The challenge of recruiting in primary care for a trial of telemonitoring in asthma : an observational study

Acknowledgments This study was funded by Asthma UK. HP is supported by a Primary Care Research Career Award from the Chief Scientist’s Office of the Scottish Government. The Primary Care Research Networks in Norfolk and Yarmouth, East Kent, North of England, and Essex and Hertfordshire identified and recruited practices. We thank the practices, practice nurses, and administrative staff for their active participation and the patients who gave their time to participate in the trial. Professor Amanda Lee was the trial statistician. We thank Dr Andrew Wilson and Neil Kendle for serving on the ITSC and Dr Brian McKinstry and Dr Chris Burton who offered advice as collaborators. ISRCTN number: NCT00512837. Contributorship: DR initiated the idea for the study and with HP led the development of the protocol, securing of funding, study administration, data analysis, interpretation of results, and writing of the paper. DP is a grant holder who contributed to development of the protocol, securing of funding, study administration, data analysis, interpretation of results, and writing of the paper. SM and SDM recruited practices and undertook the data collection. All authors had full access to all the data and were involved in interpretation of the data. SM wrote the initial draft of the paper, to which all the authors contributed. DR and HP are study guarantors.Peer reviewedPublisher PD

Clinical and cost effectiveness of mobile phone supported self monitoring of asthma: multicentre randomised controlled trial

Objective To determine whether mobile phone based monitoring improves asthma control compared with standard paper based monitoring strategies

Derivation and validation of a novel prognostic scale (modified–stroke subtype, Oxfordshire Community Stroke Project classification, age, and prestroke modified Rankin) to predict early mortality in acute stroke

Background and Purpose The stroke subtype, Oxfordshire Community Stroke Project classification, age, and prestroke modified Rankin (SOAR) score is a prognostic scale proposed for early mortality prediction after acute stroke. We aimed to evaluate whether including a measure of initial stroke severity (National Institutes of Health Stroke Scale and modified-SOAR [mSOAR] scores) would improve the prognostic accuracy. Methods Using Anglia Stroke and Heart Clinical Network data, 2008 to 2011, we assessed the performance of SOAR and mSOAR against in-hospital mortality using area under the receiver operating curve statistics. We externally validated the prognostic utility of SOAR and mSOAR using an independent cohort data set from Glasgow. We described calibration using Hosmer–Lemeshow goodness-of-fit test. Results A total of 1002 patients were included in the derivation cohort, and 105 (10.5%) died as inpatients. The area under the receiver operating curves for outcome of early mortality derived from the SOAR and mSOAR scores were 0.79 (95% confidence interval, 0.75–0.84) and 0.83 (95% confidence interval, 0.79–0.86), respectively (P=0.001). The external validation data set contained 1012 patients with stroke; of which, 121 (12.0%) patients died within 90 days. The mSOAR scores identified the risk of early mortality ranging from 3% to 42%. External validation of mSOAR score yielded an area under the receiver operating curve of 0.84 (95% confidence interval, 0.82–0.88) for outcome of early mortality. Calibration was good (P=0.70 for the Hosmer–Lemeshow test). Conclusions—Adding National Institutes of Health Stroke Scale data to create a modified-SOAR score improved prognostic utility in both derivation and validation data sets. The mSOAR may have clinical utility by using easily available data to predict mortality

Variations in Rates of Discharges to Nursing Homes after Acute Hospitalization for Stroke and the Influence of Service Heterogeneity: An Anglia Stroke Clinical Network Evaluation Study.

Nursing home placement after stroke indicates a poor outcome but numbers placed vary between hospitals. The aim of this study is to determine whether between-hospital variations in new nursing home placements post-stroke are reliant solely on case-mix differences or whether service heterogeneity plays a role. A prospective, multi-center cohort study of acute stroke patients admitted to eight National Health Service acute hospitals within the Anglia Stroke and Heart Clinical Network between 2009 and 2011 was conducted. We modeled the association between hospitals (as a fixed-effect) and rates of new discharges to nursing homes using multiple logistic regression, adjusting for important patient risk factors. Descriptive and graphical data analyses were undertaken to explore the role of hospital characteristics. Of 1335 stroke admissions, 135 (10%) were discharged to a nursing home but rates varied considerably from 6% to 19% between hospitals. The hospital with the highest adjusted odds ratio of nursing home discharges (OR 4.26; 95% CI 1.69 to 10.73), was the only hospital that did not provide rehabilitation beds in the stroke unit. Increasing hospital size appeared to be related to an increased odds of nursing home placement, although attenuated by the number of hospital stroke admissions. Our results highlight the potential influence of hospital characteristics on this important outcome, independently of patient-level factors

Leukotriene antagonists as first-line or add-on asthma controller therapy

Most randomized trials of treatment for asthma study highly selected patients under idealized conditions. METHODS: We conducted two parallel, multicenter, pragmatic trials to evaluate the real-world effectiveness of a leukotriene-receptor antagonist (LTRA) as compared with either an inhaled glucocorticoid for first-line asthma-controller therapy or a long-acting beta(2)-agonist (LABA) as add-on therapy in patients already receiving inhaled glucocorticoid therapy. Eligible primary care patients 12 to 80 years of age had impaired asthma-related quality of life (Mini Asthma Quality of Life Questionnaire [MiniAQLQ] score =6) or inadequate asthma control (Asthma Control Questionnaire [ACQ] score =1). We randomly assigned patients to 2 years of open-label therapy, under the care of their usual physician, with LTRA (148 patients) or an inhaled glucocorticoid (158 patients) in the first-line controller therapy trial and LTRA (170 patients) or LABA (182 patients) added to an inhaled glucocorticoid in the add-on therapy trial. RESULTS: Mean MiniAQLQ scores increased by 0.8 to 1.0 point over a period of 2 years in both trials. At 2 months, differences in the MiniAQLQ scores between the two treatment groups met our definition of equivalence (95% confidence interval [CI] for an adjusted mean difference, -0.3 to 0.3). At 2 years, mean MiniAQLQ scores approached equivalence, with an adjusted mean difference between treatment groups of -0.11 (95% CI, -0.35 to 0.13) in the first-line controller therapy trial and of -0.11 (95% CI, -0.32 to 0.11) in the add-on therapy trial. Exacerbation rates and ACQ scores did not differ significantly between the two groups. CONCLUSIONS: Study results at 2 months suggest that LTRA was equivalent to an inhaled glucocorticoid as first-line controller therapy and to LABA as add-on therapy for diverse primary care patients. Equivalence was not proved at 2 years. The interpretation of results of pragmatic research may be limited by the crossover between treatment groups and lack of a placebo group

Predicting asthma-related crisis events using routine electronic healthcare data

Background There is no published algorithm predicting asthma crisis events (accident and emergency [A&E] attendance, hospitalisation, or death) using routinely available electronic health record (EHR) data. Aim To develop an algorithm to identify individuals at high risk of an asthma crisis event. Design and setting Database analysis from primary care EHRs of people with asthma across England and Scotland. Method Multivariable logistic regression was applied to a dataset of 61 861 people with asthma from England and Scotland using the Clinical Practice Research Datalink. External validation was performed using the Secure Anonymised Information Linkage Databank of 174 240 patients from Wales. Outcomes were ≥1 hospitalisation (development dataset) and asthma-related hospitalisation, A&E attendance, or death (validation dataset) within a 12-month period. Results Risk factors for asthma-related crisis events included previous hospitalisation, older age, underweight, smoking, and blood eosinophilia. The prediction algorithm had acceptable predictive ability with a receiver operating characteristic of 0.71 (95% confidence interval [CI] = 0.70 to 0.72) in the validation dataset. Using a cut-point based on the 7% of the population at greatest risk results in a positive predictive value of 5.7% (95% CI = 5.3% to 6.1%) and a negative predictive value of 98.9% (95% CI = 98.9% to 99.0%), with sensitivity of 28.5% (95% CI = 26.7% to 30.3%) and specificity of 93.3% (95% CI = 93.2% to 93.4%); those individuals had an event risk of 6.0% compared with 1.1% for the remaining population. In total, 18 people would need to be followed to identify one admission. Conclusion This externally validated algorithm has acceptable predictive ability for identifying patients at high risk of asthma-related crisis events and excluding those not at high risk

Important factors in predicting mortality outcome from stroke: Findings from the Anglia Stroke Clinical Network Evaluation Study

Background: although variation in stroke service provision and outcomes have been previously investigated, it is less well known what service characteristics are associated with reduced short- and medium-term mortality. Methods: data from a prospective multicentre study (2009–12) in eight acute regional NHS trusts with a catchment population of about 2.6 million were used to examine the prognostic value of patient-related factors and service characteristics on stroke mortality outcome at 7, 30 and 365 days post stroke, and time to death within 1 year. Results: a total of 2,388 acute stroke patients (mean (standard deviation) 76.9 (12.7) years; 47.3% men, 87% ischaemic stroke) were included in the study. Among patients characteristics examined increasing age, haemorrhagic stroke, total anterior circulation stroke type, higher prestroke frailty, history of hypertension and ischaemic heart disease and admission hyperglycaemia predicted 1-year mortality. Additional inclusion of stroke service characteristics controlling for patient and service level characteristics showed varying prognostic impact of service characteristics on stroke mortality over the disease course during first year after stroke at different time points. The most consistent finding was the benefit of higher nursing levels; an increase in one trained nurses per 10 beds was associated with reductions in 30-day mortality of 11–28% (P < 0.0001) and in 1-year mortality of 8–12% (P < 0.001). Conclusions: there appears to be consistent and robust evidence of direct clinical benefit on mortality up to 1 year after acute stroke of higher numbers of trained nursing staff over and above that of other recognised mortality risk factors

Evaluation of stroke services in Anglia Stroke Clinical Network to examine the variation in acute services and stroke outcomes.

BACKGROUND: Stroke is the third leading cause of death in developed countries and the leading cause of long-term disability worldwide. A series of national stroke audits in the UK highlighted the differences in stroke care between hospitals. The study aims to describe variation in outcomes following stroke and to identify the characteristics of services that are associated with better outcomes, after accounting for case mix differences and individual prognostic factors. METHODS/DESIGN: We will conduct a cohort study in eight acute NHS trusts within East of England, with at least one year of follow-up after stroke. The study population will be a systematically selected representative sample of patients admitted with stroke during the study period, recruited within each hospital. We will collect individual patient data on prognostic characteristics, health care received, outcomes and costs of care and we will also record relevant characteristics of each provider organisation. The determinants of one year outcome including patient reported outcome will be assessed statistically with proportional hazards regression models. Self (or proxy) completed EuroQol (EQ-5D) questionnaires will measure quality of life at baseline and follow-up for cost utility analyses. DISCUSSION: This study will provide observational data about health service factors associated with variations in patient outcomes and health care costs following hospital admission for acute stroke. This will form the basis for future RCTs by identifying promising health service interventions, assessing the feasibility of recruiting and following up trial patients, and provide evidence about frequency and variances in outcomes, and intra-cluster correlation of outcomes, for sample size calculations. The results will inform clinicians, public, service providers, commissioners and policy makers to drive further improvement in health services which will bring direct benefit to the patients.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Modified early warning score and risk of mortality after acute stroke

Objective:  An accurate prediction tool may facilitate optimal management of patients with acute stroke from an early stage. We evaluated the association between admission modified early warning score (MEWS) and mortality in patients with acute stroke. Method:  Data from the Anglia Stroke Clinical Network Evaluation Study (ASCNES) were analysed. We evaluated the association between admission MEWS and four outcomes; in-patient, 7-day, 30-day and 1-year mortality. Logistic regression models were used to calculate the odds of all mortality timeframes, whereas Cox proportional hazards models were used to calculate mortality at 1 year. Five univariate and multivariate models were constructed, adjusting for confounders. Patients with a moderate (2-3) or high (≥4) scores were compared to patients with a low score (0-1). Results:  The study population consisted of 2,006 patients. A total of 1196 patients had low MEWS, 666 had moderate MEWS and 144 had a high MEWS. A high MEWS was associated with increased mortality as an in-patient (OR 4.93, 95% CI: 2.88–8.42), at 7 days (OR 7.53, 95% CI: 4.24 – 13.38), at 30 days (OR 5.74, 95% CI: 3.38 – 9.76) and 1-year (HR 2.52, 95% CI 1.88 – 3.39). At 1 year, model 5 had a 1.02 OR (95% CI 0.83 – 1.24) with moderate MEWS and 2.52 (95% CI 1.88 – 3.39) with high MEWS. Conclusion:  Elevated MEWS on admission is a potential marker for acute-stroke mortality and may therefore be a useful risk prediction tool, able to guide clinicians attempting to prognosticate outcomes for patients with acute-stroke

Hospital-Level Variations in Rates of Inpatient Urinary Tract Infections in Stroke.

Background and purpose: Urinary tract infection (UTI) is one of the most common complications following stroke and has prognostic significance. UTI rates have been shown to vary between hospitals, but it is unclear whether this is due to case-mix differences or heterogeneities in care among hospitals. Methods: A prospective multi-center cohort study of acute stroke patients admitted to eight National Health Service (NHS) acute hospital trusts within the Anglia Stroke & Heart Clinical Network between 2009 and 2011 was conducted. We modeled the association between hospital (as a fixed-effect) and inpatient UTI using a multivariable logistic regression model, adjusting for established patient-level risk factors. We graphically and descriptively analyzed heterogeneities in hospital-level characteristics. Results: We included 2,241 stroke admissions in our analysis; 171 (7.6%) acquired UTI as an inpatient. UTI rates varied significantly between the eight hospitals, ranging from 3 to 11%. The hospital that had the lowest odds of UTI [odds ratio (OR) = 0.50 (95% confidence interval (CI) 0.22-.11)] in adjusted analysis, had the highest number of junior doctors and occupational therapists per five beds of all hospitals. The hospital with the highest adjusted UTI rate [OR=2.69 (1.56-4.64)] was tertiary, the largest and had the highest volume of stroke patients, lowest number of stroke unit beds per 100 admissions, and the highest number of hospital beds per CT scanner. Conclusions: There is hospital-level variation in post-stroke UTI. Our results suggest the potential influence of service characteristics independently of patient-level factors which may be amenable to be addressed to improve the ultimate stroke outcome