Abstract WMP94: Identifying Opportunities to Improve Hypertension Management Among Hospitals Participating in Michigan’s Ongoing Stroke Registry to Accelerate Improvement of Care (MOSAIC)
Bibliographic record
Abstract
Background: Hypertension is a leading risk factor for stroke, and its management is key for stroke prevention. Michigan’s Coverdell Acute Stroke Registry collects stroke data including patient history of hypertension and discharge medications. Our aim was to identify independent predictors of having antihypertensive medication prescribed at discharge (AMPD) and to assess hospital-level performance in order to focus future quality improvement efforts. Methods: Thirty-one hospitals contributed data to MOSAIC in 2016-2017. Patients with a diagnosis of acute stroke or TIA were included; subjects receiving comfort measures only (CMO), died prior to discharge, or were discharged to hospice were excluded. Patients were considered eligible for AMPD if they had a history of hypertension. Independent factors associated with no AMPD were identified using multivariable logistic regression. Variation among participating hospitals in the treatment of hypertension at discharge was assessed by calculating the percent of hypertension patients with no AMPD. Results: Out of 19,645 patients with stroke or TIA, 15,253 (77.6%) had a history of hypertension; 12,707 (83.3%) of these cases did not receive CMO, hospice, were not discharged to an acute care facility and were discharged alive. Of these eligible cases, 934 (7.3%) had no AMPD. No AMPD was significantly higher among subjects who were younger, had hemorrhagic stroke or TIA. No AMPD was significantly lower among blacks (Table). Rates of no AMPD varied widely among hospitals; five hospitals had rates below an achievable benchmark of 15% no AMPD (Range: 2.5 - 70.0%). Conclusions: Prescription of antihypertensive medication to patients with a history of hypertension at discharge is high but use occurs less often in younger patients, and those with hemorrhagic stroke or TIA. Hospital-based data identifies substantial variability and identified individual hospitals for follow-up education and quality improvement efforts.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".