Abstract 3446: Inpatient Code Stroke Protocol: A Quality Improvement and Knowledge Translation Strategy for a Challenging Scenario
Bibliographic record
Abstract
Background: Stroke care faces a clinical challenge in treating inhospital strokes, which account for about 15% of all strokes. Prior studies showed an inequity in the assessment and treatment of inpatients who suffer a stroke versus out-of hospital. For example, inpatients have longer time to initial assessment, CT and are less likely (wait longer) to receive tissue plasminogen activator (t-PA). There is limited research evaluating the efficacy of inpatient code stroke protocols (ICSP) on access to and quality of hyper-acute stroke care. Objective: To evaluate the efficacy of the ICSP in a large tertiary care hospital. Methods: This prospective study evaluated a quality improvement strategy involving ICSP implementation at St Michael’s Hospital in 2009. The ICSP focuses on the identification of stroke symptoms and timely notification of most responsible physician, then leverages the Emergency Department code stroke process. A 3-month hospital-wide implementation period involved 60 min. education sessions with a minimum of 2 sessions per unit. Demographic factors, presenting symptoms, stroke severity, vascular risk factors as well as time of: symptoms onset, CT; and physician assessment were collected by chart abstraction after ethics approval. The primary outcomes was time from last seen normal (LSN) to CT scan. Secondary outcomes include time from LSN to initial assessment (IA), medical complications and number of patients receiving endovascular interventions or intravenous thrombolysis. The analysis was completed by comparing unadjusted and adjusted outcomes pre and post implementation of the ICSP. Descriptive statistics and robust regression was completed using SAS 9.0. Results: Overall, there were 245 inhospital strokes during the study period (152 pre and 93 post ICSP implementation). Mean age was 69.8 yrs, 60% were male. Most inpatient strokes occurred on cardiovascular services (42.9%). Main results summarized in table . There was no difference in the number of patients receiving thrombolysis or endovascular treatment. After adjustment for covariates, the ICS was associated with a significant reduction of 288 minutes (95%CI -566, -10) in time from LSN to CT. Similarly, there was significant reduction of 307 (95%CI -532, -82) in time from LSN to IA. Conclusions: Implementation of the ICSP resulted in improvements in the process indicators related to assessment and treatment of hyper-acute stroke. Similar quality improvement strategies can be implemented to ameliorate disparities between care for inpatients and outpatient presenting with an acute ischemic stroke.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.154 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".