Sentinel Stroke National Audit Programme Research Articles

The Burden of Stroke Mimics Among Hyperacute Stroke Unit Attendees with Special Emphasis on Migraine: A 10-Year Evaluation.

Stroke and migraine are common neurological illnesses that cause tremendous suffering for patients. Certain diseases can mimic the clinical manifestations of an actual stroke. Migraine is one of the most commonly reported stroke mimics. The main goals of this study are to look at the prevalence of stroke mimics on the stroke pathway of Sheffield Teaching Hospitals and how many of them are migraines. A retrospective service evaluation was conducted at the hyperacute stroke unit (HASU) of the Royal Hallamshire Hospital (RHH) in the United Kingdom. The total admissions from 2013 to 2022 were collected from the Sentinel Stroke National Audit Programme database, and the number of stroke mimics was evaluated each year. The burden of migraine stroke mimics was also evaluated. Then, a one-year sample of stroke mimics was extracted to look for the types of each mimic. From 2013 to 2022, 45.75% (n = 12156) of the stroke pathway patients (n = 26573) were stroke mimics, with an increment of up to 55% in the years 2021and 2022. During these10 years, migraine stroke mimics accounted for 10.21% of admissions (n = 1240). The three most common mimics in a one-year sample of stroke pathway patients were migraine (14.70%) (n = 373), functional neurological disorders (FNDs) (7.17%) (n = 182), and Guillain-Barré syndrome (6.66%) (n = 169). Seizures, syncope, and metabolic derangements were reported as mimics in 4.17% (n = 106), 3.14% (n = 80), and 1.77% (n = 45), respectively. About half of the HASU attendees were stroke mimics rather than actual strokes, and the most common mimics were migraines.

Impact of healthcare-associated infections within 7-days of acute stroke on health outcomes and risk of care-dependency: a multi-centre registry-based cohort study

Healthcare-associated infections (HCAIs) in patients admitted with acute conditions remain a major challenge to healthcare services. Here, we assessed the impact of HCAIs acquired within 7-days of acute stroke on indicators of care-quality outcomes and dependency. Data were prospectively collected (2014–2016) from the Sentinel Stroke National Audit Programme for 3309 patients (mean age = 76.2 yr, SD = 13.5) admitted to four UK hyperacute stroke units (HASU). Associations between variables were assessed by multivariable logistic regression (odds ratios, 95% confidence intervals), adjusted for age, sex, co-morbidities, pre-stroke disability, swallow screening, stroke type and severity. Within 7-days of admission, urinary tract infection (UTI) and pneumonia occurred in 7.6% and 11.3% of patients. Female (UTI only), older age, underlying hypertension, atrial fibrillation, previous stroke, pre-stroke disability, intracranial haemorrhage, severe stroke, and delay in swallow screening (pneumonia only) were independent risk factors of UTI and pneumonia. Compared to patients without UTI or pneumonia, those with either or both of these HCAIs were more likely to have prolonged stay (> 14-days) on HASU: 5.1 (3.8–6.8); high risk of malnutrition: 3.6 (2.9–4.5); palliative care: 4.5 (3.4–6.1); in-hospital mortality: 4.8 (3.8–6.2); disability at discharge: 7.5 (5.9–9.7); activity of daily living support: 1.6 (1.2–2.2); and discharge to care-home: 2.3 (1.6–3.3). In conclusion, HCAIs acquired within 7-days of an acute stroke led to prolonged hospitalisation, adverse health consequences and risk of care-dependency. These findings provide valuable information for timely intervention to reduce HCAIs, and minimising subsequent adverse outcomes.

Urinary incontinence indicates mortality, disability, and infections in hospitalised stroke patients.

To assess the impact of urinary incontinence (UI) on health outcomes over the entire spectrum of acute stroke severity (National Institutes of Health Stroke Scale [NIHSS] scores: 0-42), due to a paucity of data on patients with milder strokes. Data were prospectively collected (2014-2016) from the Sentinel Stroke National Audit Programme (1593 men, 1591 women; mean [SD] age 76.8 [13.3] years) admitted to four UK hyperacute stroke units (HASUs). Relationships between variables were assessed by multivariable logistic regression. Data were adjusted for age, sex, comorbidities, pre-stroke disability and intra-cranial haemorrhage, and presented as odds ratios with 95% confidence intervals. Amongst patients with no symptoms or a minor stroke (NIHSS scores of 0-4), compared to patients without UI, patients with UI had significantly greater risks of poor outcomes including: in-hospital mortality; disability at discharge; in-hospital pneumonia; urinary tract infection within 7 days of admission; prolonged length of stay on the HASU; palliative care by discharge; activity of daily living (ADL) support, and new discharge to care home. In patients with more moderate stroke (NIHSS score of 5-15) the same outcomes were identified; being at greater risk for patients with UI, except for palliative care by discharge and ADL support. With the highest stroke severity group (NIHSS score of 16-48) all outcomes were identified except in-patient mortality, pneumonia, and ADL support. However, odds ratios diminished as NIHSS scores increased. Urinary incontinence is a useful indicator of poor short-term outcomes in older patients with an acute stroke, but irrespective of stroke severity. This provides valuable information to healthcare professionals to identify at-risk individuals.

Developing and externally validating a machine learning risk prediction model for 30-day mortality after stroke using national stroke registers in the UK and Sweden

ObjectivesWe aimed to develop and externally validate a generalisable risk prediction model for 30-day stroke mortality suitable for supporting quality improvement analytics in stroke care using large nationwide stroke registers...

Determination of independent risk factors for early healthcare-associated infections acquired after acute stroke admission: A multi-centre registry-based cohort study

ObjectiveHealthcare-associated infections (HCAIs) in patients admitted with acute conditions pose a serious risk to patients and a major challenge to healthcare services. However, there is a lack of consistency in reporting aetiological risk factors, particularly in acute stroke patients. Here, we determined independent risk factors of two common HCAIs (urinary tract infection and pneumonia) acquired within 7-days of admission after an acute stroke. MethodsData were prospectively collected (2014–2016) from the Sentinel Stroke National Audit Programme for 3,309 patients (mean age=76.2yr, SD=13.5) admitted to four UK hyperacute stroke units. Associations between variables were assessed by forward stepwise multivariable logistic regression (odds ratios, 95 % confidence intervals). ResultsThe rate of urinary tract infection and/or pneumonia occurring within 7-days of admission was 15.0 %. The risk of urinary tract infection and/or pneumonia was increased amongst women: OR = 1.35 (1.08–1.68); patients from ethnic minority backgrounds: OR = 1.77 (1.01–3.10); patients aged 70–79 years: OR = 2.08 (1.42–3.06), and ≥80 years: OR = 3.20 (2.26–4.55); history of hypertension: OR = 1.59 (1.27–1.98); history of atrial fibrillation: OR = 1.67 (1.32–2.12); pre-stroke disability: OR = 2.08 (1.44–3.00); intracranial haemorrhage: OR = 1.41 (1.07–1.86); severe stroke: OR = 3.21 (2.32–4.45); swallow screening within 4–72 h: OR = 1.42 (1.08–1.86); swallow screening beyond 72 h: OR = 1.70 (1.08–2.70). History of congestive heart failure, diabetes and previous stroke did not significantly associate with HCAIs. ConclusionsA profile of independent risk factors for two common HCAIs in acute stroke was identified. These findings provide valuable information for timely intervention to reduce HCAIs, and the ability to minimise subsequent adverse outcomes.

What would other emergency stroke teams do? Using explainable machine learning to understand variation in thrombolysis practice.

The aim of this work was to understand between-hospital variation in thrombolysis use among emergency stroke admissions in England and Wales. A total of 88,928 patients who arrived at all 132 emergency stroke hospitals in England Wales within 4 h of stroke onset, from 2016 to 2018. Machine learning was applied to the Sentinel Stroke National Audit Programme (SSNAP) data set, to learn which patients in each hospital would likely receive thrombolysis. We used XGBoost machine learning models, coupled with a SHAP model for explainability; Shapley (SHAP) values, providing estimates of how patient features, and hospital identity, influence the odds of receiving thrombolysis. Thrombolysis use in patients arriving within 4 h of known or estimated stroke onset ranged 7% -49% between hospitals. The odds of receiving thrombolysis reduced 9-fold over the first 120 min of arrival-to-scan time, varied 30-fold with stroke severity, reduced 3-fold with estimated rather than precise stroke onset time, fell 6-fold with increasing pre-stroke disability, fell 4-fold with onset during sleep, fell 5-fold with use of anticoagulants, fell 2-fold between 80 and 110 years of age, reduced 3-fold between 120 and 240 min of onset-to-arrival time and varied 13-fold between hospitals. The majority of between-hospital variance was explained by the hospital, rather than the differences in local patient populations. Using explainable machine learning, we identified that the majority of the between-hospital variation in thrombolysis use in England and Wales may be explained by differences in in-hospital processes and differences in attitudes to judging suitability for thrombolysis.

Cost effectiveness analysis of the East of England stroke telemedicine service

IntroductionStroke is the fourth single leading cause of death in the UK with more than 100,000 people diagnosed with stroke annually. Timely access to urgent care and treatments, such as thrombolysis, is crucial for survival and recovery but there are national variations in care access. Materials and methodsWe explore the cost-effectiveness of an integrated telemedicine service for rapid access to stroke Consultant support in the East of England acute stroke care pathway compared to usual care during out-of-hour periods. The Sentinel Stroke National Audit Programme (SSNAP) health economics thrombolysis tool enabled us to compare the service with usual acute stroke care pathway. The tool was used to estimate costs and cost per quality-adjusted life year (QALY) gain associated with improved thrombolysis rates from a health and social care perspective. ResultsBased on SSNAP data, an average of 1,861 stroke patients were admitted out-of-hours in the participating centres annually between 2014 and 2019. Average thrombolysis rate was 9.7% when using a telemedicine service across the centres relative to the total stroke patients that presented out-of-hours. The total NHS cost savings compared to usual care were estimated at £482k and £471k while social care cost savings were £1.7m and £536k at the end of 1-year and 5-years respectively. ConclusionIntegrating a telemedicine service improves thrombolysis rates in out-of-hours acute stroke care and is associated with NHS and social care savings and QALY gains. Telemedicine is a cost-effective approach to delivering stroke care to remote communities with limited access to stroke specialists.

Using simulation and machine learning to maximise the benefit of intravenous thrombolysis in acute stroke in England and Wales: the SAMueL modelling and qualitative study

BackgroundStroke is a common cause of adult disability. Expert opinion is that about 20% of patients should receive thrombolysis to break up a clot causing the stroke. Currently, 11–12% of patients in England and Wales receive this treatment, ranging between 2% and 24% between hospitals.ObjectivesWe sought to enhance the national stroke audit by providing further analysis of the key sources of inter-hospital variation to determine how a target of 20% of stroke patients receiving thrombolysis may be reached.DesignWe modelled three aspects of the thrombolysis pathway, using machine learning and clinical pathway simulation. In addition, the project had a qualitative research arm, with the objective of understanding clinicians’ attitudes to use of modelling and machine learning applied to the national stroke audit.Participants and data sourceAnonymised data were collected for 246,676 emergency stroke admissions to acute stroke teams in England and Wales between 2016 and 2018, obtained from the Sentinel Stroke National Audit Programme.ResultsUse of thrombolysis could be predicted with 85% accuracy for those patients with a chance of receiving thrombolysis (i.e. those arriving within 4 hours of stroke onset). Machine learning models allowed prediction of likely treatment choice for each patient at all hospitals. A clinical pathway simulation predicted hospital thrombolysis use with an average absolute error of 0.5 percentage points. We found that about half of the inter-hospital variation in thrombolysis use came from differences in local patient populations, and half from in-hospital processes and decision-making. Three changes were applied to all hospitals in the model: (1) arrival to treatment in 30 minutes, (2) proportion of patients with determined stroke onset times set to at least the national upper quartile and (3) thrombolysis decisions made based on majority vote of a benchmark set of 30 hospitals. Any single change alone was predicted to increase national thrombolysis use from 11.6% to between 12.3% and 14.5% (with clinical decision-making having the most effect). Combined, these changes would be expected to increase thrombolysis to 18.3% (and to double the clinical benefit of thrombolysis, as speed increases also improve clinical benefit independently of the proportion of patients receiving thrombolysis); however, there would still be significant variation between hospitals depending on local patient population. For each hospital, the effect of each change could be predicted alone or in combination. Qualitative research with 19 clinicians showed that engagement with, and trust in, the model was greatest in physicians from units with higher thrombolysis rates. Physicians also wanted to see a machine learning model predicting outcome with probability of adverse effect of thrombolysis to counter a fear that driving thrombolysis use up may cause more harm than good.LimitationsModels may be built using data available in the Sentinel Stroke National Audit Programme only. Not all factors affecting use of thrombolysis are contained in Sentinel Stroke National Audit Programme data and the model, therefore, provides information on patterns of thrombolysis use in hospitals, but is not suitable for, or intended as, a decision aid to thrombolysis.ConclusionsMachine learning and clinical pathway simulation may be applied at scale to national audit data, allowing extended use and analysis of audit data. Stroke thrombolysis rates of at least 18% look achievable in England and Wales, but each hospital should have its own target.Future workFuture studies should extend machine learning modelling to predict the patient-level outcome and probability of adverse effects of thrombolysis, and apply co-production techniques, with clinicians and other stakeholders, to communicate model outputs.FundingThis project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full inHealth and Social Care Delivery Research; Vol. 10, No. 31. See the NIHR Journals Library website for further project information.

Factors associated with emergency readmissions after acute stroke: A retrospective audit of two hospitals.

Emergency hospital readmissions within 30 days of discharge from hospital are considered a marker for the quality of hospital care, patient experience, the discharge process and integration with community services. This paper describes the frequency and variations in cause of emergency readmissions at 30 and 90 days following discharge after acute stroke from two stroke units. Retrospective data collection of Hospital Episodes Statistics (HES) and Sentinel Stroke National Audit Programme (SSNAP) of consecutive acute stroke hospital discharges over 24 months from 2017 to 2019 from two specialist stroke units in England. HES data were used to calculate the Charlson comorbidity index (CCI). Covariates were analysed for their association with readmission rate, including: age; gender; CCI; length of stay for first stroke admission; living alone; discharge to a care home; discharge receiving stroke specialist early supported discharge (ESD) rehabilitationand stroke severity as determined by National Institute for Health Stroke Scaleon stroke admission. From 2017 to 2019 there were 1999 live discharges with a primary diagnosis of stroke. Both hospitals had a trend of increasing readmission rates with increasing stroke severity and comorbidity. Longer length of stroke admission, especially for patients with increasing stroke severity, and patients receiving ESD rehabilitation after discharge reduced 90-day readmissions. This association was stronger at 90 days than at 30 days. Different readmission event rates were found at 30 and 90 days and when events were compared between the two hospitals. Understanding differences in readmission event rates between hospitals at 30 and 90 days can support local planning of patient needs in the first weeks after stroke discharge and to investigate ways for hospital to reduce the impact of readmission. It is recommended that stroke services use both 30 and 90-day readmissions to inform service evaluation and improvement.

Use of Clinical Pathway Simulation and Machine Learning to Identify Key Levers for Maximizing the Benefit of Intravenous Thrombolysis in Acute Stroke.

Background:Expert opinion is that about 20% of emergency stroke patients should receive thrombolysis. Currently, 11% to 12% of patients in England and Wales receive thrombolysis, ranging from 2% to 24% between hospitals. The aim of this study was to assess how much variation is due to differences in local patient populations, and how much is due to differences in clinical decision-making and stroke pathway performance, while estimating a realistic target thrombolysis use.Methods:Anonymised data for 246 676 emergency stroke admissions to 132 acute hospitals in England and Wales between 2016 and 2018 was obtained from the Sentinel Stroke National Audit Programme data. We used machine learning to learn decisions on who to give thrombolysis to at each hospital. We used clinical pathway simulation to model effects of changing pathway performance. Qualitative research was used to assess clinician attitudes to these methods. Three changes were modeled: (1) arrival-to-treatment in 30 minutes, (2) proportion of patients with determined stroke onset times set to at least the national upper quartile, (3) thrombolysis decisions made based on majority vote of a benchmark set of hospitals.Results:Of the modeled changes, any single change was predicted to increase national thrombolysis use from 11.6% to between 12.3% to 14.5% (clinical decision-making having the most effect). Combined, these changes would be expected to increase thrombolysis to 18.3%, but there would still be significant variation between hospitals depending on local patient population. Clinicians engaged well with the modeling, but those from hospitals with lower thrombolysis use were most cautious about the methods.Conclusions:Machine learning and clinical pathway simulation may be applied at scale to national stroke audit data, allowing extended use and analysis of audit data. Stroke thrombolysis rates of at least 18% look achievable in England and Wales, but each hospital should have its own target.

Do Stroke Care Processes Modify Clinical Outcomes in Patients with Stroke-Associated Pneumonia? A Registry Cohort Study in England and Wales

Introduction: It is not known whether modern stroke unit care reduces the impact of stroke complications, such as stroke-associated pneumonia (SAP), on clinical outcomes. We investigated the relationship between SAP and clinical outcomes, adjusting for the confounding effects of stroke care processes and their timing. Methods: The Sentinel Stroke National Audit Programme provided patient data for all confirmed strokes between April 2013 and December 2018. SAP was defined as new antibiotic initiation for suspected pneumonia within the first 7 days from stroke admission. We compared outcomes after SAP versus non-SAP in appropriate multilevel mixed models. Each model was adjusted for patient and clinical characteristics, as well as markers of stroke care and their timing within the first 72 h. The appropriate effect estimates and corresponding 95% confidence intervals (CIs) were reported. Results: Of 201,778 patients, SAP was present in 14.2%. After adjustment for timing of acute stroke care processes and clinical characteristics, adverse outcomes remained for SAP versus non-SAP patients. In these adjusted analyses, patients with SAP maintained an increased risk of longer length of in-hospital stay (IRR of 1.27; 95% CI: 1.25, 1.30), increased odds of worse functional outcome at discharge (OR of 2.9; 95% CI: 2.9, 3.0), and increased risk of in-hospital mortality (HR of 1.78; 95% CI: 1.74, 1.82). Conclusion: We show for the first time that SAP remains associated with worse clinical outcomes, even after adjusting for processes of acute stroke care and their timing. These findings highlight the importance of continued research efforts aimed at preventing SAP.

265 Use of support vector machine to predict discharge destination on an acute stroke ward

BackgroundPredicting the discharge destination of stroke patients at admission is important for stream- lining of services, management of patient and carer expectations, and the planning and facilitating of discharge to appropriate destinations.MethodsWe reviewed data from the Sentinel Stroke National Audit Programme (SSNAP) from a single acute stroke ward in South Wales. We analysed age, modified Rankin Scale (mRS) and National Institute of Health Stroke Scale (NIHSS) data using a multi-class Support Vector Machine (SVM) to train and test a model to predict discharge destination.ResultsData was extracted from 1131 patients with acute and subacute stroke admitted to an acute stroke unit between January 1st, 2015, and December 31st, 2016. Per-class sensitivity of the model for final destination or outcome was 0.89 for home, 0.6 community hospital, 0.75 inpatient rehabilitation, 0.45 death. Per-class positive predictive value (PPV) was 0.84 for home, 0.62 community hospital, 0.72 inpatient rehabilitation, 0.55 death.ConclusionFrom data that is available from simple assessments done in routine clinical pratice it is possible to identify patients with acute stroke for whom discharge home or inpatient rehabilitation is highly likely. Collaborations between clinicians and data scientists may help to maximise the value of clinical assessment tools that are already in widespread use.Tom.Hughes2@wales.nhs.uk

Improving emergency treatment for patients with acute stroke: the PEARS research programme, including the PASTA cluster RCT

Improving emergency treatment for patients with acute stroke: the PEARS research programme, including the PASTA cluster RCT

The Timing of Stroke Care Processes and Development of Stroke Associated Pneumonia: A National Registry Cohort Study.

IntroductionTimely stroke care can result in significant improvements in stroke recovery. However, little is known about how stroke care processes relate to complications such as the development of stroke associated pneumonia (SAP). Here we investigated associations between stroke care processes, their timing and development of SAP.MethodsWe obtained patient-level data from the Sentinel Stroke National Audit Programme for all confirmed strokes between 1st April 2013 and 31st December 2018. SAP was identified if new antibiotic initiation for pneumonia occurred within the first 7 days of admission. Time to key stroke care processes in the pre-hospital, hyperacute and acute phase were investigated. A mixed effects logistic regression model estimated the association between SAP [Odds ratios (OR) with 95% CI] and each process of care after controlling for pre-determined confounders such as age, stroke severity and comorbidities.ResultsSAP was identified in 8.5% of 413,133 patients in 169 stroke units. A long time to arrival at a stroke unit after symptom onset or time last seen well [OR (95% CI) = 1.29 (1.23–1.35)], from admission to assessment by a stroke specialist [1.10 (1.06–1.14)] and from admission to assessment by a physiotherapist [1.16 (1.12–1.21)] were all independently associated with SAP. Short door to needle times were associated with lower odds of SAP [0.90 (0.83–0.97)].ConclusionTimes from stroke onset and admission to certain key stroke care processes were associated with SAP. Understanding how timing of these care processes relate to SAP may enable development of preventive interventions to reduce antibiotic use and improve clinical outcomes.

Investigating the association between inpatient stroke therapy and disability, destination on discharge, length of stay and mortality: a prospective cohort study using the Sentinel Stroke National Audit Programme

Objective‘More is better’ is a recognised mantra within stroke therapy, however, this has been developed in patients receiving long term rehabilitation. We investigated the relationship between amount of therapy received...

Temporal changes in anticoagulant prescribing and atrial fibrillation: results of interrupted time-series analysis of openly available routine data in England

Abstract Introduction The use of anticoagulants for stroke prevention in patients with atrial fibrillation (AF) has been increasing in England in the recent years (1, 2). The changes in practice coincided with several developments in the area of anticoagulants and AF, including: the emergence of evidence supporting anticoagulant use for stroke prevention in AF, changes in guideline recommendations, and the licensing of direct oral anticoagulants (DOACs) as an alternative to warfarin, which was the mainstay of stroke prevention in AF. Currently, there are four DOACs available for use: dabigatran, rivaroxaban, apixaban, and edoxaban. Recent analyses of time trends of anticoagulant prescribing in England showed an increase in DOACs prescribing and a decrease in warfarin prescribing (1, 2). However, more recent exploration of recent anticoagulant prescribing trends is needed. Aim To explore the changes in anticoagulant prescribing for AF in England. Methods Interrupted time-series analysis (ITSA) of openly available data on anticoagulant prescribing and AF. Separate analyses were conducted for data from Sentinel Stroke National Audit Programme (SSNAP) and Quality and Outcomes Framework (QOF). The timeframe of the analysis and the length of time intervals depended on the characteristics of each dataset. Results Analysis of data from SSNAP showed that the proportion of stroke patients who have a diagnosis of AF who were taking an anticoagulant alone prior to admission increased from 32.51% in the third quarter of 2013 to 62.63% in the third quarter of 2020; this coincided with a decreased proportion of stroke patients who have a diagnosis of AF taking antiplatelets alone from 37.10% to 8.31% during the same period. Analysed QOF data on anticoagulants and AF included information about AF prevalence in QOF datasets, the eligibility of patients with AF for anticoagulant based on stroke risk assessment, and the use of anticoagulants among eligible patients. Prevalence of AF in England in QOF datasets increased from 1.52% in 2012/2013 to 2.05% in 2019/2020 (ITSA gradient=0.08, p-value<.001, 95% confidence interval: 0.07- 0.09). The proportion of patients with AF who are considered eligible for anticoagulation per all patients with AF in QOF datasets increased from 44.95% in 2012/2013 to 80.87% in 2019/2020; there was an increase by 25.24% between 2014/2015 and 2015/2016 which corresponds to modifying risk assessment scores to include additional criteria. The proportion of eligible patients with AF being prescribed anticoagulants also increased in the period between 2012/2013 and 2019/2020 (ITSA gradient = 1.98, p-value<.001, 95% confidence interval: 1.52- 2.44). Conclusion The analysis of data from different sources identified an increase in anticoagulant prescribing for patients with AF in England, which is likely to translate into health gains. However, the use of aggregate data in the analysis did not allow for detailed inspection of patient characteristics, and individual anticoagulants were not specified in the datasets. Therefore, quantitative analyses of primary care data of individual anticoagulant prescribing for different patient groups with AF are needed to further understand whether additional improvements are possible in the prevention of complications from atrial fibrillation. References (1) Adderley NJ, Ryan R, Nirantharakumar K, Marshall T. Prevalence and treatment of atrial fibrillation in UK general practice from 2000 to 2016. Heart. 2019;105(1):27-33. (2) Loo SY, Dell’Aniello S, Huiart L, Renoux C. Trends in the prescription of novel oral anticoagulants in UK primary care. British Journal of Clinical Pharmacology. 2017;83(9):2096-106.

Comparison of characteristics, management and outcomes in hospital-onset and community-onset stroke: a multi-centre registry-based cohort study of acute stroke

ObjectiveHospital-onset stroke (HOS) is associated with poorer outcomes than community-onset stroke (COS). Previous studies have variably documented patient characteristics and outcome measures; here, we compare in detail characteristics, management and outcomes of HOS and COS.MethodsA total of 1656 men (mean age ± SD = 73.1 years ± 13.2) and 1653 women (79.3 years ± 13.0), with data prospectively collected (2014–2016) from the Sentinel Stroke National Audit Programme, were admitted with acute stroke in four UK hyperacute stroke units (HASU). Associations between variables were examined by chi-squared tests and multivariable logistic regression (COS as reference).ResultsThere were 272 HOS and 3037 COS patients with mean ages of 80.2 years ± 12.5 and 76.4 years ± SD13.5 and equal sex distribution. Compared to COS, HOS had higher proportions ≥ 80 years (64.0% vs 46.4%), congestive heart failure (16.9% vs 4.9%), atrial fibrillation (25.0% vs 19.7%) and pre-stroke disability (9.6% vs 5.1%), and similar history of stroke, hypertension, diabetes, stroke type and severity of stroke. After age, sex and co-morbidities adjustments, HOS had greater risk of pneumonia: OR (95%CI) = 1.9 (1.3–2.6); malnutrition: OR = 2.2 (1.7–2.9); immediate thrombolysis complications: OR = 5.3 (1.5–18.2); length of stay on HASU > 3 weeks: OR = 2.5 (1.8–3.4); post-stroke disability: OR = 1.8 (1.4–2.4); and in-hospital mortality: OR = 1.8 (1.2–2.4), as well as greater support at discharge including palliative care: OR = 1.9 (1.3–2.8); nursing care: OR = 2.0 (1.3–4.0), help for daily living activities: OR = 1.6 (1.1–2.2); and joint-care planning: OR = 1.5 (1.1–1.9).ConclusionsThis detailed analysis of underlying differences in subject characteristics between patients with HOS or COS and adverse consequences provides further insights into understanding poorer outcomes associated with HOS.

Are Differences in Dysphagia Assessment, Oral Care Provision, or Nasogastric Tube Insertion Associated with Stroke-Associated Pneumonia? A Nationwide Survey Linked to National Stroke Registry Data

Introduction: Stroke-associated pneumonia (SAP) is a common complication associated with poor outcomes. Early dysphagia screening and specialist assessment is associated with a reduced risk of SAP. Evidence about oral care and nasogastric tube (NGT) placement is equivocal. This study aimed to expose variations in dysphagia management practices and explore their associations with SAP. Participants and Methods: Speech pathologists from 166 stroke units in England and Wales were surveyed about dysphagia assessment and management, oral care, and NGT placement. Survey data were then linked to the Sentinel Stroke National Audit Programme (SSNAP), the national register of stroke. Univariable and multivariable linear regression models were fitted to estimate the association between dysphagia management practices and SAP incidence. Results: 113 hospitals completed the survey (68%). Variation was evident in dysphagia screening protocols (DSPs), oral care, and NGT practice while specialist swallow assessment data patterns were more consistent. Multivariable analysis showed no evidence of an association in incidence of SAP when using a water-only hospital DSP compared to a multiconsistency DSP (B −0.688, 95% CI: −2.912 to 1.536), when using written swallow assessment guidelines compared to not using written guidelines (B 0.671, 95% CI: −1.567 to 2.908), when teams inserted NGTs overnight compared to teams which did not (B −0.505, 95% CI: −2.759 to 1.749), and when teams had a written oral care protocol compared to those which did not (B −1.339, 95% CI: −3.551 to 0.873). Discussion and Conclusion: Variation exists in dysphagia screening and management, but there was no evidence of an association between clinical practice patterns and incidence of SAP. Further research with larger sample sizes is needed to examine association with SAP.

Large-scale implementation of stroke early supported discharge: the WISE realist mixed-methods study

Background In England, the provision of early supported discharge is recommended as part of an evidence-based stroke care pathway. Objectives To investigate the effectiveness of early supported discharge services when implemented at scale in practice and to understand how the context within which these services operate influences their implementation and effectiveness. Design A mixed-methods study using a realist evaluation approach and two interlinking work packages was undertaken. Three programme theories were tested to investigate the adoption of evidence-based core components, differences in urban and rural settings, and communication processes. Setting and interventions Early supported discharge services across a large geographical area of England, covering the West and East Midlands, the East of England and the North of England. Participants Work package 1: historical prospective patient data from the Sentinel Stroke National Audit Programme collected by early supported discharge and hospital teams. Work package 2: NHS staff (n = 117) and patients (n = 30) from six purposely selected early supported discharge services. Data and main outcome Work package 1: a 17-item early supported discharge consensus score measured the adherence to evidence-based core components defined in an international consensus document. The effectiveness of early supported discharge was measured with process and patient outcomes and costs. Work package 2: semistructured interviews and focus groups with NHS staff and patients were undertaken to investigate the contextual determinants of early supported discharge effectiveness. Results A variety of early supported discharge service models had been adopted, as reflected by the variability in the early supported discharge consensus score. A one-unit increase in early supported discharge consensus score was significantly associated with a more responsive early supported discharge service and increased treatment intensity. There was no association with stroke survivor outcome. Patients who received early supported discharge in their stroke care pathway spent, on average, 1 day longer in hospital than those who did not receive early supported discharge. The most rural services had the highest service costs per patient. NHS staff identified core evidence-based components (e.g. eligibility criteria, co-ordinated multidisciplinary team and regular weekly multidisciplinary team meetings) as central to the effectiveness of early supported discharge. Mechanisms thought to streamline discharge and help teams to meet their responsiveness targets included having access to a social worker and the quality of communications and transitions across services. The role of rehabilitation assistants and an interdisciplinary approach were facilitators of delivering an intensive service. The rurality of early supported discharge services, especially when coupled with capacity issues and increased travel times to visit patients, could influence the intensity of rehabilitation provision and teams’ flexibility to adjust to patients’ needs. This required organising multidisciplinary teams and meetings around the local geography. Findings also highlighted the importance of good leadership and communication. Early supported discharge staff highlighted the need for collaborative and trusting relationships with patients and carers and stroke unit staff, as well as across the wider stroke care pathway. Limitations Work package 1: possible influence of unobserved variables and we were unable to determine the effect of early supported discharge on patient outcomes. Work package 2: the pragmatic approach led to ‘theoretical nuggets’ rather than an overarching higher-level theory. Conclusions The realist evaluation methodology allowed us to address the complexity of early supported discharge delivery in real-world settings. The findings highlighted the importance of context and contextual features and mechanisms that need to be either addressed or capitalised on to improve effectiveness. Trial registration Current Controlled Trials ISRCTN15568163. Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 9, No. 22. See the NIHR Journals Library website for further project information.

Predicting Stroke Complications in Hospital and Functional Status at Discharge by Clustering of Cardiovascular Diseases a Multi-Centre Registry-Based Study of Acute Stroke

ObjectiveIndicators for outcomes following acute stroke are lacking. We have developed novel evidence-based criteria for identifying outcomes of acute stroke using the presence of clusters of coexisting cardiovascular disease (CVD). Materials and methodsAnalysis of prospectively collected data from the Sentinel Stroke National Audit Programme (SSNAP). A total of 1656 men (mean age ±SD=73.1yrs±13.2) and 1653 women (79.3yrs±13.0) were admitted with acute stroke (83.3% ischaemic, 15.7% intracranial haemorrhagic), 1.0% unspecified) in four major UK hyperacute stroke units (HASU) between 2014 and 2016. Four categories from cardiovascular disease Congestive heart failure, Atrial fibrillation, pre-existing Stroke and Hypertension (CASH).were constructed: CASH-0 (no coexisting CVD); CASH-1 (any one coexisting CVD); CASH-2 (any two coexisting CVD); CASH-3 (any three or all four coexisting CVD). These were tested against outcomes, adjusted for age and sex. ResultsCompared to CASH-0, individuals with CASH-3 had greatest risks of in-hospital mortality (11.1% vs 24.5%, OR=1.8, 95%CI=1.3-2.7) and disability (modified Rankin Scale score ≥4) at discharge (24.2% vs 46.2%, OR=1.9, 95%CI=1.4-2.7), urinary tract infection (3.8% vs 14.6%, OR= 3.3, 95%CI= 1.9-5.5), and pneumonia (7.1% vs 20.6%, OR= 2.6, 95%CI= 1.7-4.0); length of stay on HASU >14 days (29.8% vs 39.3%, OR=1.8, 95%CI=1.3-2.6); and joint-care planning (20.9% vs 29.8%, OR=1.4, 95%CI=1.0-2.0). ConclusionsWe present a simple tool for estimating the risk of adverse outcomes of acute stroke including death, disability at discharge, nosocomial infections, prolonged length of stay, as well as any joint care planning. CASH-0 indicates a low level and CASH-3 indicates a high level of risk of such complications after stroke.