Publications by authors named "Jim Jannes"

47 Publications

Prospective and external validation of stroke discharge planning machine learning models.

J Clin Neurosci 2022 Jan 6;96:80-84. Epub 2022 Jan 6.

Royal Adelaide Hospital, Adelaide SA 5000, Australia; University of Adelaide, Adelaide SA 5005, Australia; South Australian Health and Medical Research Institute, Adelaide SA 5000, Australia.

Machine learning may be able to help with predicting factors that aid in discharge planning for stroke patients. This study aims to validate previously derived models, on external and prospective datasets, for the prediction of discharge modified Rankin scale (mRS), discharge destination, survival to discharge and length of stay. Data were collected from consecutive patients admitted with ischaemic or haemorrhagic stroke at the Royal Adelaide Hospital from September 2019 to January 2020, and at the Lyell McEwin Hospital from January 2017 to January 2020. The previously derived models were then applied to these datasets with three pre-defined cut-off scores (high-sensitivity, Youden's index, and high-specificity) to return indicators of performance including area under the receiver operator curve (AUC), sensitivity and specificity. The number of individuals included in the prospective and external datasets were 334 and 824 respectively. The models performed well on both the prospective and external datasets in the prediction of discharge mRS ≤ 2 (AUC 0.85 and 0.87), discharge destination to home (AUC 0.76 and 0.78) and survival to discharge (AUC 0.91 and 0.92). Accurate prediction of length of stay with only admission data remains difficult (AUC 0.62 and 0.66). This study demonstrates successful prospective and external validation of machine learning models using six variables to predict information relevant to discharge planning for stroke patients. Further research is required to demonstrate patient or system benefits following implementation of these models.
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http://dx.doi.org/10.1016/j.jocn.2021.12.031DOI Listing
January 2022

Improving the accuracy of stroke clinical coding with open-source software and natural language processing.

J Clin Neurosci 2021 Dec 5;94:233-236. Epub 2021 Nov 5.

Royal Adelaide Hospital, Adelaide SA 5000, Australia; University of Adelaide, Adelaide SA 5005, Australia.

Clinical coding is an important task, which is required for accurate activity-based funding. Natural language processing may be able to assist with improving the efficiency and accuracy of clinical coding. The aims of this study were to explore the feasibility of using natural language processing for stroke hospital admissions, employed with open-source software libraries, to aid in the identification of potentially misclassified (1) category of Adjacent Diagnosis Related Groups (ADRG), (2) the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) diagnoses, and (3) Diagnosis Related Groups (DRG). Data was collected for consecutive individuals admitted to the Royal Adelaide Hospital Stroke Unit over a five-month period for misclassification identification analysis. 152 admissions were included in the study. Using free-text discharge summaries, a random forest classifier correctly identified two cases classified as B70 ("Stroke and Other Cerebrovascular Disorders") that should be classified as B02 (having received endovascular thrombectomy). A regular expression-based analysis correctly identified 33 cases in which ataxia was present but was not coded. Two cases were identified that should have been classified as B70D, rather than B70A/B/C, based on transfer to another centre within five days of admission. A variety of techniques may be useful to help identify misclassifications in ADRG, ICD-10-AM and DRG codes. Such techniques can be implemented with open-source software libraries, and may have significant financial implications. Future studies may seek to apply open-source software libraries to the identification of misclassifications of all ICD-10-AM diagnoses in stroke patients.
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http://dx.doi.org/10.1016/j.jocn.2021.10.024DOI Listing
December 2021

Daily estimates of individual discharge likelihood with deep learning natural language processing in general medicine: a prospective and external validation study.

Intern Emerg Med 2021 Jul 31. Epub 2021 Jul 31.

Royal Adelaide Hospital, Port Road, Adelaide, SA, 5000, Australia.

Machine learning, in particular deep learning, may be able to assist in the prediction of the length of stay and timing of discharge for individual patients. Artificial neural networks applied to medical text have previously shown promise in this area. In this study, a previously derived artificial neural network was applied to prospective and external validation datasets. In the prediction of discharge within the next 2 days, when the algorithm was applied to prospective and external datasets, the area under the receiver operator curve for this task were 0.78 and 0.74, respectively. The performance in the prediction of discharge within the next 7 days was more limited (area under the receiver operator curve 0.68 and 0.67). This study has shown that in prospective and external validation datasets the previously derived deep learning algorithms have demonstrated moderate performance in the prediction of which patients will be discharged within the next 2 days. Future studies may seek to further refine or evaluate the effect of the implementation of such algorithms.
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http://dx.doi.org/10.1007/s11739-021-02816-7DOI Listing
July 2021

Mixed-data deep learning in repeated predictions of general medicine length of stay: a derivation study.

Intern Emerg Med 2021 Sep 16;16(6):1613-1617. Epub 2021 Mar 16.

Royal Adelaide Hospital, Port Road, Adelaide, SA, 5000, Australia.

The accurate prediction of likely discharges and estimates of length of stay (LOS) aid in effective hospital administration and help to prevent access block. Machine learning (ML) may be able to help with these tasks. For consecutive patients admitted under General Medicine at the Royal Adelaide Hospital over an 8-month period, daily ward round notes and relevant discrete data fields were collected from the electronic medical record. These data were then split into training and testing sets (7-month/1-month train/test split) prior to use in ML analyses aiming to predict discharge within the next 2 days, discharge within the next 7 days and an estimated date of discharge (EDD). Artificial neural networks and logistic regression were effective at predicting discharge within 48 h of a given ward round note. These models achieved an area under the receiver operator curve (AUC) of 0.80 and 0.78, respectively. Prediction of discharge within 7 days of a given note was less accurate, with artificial neural network returning an AUC of 0.68 and logistic regression an AUC of 0.61. The generation of an exact EDD remains inaccurate. This study has shown that repeated estimates of LOS using daily ward round notes and mixed-data inputs are effective in the prediction of general medicine discharges in the next 48 h. Further research may seek to prospectively and externally validate models for prediction of upcoming discharge, as well as combination human-ML approaches for generating EDDs.
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http://dx.doi.org/10.1007/s11739-021-02697-wDOI Listing
September 2021

Machine learning in the prediction of medical inpatient length of stay.

Intern Med J 2020 Oct 23. Epub 2020 Oct 23.

Royal Adelaide Hospital, Adelaide, South Australia, Australia.

Length of stay (LOS) estimates are important for patients, doctors and hospital administrators. However, making accurate estimates of LOS can be difficult for medical patients. This review was conducted with the aim of identifying and assessing previous studies on the application of machine learning to the prediction of total hospital inpatient LOS for medical patients. A review of machine learning in the prediction of total hospital LOS for medical inpatients was conducted using the databases PubMed, EMBASE and Web of Science. Of the 673 publications returned by the initial search, 21 articles met inclusion criteria. Of these articles the most commonly represented medical specialty was cardiology. Studies were also identified that had specifically evaluated machine learning LOS prediction in patients with diabetes and tuberculosis. The performance of the machine learning models in the identified studies varied significantly depending on factors including differing input datasets and different LOS thresholds and outcome metrics. Common methodological shortcomings included a lack of reporting of patient demographics and lack of reporting of clinical details of included patients. The variable performance reported by the studies identified in this review supports the need for further research of the utility of machine learning in the prediction of total inpatient LOS in medical patients. Future studies should follow and report a more standardised methodology to better assess performance and to allow replication and validation. In particular, prospective validation studies and studies assessing the clinical impact of such machine learning models would be beneficial.
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http://dx.doi.org/10.1111/imj.14962DOI Listing
October 2020

Stroke prognostication for discharge planning with machine learning: A derivation study.

J Clin Neurosci 2020 Sep 5;79:100-103. Epub 2020 Aug 5.

Royal Adelaide Hospital, Adelaide, SA 5000, Australia; University of Adelaide, Adelaide, SA 5005, Australia; South Australian Health and Medical Research Institute, Adelaide, SA 5000, Australia.

Post-stroke discharge planning may be aided by accurate early prognostication. Machine learning may be able to assist with such prognostication. The study's primary aim was to evaluate the performance of machine learning models using admission data to predict the likely length of stay (LOS) for patients admitted with stroke. Secondary aims included the prediction of discharge modified Rankin Scale (mRS), in-hospital mortality, and discharge destination. In this study a retrospective dataset was used to develop and test a variety of machine learning models. The patients included in the study were all stroke admissions (both ischaemic stroke and intracerebral haemorrhage) at a single tertiary hospital between December 2016 and September 2019. The machine learning models developed and tested (75%/25% train/test split) included logistic regression, random forests, decision trees and artificial neural networks. The study included 2840 patients. In LOS prediction the highest area under the receiver operator curve (AUC) was achieved on the unseen test dataset by an artificial neural network at 0.67. Higher AUC were achieved using logistic regression models in the prediction of discharge functional independence (mRS ≤2) (AUC 0.90) and in the prediction of in-hospital mortality (AUC 0.90). Logistic regression was also the best performing model for predicting home vs non-home discharge destination (AUC 0.81). This study indicates that machine learning may aid in the prognostication of factors relevant to post-stroke discharge planning. Further prospective and external validation is required, as well as assessment of the impact of subsequent implementation.
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http://dx.doi.org/10.1016/j.jocn.2020.07.046DOI Listing
September 2020

Nurse Led Smartphone Electrographic Monitoring for Atrial Fibrillation after Ischemic Stroke: SPOT-AF.

J Stroke 2020 Sep 29;22(3):387-395. Epub 2020 Sep 29.

Heart Research Institute Charles Perkins Centre, and Concord Hospital Cardiology, University of Sydney, Sydney, Australia.

Background And Purpose: Paroxysmal atrial fibrillation (PAF) underlying acute stroke frequently evades detection by standard practice, considered to be a combination of routine electrocardiogram (ECG) monitoring, and 24-hour Holter recordings. We hypothesized that nurse-led in-hospital intermittent monitoring approach would increase PAF detection rate.

Methods: We recruited patients hospitalised for stroke/transient ischemic attack, without history of atrial fibrillation (AF), in a prospective multi-centre observational study. Patients were monitored using a smartphone-enabled handheld ECG (iECG) during routine nursing observations, and underwent 24-hour Holter monitoring according to local practice. The primary outcome was comparison of AF detection by nurse-led iECG versus Holter monitoring in patients who received both tests: secondary outcome was oral anticoagulant commencement at 3-month following PAF detection.

Results: One thousand and seventy-nine patients underwent iECG monitoring: 294 had iECG and Holter monitoring. AF was detected in 25/294 (8.5%) by iECG, and 8/294 (2.8%) by 24-hour Holter recordings (P<0.001). Median duration from stroke onset to AF detection for iECG was 3 days (interquartile range [IQR], 2 to 6) compared with 7 days (IQR, 6 to 10) for Holter recordings (P=0.02). Of 25 patients with AF detected by iECG, 11 were commenced on oral anticoagulant, compared to 5/8 for Holter. AF was detected in 8.8% (69/785 patients) who underwent iECG recordings only (P=0.8 vs. those who had both iECG and 24-hour Holter).

Conclusions: Nurse-led in-hospital iECG surveillance after stroke is feasible and effective and detects more PAF earlier and more frequently than routine 24-hour Holter recordings. Screening with iECG could be incorporated into routine post-stroke nursing observations to increase diagnosis of PAF, and facilitate institution of guideline-recommended anticoagulation.
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http://dx.doi.org/10.5853/jos.2020.00689DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568969PMC
September 2020

Two-year efficacy of varenicline tartrate and counselling for inpatient smoking cessation (STOP study): A randomized controlled clinical trial.

PLoS One 2020 29;15(4):e0231095. Epub 2020 Apr 29.

School of Nursing and Midwifery, The University of South Australia, Adelaide, South Australia, Australia.

Introduction: Varenicline tartrate is superior for smoking cessation to other tobacco cessation therapies by 52 weeks, in the outpatient setting. We aimed to evaluate the long-term (104 week) efficacy following a standard course of inpatient-initiated varenicline tartrate plus Quitline-counselling compared to Quitline-counselling alone.

Methods: Adult patients (n = 392, 20-75 years) admitted with a smoking-related illnesses to one of three hospitals, were randomised to receive either 12-weeks of varenicline tartrate (titrated from 0.5mg daily to 1mg twice-daily) plus Quitline-counselling, (n = 196) or Quitline-counselling alone, (n = 196), with continuous abstinence from smoking assessed at 104 weeks.

Results: A total of 1959 potential participants were screened for eligibility between August 2008 and December 2011. The proportion of participants who remained continuously abstinent (intention-to-treat) at 104 weeks were significantly greater in the varenicline tartrate plus counselling arm (29.2% n = 56) compared to counselling alone (18.8% n = 36; p = 0.02; odds ratio 1.78; 95%CI 1.10 to 2.86, p = 0.02). Twenty-two deaths occurred during the 104 week study (n = 10 for varenicline tartrate plus counselling and n = 12 for Quitline-counselling alone). All of these participants had known or developed underlying co-morbidities.

Conclusions: This is the first study to examine the efficacy and safety of varenicline tartrate over 104 weeks within any setting. Varenicline tartrate plus Quitline-counselling was found to be an effective opportunistic treatment when initiated for inpatient smokers who had been admitted with tobacco-related disease.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231095PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190140PMC
July 2020

Stroke unit legislation-Mandating a uniform standard of care?

Int J Stroke 2020 02 29;15(2):NP6-NP7. Epub 2020 Jan 29.

Stroke Research Programme, Discipline of Medicine, University of Adelaide, Adelaide, Australia.

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http://dx.doi.org/10.1177/1747493020903516DOI Listing
February 2020

Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study.

Intern Emerg Med 2020 09 2;15(6):989-995. Epub 2020 Jan 2.

Neurology Department, Royal Adelaide Hospital, Port Road, Adelaide, SA, 5000, Australia.

Length of stay (LOS) and discharge destination predictions are key parts of the discharge planning process for general medical hospital inpatients. It is possible that machine learning, using natural language processing, may be able to assist with accurate LOS and discharge destination prediction for this patient group. Emergency department triage and doctor notes were retrospectively collected on consecutive general medical and acute medical unit admissions to a single tertiary hospital from a 2-month period in 2019. These data were used to assess the feasibility of predicting LOS and discharge destination using natural language processing and a variety of machine learning models. 313 patients were included in the study. The artificial neural network achieved the highest accuracy on the primary outcome of predicting whether a patient would remain in hospital for > 2 days (accuracy 0.82, area under the received operator curve 0.75, sensitivity 0.47 and specificity 0.97). When predicting LOS as an exact number of days, the artificial neural network achieved a mean absolute error of 2.9 and a mean squared error of 16.8 on the test set. For the prediction of home as a discharge destination (vs any non-home alternative), all models performed similarly with an accuracy of approximately 0.74. This study supports the feasibility of using natural language processing to predict general medical inpatient LOS and discharge destination. Further research is indicated with larger, more detailed, datasets from multiple centres to optimise and examine the accuracy that may be achieved with such predictions.
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http://dx.doi.org/10.1007/s11739-019-02265-3DOI Listing
September 2020

Thrombolysis Guided by Perfusion Imaging up to 9 Hours after Onset of Stroke.

N Engl J Med 2019 05;380(19):1795-1803

From Florey Institute of Neuroscience and Mental Health (H.M., L. Churilov, N.Y., V.T., L. Carey, A.M., G.A.D.), the Department of Medicine and Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital (B.C.V.C., M.W.P., L. Churilov, S. Christensen, N.Y., G.S., A.B., B.Y., A.M., S.M.D., G.A.D.), and the Department of Radiology, Royal Melbourne Hospital (P.M.D., B.Y., P.J.M.), University of Melbourne, Parkville, the Department of Medicine, School of Clinical Science, Monash University, Clayton (H.M., T.G.P.), the Departments of Medicine and Neurology, Melbourne Medical School, University of Melbourne and Western Health, Sunshine Hospital, St. Albans (T.W.), the Department of Neurosciences, Eastern Health and Eastern Health Clinical School, Monash University, Box Hill (H.M.D., C.F.B.), the Department of Neurology, University Hospital Geelong, Deakin University, Geelong (B.C.), the Department of Neurology, Austin Hospital, Austin Health, Heidelberg (V.T.), and Occupational Therapy, School of Allied Health, College of Science, Health and Engineering, La Trobe University, Bundoora (L. Carey), VIC, the Department of Neurology, Priority Research Centre for Brain and Mental Health Research, John Hunter Hospital (C.R.L., F.M.), University of Newcastle (J.S.), Newcastle, the Department of Neurology, Westmead Hospital (N.M.), the Department of Neurology, Royal North Shore Hospital and Kolling Institute, University of Sydney (M.K.), and the Department of Neurology, St. Vincent's Hospital Sydney (R.M.), Sydney, and the Department of Neurology, Gosford Hospital, Gosford (J.S.), NSW, the Department of Neurology, Royal Adelaide Hospital (T.J.K., J.J.), the Department of Neurology, Lyell McEwin Hospital (D.F.), and the Department of Neurology, Queen Elizabeth Hospital (J.J.), Adelaide, SA, the Department of Medicine, Sunshine Coast University Hospital, Nambour (R.G.), the Department of Neurology, Royal Brisbane and Women's Hospital and the University of Queensland, Brisbane (A.A.W.), and the Department of Neurology, Gold Coast University Hospital, Southport (A.S.), and Australia and Griffith University, Gold Coast (A.S.), QLD - all in Australia; the Graduate Institute of Clinical Medical Science (C.H.) and the School of Medicine (C.-H.T.), China Medical University, and the Department of Neurology, China Medical University Hospital (C.-H.T.), Taichung, the Department of Neurology, Tri-Service General Hospital, National Defense Medical Center (J.-T.L.), the Department of Neurology, Shuang Ho Hospital (C.-J.H.), the Department of Neurology, En Chu Kong Hospital (Y.S.), the Stroke Center and Department of Neurology, National Taiwan University Hospital (J.-S.J.), and the Department of Neurology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei Medical University College of Medicine (L.-M.L.), Taipei, the Stroke Center and Department of Neurology, Changhua Christian Hospital, Changhua (M.-C.S.), the Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan (C.-H.C.), and the Department of Neurology, Kaohsiung Veterans General Hospital, Kaohsiung (C.-H.L.) - all in Taiwan; the Department of Neurology, Helsinki University Hospital, Helsinki (S. Curtze, A.M.); the Department of Neurology, Auckland City Hospital, University of Auckland, Auckland, New Zealand (P.A.B.); and the Stanford Stroke Center, Stanford University, Stanford, CA (S. Christensen).

Background: The time to initiate intravenous thrombolysis for acute ischemic stroke is generally limited to within 4.5 hours after the onset of symptoms. Some trials have suggested that the treatment window may be extended in patients who are shown to have ischemic but not yet infarcted brain tissue on imaging.

Methods: We conducted a multicenter, randomized, placebo-controlled trial involving patients with ischemic stroke who had hypoperfused but salvageable regions of brain detected on automated perfusion imaging. The patients were randomly assigned to receive intravenous alteplase or placebo between 4.5 and 9.0 hours after the onset of stroke or on awakening with stroke (if within 9 hours from the midpoint of sleep). The primary outcome was a score of 0 or 1 on the modified Rankin scale, on which scores range from 0 (no symptoms) to 6 (death), at 90 days. The risk ratio for the primary outcome was adjusted for age and clinical severity at baseline.

Results: After 225 of the planned 310 patients had been enrolled, the trial was terminated because of a loss of equipoise after the publication of positive results from a previous trial. A total of 113 patients were randomly assigned to the alteplase group and 112 to the placebo group. The primary outcome occurred in 40 patients (35.4%) in the alteplase group and in 33 patients (29.5%) in the placebo group (adjusted risk ratio, 1.44; 95% confidence interval [CI], 1.01 to 2.06; P = 0.04). Symptomatic intracerebral hemorrhage occurred in 7 patients (6.2%) in the alteplase group and in 1 patient (0.9%) in the placebo group (adjusted risk ratio, 7.22; 95% CI, 0.97 to 53.5; P = 0.05). A secondary ordinal analysis of the distribution of scores on the modified Rankin scale did not show a significant between-group difference in functional improvement at 90 days.

Conclusions: Among the patients in this trial who had ischemic stroke and salvageable brain tissue, the use of alteplase between 4.5 and 9.0 hours after stroke onset or at the time the patient awoke with stroke symptoms resulted in a higher percentage of patients with no or minor neurologic deficits than the use of placebo. There were more cases of symptomatic cerebral hemorrhage in the alteplase group than in the placebo group. (Funded by the Australian National Health and Medical Research Council and others; EXTEND ClinicalTrials.gov numbers, NCT00887328 and NCT01580839.).
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http://dx.doi.org/10.1056/NEJMoa1813046DOI Listing
May 2019

Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes: A Pilot Study.

Acad Radiol 2020 02 30;27(2):e19-e23. Epub 2019 Apr 30.

Royal Adelaide Hospital, Australia; Faculty of Health and Medical Sciences, University of Adelaide, Australia.

Rationale And Objectives: Intravenous thrombolysis decision-making and obtaining of consent would be assisted by an individualized risk-benefit ratio. Deep learning (DL) models may be able to assist with this patient selection.

Materials And Methods: Clinical data regarding consecutive patients who received intravenous thrombolysis across two tertiary hospitals over a 7-year period were extracted from existing databases. The noncontrast computed tomography brain scans for these patients were then retrieved with hospital picture archiving and communication systems. Using a combination of convolutional neural networks (CNN) and artificial neural networks (ANN) several models were developed to predict either improvement in the National Institutes of Health Stroke Scale of ≥4 points at 24 hours ("NIHSS24"), or modified Rankin Scale 0-1 at 90 days ("mRS90"). The developed CNN and ANN were then applied to a test set. The THRIVE, HIAT, and SPAN-100 scores were also calculated for the patients in the test set and used to predict NIHSS24 and mRS90.

Results: Data from 204 individuals were included in the project. The best performing DL model for prediction of mRS90 was a combination CNN + ANN based on clinical data and computed tomography brain (accuracy = 0.74, F1 score = 0.69). The best performing model for NIHSS24 prediction was also the combination CNN + ANN (accuracy = 0.71, F1 score = 0.74).

Conclusion: DL models may aid in the prediction of functional thrombolysis outcomes. Further investigation with larger datasets and additional imaging sequences is indicated.
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http://dx.doi.org/10.1016/j.acra.2019.03.015DOI Listing
February 2020

Deep Learning Natural Language Processing Successfully Predicts the Cerebrovascular Cause of Transient Ischemic Attack-Like Presentations.

Stroke 2019 03;50(3):758-760

From the Royal Adelaide Hospital, Adelaide, Australia (S.B., L.O.-R., T.K., S.P., J.J.).

Background and Purpose- Triaging of referrals to transient ischemic attack (TIA) clinics is aided by risk stratification. Deep learning-based natural language processing, a type of machine learning, may be able to assist with the prediction of cerebrovascular cause of TIA-like presentations from free-text information. Methods- Consecutive TIA clinic notes were retrieved from existing databases. Texts associated with cerebrovascular and noncerebrovascular diagnoses were preprocessed before classification experiments, using a variety of classifier models, based on only the free-text description of the history of presenting complaint. The primary outcome was area under the curve (AUC) of the receiver operator curve. The model with the greatest AUC was then used in classification experiments in which it was provided with additional clinical information. Results- Of the classifier models trialed on the history of presenting complaint, the convolutional neural network achieved the greatest predictive capability (AUC±SD; 81.9±2.0). The effects of additional clinical information on AUC were variable. The greatest AUC was achieved when the convolutional neural network was provided with the history of presenting complaint and magnetic resonance imaging report (88.3±3.6). Conclusions- Deep learning-based natural language processing, in particular convolutional neural networks, based on medical free-text, may prove effective in prediction of the cause of TIA-like presentations. Future research investigating the role of the application of deep learning-based natural language processing to the automated triaging of clinic referrals in TIA, and potentially other specialty areas, is indicated.
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http://dx.doi.org/10.1161/STROKEAHA.118.024124DOI Listing
March 2019

Genetics of the thrombomodulin-endothelial cell protein C receptor system and the risk of early-onset ischemic stroke.

PLoS One 2018 1;13(11):e0206554. Epub 2018 Nov 1.

University of Adelaide, Adelaide, Australia.

Background And Purpose: Polymorphisms in coagulation genes have been associated with early-onset ischemic stroke. Here we pursue an a priori hypothesis that genetic variation in the endothelial-based receptors of the thrombomodulin-protein C system (THBD and PROCR) may similarly be associated with early-onset ischemic stroke. We explored this hypothesis utilizing a multi-stage design of discovery and replication.

Methods: Discovery was performed in the Genetics-of-Early-Onset Stroke (GEOS) Study, a biracial population-based case-control study of ischemic stroke among men and women aged 15-49 including 829 cases of first ischemic stroke (42.2% African-American) and 850 age-comparable stroke-free controls (38.1% African-American). Twenty-four single-nucleotide-polymorphisms (SNPs) in THBD and 22 SNPs in PROCR were evaluated. Following LD pruning (r2≥0.8), we advanced uncorrelated SNPs forward for association analyses. Associated SNPs were evaluated for replication in an early-onset ischemic stroke population (onset-age<60 years) consisting of 3676 cases and 21118 non-stroke controls from 6 case-control studies. Lastly, we determined if the replicated SNPs also associated with older-onset ischemic stroke in the METASTROKE data-base.

Results: Among GEOS Caucasians, PROCR rs9574, which was in strong LD with 8 other SNPs, and one additional independent SNP rs2069951, were significantly associated with ischemic stroke (rs9574, OR = 1.33, p = 0.003; rs2069951, OR = 1.80, p = 0.006) using an additive-model adjusting for age, gender and population-structure. Adjusting for risk factors did not change the associations; however, associations were strengthened among those without risk factors. PROCR rs9574 also associated with early-onset ischemic stroke in the replication sample (OR = 1.08, p = 0.015), but not older-onset stroke. There were no PROCR associations in African-Americans, nor were there any THBD associations in either ethnicity.

Conclusion: PROCR polymorphisms are associated with early-onset ischemic stroke in Caucasians.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0206554PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6211695PMC
April 2019

Evaluation of a CTA-Triage Based Transient Ischemic Attack Service: A Retrospective Single Center Cohort Study.

J Stroke Cerebrovasc Dis 2018 Dec 5;27(12):3436-3442. Epub 2018 Sep 5.

Royal Adelaide Hospital, Department of Neurology, Adelaide, Australia.

Objective: We designed a computed tomography angiography (CTA)-based algorithm for patients presenting to hospital with a transient ischemic attack (TIA) which identified high-risk patients, as well as inpatient versus semiurgent outpatient management following MRI, and we hypothesised that this would be effective.

Methods: Patients seen in the ED at the Royal Adelaide Hospital from March 3, 2012 to November 30, 2016 with TIA-like symptoms were assessed for a cardioembolic source (clinical assessment, electrocardiogram) and underwent intra and extracranial CTA. Patients with a referable >50% stenosis were admitted and given dual antiplatelets. Most high-risk cardiac source patients were also admitted and anticoagulated. Other patients were loaded with aspirin, or changed to clopidogrel if on aspirin, and reviewed as outpatients following semiurgent MRI (3-4 days). We assessed the 90-day recurrent stroke risk in this cohort as a whole, and in those with a final cerebrovascular diagnosis.

Results: 1167 patients were diagnosed in Emergency as TIA and referred via our algorithm. A total of 150 were admitted, 78 had "high-risk" features. A total of 1017 patients were reviewed in the TIA clinic. The average age of the total cohort was 65.8 years old. Final diagnosis was TIA/minor stroke in 69% admitted patients and 30% clinic patients (P value < .0001). The 90-day recurrent stroke risk in these patients was 2.0% (5.8% admitted vs .7% clinic patients, P value < .0001). In those with noncerebrovascular diagnoses, there were no recurrent strokes within 90 days.

Conclusions: Stroke risk is very low using CTA guided semiurgent clinic review algorithm.
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http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2018.08.006DOI Listing
December 2018

Transient Ischaemic Attack Rarely Precedes Stroke in a Cohort with Low Proportions of Large Artery Atherosclerosis: A Population-Based Study.

Cerebrovasc Dis Extra 2018 23;8(2):101-105. Epub 2018 Aug 23.

Stroke and Neurology Department, Royal Adelaide Hospital, Adelaide, South Australia, Australia.

Background: Ischaemic stroke is reportedly preceded by transient ischaemic attack (TIA) in 15-30% of all cases. The risk of stroke following TIA is highest in the presence of unstable atherosclerotic plaques in large arteries. The recent population-based Adelaide Stroke Incidence Study describes a population with a low proportion (16%) of stroke attributable to large artery atherosclerosis (LAA). We hypothesized that this population-based ischaemic stroke cohort would have a lower rate of preceding TIA than previously reported.

Methods: This paper is a prospective ascertainment of all suspected TIAs and strokes in a 12-month period from 2009 to 2010. Ischaemic stroke pathogenesis was classified by the TOAST criteria. Details of preceding TIA events were scrutinised.

Results: In this 12-month period, 318 stroke events in 301 individuals were recorded. Of the total 258 ischaemic strokes, 16% (95% confidence interval [CI] 12-22) were from LAA. Of 258 ischaemic stroke patients, only 11 (4%; 95% CI 2-7) reported symptoms in the preceding 90 days consistent with TIA. Nine (82%) sought medical attention. The median ABCD2 score in this group was 4.5 (IQR: 3-7), and the median time of event prior to stroke was 20 days (IQR: 4-32).

Conclusion: In our population-based cohort, rates of TIA preceding ischaemic stroke were much lower than previously reported, probably reflective of effective secondary prevention (active TIA clinics) and primary prevention (limiting LAA prevalence). In our population, further enhancements in TIA care will be of limited yield.
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http://dx.doi.org/10.1159/000491936DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6120413PMC
October 2018

Ischaemic stroke may symptomatically manifest as migraine aura.

J Clin Neurosci 2018 Sep 19;55:62-64. Epub 2018 Jul 19.

Department of Neurology, Royal Adelaide Hospital, Adelaide, Australia.

Migraine aura is a common stroke mimic. We hypothesised that some patients with typical migraine aura symptoms might have embolic stroke detected as the precipitant. We identified fourteen patients who presented with symptoms consistent with a clinical diagnosis of migraine aura, but demonstrated subsequent evidence of acute infarction on magnetic resonance imaging (MRI). In all patients, migraine aura symptoms were not directly attributable to the vascular lesion on MRI. 50% of patients were classified as having an embolic stroke of undetermined source. Of these, a patent foramen ovale was identified in 4/5 of those who underwent transoesophageal echocardiogram, with large right-to-left shunt demonstrated in three. The results from our cohort suggest that migraine aura can be the presenting feature of acute ischaemic stroke, with local ischaemia presumably triggering a widely migrating cortical wave of spreading depolarization.
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http://dx.doi.org/10.1016/j.jocn.2018.07.017DOI Listing
September 2018

Smartphone electrographic monitoring for atrial fibrillation in acute ischemic stroke and transient ischemic attack.

Int J Stroke 2017 10 8;12(7):786-789. Epub 2017 Mar 8.

1 Melbourne Brain Centre, Royal Melbourne Hospital, the University of Melbourne, Victoria, Australia.

Rationale Paroxysmal atrial fibrillation is a common and preventable cause of devastating strokes. However, currently available monitoring methods, including Holter monitoring, cardiac telemetry and event loop recorders, have drawbacks that restrict their application in the general stroke population. AliveCor™ heart monitor, a novel device that embeds miniaturized electrocardiography (ECG) in a smartphone case coupled with an application to record and diagnose the ECG, has recently been shown to provide an accurate and sensitive single lead ECG diagnosis of atrial fibrillation. This device could be used by nurses to record a 30-s ECG instead of manual pulse taking and automatically provide a diagnosis of atrial fibrillation. Aims To compare the proportion of patients with paroxysmal atrial fibrillation detected by AliveCor™ ECG monitoring with current standard practice. Sample size 296 Patients. Design Consecutive ischemic stroke and transient ischemic attack patients presenting to participating stroke units without known atrial fibrillation will undergo intermittent AliveCor™ ECG monitoring administered by nursing staff at the same frequency as the vital observations of pulse and blood pressure until discharge, in addition to the standard testing paradigm of each participating stroke unit to detect paroxysmal atrial fibrillation. Study outcome Proportion of patients with paroxysmal atrial fibrillation detected by AliveCor™ ECG monitoring compared to 12-lead ECG, 24-h Holter monitoring and cardiac telemetry. Discussion Use of AliveCor™ heart monitor as part of routine stroke unit nursing observation has the potential to be an inexpensive non-invasive method to increase paroxysmal atrial fibrillation detection, leading to improvement in stroke secondary prevention.
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http://dx.doi.org/10.1177/1747493017696097DOI Listing
October 2017

Validating a Predictive Model of Acute Advanced Imaging Biomarkers in Ischemic Stroke.

Stroke 2017 03 19;48(3):645-650. Epub 2017 Jan 19.

From the Departments of Neurology, John Hunter Hospital, University of Newcastle, Australia (A.B., C.L., L.L., N.J.S., M.P.); Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China (X.C., M.L., Q.D.); Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, ON, Canada (R.A.); Department of Neurology, Royal Adelaide Hospital, Australia (T.K., J.J.); Department of Neurology, Gosford Hospital, Australia (B.O.); Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Canada (K.B.); and Department of Neurology, Baotou Central Hospital, China (J.Z.).

Background And Purpose: Advanced imaging to identify tissue pathophysiology may provide more accurate prognostication than the clinical measures used currently in stroke. This study aimed to derive and validate a predictive model for functional outcome based on acute clinical and advanced imaging measures.

Methods: A database of prospectively collected sub-4.5 hour patients with ischemic stroke being assessed for thrombolysis from 5 centers who had computed tomographic perfusion and computed tomographic angiography before a treatment decision was assessed. Individual variable cut points were derived from a classification and regression tree analysis. The optimal cut points for each assessment variable were then used in a backward logic regression to predict modified Rankin scale (mRS) score of 0 to 1 and 5 to 6. The variables remaining in the models were then assessed using a receiver operating characteristic curve analysis.

Results: Overall, 1519 patients were included in the study, 635 in the derivation cohort and 884 in the validation cohort. The model was highly accurate at predicting mRS score of 0 to 1 in all patients considered for thrombolysis therapy (area under the curve [AUC] 0.91), those who were treated (AUC 0.88) and those with recanalization (AUC 0.89). Next, the model was highly accurate at predicting mRS score of 5 to 6 in all patients considered for thrombolysis therapy (AUC 0.91), those who were treated (0.89) and those with recanalization (AUC 0.91). The odds ratio of thrombolysed patients who met the model criteria achieving mRS score of 0 to 1 was 17.89 (4.59-36.35, <0.001) and for mRS score of 5 to 6 was 8.23 (2.57-26.97, <0.001).

Conclusions: This study has derived and validated a highly accurate model at predicting patient outcome after ischemic stroke.
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http://dx.doi.org/10.1161/STROKEAHA.116.015143DOI Listing
March 2017

Perfusion computed tomography in patients with stroke thrombolysis.

Brain 2017 03;140(3):684-691

Departments of Neurology, John Hunter Hospital, University of Newcastle, Lookout Road, New Lambton Heights, NSW 2305, Australia.

See Saver (doi:10.1093/awx020) for a scientific commentary on this article.Stroke shortens an individual's disability-free life. We aimed to assess the relative prognostic influence of pre- and post-treatment perfusion computed tomography imaging variables (e.g. ischaemic core and penumbral volumes) compared to standard clinical predictors (such as onset-to-treatment time) on long-term stroke disability in patients undergoing thrombolysis. We used data from a prospectively collected international, multicentre, observational registry of acute ischaemic stroke patients who had perfusion computed tomography and computed tomography angiography before treatment with intravenous alteplase. Baseline perfusion computed tomography and follow-up magnetic resonance imaging were analysed to derive the baseline penumbra volume, baseline ischaemic core volume, and penumbra salvaged from infarction. The primary outcome measure was the effect of imaging and clinical variables on Disability-Adjusted Life Year. Clinical variables were age, sex, National Institutes of Health Stroke Scale score, and onset-to-treatment time. Age, sex, country, and 3-month modified Rankin Scale were extracted from the registry to calculate disability-adjusted life-year due to stroke, such that 1 year of disability-adjusted life-year equates to 1 year of healthy life lost due to stroke. There were 772 patients receiving alteplase therapy. The number of disability-adjusted life-year days lost per 1 ml of baseline ischaemic core volume was 17.5 (95% confidence interval, 13.2-21.9 days, P < 0.001). For every millilitre of penumbra salvaged, 7.2 days of disability-adjusted life-year days were saved (β = -7.2, 95% confidence interval, -10.4 to -4.1 days, P < 0.001). Each minute of earlier onset-to-treatment time resulted in a saving of 4.4 disability-free days after stroke (1.3-7.5 days, P = 0.006). However, after adjustment for imaging variables, onset-to-treatment time was not significantly associated with savings in disability-adjusted life-year days. Pretreatment perfusion computed tomography can (independently of clinical variables) predict significant gains, or loss, of disability-free life in patients undergoing reperfusion therapy for stroke. The effect of earlier treatment on disability-free life appears explained by salvage of penumbra, particularly when the ischaemic core is not too large.
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http://dx.doi.org/10.1093/brain/aww338DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5382947PMC
March 2017

Stroke Epidemiology in an Australian Rural Cohort (SEARCH).

Int J Stroke 2017 02 30;12(2):161-168. Epub 2016 Sep 30.

4 The George Institute for Global Health, Sydney, Australia.

Background Stroke rates in Australia and New Zealand have been declining since 1990 but all studies have been completed in large urban centers. Aim We report the first Australasian stroke incidence study in a rural population. Methods The authors applied the principle of complete ascertainment, used the WHO standard definition of stroke and classified ischemic stroke by the TOAST criteria. Data were collected from five rural centers defined by postcode of residence, over a 2-year period with 12 months of follow up of all cases. Results There were 217 strokes in 215 individuals in a population of 96,036 people, over 2 years, giving a crude attack rate of 113 per 100,000 per year. The 181 first-ever strokes (83% of total), standardized to the WHO world population, occurred at a rate of 50/100,000 (95% CI: 43-58). The 28-day fatality for first-ever strokes was 24% (95% CI: 18-31) and 77% (95% CI: 71-83) were classified as ischemic (140/181), 15% (95% CI: 10-21) intracerebral hemorrhage, 3% (95% CI: 1-6) due to subarachnoid hemorrhage and 5% (95% CI: 2-9) were unknown. A high proportion of first-ever ischemic strokes (44%) were cardioembolic, mostly (77%) due to atrial arrhythmias. Of the 38 with known atrial arrhythmias prior to stroke, only six (16%) were therapeutically anticoagulated. Conclusions This rural companion study of a recent Australian urban stroke incidence study confirms the downward trend of stroke incidence in Australia, and reiterates that inadequate anticoagulation of atrial arrhythmia remains a preventable cause of ischemic stroke.
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http://dx.doi.org/10.1177/1747493016670174DOI Listing
February 2017

Too good to treat? ischemic stroke patients with small computed tomography perfusion lesions may not benefit from thrombolysis.

Ann Neurol 2016 Aug 26;80(2):286-93. Epub 2016 Jul 26.

Departments of Neurology, John Hunter Hospital, University of Newcastle, Newcastle, Australia.

Objective: Although commonly used in clinical practice, there remains much uncertainty about whether perfusion computed tomography (CTP) should be used to select stroke patients for acute reperfusion therapy. In this study, we tested the hypothesis that a small acute perfusion lesion predicts good clinical outcome regardless of thrombolysis administration.

Methods: We used a prospectively collected cohort of acute ischemic stroke patients being assessed for treatment with IV-alteplase, who had CTP before a treatment decision. Volumetric CTP was retrospectively analyded to identify patients with a small perfusion lesion (<15ml in volume). The primary analysis was excellent 3-month outcome in patients with a small perfusion lesion who were treated with alteplase compared to those who were not treated.

Results: Of 1526 patients, 366 had a perfusion lesion <15ml and were clinically eligible for alteplase (212 being treated and 154 not treated). Median acute National Institutes of Health Stroke Scale score was 8 in each group. Of the 366 patients with a small perfusion lesion, 227 (62%) were modified Rankin Scale (mRS) 0 to 1 at day 90. Alteplase-treated patients were less likely to achieve 90-day mRS 0 to 1 (57%) than untreated patients (69%; relative risk [RR] = 0.83; 95% confidence interval [CI], 0.71-0.97; p = 0.022) and did not have different rates of mRS 0 to 2 (72% treated patients vs 77% untreated; RR, 0.93; 95% CI, 0.82-1.95; p = 0.23).

Interpretation: This large observational cohort suggests that a portion of ischemic stroke patients clinically eligible for alteplase therapy with a small perfusion lesion have a good natural history and may not benefit from treatment. Ann Neurol 2016;80:286-293.
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http://dx.doi.org/10.1002/ana.24714DOI Listing
August 2016

Determining the Number of Ischemic Strokes Potentially Eligible for Endovascular Thrombectomy: A Population-Based Study.

Stroke 2016 05 17;47(5):1377-80. Epub 2016 Mar 17.

From the Department of Neurology, Royal Adelaide Hospital, Adelaide, Australia (N.H.C., J.J., T.J.K.); Department of Neurology, Lyell McEwin Hospital, Adelaide, Australia (J.M.L., T.J.K.); and Department of Medicine, University of Adelaide, Adelaide, Australia (J.N., J.J., T.J.K.).

Background And Purpose: Endovascular thrombectomy (ET) is standard-of-care for ischemic stroke patients with large vessel occlusion, but estimates of potentially eligible patients from population-based studies have not been published. Such data are urgently needed to rationally plan hyperacute services. Retrospective analysis determined the incidence of ET-eligible ischemic strokes in a comprehensive population-based stroke study (Adelaide, Australia 2009-2010).

Methods: Stroke patients were stratified via a prespecified eligibility algorithm derived from recent ET trials comprising stroke subtype, pathogenesis, severity, premorbid modified Rankin Score, presentation delay, large vessel occlusion, and target mismatch penumbra. Recognizing centers may interpret recent ET trials either loosely or rigidly; 2 eligibility algorithms were applied: restrictive (key criteria modified Rankin Scale score 0-1, presentation delay <3.5 hours, and target mismatch penumbra) and permissive (modified Rankin Scale score 0-3 and presentation delay <5 hours).

Results: In a population of 148 027 people, 318 strokes occurred in the 1-year study period (crude attack rate 215 [192-240] per 100 000 person-years). The number of ischemic strokes eligible by restrictive criteria was 17/258 (7%; 95% confidence intervals 4%-10%) and by permissive criteria, an additional 16 were identified, total 33/258 (13%; 95% confidence intervals 9%-18%). Two of 17 patients (and 6/33 permissive patients) had thrombolysis contraindications. Using the restrictive algorithm, there were 11 (95% confidence intervals 4-18) potential ET cases per 100 000 person-years or 22 (95% confidence intervals 13-31) using the permissive algorithm.

Conclusions: In this cohort, ≈7% of ischemic strokes were potentially eligible for ET (13% with permissive criteria). In similar populations, the permissive criteria predict that ≤22 strokes per 100 000 person-years may be eligible for ET.
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http://dx.doi.org/10.1161/STROKEAHA.116.013165DOI Listing
May 2016

Global White Matter Hypoperfusion on CT Predicts Larger Infarcts and Hemorrhagic Transformation after Acute Ischemia.

CNS Neurosci Ther 2016 Mar 18;22(3):238-43. Epub 2016 Jan 18.

Departments of Neurology, John Hunter Hospital, University of Newcastle, Newcastle, NSW, Australia.

Introduction: Presence of white matter hyperintensity (WMH) on MRI is a marker of cerebral small vessel disease and is associated with increased small vessel stroke and increased risk of hemorrhagic transformation (HT) after thrombolysis.

Aim: We sought to determine whether white matter hypoperfusion (WMHP) on perfusion CT (CTP) was related to WMH, and if WMHP predisposed to acute lacunar stroke subtype and HT after thrombolysis.

Methods: Acute ischemic stroke patients within 12 h of symptom onset at 2 centers were prospectively recruited between 2011 and 2013 for the International Stroke Perfusion Imaging Registry. Participants routinely underwent baseline CT imaging, including CTP, and follow-up imaging with MRI at 24 h.

Results: Of 229 ischemic stroke patients, 108 were Caucasians and 121 Chinese. In the contralateral white matter, patients with acute lacunar stroke had lower cerebral blood flow (CBF) and cerebral blood volume (CBV), compared to those with other stroke subtypes (P = 0.041). There were 46 patients with HT, and WMHP was associated with increased risk of HT (R(2) = 0.417, P = 0.002). Compared to previously reported predictors of HT, WMHP performed better than infarct core volume (R(2) = 0.341, P = 0.034), very low CBV volume (R(2) = 0.249, P = 0.026), and severely delayed perfusion (Tmax>14 second R(2) = 0.372, P = 0.011). Patients with WMHP also had larger acute infarcts and increased infarct growth compared to those without WMHP (mean 28 mL vs. 13 mL P < 0.001).

Conclusion: White matter hypoperfusion remote to the acutely ischemic region on CTP is a marker of small vessel disease and was associated with increased HT, larger acute infarct cores, and greater infarct growth.
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http://dx.doi.org/10.1111/cns.12491DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6492900PMC
March 2016

Genome-Wide Association Analysis of Young-Onset Stroke Identifies a Locus on Chromosome 10q25 Near HABP2.

Stroke 2016 Feb 5;47(2):307-16. Epub 2016 Jan 5.

From the Veterans Affairs Maryland Health Care System, Baltimore, MD (Y.-C.C., S.J.K., J.W.C., B.D.M.); University of Maryland School of Medicine, Baltimore (Y.-C.C., H.X., S.J.K., J.W.C., J.R.O., B.D.M.); The University of Gothenburg, Gothenburg, Sweden (T.M.S., C.J.); University of Rostock, Rostock, Germany (A.-K.G., A. Rolfs); University of Nottingham Malaysia Campus, Selangor Darul Ehsa, Malaysia (W.K.H.); University of Cambridge, Cambridge, UK (M.T., J.D., S.B., H.S.M., S.D., D.S.); Institut Pasteur de Lille, F-59000 Lille, France (P.A.); University of Newcastle, Australia (E.G.H.); Ludwig-Maximilians-Universität, Munich, Germany (R.M., K.S., M.D.); Wellcome Trust Sanger Institute, Cambridge, UK (J.D.); Center for Non-Communicable Diseases, Karachi, Pakistan (A. Rasheed, D.S.); University of Pennsylvania (W.Z., D.S.); Basel University Hospital, Switzerland (S.E.); Heidelberg University Hospital, Germany (C.G.-G.); Centre d'Étude du Polymorphisme Humain, Paris, France (Y.K.); RIKEN Center for Integrative Medical Sciences, Yokohama, Japan (Y.K.); National Genotyping Center, Evry, France (M.L.); Genome Quebec, McGill University, Montreal, Canada (M.L.); Lille University Hospital, France (D.L., S.D.); KU Leuven - University of Leuven, Leuven, Belgium (V.T.); Vesalius Research Center, VIB, Leuven, Belgium (V.T.); University Hospitals Leuven, Leuven, Belgium (V.T.); Helsinki University Central Hospital, Helsinki, Finland (T.M.M., T.T.); Università degli Studi di Brescia, Brescia, Italy (A. Pezzini); Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy (E.A.P., G.B.B.); University of Lund, Sweden (B.N.); University of Oxford, John Radcliffe Hospital (P.M.R.); University of Edinburgh, Edinburgh, UK (C.S.); Jagiellonian University Medical College, Krakow, Poland (A.S.); Lund University, Lund, Sweden (A.L.); Skåne University Hospital, Lund, Sweden (A.L.); University of Glasgow, Glasgow, UK (M.R.W.); University of Adelaide, Australia (J.J.); Mount Sinai Hos

Background And Purpose: Although a genetic contribution to ischemic stroke is well recognized, only a handful of stroke loci have been identified by large-scale genetic association studies to date. Hypothesizing that genetic effects might be stronger for early- versus late-onset stroke, we conducted a 2-stage meta-analysis of genome-wide association studies, focusing on stroke cases with an age of onset <60 years.

Methods: The discovery stage of our genome-wide association studies included 4505 cases and 21 968 controls of European, South-Asian, and African ancestry, drawn from 6 studies. In Stage 2, we selected the lead genetic variants at loci with association P<5×10(-6) and performed in silico association analyses in an independent sample of ≤1003 cases and 7745 controls.

Results: One stroke susceptibility locus at 10q25 reached genome-wide significance in the combined analysis of all samples from the discovery and follow-up stages (rs11196288; odds ratio =1.41; P=9.5×10(-9)). The associated locus is in an intergenic region between TCF7L2 and HABP2. In a further analysis in an independent sample, we found that 2 single nucleotide polymorphisms in high linkage disequilibrium with rs11196288 were significantly associated with total plasma factor VII-activating protease levels, a product of HABP2.

Conclusions: HABP2, which encodes an extracellular serine protease involved in coagulation, fibrinolysis, and inflammatory pathways, may be a genetic susceptibility locus for early-onset stroke.
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http://dx.doi.org/10.1161/STROKEAHA.115.011328DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4729659PMC
February 2016

Endovascular treatment for acute ischemic stroke: experience in South Australia.

Int J Stroke 2015 Aug;10(6):E64-5

Stroke Research Programme, Discipline of Medicine, University of Adelaide, Adelaide, SA, Australia.

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http://dx.doi.org/10.1111/ijs.12580DOI Listing
August 2015

Genetic overlap between diagnostic subtypes of ischemic stroke.

Stroke 2015 Mar 22;46(3):615-9. Epub 2015 Jan 22.

From the School of Medicine and Public Health (E.G.H., J.A.), School of Biomedical Sciences and Pharmacy (L.F.L., R.J.S.), and School of Nursing and Midwifery (J.M.M.), University of Newcastle, Australia; Clinical Research Design, IT and Statistical Support Unit, Public Health Research Program, Hunter Medical Research Institute, Newcastle, Australia (E.G.H., C.O., J.A.); Department of Clinical Neurosciences, University of Cambridge, UK (M.T., S.B., H.S.M.); Institute for Stroke and Dementia Research, Klinikum der Universität Mün-chen, Ludwig-Maximilians-Universität, Munich, Germany (R.M., M.D.); Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA (G.F., J.R.); Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA (G.F., J.R.); Clinical Trial Service Unit and Epidemiological Studies Unit (J.C.H., R.C.) and Stroke Prevention Research Unit, Nuffield Department of Clinical Neuroscience (P.M.R.), University of Oxford, UK; Department of Medicine (Y.-C.C., B.D.M.) and Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine, Baltimore, MD; Imperial College Cerebrovascular Research Unit (ICCRU), Imperial College, London, UK (I.C.); Cardiovascular Health Research Unit, Department of Medicine (J.C.B., B.M.P., K.L.W.) and Department of Epidemiology (B.M.P.), University of Washington, Seattle, WA; Institute of Molecular Medicine and Human Genetics Center, the University of Texas Health Science Center at Houston, TX (E.B., M.F.); Department of Cerebrovascular Disease, IRCCS Istituto Neurologico Carlo Besta, Milan, Italy (G.B.B., E.A.P.); Department of Neurology, Veterans Affairs Medical Center, Baltimore, MD (J.W.C., S.J.K.); Department of Neurology, Rhode Island Hospital, Providence, RI (K.L.F.); Departments of Epidemiology, Neurology, and Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands (M.A.I.); School of Medicine, University of Adelaide, Australia (J.J.); H

Background And Purpose: Despite moderate heritability, the phenotypic heterogeneity of ischemic stroke has hampered gene discovery, motivating analyses of diagnostic subtypes with reduced sample sizes. We assessed evidence for a shared genetic basis among the 3 major subtypes: large artery atherosclerosis (LAA), cardioembolism, and small vessel disease (SVD), to inform potential cross-subtype analyses.

Methods: Analyses used genome-wide summary data for 12 389 ischemic stroke cases (including 2167 LAA, 2405 cardioembolism, and 1854 SVD) and 62 004 controls from the Metastroke consortium. For 4561 cases and 7094 controls, individual-level genotype data were also available. Genetic correlations between subtypes were estimated using linear mixed models and polygenic profile scores. Meta-analysis of a combined LAA-SVD phenotype (4021 cases and 51 976 controls) was performed to identify shared risk alleles.

Results: High genetic correlation was identified between LAA and SVD using linear mixed models (rg=0.96, SE=0.47, P=9×10(-4)) and profile scores (rg=0.72; 95% confidence interval, 0.52-0.93). Between LAA and cardioembolism and SVD and cardioembolism, correlation was moderate using linear mixed models but not significantly different from zero for profile scoring. Joint meta-analysis of LAA and SVD identified strong association (P=1×10(-7)) for single nucleotide polymorphisms near the opioid receptor μ1 (OPRM1) gene.

Conclusions: Our results suggest that LAA and SVD, which have been hitherto treated as genetically distinct, may share a substantial genetic component. Combined analyses of LAA and SVD may increase power to identify small-effect alleles influencing shared pathophysiological processes.
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http://dx.doi.org/10.1161/STROKEAHA.114.007930DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4342266PMC
March 2015

Safety of varenicline tartrate and counseling versus counseling alone for smoking cessation: a randomized controlled trial for inpatients (STOP study).

Nicotine Tob Res 2014 Nov 16;16(11):1495-502. Epub 2014 Jul 16.

University of South Australia, Adelaide, South Australia, Australia.

Introduction: Inpatient medical settings offer an opportunistic environment for initiating smoking cessation interventions to patients reflecting on their health. Current evidence has shown the superior efficacy of varenicline tartrate (VT) for smoking cessation compared with other tobacco cessation therapies; however, recent evidence also has highlighted concerns about the safety and tolerability of VT. Given these apprehensions, we aimed to evaluate the safety and effectiveness of VT plus quitline-counseling compared to quitline-counseling alone in the inpatient medical setting.

Methods: Adult patients (n = 392, 20-75 years) admitted with a smoking-related illnesses to 3 hospitals were randomized to receive either 12 weeks of varenicline tartrate (titrated from 0.5mg daily to 1mg twice daily) plus quitline-counseling (VT+C), (n = 196) or quitline-counseling alone (n = 196).

Results: VT was well tolerated in the inpatient setting among subjects admitted with acute smoking-related illnesses (mean age 52.8±2.89 and 53.7±2.77 years in the VT+C and counseling alone groups, respectively). The most common self-reported adverse event during the 12-week treatment phase was nausea (16.3% in the VT+C group compared with 1.5% in the counseling alone group). Thirteen deaths occurred during the study period (n = 6 were in the VT+C arm compared with n = 7 in the counseling alone arm). All of these subjects had known comorbidities or developed underlying comorbidities.

Conclusions: VT appears to be a safe and well-tolerated opportunistic treatment for inpatient smokers who have related chronic disease. Based on the proven efficacy of varenicline from outpatient studies and our recent inpatient evidence, we suggest it be considered as part of standard care in the hospital setting.
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http://dx.doi.org/10.1093/ntr/ntu112DOI Listing
November 2014
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