Influence of pathophysiological patterns of coronary artery disease on the safety and efficacy of percutaneous coronary intervention
Notice bibliographique
Résumé
Abstract Background Impaired blood flow after PCI, reflected by low FFR, portends a worse prognosis. Prior to intervention, pressure gradient distributions offer insights into the likelihood of subsequent PCI success. The pullback pressure gradient (PPG) serves as a quantifiable measure of CAD patterns: values approximating 1 signify focal disease, while values approaching 0 suggest diffuse disease. This study aimed to assess the impact of pathophysiological CAD patterns on the safety and efficacy of PCI. Methods PPG Global was a prospective, investigator-initiated, multicenter study, single-arm that enrolled patients with at least one lesion with an FFR ≤ 0.80 scheduled to be treated by PCI. The study enrolled 1004 patients (1057 vessels). A standardised physiological assessment was performed, including online PPG calculation from manual FFR pullbacks. CAD patterns were defined as predominantly focal or diffuse disease based on the median PPG value. The study was powered to ascertain the predictive capacity of PPG for optimal PCI results (defined as FFR≥0.88 after PCI) as assessed by the AUC. Immediate PCI outcomes were assessed using post-PCI FFR and CFR. Following PCI, biomarkers (troponin) were collected. An independent clinical events committee adjudicated periprocedural myocardial infarction. The assessment of peri-procedural myocardial infarction (MI) adhered to the criteria outlined in the 4th Universal Definition of Myocardial Infarction. Target vessel failure (TVF) was defined as cardiac death, myocardial infarction and target vessel revascularisation. Results One thousand and four patients with 1057 vessels were included. The mean FFR was 0.68 ± 0.12, PPG 0.62 ± 0.17, post-PCI FFR 0.87 ± 0.07, and post-PCI CFR was 3.19 ± 1.93. PPG was significantly correlated with the change in FFR after PCI (r=0.65, 95% CI: 0.61-0.69, p<0.001) and showed an area under the curve of 0.82 (95% CI: 0.79-0.84) to predict optimal revascularisation. Suboptimal FFR (<0.88) after an angiographically successful PCI occurred in 471 vessels (53.5%) and was significantly higher in patients with diffuse disease (37.1% vs 74.0%, p<0.001). The change in CFR was sixfold higher in patients with high PPG (delta CFR focal 1.18 ± 1.94 and diffuse 0.19 ± 1.52, p<0.001). The rate of in-hospital TVF was similar between patients with focal vs diffuse disease (5.1% vs 8.5%, p=0.060). The incidence of periprocedural MI was significantly higher in patients with diffuse disease (5.9% vs 9.8%, p=0.050; OR 1.83, 95% CI 1.02 to 3.34). Conclusions Pathophysiological CAD patterns distinctly affect the safety and efficacy of PCI. PCI in focal disease was associated with improved physiological outcomes and a lower rate of periprocedural myocardial infarction compared to diffuse disease. Quantifying PPG before intervention reliably predicted post-PCI FFR. Further investigation through a randomised trial is warranted to explore the potential advantages of a PPG-guided PCI strategy.PPG and Revascularisation OutcomesPPG and FFR correlation Pre and Post-PCI
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 | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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 ».