Effect of Octamer-Binding Transcription Factor 4 Overexpression on the Neural Induction of Human Dental Pulp Stem Cells.

Stem cell-based therapy is a potential alternative strategy for brain repair, with neural stem cells (NSC) presenting as the most promising candidates. Obtaining sufficient quantities of NSC for clinical applications is challenging, therefore alternative cell types, such as neural crest-derived dental pulp stem cells (DPSC), may be considered. Human DPSC possess neurogenic potential, exerting positive effects in the damaged brain through paracrine effects. However, a method for conversion of DPSC into NSC has yet to be developed. Here, overexpression of octamer-binding transcription factor 4 (OCT4) in combination with neural inductive conditions was used to reprogram human DPSC along the neural lineage. The reprogrammed DPSC demonstrated a neuronal-like phenotype, with increased expression levels of neural markers, limited capacity for sphere formation, and enhanced neuronal but not glial differentiation. Transcriptomic analysis further highlighted the expression of genes associated with neural and neuronal functions. In vivo analysis using a developmental avian model showed that implanted DPSC survived in the developing central nervous system and respond to endogenous signals, displaying neuronal phenotypes. Therefore, OCT4 enhances the neural potential of DPSC, which exhibited characteristics aligning with neuronal progenitors. This method can be used to standardise DPSC neural induction and provide an alternative source of neural cell types.

Developing and Planning a Protocol for Implementing Health Promoting Animal Assisted Interventions (AAI) in a Tertiary Health Setting.

The Ottawa Charter identifies that multiple levels of government, non-government, community, and other organizations should work together to facilitate health promotion, including in acute settings such as hospitals. We outline a method and protocol to achieve this, namely an Action Research (AR) framework for an Animal Assisted Intervention (AAI) in a tertiary health setting. Dogs Offering Support after Stroke (DOgSS) is an AR study at a major tertiary referral hospital. AAI has been reported to improve mood and quality of life for patients in hospitals. Our project objectives included applying for funding, developing a hospital dog visiting Action Research project, and, subsequent to ethics and governance approvals and finance, undertaking and reporting on the Action Research findings. The Action Research project aimed to investigate whether AAI (dog-visiting) makes a difference to the expressed mood of stroke patients and their informal supports (visiting carers/family/friends), and also the impact these visits have on hospital staff and volunteers, as well as the dog handler and dog involved. We provide our protocol for project management and operations, setting out how the project is conducted from conception to assess human and animal wellbeing and assist subsequent decision-making about introducing dog-visiting to the Stroke Unit. The protocol can be used or adapted by other organizations to try to avoid pitfalls and support health promotion in one of the five important action areas of the Ottawa Charter, namely that of reorienting health services.

FAST-IT: Find A Simple Test - In TIA (transient ischaemic attack): a prospective cohort study to develop a multivariable prediction model for diagnosis of TIA through proteomic discovery and candidate lipid mass spectrometry, neuroimaging and machine learning-study protocol.

INTRODUCTION: Transient ischaemic attack (TIA) may be a warning sign of stroke and difficult to differentiate from minor stroke and TIA-mimics. Urgent evaluation and diagnosis is important as treating TIA early can prevent subsequent strokes. Recent improvements in mass spectrometer technology allow quantification of hundreds of plasma proteins and lipids, yielding large datasets that would benefit from different approaches including machine learning. Using plasma protein, lipid and radiological biomarkers, our study will develop predictive algorithms to distinguish TIA from minor stroke (positive control) and TIA-mimics (negative control). Analysis including machine learning employs more sophisticated modelling, allowing non-linear interactions, adapting to datasets and enabling development of multiple specialised test-panels for identification and differentiation. METHODS AND ANALYSIS: Patients attending the Emergency Department, Stroke Ward or TIA Clinic at the Royal Adelaide Hospital with TIA, minor stroke or TIA-like symptoms will be recruited consecutively by staff-alert for this prospective cohort study. Advanced neuroimaging will be performed for each participant, with images assessed independently by up to three expert neurologists. Venous blood samples will be collected within 48 hours of symptom onset. Plasma proteomic and lipid analysis will use advanced mass spectrometry (MS) techniques. Principal component analysis and hierarchical cluster analysis will be performed using MS software. Output files will be analysed for relative biomarker quantitative differences between the three groups. Differences will be assessed by linear regression, one-way analysis of variance, Kruskal-Wallis H-test, χ2 test or Fisher's exact test. Machine learning methods will also be applied including deep learning using neural networks. ETHICS AND DISSEMINATION: Patients will provide written informed consent to participate in this grant-funded study. The Central Adelaide Local Health Network Human Research Ethics Committee approved this study (HREC/18/CALHN/384; R20180618). Findings will be disseminated through peer-reviewed publication and conferences; data will be managed according to our Data Management Plan (DMP2020-00062).

Automated information extraction from free-text medical documents for stroke key performance indicators: a pilot study.

Automated information extraction might be able to assist with the collection of stroke key performance indicators (KPI). The feasibility of using natural language processing for classification-based KPI and datetime field extraction was assessed. Using free-text discharge summaries, random forest models achieved high levels of performance in classification tasks (area under the receiver operator curve 0.95-1.00). The datetime field extraction method was successful in 29 of 43 (67.4%) cases. Further studies are indicated.

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

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.

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

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.

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

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.

Normal aging, motor neurone disease, and Alzheimer's disease are characterized by cortical changes in inflammatory cytokines.

The role of increased brain inflammation in the development of neurodegenerative diseases is unclear. Here, we have compared cytokine changes in normal aging, motor neurone disease (MND), and Alzheimer's disease (AD). After an initial analysis, six candidate cytokines, interleukin (IL)- 4, 5, 6, 10, macrophage inhibitory protein (MIP)-1α, and fibroblast growth factor (FGF)-2, showing greatest changes were assayed in postmortem frozen human superior frontal gyri (n = 12) of AD patients, aging and young adult controls along with the precentral gyrus (n = 12) of MND patients. Healthy aging was associated with decreased anti-inflammatory IL-10 and FGF-2 levels. AD prefrontal cortex was associated with increased levels of IL-4, IL-5, and FGF-2, with the largest increase seen for FGF-2. Notwithstanding differences in the specific frontal lobe gyrus sampled, MND patients' primary motor cortex (precentral gyrus) was associated with increased levels of IL-5, IL-6, IL-10, and FGF-2 compared to the aging prefrontal cortex (superior frontal gyrus). Immunocytochemistry showed that FGF-2 is expressed in neurons, astrocytes, and microglia in normal aging prefrontal cortex, AD prefrontal cortex, and MND motor cortex. We report that healthy aging and age-related neurodegenerative diseases have different cortical inflammatory signatures that are characterized by increased levels of anti-inflammatory cytokines and call into question the view that increased inflammation underlies the development of age-related neurodegenerative diseases.

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

[This corrects the article DOI: 10.1371/journal.pone.0231095.].

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

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.

The BMP antagonist gremlin 1 contributes to the development of cortical excitatory neurons, motor balance and fear responses.

Bone morphogenetic protein (BMP) signaling is required for early forebrain development and cortical formation. How the endogenous modulators of BMP signaling regulate the structural and functional maturation of the developing brain remains unclear. Here, we show that expression of the BMP antagonist Grem1 marks committed layer V and VI glutamatergic neurons in the embryonic mouse brain. Lineage tracing of Grem1-expressing cells in the embryonic brain was examined by administration of tamoxifen to pregnant Grem1creERT; Rosa26LSLTdtomato mice at 13.5 days post coitum (dpc), followed by collection of embryos later in gestation. In addition, at 14.5 dpc, bulk mRNA-seq analysis of differentially expressed transcripts between FACS-sorted Grem1-positive and -negative cells was performed. We also generated Emx1-cre-mediated Grem1 conditional knockout mice (Emx1-Cre;Grem1flox/flox) in which the Grem1 gene was deleted specifically in the dorsal telencephalon. Grem1Emx1cKO animals had reduced cortical thickness, especially layers V and VI, and impaired motor balance and fear sensitivity compared with littermate controls. This study has revealed new roles for Grem1 in the structural and functional maturation of the developing cortex.

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

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.

Evidence for a Window of Enhanced Plasticity in the Human Motor Cortex Following Ischemic Stroke.

BACKGROUND: In preclinical models, behavioral training early after stroke produces larger gains compared with delayed training. The effects are thought to be mediated by increased and widespread reorganization of synaptic connections in the brain. It is viewed as a period of spontaneous biological recovery during which synaptic plasticity is increased. OBJECTIVE: To look for evidence of a similar change in synaptic plasticity in the human brain in the weeks and months after ischemic stroke. METHODS: We used continuous theta burst stimulation (cTBS) to activate synapses repeatedly in the motor cortex. This initiates early stages of synaptic plasticity that temporarily reduces cortical excitability and motor-evoked potential amplitude. Thus, the greater the effect of cTBS on the motor-evoked potential, the greater the inferred level of synaptic plasticity. Data were collected from separate cohorts (Australia and UK). In each cohort, serial measurements were made in the weeks to months following stroke. Data were obtained for the ipsilesional motor cortex in 31 stroke survivors (Australia, 66.6 ± 17.8 years) over 12 months and the contralesional motor cortex in 29 stroke survivors (UK, 68.2 ± 9.8 years) over 6 months. RESULTS: Depression of cortical excitability by cTBS was most prominent shortly after stroke in the contralesional hemisphere and diminished over subsequent sessions (P = .030). cTBS response did not differ across the 12-month follow-up period in the ipsilesional hemisphere (P = .903). CONCLUSIONS: Our results provide the first neurophysiological evidence consistent with a period of enhanced synaptic plasticity in the human brain after stroke. Behavioral training given during this period may be especially effective in supporting poststroke recovery.

Clinical Translation of Cell Therapies in Stroke (CT2S) Checklist-a pragmatic tool to accelerate development of cell therapy products.

BACKGROUND: Cell therapies present an exciting potential but there is a long history of expensive translational failures in stroke research. Researchers engaged in cell therapy research would benefit from a practical framework that can help in planning research and development of investigational cell therapies into viable medical products. METHODS: We developed a checklist using a mixed methodology approach to evaluate the impact of study design, regulatory policy, ethical, and health economic considerations for efficient implementation of early phase cell therapy studies. RESULTS: The checklist comprises a series of questions arranged under four domains: the first concerns study design such as characterization of target study population, trial design, endpoints and operational fit of dosage, time, and route of administration to target populations. A second domain addresses the data package required for regulatory approval relevant to the intended use (allogeneic/autologous; homologous/non-homologous; nature of cell processing). The third domain comprises patient involvement to ensure relevant data is collected via targeted study design. The final domain requires the team to determine the critical data elements that could be built into study design to enable health economic data collection to be started at an early phase of the study. CONCLUSIONS: The CT2S checklist can help to determine areas of expertise gaps and enable research groups to appropriately allocate resources for capacity building. Use of this checklist will allow identification of key areas where trial planning needs to be optimized, as well as helping to identify resources that need to be secured. The CT2S checklist can also serve as a general cell therapy research decision aid to improve research output and accelerate new cell therapy development.

Delayed Treatment with Human Dental Pulp Stem Cells Accelerates Functional Recovery and Modifies Responses of Peri-Infarct Astrocytes Following Photothrombotic Stroke in Rats.

Dental pulp contains multipotent mesenchymal stem cells that improve outcomes when administered early after temporary middle cerebral artery occlusion in rats. To further assess the therapeutic potential of these cells, we tested whether functional recovery following stroke induced by photothrombosis could be modified by a delayed treatment that was initiated after the infarct attained maximal volume. Photothrombosis induces permanent focal ischemia resulting in tissue changes that better reflect key aspects of the many human strokes in which early restoration of blood flow does not occur. Human dental pulp stem cells (approximately 400 × 103 viable cells) or vehicle were injected into the infarct and adjacent brain tissue of Sprague-Dawley rats at 3 days after the induction of unilateral photothrombotic stroke in the sensorimotor cortex. Forepaw function was tested up to 28 days after stroke. Cellular changes in peri-infarct tissue at 28 days were assessed using immunohistochemistry. Rats treated with the stem cells showed faster recovery compared with vehicle-treated animals in a test of forelimb placing in response to vibrissae stimulation and in first attempt success in a skilled forelimb reaching test. Total success in the skilled reaching test and forepaw use during exploration in a Perspex cylinder were not significantly different between the 2 groups. At 28 days after stroke, rats treated with the stem cells showed decreased immunolabeling for glial fibrillary acidic protein in tissue up to 1 mm from the infarct, suggesting decreased reactive astrogliosis. Synaptophysin, a marker of synapses, and collagen IV, a marker of capillaries, were not significantly altered at this time by the stem-cell treatment. These results indicate that dental pulp stem cells can accelerate recovery without modifying initial infarct formation. Decreases in reactive astrogliosis in peri-infarct tissue could have contributed to the change by promoting adaptive responses in neighboring neurons.

Plasmin Generation Potential and Recanalization in Acute Ischaemic Stroke; an Observational Cohort Study of Stroke Biobank Samples.

Rationale: More than half of patients who receive thrombolysis for acute ischaemic stroke fail to recanalize. Elucidating biological factors which predict recanalization could identify therapeutic targets for increasing thrombolysis success. Hypothesis: We hypothesize that individual patient plasmin potential, as measured by in vitro response to recombinant tissue-type plasminogen activator (rt-PA), is a biomarker of rt-PA response, and that patients with greater plasmin response are more likely to recanalize early. Methods: This study will use historical samples from the Barcelona Stroke Thrombolysis Biobank, comprised of 350 pre-thrombolysis plasma samples from ischaemic stroke patients who received serial transcranial-Doppler (TCD) measurements before and after thrombolysis. The plasmin potential of each patient will be measured using the level of plasmin-antiplasmin complex (PAP) generated after in-vitro addition of rt-PA. Levels of antiplasmin, plasminogen, t-PA activity, and PAI-1 activity will also be determined. Association between plasmin potential variables and time to recanalization [assessed on serial TCD using the thrombolysis in brain ischemia (TIBI) score] will be assessed using Cox proportional hazards models, adjusted for potential confounders. Outcomes: The primary outcome will be time to recanalization detected by TCD (defined as TIBI ≥4). Secondary outcomes will be recanalization within 6-h and recanalization and/or haemorrhagic transformation at 24-h. This analysis will utilize an expanded cohort including ~120 patients from the Targeting Optimal Thrombolysis Outcomes (TOTO) study. Discussion: If association between proteolytic response to rt-PA and recanalization is confirmed, future clinical treatment may customize thrombolytic therapy to maximize outcomes and minimize adverse effects for individual patients.

Stroke incidence and subtypes in Aboriginal people in remote Australia: a healthcare network population-based study.

OBJECTIVES: We aimed to compare the incidence, subtypes and aetiology of stroke, and in-hospital death due to stroke, between Aboriginal and non-Aboriginal people in Central Australia, a remote region of Australia where a high proportion Aboriginal people reside (40% of the population). We hypothesised that the rates of stroke, particularly in younger adults, would be greater in the Aboriginal population, compared with the non-Aboriginal population; we aimed to elucidate causes for any identified disparities. DESIGN: A retrospective population-based study of patients hospitalised with stroke within a defined region from 1 January 2011 to 31 December 2014. SETTING: Alice Springs Hospital, the only neuroimaging-capable acute hospital in Central Australia, serving a network of 50 healthcare facilities covering 672 000 km2. PARTICIPANTS: 161 residents (63.4% Aboriginal) of the catchment area admitted to hospital with stroke. PRIMARY AND SECONDARY OUTCOME MEASURES: Rates of first-ever stroke, overall (all events) stroke and in-hospital death. RESULTS: Of 121 residents with first-ever stroke, 61% identified as Aboriginal. Median onset-age (54 years) was 17 years younger in Aboriginal patients (p<0.001), and age-standardised stroke incidence was threefold that of non-Aboriginal patients (153 vs 51 per 100 000, incidence rate ratio 3.0, 95% CI 2 to 4). The rate ratios for the overall rate of stroke (first-ever and recurrent) were similar. In Aboriginal patients aged <55 years, the incidence of ischaemic stroke was 14-fold greater (95% CI 4 to 45), and intracerebral haemorrhage 19-fold greater (95% CI 3 to 142) than in non-Aboriginal patients. Crude prevalence of diabetes mellitus (70.3% vs 34.0%, p<0.001) and hypercholesterolaemia (68.9% vs 51.1%, p=0.049) was greater, and age-standardised in-hospital deaths were fivefold greater (35 vs 7 per 100 000, 95% CI 2 to 11) in Aboriginal patients than in non-Aboriginal patients. CONCLUSIONS: Stroke incidence (both subtypes) and in-hospital deaths for remote Aboriginal Australians are dramatically greater than in non-Aboriginal people, especially in patients aged <55 years.

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

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.

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

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.

Developing a multivariable prediction model for functional outcome after reperfusion therapy for acute ischaemic stroke: study protocol for the Targeting Optimal Thrombolysis Outcomes (TOTO) multicentre cohort study.

INTRODUCTION: Intravenous thrombolysis (IVT) with recombinant tissue plasminogen activator (rt-PA) is the only approved pharmacological reperfusion therapy for acute ischaemic stroke. Despite population benefit, IVT is not equally effective in all patients, nor is it without significant risk. Uncertain treatment outcome prediction complicates patient treatment selection. This study will develop and validate predictive algorithms for IVT response, using clinical, radiological and blood-based biomarker measures. A secondary objective is to develop predictive algorithms for endovascular thrombectomy (EVT), which has been proven as an effective reperfusion therapy since study inception. METHODS AND ANALYSIS: The Targeting Optimal Thrombolysis Outcomes Study is a multicenter prospective cohort study of ischaemic stroke patients treated at participating Australian Stroke Centres with IVT and/or EVT. Patients undergo neuroimaging using multimodal CT or MRI at baseline with repeat neuroimaging 24 hours post-treatment. Baseline and follow-up blood samples are provided for research use. The primary outcome is good functional outcome at 90 days poststroke, defined as a modified Rankin Scale (mRS) Score of 0-2. Secondary outcomes are reperfusion, recanalisation, infarct core growth, change in stroke severity, poor functional outcome, excellent functional outcome and ordinal mRS at 90 days. Primary predictive models will be developed and validated in patients treated only with rt-PA. Models will be built using regression methods and include clinical variables, radiological measures from multimodal neuroimaging and blood-based biomarkers measured by mass spectrometry. Predictive accuracy will be quantified using c-statistics and R2. In secondary analyses, models will be developed in patients treated using EVT, with or without prior IVT, reflecting practice changes since original study design. ETHICS AND DISSEMINATION: Patients, or relatives when patients could not consent, provide written informed consent to participate. This study received approval from the Hunter New England Local Health District Human Research Ethics Committee (reference 14/10/15/4.02). Findings will be disseminated via peer-reviewed publications and conference presentations.