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Background: Administrative data are used extensively for research purposes, but there remains limited information on the quality of these data for identifying comorbidities related to stroke., Objective: To compare the prevalence of comorbidities of stroke identified using International Classification Diseases, Australian Modification (ICD-10-AM) or Anatomical Therapeutic Chemical codes, with those from (i) self-reported data and (ii) published studies., Method: The cohort included patients with stroke or transient ischaemic attack admitted to hospitals (2012-2016; Victoria and Queensland) in the Australian Stroke Clinical Registry (N = 26,111). Data were linked with hospital and pharmaceutical datasets to ascertain comorbidities using published algorithms. The sensitivity, specificity, and positive predictive value of these comorbidities were compared with survey responses from 623 patients (reference standard). An indirect comparison was also performed with clinical data from published stroke studies., Results: The sensitivity of hospital ICD-10-AM data was poor for most comorbidities, except for diabetes (93.0%). Specificity was excellent for all comorbidities (87-96%), except for hypertension (70.5%). Compared to published stroke studies (3 clinical trials and 1 incidence study), the prevalence of diabetes and atrial fibrillation in our cohort was similar using ICD-10-AM codes, but lower for dyslipidaemia and anxiety/depression. Whereas in the pharmaceutical dispensing data, the sensitivity was excellent for dyslipidaemia (94%) and modest for anxiety/depression (77%). In the pharmaceutical data, specificity was modest for hypertension (78%) and anxiety or depression (76%), but specificity was poor for dyslipidaemia (19%) and heart disease (46%)., Conclusion: Variation was observed in the reporting of comorbidities of stroke in administrative data, and consideration of multiple sources of data may be necessary for research. Further work is needed to improve coding and clinical documentation for reporting of comorbidities in administrative data., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Stroke is a high-cost condition. Detailed patient-level assessments of the costs of care received and outcomes achieved provide useful information for organisation and optimisation of the health system., Objectives: To describe the costs of hospital care for stroke and transient ischaemic attack (TIA) and investigate factors associated with costs., Methods: Retrospective cohort study using data from the Australian Stroke Clinical Registry (AuSCR) collected between 2009 and 2013 linked to hospital administrative data and clinical costing data in Queensland. Clinical costing data include standardised assignment of costs from hospitals that contribute to the National Hospital Costing programme. Patient-level costs for each hospital admission were described according to the demographic, clinical and treatment characteristics of patients. Multivariable median regression with clustering by hospital was used to determine factors associated with greater costs., Results: Among 22 hospitals, clinical costing data were available for 3909 of 5522 patient admissions in the AuSCR (71%). Compared to those without clinical costing data, patients with clinical costing data were more often aged <65 years (30% with cost data vs 24% without cost data, p < 0.001) and male (56% with cost data vs 49% without cost data, p < 0.001). Median cost of an acute episode was $7945 (interquartile range $4176 to $14970) and the median length of stay was 5 days (interquartile range 2 to 10 days). The most expensive cost buckets were related to medical ( n = 3897, median cost $1577), nursing ( n = 3908, median cost $2478) and critical care ( n = 434, median cost $3064). Factors associated with greater total costs were a diagnosis of intracerebral haemorrhage, greater socioeconomic position, in-hospital stroke and prior history of stroke., Conclusion: Medical and nursing costs were incurred by most patients admitted with stroke or TIA, and were relatively more expensive on average than other cost buckets such as imaging and allied health., Implications: Scaling this data linkage to national data collections may provide valuable insights into activity-based funding at public hospitals. Regular report of these costs should be encouraged to optimise economic evaluations.

Rationale: To address unmet needs, electronic messages to support person-centered goal attainment and secondary prevention may avoid hospital presentations/readmissions after stroke, but evidence is limited., Hypothesis: Compared to control participants, there will be a 10% lower proportion of intervention participants who represent to hospital (emergency/admission) within 90 days of randomization., Methods and Design: Multicenter, double-blind, randomized controlled trial with intention-to-treat analysis. The intervention group receives 12 weeks of personalized, goal-centered, and administrative electronic messages, while the control group only receive administrative messages. The trial includes a process evaluation, assessment of treatment fidelity, and an economic evaluation. Participants: Confirmed stroke (modified Rankin Score: 0-4), aged ≥18 years with internet/mobile phone access, discharged directly home from hospital. Randomization: 1:1 computer-generated, stratified by age and baseline disability. Outcomes assessments: Collected at 90 days and 12 months following randomization., Outcomes: Primary outcomes include hospital emergency presentations/admissions within 90 days of randomization. Secondary outcomes include goal attainment, self-efficacy, mood, unmet needs, disability, quality-of-life, recurrent stroke/cardiovascular events/deaths at 90 days and 12 months, and death and cost-effectiveness at 12 months. Sample size: To test our primary hypothesis, we estimated a sample size of 890 participants (445 per group) with 80% power and two-tailed significance threshold of α = 0.05. Given uncertainty for the effect size of this novel intervention, the sample size will be adaptively re-estimated when outcomes for n = 668 are obtained, with maximum sample capped at 1100., Discussion: We will provide new evidence on the potential effectiveness, implementation, and cost-effectiveness of a tailored eHealth intervention for survivors of stroke.

Introduction: Chest infections following acute stroke contribute to increased morbidity and mortality. We aimed to investigate factors associated with chest infections that occur within 30 days of stroke, the impact on 90-day survival, and the role of stroke unit care., Methods: Patient-level data from the Australian Stroke Clinical Registry (2010-13; 23 Queensland hospitals), were linked with Queensland hospital admission, emergency department (ED), and national death registry data. Acute chest infections were determined using ICD-10 codes from the stroke admission, hospital readmissions, ED contacts, and cause of death data. Patients aged ≥18 years without a prior stroke or chronic respiratory condition were included. Multilevel (hospital and patient) multivariable regression and survival analysis were used to identify associated factors and the influence on 90-day survival., Results: Overall, 3149 patients (77% ischemic stroke, 47% female, median age 74 years) were included; 3.1% developed a chest infection within 30 days. Associated factors included: admission to intensive care (OR: 8.26, 95% CI: 4.07, 16.76); and urinary tract infection (OR: 3.09, 95% CI: 1.89, 5.04). Patients not treated in stroke units had a two-fold greater odds of chest infections (OR: 1.96, 95% CI: 1.25, 3.05). Chest infection afforded a greater hazard of death at 90 days (HR: 1.42, 95% CI 1.04, 1.93). This was reduced for those admitted to a stroke unit (HR: 1.31, 95% CI 0.99, 1.75)., Conclusion: Results emphasize the need for active prevention and highlight the importance of stroke unit care in mitigating risk and improving survival in those with stroke-related chest infections.

Rationale The effectiveness of clinician-focused interventions to improve stroke care is uncertain. Aims To determine whether an organizational intervention can improve the quality of stroke care over usual care. Sample size estimates To detect an absolute 10% difference in overall performance (composite outcome), a minimum of 21 hospitals and 843 patients per group was determined. Methods and design Before and after controlled design in hospitals in Queensland, Australia. Intervention Externally facilitated program (StrokeLink) using outreach workshops incorporating clinical performance feedback, patient outcomes (survival, quality of life at 90-180 days), local barrier assessments to best practice care, action planning, and ongoing support. Descriptive and multivariable analyses adjusted for patient correlations by hospital (intention-to-treat method). Context Concurrent implementation of financial incentives to increase stroke unit access and use of the Australian Stroke Clinical Registry for performance monitoring. Study outcome(s) Primary outcome: net change in composite score (i.e. total number of process indicators achieved divided by the sum of eligible indicators for each cohort)., Secondary Outcomes: change in individual indicators, change in composite score comparing hospitals that did or did not develop action plans (per-protocol analysis), impact on 90-180-day health outcomes. Sensitivity analyses: hospital self-rated status, alternate cross-sectional audit data (Stroke Foundation). To account for temporal effects, comparison of Queensland hospital performance relative to other Australian hospitals will also be undertaken. Discussion Twenty-one hospitals were recruited; however, one was unable to participate within the study time frame. Workshops were held between 11 March 2014 and 7 November 2014. Data are ready for analysis.