Risk Models for Monitoring Postoperative Complication Rates After Paediatric Cardiac Surgery
Bibliographic record
Abstract
OBJECTIVES: As postoperative mortality for paediatric cardiac surgery is very low, we aimed to develop methods for monitoring of postoperative complication rates, given their impact upon children's health and well-being. METHODS: We used national registry data to develop and evaluate a suite of risk adjustment models for the outcomes of 6 defined postoperative complications, designed for use in complication monitoring for quality assurance. RESULTS: There were 23 423 30-day postoperative episodes in children under the age of 18 years undergoing cardiac surgery between 2015 and 2021 in England and Wales, with 361 (1.5%) deaths <30 days. Two hundred fifty-seven (1.9%) of 13 556 postoperative episodes in infants (<1 year) involved necrotizing enterocolitis; 158 (1.3%) of 12 408 postoperative episodes between 2018 and 2021 involved prolonged pleural effusion; and among the full sample of postoperative episodes, there were 526 (2.2%) acute neurological events, 446 (1.9%) extracorporeal life supports, 740 (3.6%) renal replacement therapies, and 1006 (4.3%) unplanned reinterventions within 30 days of surgery. The risk adjustment models were developed using clinical factors first defined for mortality monitoring. The models for prolonged pleural effusion, extracorporeal life support, and renal replacement performed very well with area under the curve (AUC) statistics >0.85. The performance of the models for necrotizing enterocolitis, acute neurological event, and unplanned reintervention was less good (AUC statistics 0.74-0.79). CONCLUSIONS: Although complications are more complex outcome measures than mortality, national registry data can be used to capture them and to evaluate methods for risk adjustment of these outcomes. These methods may enable future risk-adjusted monitoring of complication metrics for quality assurance.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".