Abstract 122: Impact of Using Clinical Data to Profile Hospitals for 30-day Stroke Mortality: Data from the Ontario Stroke Audit
Bibliographic record
Abstract
Background: Risk-adjustment models that use administrative data may not fully account for case-mix differences between hospitals. Supplementing these models with clinical data may improve their accuracy. We examined the impact of using data from a clinical registry in addition to administrative data on profiling Ontario hospitals for 30-day stroke mortality. Methods: Ontario Stroke Audit (OSA) data on 7006 acute stroke cases admitted to 54 Ontario hospitals between 2010-11 were obtained. We calculated 30-day risk-standardized mortality (RSMR) from an established risk adjustment model that relied on only hospital discharge data. This model (termed ADMIN) included age, sex, past medical history (including atrial fibrillation, stroke, diabetes, hypertension, heart disease), ambulance arrival, and stroke-type. We then modified this model to create a CLINICAL model by adding statistically significant clinical variables from the OSA data. In addition to the ADMIN model variables, the CLINICAL model included dysphagia, cognitive status, pre-stroke function, and stroke severity. We compared model performance (c-statistics, calibration [Hosmer-Lemeshow chi-square statistic (HLCS], Integrated Discrimination Improvement [IDI]), and hospital rankings (based on RSMR) using Spearman rank correlation. Results: Model performance was noticeably better for the CLINICAL model compared to the ADMIN model; discrimination was higher (c-statistic 0.82 vs. 0.70), calibration better (HLCS p= 0.08 vs <0.01) and predictive accuracy greater (IDI= 0.13, p <0.001). Comparison of RSMRs between the two models showed only moderate correlation (r = 0.653) with substantial variation for some hospitals (Figure). Conclusions: The addition of variables from clinical data to an established administrative data model had a marked effect on hospital rankings. These results suggest that clinical variables could have a meaningful impact on the accuracy of hospital report cards.
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".