Abstract 16359: Incidence of Fontan Associated Liver Disease and Its Impact on Mortality in Patients With Single Ventricle Physiology
Bibliographic record
Abstract
Introduction: Data on the burden of clinically significant Fontan associated liver disease (FALD) and its relationship to mortality is scant. We performed a retrospective cohort study to assess the incidence of FALD and its association with mortality. Methods: Data source was the Quebec Congenital Heart Disease (CHD) Database, a population-based cohort of over 100,000 CHD patients followed from 1983-2017. Fontan patients surviving longer than 30 days post-Fontan were identified, each were matched to 20 VSD patients on age and sex. The Fontan-VSD cohort were used to assess the association between Fontan and the risk of developing FALD. The VSD cohort served as “Non-exposed to Fontan” group. FALD was defined as at least one hospitalization due to liver disease. Kaplan-Meier curves were used to estimate and compare the cumulative probability of 1) developing FALD between Fontan and VSD patients; and 2) mortality between Fontan patients w/o FALD. Results: A total of 512 Fontan patients and 10,232 VSD patients were included. The cumulative probability of developing FALD at 10 and 25 years of follow-up was higher in Fontan patients (13.0% and 37.1%, respectively), compared to 0.7% and 2.0% for VSD patients respectively (p-value <0.0001-Logrank test). In Fontan patients with FALD, the cumulative probability of mortality by 5 years after the diagnosis of FALD was 12.6%, 11 times higher than the risk in Fontan Patients without FALD (Figure) . Calendar year of Fontan operation was found to be an important predictor of developing FALD. Conclusions: This is the first study documenting the impact of FALD on mortality, using a large cohort with long-term follow up. The findings support the use of well-defined surveillance protocols to identify potential precipitants of FALD before liver disease becomes irreversible.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".