Objectives: Fractional flow reserve (FFR) has been suggested to have value in acute coronary syndromes (ACSs). The clinical and prognostic value of ischaemia reduction assessed by post-percutaneous coronary intervention (PCI) FFR has not been studied in this population.
Methods: Consecutive stable ischaemic heart disease (SIHD) (N=390) and patients with ACS (N=189) who had pre-PCI FFR and post-PCI FFR were followed for 2.4±1.5 years. Primary endpoint was major adverse cardiac events (MACE) (composite of myocardial infarction, target vessel revascularisation and death).
Results: In patients with ACS, PCI led to significant improvement in FFR from 0.62±0.15 to post-PCI FFR 0.88±0.08 (p<0.0001). Post-PCI FFR identified 29 patients (15%) who had persistently low FFR<0.80 (0.75±0.06) despite angiographically optimal results prompting subsequent interventions improving repeat FFR (0.85±0.06; p<0.0001). The difference in MACE events between patients with ACS and patients with SIHD varied according to the post-PCI FFR value (interaction p=0.044). Receiver operator curve analysis identified a final FFR cut-off of ≤0.91 as having the best predictive accuracy for MACE in the ACS study population (30% vs 19%; p=0.03). Patients with ACS achieving final FFR of >0.91 had similar outcomes compared with patients who had SIHD (19% vs 16%; p=0.51). However, in patients with final FFR of ≤0.91 there was increased MACE versus patients with SIHD (30% vs 16%; p<0.01).
Conclusions: Post-PCI FFR is valuable in assessing the functional outcome of PCI in patients with ACS. Use of post-PCI FFR in patients with ACS allows for functional optimisation of PCI results and is predictive of long-term outcomes in patients with ACS.
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http://dx.doi.org/10.1136/heartjnl-2016-309422 | DOI Listing |
JCI Insight
January 2025
Department of Biomedical Engineering, Oregon Health and Science University, Portland, United States of America.
Spatial profiling of tissues promises to elucidate tumor-microenvironment interactions and generate prognostic and predictive biomarkers. We analyzed single-cell, spatial data from three multiplex imaging technologies: cyclic immunofluorescence (CycIF) data we generated from 102 breast cancer patients with clinical follow-up, and publicly available imaging mass cytometry and multiplex ion-beam imaging datasets. Similar single-cell phenotyping results across imaging platforms enabled combined analysis of epithelial phenotypes to delineate prognostic subtypes among estrogen-receptor positive (ER+) patients.
View Article and Find Full Text PDFHeart Fail Rev
January 2025
Division of Cardiovascular Medicine, University of Utah Health & School of Medicine, 30 N Mario Capecchi Drive, HELIX Building 3rd Floor, Salt Lake City, UT, 84112, USA.
Right heart catheterization (RHC) provides critical hemodynamic insights by measuring atrial, ventricular, and pulmonary artery pressures, as well as cardiac output (CO). Although the use of RHC has decreased, its application has been linked to improved outcomes. Advanced hemodynamic markers such as cardiac power output (CPO), aortic pulsatility index (API), pulmonary artery pulsatility index (PAPi), right atrial pressure to pulmonary capillary wedge pressure ratio (RAP/PCWP) and right ventricular stroke work index (RVSWI) have been introduced to enhance risk stratification in cardiogenic shock (CS) and end-stage heart failure (HF) patients.
View Article and Find Full Text PDFAnn Hematol
January 2025
Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
In a previous preliminary study, radiomic features from the largest and the hottest lesion in baseline F-FDG PET/CT (bPET/CT) of classical Hodgkin's Lymphoma (cHL) predicted early response-to-treatment and prognosis. Aim of this large retrospectively-validated study is to evaluate the predictive role of two-lesions radiomics in comparison with other clinical and conventional PET/CT models. cHL patients with bPET/CT between 2010 and 2020 were retrospectively included and randomized into training-validation sets.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Artificial Intelligence, Jilin University, 3003 Qianjin Street, 130012 Changchun, China.
Accurate identification of causal genes for cancer prognosis is critical for estimating disease progression and guiding treatment interventions. In this study, we propose CPCG (Cancer Prognosis's Causal Gene), a two-stage framework identifying gene sets causally associated with patient prognosis across diverse cancer types using transcriptomic data. Initially, an ensemble approach models gene expression's impact on survival with parametric and semiparametric hazard models.
View Article and Find Full Text PDFRadiology
January 2025
From the Department of Cardiology (T.P., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), MIRACL.ai (Multimodality Imaging for Research and Analysis Core Laboratory: and Artificial Intelligence) (T.P., S.T., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), Inserm MASCOT-UMRS 942 (T.P., K.H., T.A.S., T.G., A.L., E.G., A.U., J.G.D., P.H.), and Department of Radiology (T.P., V.B., L.H., T.G.), Université Paris Cité, University Hospital of Lariboisière, Assistance Publique-Hôpitaux de Paris, Paris, France; Cardiovascular Magnetic Resonance Laboratory (T.P., T.H., T.U., F.S., S.C., P.G., J.G.) and Cardiac Computed Tomography Laboratory (T.P., T.H., T.L., B.C., T.U., F.S., S.C., H.B., A.N., M.A., P.G., J.G.), Hôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud, Ramsay Santé, 6 Avenue du Noyer Lambert, 91300 Massy, France; Scientific Partnerships, Siemens Healthcare France, Saint-Denis, France (S.T.); Department of Cardiology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium (A.U.); and Department of Cardiovascular Imaging, American Hospital of Paris, Neuilly, France (O.V., M.S.).
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.
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