Early anticoagulation in patients with stroke and atrial fibrillation is associated with fewer ischaemic lesions at 1 month: the ATTUNE study.

BACKGROUND: The optimal time to commence anticoagulation in patients with atrial fibrillation (AF) after ischaemic stroke or transient ischaemic attack (TIA) is unclear, with guidelines differing in recommendations. A limitation of previous studies is the focus on clinically overt stroke, rather than radiologically obvious diffusion-weighted imaging ischaemic lesions. We aimed to quantify silent ischaemic lesions and haemorrhages on MRI at 1 month in patients commenced on early (<4 days) vs late (≥4 days) anticoagulation. We hypothesised that there would be fewer ischaemic lesions and more haemorrhages in the early anticoagulant group at 1-month MRI. METHODS: A prospective multicentre, observational cohort study was performed at 11 Australian stroke centres. Clinical and MRI data were collected at baseline and follow-up, with blinded imaging assessment performed by two authors. Timing of commencement of anticoagulation was at the discretion of the treating stroke physician. RESULTS: We recruited 276 patients of whom 208 met the eligibility criteria. The average age was 74.2 years (SD±10.63), and 79 (38%) patients were female. Median National Institute of Health Stroke Scale score was 5 (IQR 1-12). Median baseline ischaemic lesion volume was 5 mL (IQR 2-17). There were a greater number of new ischaemic lesions on follow-up MRI in patients commenced on anticoagulation ≥4 days after index event (17% vs 8%, p=0.04), but no difference in haemorrhage rates (22% vs 32%, p=0.10). Baseline ischaemic lesion volume of ≤5 mL was less likely to have a new haemorrhage at 1 month (p=0.02). There was no difference in haemorrhage rates in patients with an initial ischaemic lesion volume of >5 mL, regardless of anticoagulation timing. CONCLUSION: Commencing anticoagulation <4 days after stroke or TIA is associated with fewer ischaemic lesions at 1 month in AF patients. There is no increased rate of haemorrhage with early anticoagulation. These results suggest that early anticoagulation after mild-to-moderate acute ischaemic stroke associated with AF might be safe, but randomised controlled studies are needed to inform clinical practice.

Comparison of Automated Spike Detection Software in Detecting Epileptiform Abnormalities on Scalp-EEG of Genetic Generalized Epilepsy Patients.

PURPOSE: Despite availability of commercial EEG software for automated epileptiform detection, validation on real-world EEG datasets is lacking. Performance evaluation of two software packages on a large EEG dataset of patients with genetic generalized epilepsy was performed. METHODS: Three epileptologists labelled IEDs manually of EEGs from three centres. All Interictal epileptiform discharge (IED) markings predicted by two commercial software (Encevis 1.11 and Persyst 14) were reviewed individually to assess for suspicious missed markings and were integrated into the reference standard if overlooked during manual annotation during a second phase. Sensitivity, precision, specificity, and F1-score were used to assess the performance of the software packages against the adjusted reference standard. RESULTS: One hundred and twenty-five routine scalp EEG recordings from different subjects were included (total recording time, 310.7 hours). The total epileptiform discharge reference count was 5,907 (including spikes and fragments). Encevis demonstrated a mean sensitivity for detection of IEDs of 0.46 (SD 0.32), mean precision of 0.37 (SD 0.31), and mean F1-score of 0.43 (SD 0.23). Using the default medium setting, the sensitivity of Persyst was 0.67 (SD 0.31), with a precision of 0.49 (SD 0.33) and F1-score of 0.51 (SD 0.25). Mean specificity representing non-IED window identification and classification was 0.973 (SD 0.08) for Encevis and 0.968 (SD 0.07) for Persyst. CONCLUSIONS: Automated software shows a high degree of specificity for detection of nonepileptiform background. Sensitivity and precision for IED detection is lower, but may be acceptable for initial screening in the clinical and research setting. Clinical caution and continuous expert human oversight are recommended with all EEG recordings before a diagnostic interpretation is provided based on the output of the software.

Moving the field forward: detection of epileptiform abnormalities on scalp electroencephalography using deep learning-clinical application perspectives.

The application of deep learning approaches for the detection of interictal epileptiform discharges is a nascent field, with most studies published in the past 5 years. Although many recent models have been published demonstrating promising results, deficiencies in descriptions of data sets, unstandardized methods, variation in performance evaluation and lack of demonstrable generalizability have made it difficult for these algorithms to be compared and progress to clinical validity. A few recent publications have provided a detailed breakdown of data sets and relevant performance metrics to exemplify the potential of deep learning in epileptiform discharge detection. This review provides an overview of the field and equips computer and data scientists with a synopsis of EEG data sets, background and epileptiform variation, model evaluation parameters and an awareness of the performance metrics of high impact and interest to the trained clinical and neuroscientist EEG end user. The gold standard and inter-rater disagreements in defining epileptiform abnormalities remain a challenge in the field, and a hierarchical proposal for epileptiform discharge labelling options is recommended. Standardized descriptions of data sets and reporting metrics are a priority. Source code-sharing and accessibility to public EEG data sets will increase the rigour, quality and progress in the field and allow validation and real-world clinical translation.

Deep learning for automated epileptiform discharge detection from scalp EEG: A systematic review.

Automated interictal epileptiform discharge (IED) detection has been widely studied, with machine learning methods at the forefront in recent years. As computational resources become more accessible, researchers have applied deep learning (DL) to IED detection with promising results. This systematic review aims to provide an overview of the current DL approaches to automated IED detection from scalp electroencephalography (EEG) and establish recommendations for the clinical research community. We conduct a systematic review according to the PRISMA guidelines. We searched for studies published between 2012 and 2022 implementing DL for automating IED detection from scalp EEG in major medical and engineering databases. We highlight trends and formulate recommendations for the research community by analyzing various aspects: data properties, preprocessing methods, DL architectures, evaluation metrics and results, and reproducibility. The search yielded 66 studies, and 23 met our inclusion criteria. There were two main DL networks, convolutional neural networks in 14 studies and long short-term memory networks in three studies. A hybrid approach combining a hidden Markov model with an autoencoder was employed in one study. Graph convolutional network was seen in one study, which considered a montage as a graph. All DL models involved supervised learning. The median number of layers was 9 (IQR: 5-21). The median number of IEDs was 11 631 (IQR: 2663-16 402). Only six studies acquired data from multiple clinical centers. AUC was the most reported metric (median: 0.94; IQR: 0.94-0.96). The application of DL to IED detection is still limited and lacks standardization in data collection, multi-center testing, and reporting of clinically relevant metrics (i.e. F1, AUCPR, and false-positive/minute). However, the performance is promising, suggesting that DL might be a helpful approach. Further testing on multiple datasets from different clinical centers is required to confirm the generalizability of these methods.

Perfusion-Derived Dynamic 4D CT Angiography Identifies Carotid Pseudo-Occlusion in Hyperacute Stroke.

BACKGROUND: Differentiation between true acute tandem occlusion involving the extracranial internal carotid artery (ICA) from pseudotandem occlusion with a patent extracranial ICA has important prognostic and therapeutic implications. We explored the utility of perfusion-derived 4-dimensional CT angiogram (4D-CTA) in identifying carotid pseudo-occlusion in a single-center pilot study. METHODS: Acute stroke patients with delayed antegrade ICA flow on 4D-CTA despite an apparent tandem occlusion on conventional single-phase CTA were prospectively identified over a 2.5-year period (2013-2015). RESULTS: Eight patients were identified. Delayed antegrade intracranial flow from the apparently occluded ICA was detected up to 50 seconds after contrast administration on 4D-CTA. The distal intracranial ICA was the most common site of true occlusion. Reconstruction of the 4D-CTA images required an additional processing time of 2-3 minutes. CONCLUSIONS: 4D-CTA is a novel noninvasive technique that can identify carotid pseudo-occlusion in the acute stroke setting. Our preliminary findings suggest that 4D-CTA can be easily incorporated into an existing acute stroke neuroimaging protocol.

Is FAST stroke smart? Do the content and language used in awareness campaigns describe the experience of stroke symptoms?

BACKGROUND: Public education campaigns for stroke are used worldwide. However, there are few published evaluations of such campaigns. AIM: This cross-sectional study examined patient and bystander awareness of two Australian campaigns, 'FAST' (face, arm, speech, time) and 'Signs of Stroke', and evaluated the campaigns ability to identify stroke and to describe the symptom experience. METHODS: Interviews were conducted with either stroke patients or a key bystander for consecutive eligible cases admitted to two metropolitan hospitals between August 2006 and April 2008. Participants were asked to describe awareness of the FAST campaign, the symptoms experienced and to evaluate the symptom descriptions of Signs of Stroke against their own experience. RESULTS: A total of 100 patients and 70 bystanders were interviewed for 170 cases (71% of eligible cases). Only 12% of those interviewed were aware of the FAST campaign, and of these few (19%) were able to recall all FAST symptoms, with only one bystander using the FAST assessments to identify stroke. At least one FAST symptom was reported by 84% and one Signs of Stroke symptom by 100%. Less than half of those experiencing 'weakness or paralysis' thought this description exactly described their experience. Common descriptors of symptoms were: face or mouth droop or drop; trouble using, coordinating or moving arm, hand or leg; trouble walking or standing; numbness; and slurring or loss of speech. CONCLUSION: Awareness of the FAST and Signs of Stroke campaigns was low, with poor recall and little use of the FAST assessments. Some symptom descriptions used in the campaign were not reflective of the symptom experience. The content and language of these campaigns could be improved to portray stroke symptoms more realistically.

Incidence of stroke subtypes in the North East Melbourne Stroke Incidence Study (NEMESIS): differences between men and women.

BACKGROUND AND PURPOSE: Incidence rates of stroke subtypes may be imprecise when samples are small. We aimed to determine the incidence of stroke subtypes in a large geographically defined population. METHODS: Multiple overlapping sources were used to ascertain all strokes occurring in 22 postcodes (population of 306,631) of Melbourne, Australia, between 1997 and 1999. Stroke subtypes were defined by CT, MRI and autopsy. The Mantel-Haenszel age-adjusted rate ratio (MH RR) was used to compare incidence rates between men and women. RESULTS: We identified 1,421 strokes among 1,337 residents, 1,035 (72.8%) being first-ever strokes. Incidence (number/100,000 population/year), adjusted to the European population 45-84 years, was 197 (95% confidence interval, CI, 169-224) for ischemic stroke (IS), 47 (95% CI 33-60) for intracerebral haemorrhage (ICH) and 19 (95% CI 10-27) for subarachnoid haemorrhage (SAH). Compared with women, men in this age group had a greater incidence of IS (MH RR 1.65, 95% CI 1.39-1.96, p < 0.0001) and ICH (MH RR 1.46, 95% CI 1.01-2.10, p = 0.0420), but lesser rates of SAH (MH RR 0.34, 95% CI 0.16-0.69, p = 0.0031). CONCLUSIONS: In this population-based study, the incidence of IS and ICH was greater among men than women, while women had a greater incidence of SAH. More effort may need to be directed at modifying risk factors for IS and ICH in men.

Greater incidence of both fatal and nonfatal strokes in disadvantaged areas: the Northeast Melbourne Stroke Incidence Study.

BACKGROUND AND PURPOSE: Greater stroke mortality has been reported among lower socioeconomic groups. We aimed to determine whether fatal, nonfatal, and overall stroke incidence varied by socioeconomic status. METHODS: All suspected strokes occurring in 22 postcodes (population of 306,631) of Melbourne, Australia, during a 24-month period between 1997 and 1999 were found and assessed. Multiple overlapping sources were used to ascertain cases with standard clinical definitions for stroke. Socioeconomic disadvantage was assigned in 4 bands from least to greatest using an area-based measure developed by the Australian Bureau of Statistics. RESULTS: Overall stroke incidence (number per 100,000 population per year), adjusted to the European population 45 to 84 years of age, increased with increasing socioeconomic disadvantage: 200 (95% CI, 173 to 228); 251 (95% CI, 220 to 282); 309 (95% CI, 274 to 343); and 366 (95% CI, 329 to 403; chi2 for ranks; P<0.0001). Similar incidence patterns were observed for both fatal and nonfatal stroke. Nonfatal stroke contributed most to this incidence pattern: 146 (95% CI, 122 to 169); 181 (95% CI, 155 to 207); 223 (95% CI, 194 to 252); and 280 (95% CI, 247 to 313; chi2 for ranks; P<0.0001). CONCLUSIONS: In this population-based study, both fatal and nonfatal stroke incidence increased with increasing socioeconomic disadvantage. The greater contributor to this incidence pattern was nonfatal stroke incidence. This may have implications for service provision to those least able to afford it. Area-based identification of those most disadvantaged may provide a simple and effective way of targeting regions for stroke prevention strategies.

Stroke units, tissue plasminogen activator, aspirin and neuroprotection: which stroke intervention could provide the greatest community benefit?

BACKGROUND: Although a number of acute stroke interventions are of proven efficacy, there is uncertainty about their community benefits. We aimed to assess this within a defined population. METHODS: Eligibility for tissue plasminogen activator (tPA), aspirin, stroke unit management and neuroprotection were assessed among incident stroke cases within the community-based North East Melbourne Stroke Incidence Study. RESULTS: Among 306,631 people, there were 645 incident strokes managed in hospital. When eligible patients were extrapolated to the Australian population, for every 1,000 cases, 46 (95% CI 17-69) could have been saved from death or dependency with stroke unit management, 6 (95% CI 1-11) by using aspirin, 11 (95% CI 5-17) or 10 (95% CI 3-16) by using tPA at 3 and 6 h, respectively. CONCLUSIONS: Although tPA is the most potent intervention, management in stroke units has the greatest population benefit and should be a priority.

Quality of stroke care within a hospital: effects of a mobile stroke service.

OBJECTIVE: An Australian stroke services study (SCOPES) has developed a framework to compare different forms of acute stroke services, the gold standard being localised stroke units. We aimed to use this framework to assess changes in the quality of stroke care over time as a sequential audit process. DESIGN AND SETTING: A retrospective medical record audit comparing 100 sequential stroke admissions (July 2002 to June 2003) two years after institution of a mobile stroke service (MSS) with 100 historical controls (September 1998 to October 1999) at a 260-bed hospital in Melbourne. The MSS results were also compared with stroke units in SCOPES. MAIN OUTCOME MEASURES: Adherence to quality indicators and standard measures of outcome (complications, length of stay and discharge disability) after implementing the MSS. RESULTS: Significant improvements were seen in prophylaxis for deep-vein thrombosis, incontinence management, premorbid function documentation, frequent neurological observations and early occupational therapy. The MSS demonstrated fewer severe complications (9% versus 24%; P = 0.004), reduced median length of stay (discharged patients: 12.0 days versus 18.5 days; P = 0.003) and more patients were independent at discharge (32% versus 9%; P < 0.001). Comparison with SCOPES stroke units showed our MSS could improve in incontinence management and appropriate use of antiplatelet therapy. CONCLUSION: Institution of the MSS was associated with improvements in the quality of stroke care. This study demonstrates application of an audit procedure for quality improvement in hospital stroke management and the potential to improve stroke services in smaller centres.

Quality of life after stroke: the North East Melbourne Stroke Incidence Study (NEMESIS).

BACKGROUND AND PURPOSE: Health-related quality of life (HRQoL) data are scarce from unselected populations. The aims were to assess HRQoL at 2 years poststroke, to identify determinants of HRQoL in stroke survivors, and to identify predictors at stroke onset of subsequent HRQoL. METHODS: All first-ever cases of stroke in a population of 306 631 over a 1-year period were assessed. Stroke severity, comorbidity, and demographic information were recorded. Two-year poststroke HRQoL was assessed using the Assessment of Quality of Life (AQoL) instrument (deceased patients score=0). Handicap, disability, physical impairment, depression, anxiety, living arrangements, and recurrent stroke at 2 years were documented. If necessary, proxy assessments were obtained, except for mood. Linear regression analyses were performed to identify factors independently associated with HRQoL. RESULTS: Of 266 incident cases alive at 2 years, 225 (85%) were assessed. The mean AQoL utility score for all survivors was 0.47 (95% CI, 0.42 to 0.52). Almost 25% of survivors had a score of < or =0.1. The independent determinants of HRQoL in survivors were handicap, physical impairment, anxiety and depression, disability, institutionalization, dementia, and age. The factors present at stroke onset that independently predicted HRQoL at 2 years poststroke were age, female sex, initial NIHSS score, neglect, and low socioeconomic status. CONCLUSIONS: A substantial proportion of stroke survivors have very poor HRQoL. Interventions targeting handicap and mood have the potential to improve HRQoL independently of physical impairment and disability.

Determinants of handicap after stroke: the North East Melbourne Stroke Incidence Study (NEMESIS).

BACKGROUND AND PURPOSE: Handicap, although more relevant to the patient than impairment or disability, has received little attention in people with stroke. The aim of this study was to identify, in an unselected population, factors determining handicap at 2 years after stroke. METHODS: All first-ever cases of stroke in a population of 306 631 over a 1-year period were assessed. Stroke severity, comorbidity, and demographic information was recorded. Among survivors, 2-year poststroke handicap was assessed with the London Handicap Scale. Disability, physical impairment, depression, anxiety, living arrangements, and recurrent stroke at 2 years were documented. If necessary, proxy assessments were obtained, except for mood. Linear regression analyses were performed to identify factors independently associated with handicap. First, all assessments (proxy and nonproxy) were examined; then, the nonproxy assessments were used to examine the effects of mood. RESULTS: Of 266 patients with incident stroke who were alive at 2 years, 226 (85%) were assessed. Significant determinants of handicap on univariable analysis were age, female sex, socioeconomic status, alcohol intake, stroke subtype, initial stroke severity; 2-year physical impairment, disability, depression and anxiety scores; institutionalization; and recurrent stroke. On multivariable analysis, the independent determinants of handicap were age and 2-year physical impairment and disability. In analysis restricted to nonproxy data, depression and anxiety were also independently associated with handicap. CONCLUSIONS: Age, concurrent disability, and physical impairment were more important determinants of handicap than other demographic factors or initial stroke severity. Because depression and anxiety were independently associated with handicap, their treatment may potentially reduce handicap in stroke patients.