P1302NONINVASIVE ASSESSMENT OF PULMONARY HYPERTENSION USING QUANTITATIVE IMAGING IN HEMODIALYSIS PATIENTS
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».