P1302NONINVASIVE ASSESSMENT OF PULMONARY HYPERTENSION USING QUANTITATIVE IMAGING IN HEMODIALYSIS PATIENTS
Bibliographic record
Abstract
Abstract Background and Aims Pulmonary hypertension (PH) is highly prevalent in the hemodialysis (HD) patient population. Right heart catheterism remains the gold standard for PH diagnosis and etiological stratification – this makes a comprehensive investigation of PH challenging in these patients. The PEPPER study suggested that postcapillary PH is the most common form of PH in HD patients, as the result of volume overload and left ventricular dysfunction. We hypothesized that novel quantitative imaging-derived biomarkers, such as pulmonary vessel volume and pulmonary artery volume, would improve our insight on the relationship between PH, volume status and left ventricular dysfunction in HD patients. In this study, we explored the combined role of noncontrast chest CT and echocardiography to investigate PH in a sample of HD patients. Method Study participants underwent noncontrast chest CT and doppler echocardiography on a non-HD day. To avoid potential confounders, chronic hemodialysis patients with previously diagnosed chronic lung disease, cancer and infections were excluded, and smoking history was limited to 20 packs/year. Pulmonary vessel volume was automatically segmented and measured using commercial software (VIDA Diagnostics Inc., Coralville, USA). Total pulmonary artery (PA) volume was segmented manually from CT, including 25 mm of the main, left and right pulmonary arteries starting from the bifurcation; volumes were calculated using a combination of in-house software (3D Quantify, Robarts Research Institute, London, Ontario, Canada; MATLAB MathWorks, Inc., Natick, Massachusetts, USA). PA volume and pulmonary vessel volume were indexed by body surface area (BSA), to correct for body size. Left atrial volume and PA systolic pressure were measured from doppler echocardiography according to current clinical guidelines. Associations between quantitative imaging biomarkers and demographics were assessed with Pearson and Spearman correlation, as appropriate. Linear fitting was performed with linear regression. Results Five HD patients were studied. Two patients had PA systolic pressure ≥ 35 mmHg. Preliminary analysis showed a nonlinear trend correlation between PA systolic pressure and pulmonary vessel volume/BSA (Panel A), PA systolic pressure and pulmonary artery volume/BSA (Panel B). Additionally, pulmonary vessel volume showed a significant, positive linear correlation with total pulmonary artery volume (Panel C) and left atrial volume (Panel D). Conclusion Preliminary correlations between pulmonary vessel volume, pulmonary artery volume, left atrial volume and PA systolic pressure suggest that intravascular volume and left ventricular dysfunction may play a significant role in determining PH in HD patients. Quantitative imaging allows screening for PH and provides additional, noninvasive, and relevant clinical information on the pathophysiology of PH in HD patients. Correlation for PA Systolic Pressure (mmHg) with pulmonary vessel volume/BSA and total PA volume/BSA (Panels A and B, respectively). Correlation for pulmonary vessel volume with left atrial volume and total PA volume (Panels C and D, respectively).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".