Abstract WP378: Characteristics and Outcomes of Stroke Patients Transferred to Hospitals Participating in the Michigan Coverdell Acute Stroke Registry 2009-2011
Bibliographic record
Abstract
Characteristics and Outcomes of Stroke Patients Transferred to Hospitals Participating in the Michigan Coverdell Acute Stroke Registry, 2009-2011 Background: As stroke systems of care are evolving the number of stroke patients transferred between hospitals is increasing. Our objectives were to describe the characteristics of acute stroke patients who were transferred to hospitals participating in the Michigan Coverdell Acute Stroke Registry, and to determine the independent association between transfer status (TS) and in-hospital mortality (IHM) and in-hospital complications (IHC). Methods: From 2009-2011, 30934 acute ischemic (IS) and hemorrhagic stroke (HS) patients were admitted to 35 registry hospitals. Patients with an in-hospital stroke, TIA, or unknown arrival mode were excluded (N= 14,732). Independent factors associated with TS and predictors of IHC (defined as deep vein thrombosis, pneumonia, and/or UTI) were identified using multivariable logistic regression models. Results: The mean age of the 16202 admissions was 69.2 years, 51% were female, 68% were white, 83% had an IS, 7.4% died in-hospital, and 13.8% had an IHC. Overall, 19% (N= 3091) were transferred to a registry hospital. The transfer rate increased from 2009-2011 (16.9% -21.1%), and was higher in HS vs. IS patients (37.6% vs. 15.3%). Significant predictors of TS were year, age, gender, race, stroke type, pre-stroke ambulatory status, nursing home residence, and medical history of diabetes or prior stroke. Length of stay (LOS) was longer for transferred patients vs. non-transferred (7.9 vs. 5.3 days, p<0.0001). Transferred patients were more likely to die in-hospital vs. non-transferred (12.0% vs. 6.4%, P <0.001), and develop IHC (18.4% vs. 12.8%, P < 0.001). After adjusting for confounding variables, TS remained a significant predictor of IHM (adjusted odds ratios [aOR] = 1.46, 95%CI =1.14- 1.88), and IHC (aOR= 1.58, 95%CI =1.34- 1.87). Conclusions: The frequency of hospital transfers increased markedly in this registry. Transferred patients experience higher rates of IHM, IHC and longer LOS. Further studies are needed to understand the relationship between TS and outcomes and the implication for improved clinical care and reducing poor outcomes in this higher risk group.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".