Abstract 4145614: Neuroimaging findings in adults with congenital heart disease: associations with demographic-clinical factors and neurocognition
Bibliographic record
Abstract
Background: People with congenital heart disease (CHD) face developmental and acquired risks to their neurocognitive health. Mechanisms and clinical-neuroimaging correlates are not well-defined. We report baseline neuroimaging findings, their associations with demographic/clinical factors, and cognitive performance, from a longitudinal study of brain health in adult CHD (ACHD). Methods: Participants (n=99) aged >18y with moderate-severe complexity CHD were recruited from the ACHD referral centre for Western Canada. They underwent clinical review and had bloodwork, Holter, echocardiography MRI brain, cognitive testing (MoCA, NIH Toolbox [NIHTB]). Forward stepwise linear and logistic regression models were used to identify demographic/clinical factors that predicted MRI findings. Relationships between demographic/clinical factors with MRI findings, and MRI findings with cognition, were then explored with multivariable linear and logistic regression as appropriate. Results: Median(IQR) age 35y (29-40); 42% female.(Figure 1) CHD complexity (21% severe) was independently associated with lower total brain volume (TBV). TBV independently predicted cognitive performance (B[95%CI] for MoCA change/100mm3: 2.73x10 -3 [3.0x10 -4 - 5.1 x 10 -3 ]; p=0.03; NIHTB 8.39x10 -3 [2.2x10 -3 - 1.46x10 -2 ]; p=0.009). History of dyslipidemia was independently associated with white matter hyperintensity (WMH) volume but not presence of WMH. Cerebral microbleeds (CMB, 60% of participants) and lacunes (16%) were independently associated with history and number of cardiopulmonary bypass surgeries. None of WMH, CMB or lacunes predicted cognitive performance. Conclusions: In this high-functioning cohort of mostly younger ACHD, neuroimaging abnormalities were common. TBV was independently associated with CHD severity. WMH were associated with dyslipidemia; CMB and lacunes with bypass. Only TBV predicted cognitive performance. Acknowledging our modest cohort size with heterogenous CHD types, our results suggest that the pathophysiology impacting brain health reflects a combination of early- and later-life factors. Longitudinal studies may identify optimal preventative interventions and their timing; dyslipidemia may be a modifiable target.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".