Protocol of a randomized controlled trial investigating the effectiveness of Recovery-focused Community support to Avoid readmissions and improve Participation after Stroke (ReCAPS).

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.

A systematic review protocol of timing, efficacy and cost effectiveness of upper limb therapy for motor recovery post-stroke.

BACKGROUND: Improving upper limb (UL) motor recovery after stroke represents a major clinical and scientific goal. We aim to complete three systematic reviews to estimate the (1) association between time to start of UL therapy and motor recovery, (2) relative efficacy of different UL therapy approaches post-stroke and (3) cost-effectiveness of UL therapy interventions. METHODS: We have designed a systematic review protocol to address three systematic review questions that were each registered with PROSPERO. The search will be conducted in MEDLINE, EMBASE, and Cochrane Controlled Register of Trials. We will include randomised controlled trials, non-randomised clinical trials, before-after studies and observational studies of adult stroke survivors with an average stroke onset < 6 months, undergoing hospital-based therapy to improve UL function. Eligible interventions will aim to promote UL functional recovery. Two reviewers will independently screen, select and extract data. Study risk of bias will be appraised using appropriate tools. Clinical measures of motor recovery will be investigated (primary measure Fugl Meyer UL assessment), as well as measures of health-related quality of life (primary measure EQ-5D) and all cost-effectiveness analyses completed. Secondary outcomes include therapy dose (minutes, weeks, repetitions as available) and safety (i.e. adverse events, serious adverse events). A narrative synthesis will describe quality and content of the evidence. If feasible, we will conduct random effects meta-analyses where appropriate. DISCUSSION: We anticipate the findings of this review will increase our understanding of UL therapy and inform the generation of novel, data-driven hypotheses for future UL therapy research post-stroke. SYSTEMATIC REVIEW REGISTRATION: PROSPERO, http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42018019367, http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42018111629, http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42018111628.

Derivation and external validation of a case mix model for the standardized reporting of 30-day stroke mortality rates.

BACKGROUND AND PURPOSE: Case mix adjustment is required to allow valid comparison of outcomes across care providers. However, there is a lack of externally validated models suitable for use in unselected stroke admissions. We therefore aimed to develop and externally validate prediction models to enable comparison of 30-day post-stroke mortality outcomes using routine clinical data. METHODS: Models were derived (n=9000 patients) and internally validated (n=18 169 patients) using data from the Sentinel Stroke National Audit Program, the national register of acute stroke in England and Wales. External validation (n=1470 patients) was performed in the South London Stroke Register, a population-based longitudinal study. Models were fitted using general estimating equations. Discrimination and calibration were assessed using receiver operating characteristic curve analysis and correlation plots. RESULTS: Two final models were derived. Model A included age (<60, 60-69, 70-79, 80-89, and ≥90 years), National Institutes of Health Stroke Severity Score (NIHSS) on admission, presence of atrial fibrillation on admission, and stroke type (ischemic versus primary intracerebral hemorrhage). Model B was similar but included only the consciousness component of the NIHSS in place of the full NIHSS. Both models showed excellent discrimination and calibration in internal and external validation. The c-statistics in external validation were 0.87 (95% confidence interval, 0.84-0.89) and 0.86 (95% confidence interval, 0.83-0.89) for models A and B, respectively. CONCLUSIONS: We have derived and externally validated 2 models to predict mortality in unselected patients with acute stroke using commonly collected clinical variables. In settings where the ability to record the full NIHSS on admission is limited, the level of consciousness component of the NIHSS provides a good approximation of the full NIHSS for mortality prediction.