Interdisciplinary interactions, social systems and technical infrastructure required for successful implementation of mobile stroke units: A qualitative process evaluation.

RATIONALE: Mobile stroke units (MSUs) are increasingly being implemented to provide acute stroke care in the prehospital environment, but a comprehensive implementation evaluation has not been undertaken. AIM: To identify successes and challenges in the pre- and initial operations of the first Australian MSU service from an interdisciplinary perspective. METHODS: Process evaluation of the Melbourne MSU with a mixed-methods design. Purposive sampling targeted key stakeholder groups. Online surveys (administered June-September 2019) and semistructured interviews (October-November 2019) explored experiences. Directed content analysis (raters' agreement 85%) and thematic analysis results are presented using the Interactive Sociotechnical Analysis framework. RESULTS: Participants representing executive/program operations, MSU clinicians and hospital-based clinicians completed 135 surveys and 38 interviews. Results converged, with major themes addressing successes and challenges: stakeholders, vehicle, knowledge, training/education, communication, work processes and working relationships. CONCLUSIONS: Successes and challenges of establishing a new MSU service extend beyond technical, to include operational and social aspects across prehospital and hospital environments.

Sustaining a New Model of Acute Stroke Care: A Mixed-Method Process Evaluation of the Melbourne Mobile Stroke Unit.

BACKGROUND: Internationally, Mobile Stroke Unit (MSU) ambulances have changed pre-hospital acute stroke care delivery. MSU clinical and cost-effectiveness studies are emerging, but little is known about important factors for achieving sustainability of this innovative model of care. METHODS: Mixed-methods study from the Melbourne MSU (operational since November 2017) process evaluation. Participant purposive sampling included clinical, operational and executive/management representatives from Ambulance Victoria (AV) (emergency medical service provider), the MSU clinical team, and receiving hospitals. Sustainability was defined as ongoing MSU operations, including MSU workforce and future model considerations. Theoretically-based on-line survey with Unified Theory of Acceptance and Use of Technology (UTAUT), Self Determination Theory (SDT, Intrinsic Motivation), and open-text questions targeting barriers and benefits was administered (June-September 2019). Individual/group interviews were conducted, eliciting improvement suggestions and requirements for ongoing use. Descriptive and regression analyses (quantitative data) and directed content and thematic analysis (open text and interview data) were conducted. RESULTS: There were 135 surveys completed. Identifying that the MSU was beneficial to daily work (ß=0.61), not experiencing pressure/tension about working on the MSU (ß=0.17) and thinking they did well working within the team model (ß=0.17) were significantly associated with wanting to continue working within the MSU model [R2=0.76; F(15, 60)=12.76, P<.001]. Experiences varied between those on the MSU team and those working with the MSU. Advantages were identified for patients (better, faster care) and clinicians (interdisciplinary learning). Disadvantages included challenges integrating into established systems, and establishing working relationships. Themes identified from 35 interviews were MSU team composition, MSU vehicle design and layout, personnel recruitment and rostering, communication improvements between organisations, telemedicine options, MSU operations and dispatch specificity. CONCLUSION: Important factors affecting the sustainability of the MSU model of stroke care emerged. A cohesive team approach, with identifiable benefits and good communication between participating organisations is important for clinical and operational sustainability.

Sleep disordered breathing in chronic stroke survivors. A study of the long term follow-up of the SCOPES cohort using home based polysomnography.

Prevalence of sleep-disordered breathing (SDB) (apnea-hypopnea index [AHI] > or = 5) in acute stroke patients ranges between 44% and 95%, compared to the community prevalence, 9 to 35% for women and 8 to 57% for men [age range 30-60 years]. Limited data exists beyond 3 months following stroke. We assessed the prevalence of SDB amongst stroke survivors at 3 years and compared results to data reported in normal and elderly populations. 90/143 eligible stroke survivors from an existing cohort underwent a home based sleep study. Mean age of the 78 subjects with a valid sleep study was 64 years (SD 15). Prevalence of SDB (AHI > or = 5) was 81% (95% CI 72% to 90%) and sleep apnoea syndrome (AHI > or = 5 plus ESS score > or =11) was 20% (95% CI 11% to 29%). Important predictors for AHI > or = 15 were haemorrhagic stroke (aOR12.06 [1.42-102.74]) and stroke severity at 1 month (aOR4.15 [1.05-16.38]). Large case-control studies are needed.

Improved ischemic stroke outcome prediction using model estimation of outcome probability: the THRIVE-c calculation.

BACKGROUND AND PURPOSE: The Totaled Health Risks in Vascular Events (THRIVE) score is a previously validated ischemic stroke outcome prediction tool. Although simplified scoring systems like the THRIVE score facilitate ease-of-use, when computers or devices are available at the point of care, a more accurate and patient-specific estimation of outcome probability should be possible by computing the logistic equation with patient-specific continuous variables. METHODS: We used data from 12 207 subjects from the Virtual International Stroke Trials Archive and the Safe Implementation of Thrombolysis in Stroke - Monitoring Study to develop and validate the performance of a model-derived estimation of outcome probability, the THRIVE-c calculation. Models were built with logistic regression using the underlying predictors from the THRIVE score: age, National Institutes of Health Stroke Scale score, and the Chronic Disease Scale (presence of hypertension, diabetes mellitus, or atrial fibrillation). Receiver operator characteristics analysis was used to assess model performance and compare the THRIVE-c model to the traditional THRIVE score, using a two-tailed Chi-squared test. RESULTS: The THRIVE-c model performed similarly in the randomly chosen development cohort (n = 6194, area under the curve = 0·786, 95% confidence interval 0·774-0·798) and validation cohort (n = 6013, area under the curve = 0·784, 95% confidence interval 0·772-0·796) (P = 0·79). Similar performance was also seen in two separate external validation cohorts. The THRIVE-c model (area under the curve = 0·785, 95% confidence interval 0·777-0·793) had superior performance when compared with the traditional THRIVE score (area under the curve = 0·746, 95% confidence interval 0·737-0·755) (P < 0·001). CONCLUSION: By computing the logistic equation with patient-specific continuous variables in the THRIVE-c calculation, outcomes at the individual patient level are more accurately estimated. Given the widespread availability of computers and devices at the point of care, such calculations can be easily performed with a simple user interface.

The THRIVE score predicts symptomatic intracerebral hemorrhage after intravenous tPA administration in SITS-MOST.

BACKGROUND: The Totaled Health Risks in Vascular Events (THRIVE) score is a clinical prediction score that predicts ischemic stroke outcomes in patients receiving intravenous tissue plasminogen activator, endovascular stroke treatment, or no acute therapy. We have previously found an association between THRIVE and risk of post-tissue plasminogen activator symptomatic intracranial hemorrhage in the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator trial and risk of radiographic hemorrhage in Virtual International Stroke Trials Archive. AIMS: The study aims to validate the relationship between THRIVE and symptomatic intracranial hemorrhage among tissue plasminogen activator-treated patients in the large Safe Implementation of Thrombolysis in Stroke - Monitoring Study (SITS-MOST). METHODS: This is a retrospective analysis of the prospective SITS-MOST to examine the relationship between THRIVE and symptomatic intracranial hemorrhage after tissue plasminogen activator treatment. Symptomatic intracranial hemorrhage after tissue plasminogen activator was defined according to each of three standard definitions: the NINDS, European Cooperative Acute Stroke Study (ECASS), and Safe Implementation of Thrombolysis in Stroke (SITS) criteria. Multivariable logistic regression was used to confirm the relationship of THRIVE and individual THRIVE components with the risk of symptomatic intracranial hemorrhage and to examine the relationship of THRIVE, symptomatic intracranial hemorrhage, and functional outcome. RESULTS: The odds ratio for symptomatic intracranial hemorrhage at each increased level of THRIVE score is 1·34 (95% CI 1·27 to 1·41, P < 0·001) for symptomatic intracranial hemorrhage by NINDS criteria, 1·36 (95% CI 1·27 to 1·46, P < 0·001) for symptomatic intracranial hemorrhage by ECASS criteria, and 1·21 (95% CI 1·09 to 1·36, P < 0·001) for symptomatic intracranial hemorrhage by SITS criteria. In receiver-operator characteristics analysis, the C-statistic for THRIVE prediction of symptomatic intracranial hemorrhage was 0·65 (95% CI 0·62 to 0·67) for symptomatic intracranial hemorrhage by NINDS criteria, 0·66 (95% CI 0·63 to 0·69) for symptomatic intracranial hemorrhage by ECASS criteria, and 0·61 (95% CI 0·56 to 0·66) for symptomatic intracranial hemorrhage by SITS criteria. Each component of the THRIVE score predicts the risk of symptomatic intracranial hemorrhage, with independent impact of each component in multivariable analysis. CONCLUSIONS: The THRIVE score predicts the risk of symptomatic intracranial hemorrhage after intravenous tissue plasminogen activator administration. This external validation of the relationship between THRIVE and symptomatic intracranial hemorrhage in a prospective study further strengthens the role of the THRIVE score in the prediction of poststroke outcomes.