Mortality Following Pediatric Congenital Heart Surgery: An Analysis of the Causes of Death Derived From the National Death Index
Bibliographic record
Abstract
Background Prior research has focused on early outcomes after congenital heart surgery, but less is known about later risks. We aimed to determine the late causes of death among children (<21 years of age) surviving their initial congenital heart surgery. Methods and Results This is a retrospective cohort study from the Pediatric Cardiac Care Consortium, a US-based registry of interventions for congenital heart defects (CHD). Excluding patients with chromosomal anomalies or inadequate identifiers, we matched those surviving their first congenital heart surgery (1982-2003) against the National Death Index through 2014. Causes of death were obtained from the National Death Index to calculate cause-specific standardized mortality ratios (SMRs). Among 31 132 patients, 2527 deaths (8.1%) occurred over a median follow-up period of 18 years. Causes of death varied by time after surgery and severity of CHD but, overall, 69.9% of deaths were attributed to the CHD or another cardiovascular disorder, with a SMR for CHD/cardiovascular disorder of 67.7 (95% confidence interval: 64.5-70.8). Adjusted odds ratios revealed increased risk of death from CHD/cardiovascular disorder in females [odds ratio=1.28; 95% confidence interval (1.04-1.58); P=0.018] with leading cardiovascular disorder contributing to death being cardiac arrest (16.8%), heart failure (14.8%), and arrhythmias (9.1%). Other major causes of death included coexisting congenital malformations (4.7%, SMR: 7.0), respiratory diseases (3.6%, SMR: 8.2), infections (3.4%, SMR: 8.2), and neoplasms (2.1%, SMR: 1.9). Conclusions Survivors of congenital heart surgery face long-term risks of premature mortality mostly related to residual CHD pathology, heart failure, and arrhythmias, but also to other noncardiac conditions. Ongoing monitoring is warranted to identify target factors to address residual morbidities and improve long-term outcomes.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".