Cardiovascular events more than 6 months after pregnancy in patients with congenital heart disease
Bibliographic record
Abstract
OBJECTIVES: Patients with congenital heart disease (CHD) are increasingly pursuing pregnancy, highlighting the need for data on late cardiovascular events (more than 6 months after delivery). We aimed to determine the incidence of late cardiovascular events in postpartum patients with CHD and evaluate the accuracy of the existing risk scores in predicting these events. STUDY DESIGN: We identified patients with CHD who delivered between 2008 and 2020 at a tertiary centre and had follow-up data for greater than 6 months post partum. Late cardiovascular events were defined as heart failure, arrhythmia, thromboembolic events, endocarditis, urgent cardiovascular interventions or death. Survival analysis and Cox proportional model were used to estimate the incidence of late cardiovascular events and determine the hazard ratio of factors associated with these events. RESULTS: Of 117 patients, 19% had 36 late cardiovascular events over a median follow-up of 3.8 years. Annual incidence of any late cardiovascular event was 5.7%. Hazards of late cardiovascular events were significantly higher among those with higher Cardiac Disease in Pregnancy Study (CARPREG) II and Zwangerschap bij Aangeboren HARtAfwijking-Pregnancy in Women With Congenital Heart Disease (ZAHARA) risk scores and among patients with prepregnancy New York Heart Association class≥II. C-statistic to predict the late cardiovascular events was highest for ZAHARA (0.7823), followed by CARPREG II (0.6902) and prepregnancy New York Heart Association class≥ II (0.6677). CONCLUSIONS: Currently available risk tools designed for prognostication during the peripartum period can also be used to determine risks of late maternal cardiovascular events among those with CHD. These findings provide important new information for counselling and risk modification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".