Abstract 4139555: Transitions Between Heart Failure States in Adult Patients with Congenital Heart Disease
Bibliographic record
Abstract
Background: The growing number of adult congenital heart disease (CHD) patients facing heart failure (HF) related complications after the age of 40 has become a major concern. There is a lack of data to capture the dynamic nature of HF progression across the adulthood, with the consideration of various contributing factors. This application of multistate models paves a promising way to accurately track health state changes, thus to provide data to inform surveillance and interventions to prevent disease progression, potentially improving personalized treatment plans for a significant patient population. Aims: This study aimed to (1) determine the expected time a patient remains in each HF state, based on predisposing factors; (2) evaluate the probabilities that, across different ages, a patient will either remain at the same state, transition to the next HF state, or die; (3) identify the risk factors of transitioning between health states related to HF among CHD patients. Methods: The dataset was derived from the Quebec CHD database which encompasses 137,493 patients, spanning 35 years of follow-up from 1983 to 2017. We constructed a multistate model to include 6 states: no HF history (0HF), having one (1HF), two (2HF), three (3HF) or more than four HF events (4+HF), and death. Each HF state transition was modeled by Cox proportional hazards regression using the same predictors, including sex, presence of severe congenital heart defects, and comorbidity history. Results: The study included 83,669 adult patients with CHD. Among them, 32,934 HF events and 16,348 deaths were observed during a total of 1,732,942 person-years of follow-up. With each HF occurrence, patients showed faster progression to subsequent HF events. Patients with severe CHD lesion had nearly two decades fewer HF-free years compared to those with non-severe lesions (Figure). Early age of initial HF increased the risk of additional HF events and mortality. Comorbidities such as diabetes and chronic kidney disease markedly reduced the duration that a CHD patient remains HF-free. Conclusion: The study findings highlight the need for early intervention and personalized treatment strategies in managing HF progression in CHD patients.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".