Trans-stenotic pressure gradient as derived from CT improves patient management: ADVANCE registry
Notice bibliographique
Résumé
Abstract Background The change in fractional flow reserve derived from CT (FFRCT) value across a coronary stenosis (ΔFFRCT) improves the physiological characterization of coronary artery disease (CAD). The role of ΔFFRCT in guiding risk-stratification and downstream testing in patients with stable CAD is unknown. Purpose To investigate the incremental value of ΔFFRCT at predicting early revascularization and improving efficacy of resource utilization. Methods Patients with CAD on CT coronary angiography (CTCA) were enrolled in an international multicenter registry. Patients with non-evaluable FFRCT analysis were excluded. The CTCA was assessed for: stenosis severity as per CAD-Reporting and Data System (CAD-RADS), lesion length and lesion-specific FFRCT measured 2 cm distal to stenosis. Risk factors and actual treatment (revascularization vs medical therapy) at 90-day follow-up were recorded. Multivariable logistic regression analysis for early revascularization was conducted. The incremental discrimination for revascularization prediction was compared among 3 models (model 1: risk factors + lesion length and location + CAD-RADS; model 2: model 1 + lesion-specific FFRCT; model 3: model 2 + ΔFFRCT). Simulating ICA referral for patients with CAD-RADS ≥3 and lesion-specific FFRCT ≤0.8, the potential impact of ΔFFRCT at reducing ICA referral and improving the ratio of subsequent revascularization was assessed. Results Of 4730 patients (66±10 years; 34% female), 2092 (42.7%) underwent ICA and 1168 (24.7%) underwent early revascularization. With increasing ΔFFRCT, a higher incidence of revascularization (Figure 1A) and an increase in the revascularization to ICA ratio was observed (Figure 1B). ΔFFRCT >0.13 was the optimal cut-off for predicting revascularization as determined by the Youden index. ΔFFRCT remained an independent predictor for early revascularization (odds ratio per 0.05 increase with 95% CI, 1.31 [1.26–1.35]; p<0.0001) after adjusting for risk factors, CAD-RADS, lesion length and location, and FFRCT. Among the 3 models, model 3, which included ΔFFRCT showed the highest AUC and improved discrimination power compared to model 2 (0.87 [0.86–0.88] vs 0.85 [0.84–0.86]; p<0.0001] (Figure 2), with the greatest incremental value for ΔFFRCT observed in patients with lesion-specific FFRCT between 0.71–0.80. In patients with CAD-RADS ≥3 and lesion-specific FFRCT ≤0.8, a diagnostic strategy incorporating ΔFFRCT >0.13 would potentially reduce ICA referral by 32.2% (1638 to 1110) and improve the revascularization to ICA ratio from 65.2% [1068/1638] to 73.1% [811/1110]. Conclusions The characterization of CAD with ΔFFRCT improves the identification of patients requiring early revascularization as compared to a standard diagnostic strategy of CTCA with FFRCT, particularly for those with lesion-specific FFRCT of 0.71–0.80. ΔFFRCT has the potential to aid decision making for ICA referral and improve the efficiency of resource utilization. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): HeartFlow, Inc., Redwood City, CA, USA ΔFFRCT and actual treatmentROC curve for early revascularization
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 | 0,006 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».