Physical Functioning, Mental Health, and Quality of Life in Different Congenital Heart Defects: Comparative Analysis in 3538 Patients From 15 Countries
Bibliographic record
Abstract
BACKGROUND: We compared physical functioning, mental health, and quality of life (QoL) of patients with different subtypes of congenital heart disease (CHD) in a large international sample and investigated the role of functional class in explaining the variance in outcomes across heart defects. METHODS: In the cross-sectional Assessment of Patterns of Patient-Reported Outcome in Adults with Congenital Heart Disease-International Study (APPROACH-IS), we enrolled 4028 adult patients with CHD from 15 countries. Diagnostic groups with at least 50 patients were included in these analyses, yielding a sample of 3538 patients (median age: 32 years; 52% women). Physical functioning, mental health, and QoL were measured with the SF-12 health status survey, Hospital Anxiety and Depression Scale (HADS), linear analog scale (LAS) and Satisfaction with Life Scale, respectively. Functional class was assessed using the patient-reported New York Heart Association (NYHA) class. Multivariable general linear mixed models were applied to assess the relationship between the type of CHD and patient-reported outcomes, adjusted for patient characteristics, and with country as random effect. RESULTS: Patients with coarctation of the aorta and those with isolated aortic valve disease reported the best physical functioning, mental health, and QoL. Patients with cyanotic heart disease or Eisenmenger syndrome had worst outcomes. The differences were statistically significant, above and beyond other patient characteristics. However, the explained variances were small (0.6% to 4.1%) and decreased further when functional status was added to the models (0.4% to 0.9%). CONCLUSIONS: Some types of CHD predict worse patient-reported outcomes. However, it appears that it is the functional status associated with the heart defect rather than the heart defect itself that shapes the 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".