MRI pulmonary artery flow detects lung vascular pathology in preterms with lung disease
Bibliographic record
Abstract
Background Pulmonary vascular disease (PVD) affects the majority of preterm neonates with bronchopulmonary dysplasia (BPD) and significantly determines long-term mortality through undetected progression into pulmonary hypertension. Our objectives were to associate characteristics of pulmonary artery (PA) flow and cardiac function with BPD-associated PVD near term using advanced magnetic resonance imaging (MRI) for improved risk stratification. Methods Preterms <32 weeks postmenstrual age (PMA) with/without BPD were clinically monitored including standard echocardiography and prospectively enrolled for 3 T MRI in spontaneous sleep near term (AIRR (Attention to Infants at Respiratory Risks) study). Semi-manual PA flow quantification (phase-contrast MRI; no BPD n=28, mild BPD n=35 and moderate/severe BPD n=25) was complemented by cardiac function assessment (cine MRI). Results We identified abnormalities in PA flow and cardiac function,i.e.increased net forward volume right/left ratio, decreased mean relative area change and pathological right end-diastolic volume, to sensitively detect BPD-associated PVD while correcting for PMA (leave-one-out area under the curve 0.88, sensitivity 0.80 and specificity 0.81). We linked these changes to increased right ventricular (RV) afterload (RV–arterial coupling (p=0.02), PA mid-systolic notching (t2; p=0.015) and cardiac index (p=1.67×10−8)) and correlated echocardiographic findings. Identified in moderate/severe BPD, we successfully applied the PA flow model in heterogeneous mild BPD cases, demonstrating strong correlation of PVD probability with indicators of BPD severity,i.e.duration of mechanical ventilation (rs=0.63, p=2.20×10−4) and oxygen supplementation (rs=0.60, p=6.00×10−4). Conclusions Abnormalities in MRI PA flow and cardiac function exhibit significant, synergistic potential to detect BPD-associated PVD, advancing the possibilities of risk-adapted monitoring.
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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 | 0.003 |
| 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 | 0.001 | 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".