Predictors of Developing Heart Failure in Adults with Congenital Heart Defects
Bibliographic record
Abstract
Background: The population of adults with congenital heart defects (ACHD) is growing. The leading cause of premature death in these patients is heart failure (HF). However, there is still limited information on the predictive factors for HF in ACHD patients. Objectives: This study re-examined a group of patients with repaired or palliated congenital heart defects (CHD) that were initially studied in 2003. A follow-up period of 15 years has allowed us to identify and evaluate predictors for the development of HF in ACHD. Methods: All patients with repaired or palliated CHD who participated in the initial study (n = 364) were invited for a follow-up examination. The effects of maximum oxygen uptake (VO2max) during exercise stress testing, the cardiac biomarker N-terminal pro brain natriuretic peptide (NT-proBNP), and QRS complex on the development of HF during the follow-up period were investigated. Results: From May 2017 to April 2019, 249 of the initial 364 (68%) patients participated in the follow-up study. Of these, 21% were found to have mild CHD, 60% had moderate CHD, and 19% had complex CHD. Significant predictors for the development of HF were: NT-proBNP level >1.7 times the upper normal limit, VO2max <73% of predicted values, and QRS complex duration >120 ms. Combination of these three parameters resulted in the highest area-under-the-curve of 0.75, with a sensitivity of 75% and specificity of 63% for predicting the development of HF. Conclusions: In this cohort of ACHD patients, the combination of VO2max%, NT-proBNP, and QRS duration was predictive of HF development over a 15-year follow-up period. Enhanced surveillance of these parameters in patients with ACHD may be beneficial for the prevention of HF and early intervention.
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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 | 0.001 | 0.000 |
| 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".