Abstract WP317: The Impact of Post-acute Discharge Disposition on Outcomes in the SWIFT PRIME Trial
Bibliographic record
Abstract
Intro: Post-acute stroke care in an inpatient rehabilitation facility (IRF) demonstrates better outcomes compared to a skilled nursing facility (SNF). With advancements in endovascular acute stroke, the impact that post-acute care plays is unclear. Here, we analyze a successful endovascular acute stroke trial to demonstrate that more improvement is seen in patients discharged to an IRF compared to a SNF. Methods: From SWIFT PRIME, a prospective, multi-center randomized acute endovascular trial, subject characteristics, and modified Rankin scores (mRS) were obtained. Post-acute hospital discharge was classified as home, IRF, and SNF. A favorable outcome was defined as 90 day mRS ≤ 2 and improvement was defined as ≥ 1 point decrease in mRS score. The effect of each disposition on a favorable outcome was calculated overall and stratified by stroke severity class (defined as discharge mRS 0-3, 4, 5) Results: A total of 165 subjects (mean age 64.8 years, mean initial NIHSS= 16.5, and 50 % male) were analyzed. Discharge disposition included: 51 (31%) going home, 92 (56%) IRF, 22 (13%) SNF. The baseline characteristics were similar between patients that went to IRF and SNF: age (p =0.76), gender (p= 0.81), baseline NIHSS (p=0.055), final infarct volumes (p=0.20), and recanalization rates (p=0.19). However, IRF subjects had lower NIHSS (p<0.001) and mRS (p=0.017) at day 7. Time to treatment defined as symptom onset to groin puncture was not significantly associated with discharge disposition (p=0.119). Only 1/22 (4.5%) subjects who were discharged to SNF achieved a 90 day mRS ≤2, compared to 41/92 (44.6%) in the IRF group or 48/51 (94.1%) in the home group (p < 0.001). When stratified by stroke severity: for mRS=0-3, there were no differences in favorable outcomes; mRS=4, 1/7 (14.3%) showed improvement at SNF compared to 21/27 (77.8%) at IRF (p=0.008); mRS =5, 5/14 (35.7%) showed improvement at SNF compared to 28/37 (75.7%) at IRF (p=0.013). Conclusions: Despite having similar characteristics following acute stroke treatment, not only did subjects who went to SNF compared to IRF have more unfavorable outcomes, they were less likely to make improvement. These findings show the continued importance of post-stroke rehabilitation, even in the endovascular era.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".