Abstract 11485: New Metrics of Postoperative Mechanical Ventilation Duration After Congenital Heart Surgery Reveal Variation Across Hospitals
Bibliographic record
Abstract
Background: Pediatric cardiac surgical programs aim to limit duration of postoperative mechanical ventilation (POMV) to reduce complications and hospital stay. Measuring casemix-adjusted duration of POMV across hospitals might elucidate differential performance and identify improvement opportunities. Methods: All surgical hospitalizations in the Pediatric Cardiac Critical Care Consortium (PC 4 ) clinical registry from 10/2013-8/2015 were used to create a model predicting casemix-adjusted total duration of POMV using zero-inflated negative binomial regression and validated with 1000 bootstrap samples. From the model we developed metrics based on observed-to-expected POMV: early extubation success/failure, POMV reduction, and total hours of POMV saved/lost ( Table 1 ). We ranked hospitals on each metric (1-15, 1=best) and calculated an average ranking across metrics to identify high and low performing hospitals. Results: The cohort included 4739 hospitalizations from 15 hospitals: 53% were infants and 22% had high complexity surgery. The final model included age, weight-for-age z-score, prematurity, pre-operative MV, extracardiac anomalies, procedure complexity, and bypass time. The model was well-calibrated to predict mean duration of POMV for groups of patients. Table 1 displays the range and median of hospital rates on each of the four metrics, demonstrating variation across the group. The average ranks across these POMV duration metrics suggested two positive outlying hospitals (average rank across all 4 metrics = 1.75) and five hospitals with consistently lower performance (average 8.75-11.25). Conclusions: We developed novel casemix-adjusted metrics of hospital performance to limit duration of POMV following pediatric cardiac surgery, and identified wide variation in relative performance across centers. These metrics may suggest opportunities for improvement when evaluated in context with other perioperative quality measures.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".