in Singapore was recruited. Logistic recalibrations were performed to obtain updated calibration slopes and intercepts for all models. The discrimination (c-statistic), calibration (Hosmer-Lemeshow test, le Cessie-van Houwelingen-Copas-Hosmer test, Akaike information criterion), overall performance (Brier score, R2), and clinical usefulness (decision curve analysis) of the risk prediction models were examined. RESULTS: Overall, the recalibrated PREDICT performed best among the three models in our study cohort based on the novel matrix comprising of Akaike information criterion and c-statistic. The PREDICT model had the highest R2 (0.26) and lowest Brier score (0.14). Decision curve analyses showed that recalibrated PREDICT was more clinically useful than 9-point and BRAIN models over the greatest range of threshold probabilities. The two scores (PREDICT and 9-point) which incorporated computed tomography (CT) angiography spot sign outperformed the one without (BRAIN). CONCLUSIONS: To our knowledge, this is the first study to validate HE scores, namely PREDICT, 9-Point and BRAIN, in a multi-ethnic Asian ICH patient population. The PREDICT score was the best performing model in our study cohort, based on the performance metrics employed in this study. Our findings also showed support for CT angiography spot sign as a predictor of outcome after ICH. Although the models assessed are sufficient for risk stratification, the discrimination and calibration are at best moderate and could be improved.

Effects of surgery on neurocognitive function in patients with glioma: a meta-analysis of immediate post-operative and long-term follow-up neurocognitive outcomes.

PURPOSE: This study aims to identify the neuropsychological tests commonly used for assessment in each neurocognitive domain, and quantify the post-operative changes in neurocognitive function in the immediate post-operation and follow-up. METHODS: With the use of the PubMed, a comprehensive search of the English literature was performed following PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines. There were 1021 publications identified for screening. Standardized mean differences (SMD) in neuropsychological task performance were calculated both for immediate post-operation (up to 1 week) and follow-up (up to 6 months). RESULTS: Out of 12 studies which met the inclusion criteria, 11 studies were analyzed in this meta-analysis, with a total of 313 patients (age range 18-82, 50% males) with intracranial gliomas (45% high-grade, 55% low-grade). Complex attention, language and executive function were the most frequently tested neurocognitive domains. Surgery had a positive impact in the domains of complex attention, language, learning and memory tasks in the immediate post-operative period and sustained improvement at follow-up. In contrast, surgery was found to negatively impact performance for executive function in the immediate post-operative period with sustained decline in performance in the long term. CONCLUSIONS: This meta-analysis suggests that surgery for glioma confers a benefit for the domains of complex attention, language, learning and memory, while negatively affecting executive function, in the periods immediately after surgery and at 6 months follow-up. In addition, awake surgery seemed to confer a beneficial effect on neurocognitive functions. Future research should attempt to standardize a battery of neuropsychological tests for patients undergoing surgical resection for glioma, perhaps with a particular focus on executive function.

A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson's disease.

OBJECTIVE: This study seeks to systematically review the selection of features and algorithms for machine learning and automation in deep brain stimulation surgery (DBS) for Parkinson's disease. This will assist in consolidating current knowledge and accuracy levels to allow greater understanding and research to be performed in automating this process, which could lead to improved clinical outcomes. METHODS: A systematic literature review search was conducted for all studies that utilized machine learning and DBS in Parkinson's disease. RESULTS: Ten studies were identified from 2006 utilizing machine learning in DBS surgery for Parkinson's disease. Different combinations of both spike independent and spike dependent features have been utilized with different machine learning algorithms to attempt to delineate the subthalamic nucleus (STN) and its surrounding structures. CONCLUSION: The state-of-the-art algorithms achieve good accuracy and error rates with relatively short computing time, however, the currently achievable accuracy is not sufficiently robust enough for clinical practice. Moreover, further research is required for identifying subterritories of the STN. SIGNIFICANCE: This is a comprehensive summary of current machine learning algorithms that discriminate the STN and its adjacent structures for DBS surgery in Parkinson's disease.

Detection of Subthalamic Nucleus using Time-Frequency Features of Microelectrode recordings and Random Forest Classifier.

Accurate localization of subthalamic nucleus (STN) is a key prior in deep brain stimulation (DBS) surgery for the patients with advanced Parkinson's disease (PD). Microelectrode recordings (MERs) along with preplanned trajectories are often employed for the STN localization and it remains challenging task. These MER signals are nonstationary and multicomponent in nature. In this study, we propose a system based on time-frequency features of MERs to differentiate the STN and non-STN regions. We assessed the system with 50 MER trajectories from 26 PD patients who have undergone DBS surgery. The signals are pre-processed and subjected to six-level wavelet decomposition. Then, the entropy is computed from the detailed and approximate coefficients. These features are fed to the random forest classifier and the model is evaluated by leave one patient out cross-validation. The results show that entropy associated with detailed wavelet coefficients (D1and D2) are higher in STN where as it is lower in other wavelet scales. All extracted features except entropy from approximate coefficients are found to have significant difference between non-STN and STN (p<; 0.05). The random forest classifier achieves about 83% accuracy and 87% precision in differentiating the STN and non-STN regions.

Overall survival prediction in glioblastoma multiforme patients from volumetric, shape and texture features using machine learning.

Glioblastoma multiforme (GBM) are aggressive brain tumors, which lead to poor overall survival (OS) of patients. OS prediction of GBM patients provides useful information for surgical and treatment planning. Radiomics research attempts at predicting disease prognosis, thus providing beneficial information for personalized treatment from a variety of imaging features extracted from multiple MR images. In this study, MR image derived texture features, tumor shape and volumetric features, and patient age were obtained for 163 patients. OS group prediction was performed for both 2-class (short and long) and 3-class (short, medium and long) survival groups. Support vector machine classification based recursive feature elimination method was used to perform feature selection. The performance of the classification model was assessed using 5-fold cross-validation. The 2-class and 3-class OS group prediction accuracy obtained were 98.7% and 88.95% respectively. The shape features used in this work have been evaluated for OS prediction of GBM patients for the first time. The feature selection and prediction scheme implemented in this study yielded high accuracy for both 2-class and 3-class OS group predictions. This study was performed using routinely acquired MR images for GBM patients, thus making the translation of this work into a clinical setup convenient.

Long-term recovery profile of patients with severe disability or in vegetative states following severe primary intracerebral hemorrhage.

PURPOSE: We conducted a single-center retrospective review to investigate the long-term recovery of patients who were severely disabled or vegetative secondary to primary intracerebral hemorrhage upon discharge from hospital from January 2009 to November 2013. METHODS: Patients were categorized into two groups based on their Glasgow outcome scale (GOS) scores at discharge, namely vegetative state (GOS 2; n = 91) and severely disabled (GOS 3; n = 278). Long-term outcomes at three years post discharge were defined as death, stable, deterioration and improvement from discharge to follow-up. RESULTS: Lower mortality (29% versus 69%) and higher neurological improvement rates at three years (33% versus 10%) were observed in the SD compared to VS group (both p = .0001). Age was a significant predictor of survival in the VS group (p = .03) and the SD group (p = .012). Age was also the only predictor of neurological improvement in the SD group (p = .01). CONCLUSIONS: Neurological status at discharge from hospital was not truly indicative of long-term prognosis for patients who were severely disabled or vegetative. Patients in both groups can potentially improve in the long term and may benefit from prolonged rehabilitation programmes to maximize their recovery potential.

Awake Craniotomy for Resection of Brain Metastases: A Systematic Review.

BACKGROUND: Surgery for brain metastases aims to reduce mass effect and achieve local control through maximizing resection. There is increasing recognition that awake craniotomy (AC) is especially relevant for resection of brain metastases in eloquent areas. This study seeks to examine the neurologic outcomes of using AC for brain metastases resection. METHODS: A systematic search for studies examining the role of AC in patients with brain metastases was conducted via PubMed without limitations on the year of publication, language, or study design, using the following search terms: (cerebral OR brain) and (metastases OR tumor) and (awake OR intraoperative OR eloquent OR supramarginal). Studies were included if patients underwent AC for resection of brain metastases and data on pre- and postoperative neurologic function were available. RESULTS: Seven studies published between 2001 and 2017 with 104 patients who underwent 107 ACs were identified. Gross total resection was achieved in 61% of patients, supramarginal resection was achieved in 32%, and subtotal resection was achieved in 7%. Immediately after operation, 73% of patients experienced no change or improvement in neurologic outcomes, whereas 27% experienced worsening. In the long term, 96% of those with postoperative worsening of function experienced improvements in neurologic function. CONCLUSIONS: Most patients experienced improvements in neurologic function immediately after AC, and most patients that experienced short-term worsening of neurologic function after the procedure showed excellent recovery. AC should be considered as a technique to optimize outcomes in brain metastases in eloquent areas.

A Skull-Mounted Robot with a Compact and Lightweight Parallel Mechanism for Positioning in Minimally Invasive Neurosurgery.

Robotic systems play an increasingly important role in improving feasibility and effectiveness of minimally invasive neurosurgery (MIN). However, large footprint, bulky size, and complex mechanisms limit the clinical application of existing robotic neurosurgery solutions. This paper proposes a novel skull-mounted robot with a compact and lightweight parallel mechanism for positioning of surgical tools in MIN. The system serves as a mechanical guide for automatic positioning of needles, catheters, probes, or electrodes. A parallel mechanism with 4 degrees of freedom (DOFs) is adopted, with the aim of providing sufficient accuracy and load capacity. The volume of the robot is only 50 mm × 50 mm × 40 mm and the weight is 73 g. The miniature design allows the robot to be mounted on the skull easily without consuming space in the operating room while avoiding the patient's immobilization, simplifying the registration operation, and increasing patient comfort and tolerability. The mechanical design, kinematics and workspace are analyzed and described in detail. Three experiments on the prototype are conducted to test the stiffness, accuracy and performance. Results show that the deflection is less than 0.1 mm for holding common surgical tools and the tracking errors are less than 1.2 mm and 1.9° which is acceptable for MIN. The robot can be easily and firmly mounted on the skull model and cadaver head, and flexibly manipulated on the skull model.

Associations between Patient Characteristics and a New, Early Do-Not-Attempt Resuscitation Order after Intracerebral Hemorrhage.

BACKGROUND: Decisions to limit care, including use of a do-not-resuscitate (DNR) order, are associated with increased risk of death after intracerebral hemorrhage (ICH). Given the value that patient surrogates place on the physician's perception of prognosis, understanding prognostic indicators that influence clinical judgment of outcomes is critical. OBJECTIVE: The purpose of this study was to understand the patient variables and comorbid illnesses associated with DNR orders placed on patients within 72 hours after ICH. DESIGN: Single-center, retrospective review of medical records of 198 consecutive patients with an admission diagnosis of primary supratentorial ICH between July 2007 and December 2010. SUBJECTS: Patients who did not experience a DNR order placement during their primary admission for ICH (non-DNR group) were compared to patients who received a new DNR order in the first 72 hours of admission (DNR group). MEASUREMENTS: Patient characteristics obtained include demographic data, past medical history, clinical data pertaining to the admission for the ICH, and radiographic images. Demographic, medical, and ICH injury data during the first three days of admission were collected. RESULTS: Multiple differences in patient and hospital factors were found between patients receiving a new, early DNR order and those who did not receive a DNR order after ICH. In regression modeling, Caucasian race, direct admission, and higher ICH score were associated with placement of a new DNR order early in the course of injury. CONCLUSIONS: Race, transfer procedures, and injury severity may be important factors associated with placement of new, early DNR orders in patients after ICH.