Abstract T P14: In-Hospital Cost Analysis of the Addition of Hyperacute MRI for Selection of Patients for Endovascular Stroke Therapy
Bibliographic record
Abstract
Background: Patient selection is important for acute endovascular stroke therapy. We previously reported that a hyperacute MRI protocol for patient selection was associated with decreased utilization of endovascular stroke therapy and improved outcomes. A cost analysis comparing the pre and post-MRI protocol periods was performed to determine if the previous findings translated into cost savings. Methods: We retrospectively identified patients considered for endovascular stroke therapy from January 2008 to August 2012 who were ≤8 hours from stroke symptoms onset. Prior to April 30, 2010 selection was based on results of the CT/CTA alone (pre-hyperacute), whereas afterwards selection was based on results of MRI (hyperacute MRI). Demographic, outcomes and financial information was collected. Log-transformed average daily direct costs were regressed on time period. The regression model included demographic and clinical covariates as potential confounders. Multiple imputation was used to account for missing data. Results: We identified 267 patients, 88 in pre-hyperacute MRI and 179 in hyperacute MRI protocol. Length of stay was not significantly different in both groups (10.6 vs. 9.9 days; p< 0.42). The median of average daily direct costs was reduced by 24.5% (95% CI = 14.1% to 33.7%; p<0.001). Decreases in the proportion of cost from imaging (including endovascular intervention) and anesthesia services was seen, whereas increases were seen in the neurological and pharmacy charges (Figure). Conclusions: Use of the hyperacute MRI protocol translated into reduced costs, in addition to reduced utilization of the invasive therapy and better outcomes. MRI selection of patients is an effective strategy, both for patient and hospital systems.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".