Trends in mortality rate in patients with congenital heart disease undergoing noncardiac surgical procedures at children’s hospitals
Bibliographic record
Abstract
Advances made in pediatric cardiology, cardiac surgery and critical care have significantly improved the survival rate of patients with congenital heart disease (CHD) leading to an increase in children with CHD presenting for noncardiac surgical procedures. This study aims (1) to describe the trend and perioperative mortality rates in patients with CHD undergoing noncardiac surgical procedures at children's hospitals over the past 5 years and (2) to describe the patient characteristics and the most common type of surgical procedures. The Pediatric Health Information System (PHIS) is an administrative database that contains inpatient, observation, and outpatient surgical data from 52 freestanding children's hospitals. Thirty-nine of the 52 hospitals submitted data on all types of patient encounters for the duration of the study from 2015 to 2019. The total numbers of non-cardiac surgical encounters among patients with history of a CHD diagnosis significantly increased each year from 38,272 in 2015 to 45,993 in 2019 (P < 0.001). Despite the increase in case numbers, there has been a significant decline in mortality rates to the most recent incidence of 1.06% in 2019. Careful patient selection and medical optimization of patients aligned with specific expertise at dedicated children's hospitals may lead to improvement in mortality rate. Future studies comparing the outcomes of patients with cardiac disease based on hospital type and volume as well as type of providers may help determine the future of care including potential need for regionalization of noncardiac care for this vulnerable patient population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".